Fan working parameter adjusting method and system for automobile seat

Abstract

The invention relates to the technical field of intelligent cabins, and discloses a fan working parameter control method and system for an automobile seat, and the method comprises the steps: obtaining the object characteristics of a driver and passengers sitting on the automobile seat and the environment characteristics in a cabin; constructing a feature matrix for controlling working parameters of the fan based on the object features and the environment features, and determining main feature factors and secondary feature factors in the feature matrix; adding different disturbance characteristics to the secondary characteristic factors under the condition of keeping the primary characteristic factors unchanged, and constructing a plurality of disturbance matrixes; screening out a target fan adjustment model from a plurality of fan adjustment models obtained by pre-training; and obtaining fan adjustment parameters output by the target fan adjustment model for the feature matrix, and performing fan adjustment based on the obtained fan adjustment parameters. The fan adjusting device has the beneficial effect that the personalized fan adjusting process can be finely realized.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent cockpits, and in particular to a method and system for adjusting operating parameters of a fan for a car seat. Background Art

[0002] The existing fan adjustment method of the car seat is usually manually adjusted by the driver and passengers. For example, the cockpit may provide a plurality of different fan intensities, which are manually switched by the driver and passengers.

[0003] With the popularity of smart cockpits, the demand for intelligent fan adjustment is becoming increasingly urgent. However, the existing fan adjustment method is only linked with the smart voice assistant to adjust the fan intensity through the voice of the driver and passengers. Although this adjustment method is simple, it cannot be personalized for different driving scenarios. Summary of the invention

[0004] The present application provides a method and system for adjusting operating parameters of a fan for a car seat, which can realize a personalized fan adjustment method to enhance the riding experience of the driver and passengers.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include:

[0006] In a first aspect, an embodiment of the present application provides a method for controlling operating parameters of a fan for a car seat, the method comprising:

[0007] Acquire object features of a driver and passenger sitting on a car seat and environmental features in the cabin, wherein the object features are used to characterize at least the gender, preference, and contact area of ​​the driver and passenger with the car seat, and the environmental features are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner;

[0008] Constructing a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determining main feature factors and secondary feature factors in the feature matrix;

[0009] While keeping the main characteristic factor unchanged, adding different disturbance features to the secondary characteristic factor, and constructing a plurality of disturbance matrices according to the main characteristic factor and the secondary characteristic factor after adding the disturbance features;

[0010] A target fan adjustment model is selected from the plurality of fan adjustment models obtained by pre-training, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices and obtains respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters;

[0011] The fan adjustment parameters output by the target fan adjustment model for the characteristic matrix are obtained, and the fan adjustment is performed based on the obtained fan adjustment parameters.

[0012] In one embodiment, the preference is used to characterize the driver's or passenger's preference for cold or warm. The preference is represented by a numerical value in a specified interval, wherein a smaller numerical value indicates a greater preference for cold, and a higher numerical value indicates a greater preference for warm.

[0013] In one embodiment, constructing a feature matrix for controlling fan operating parameters based on the object features and the environment features includes:

[0014] The object features and environmental features are collected according to a preset sampling period, and the object features and environmental features with the same timestamp are spliced ​​into a timestamp sequence;

[0015] The time stamp sequences are arranged in sequence according to a time sequence relationship to form a feature matrix for controlling the fan operating parameters.

[0016] In one embodiment, determining the primary characteristic factors and the secondary characteristic factors in the characteristic matrix includes:

[0017] Identifying feature data of each feature factor at different time stamps in the feature matrix;

[0018] For any feature data, the feature data is used as index data, and query data and attribute data are determined from other feature data, and an index value of the index data is calculated based on the association between the index data and the query data and the attribute data;

[0019] According to the index value of each characteristic data, the main characteristic factor and the secondary characteristic factor are determined.

[0020] In one embodiment, calculating the index value of the index data includes:

[0021] According to the same time span, the index data, the query data and the attribute data are divided respectively to obtain respective local data;

[0022] For any first local data of the index data, calculating the similarity between the first local data and each local data of the query data to obtain multiple similarities;

[0023] Using the multiple similarities as weight coefficients, weighting each local data of the attribute data, and concatenating the weighted local data to obtain an index feature;

[0024] The index feature is input into an index value calculation model to obtain an index value corresponding to the index feature.

[0025] In one embodiment, each of the fan adjustment models is trained in the following manner:

[0026] For any main characteristic factor, obtain a training sample of the main characteristic factor, and set a first label value of a fan adjustment parameter for the training sample;

[0027] On the basis of the training samples, disturbance features are added to the secondary feature factors to generate disturbance samples corresponding to the training samples, and a second label value of the fan adjustment parameter is set for each disturbance sample, wherein when cluster analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different cluster clusters;

[0028] The fan adjustment model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan adjustment model for the training samples and the disturbance samples correspond to the first label value and the second label value respectively;

[0029] The fan adjustment model obtained through training is used as a fan adjustment model of the main characteristic factor.

[0030] In a second aspect, the present application further provides a fan operating parameter control system for a car seat, the system comprising:

[0031] a feature acquisition unit, configured to acquire object features of a driver and passenger sitting on a car seat and environmental features in the cabin, wherein the object features are used to characterize at least the gender, preference, and contact area of ​​the driver and passenger with the car seat, and the environmental features are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner;

[0032] A factor determination unit, configured to construct a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determine main feature factors and secondary feature factors in the feature matrix;

[0033] A perturbation unit, used for adding different perturbation features to the secondary characteristic factors while keeping the primary characteristic factors unchanged, and constructing a plurality of perturbation matrices according to the primary characteristic factors and the secondary characteristic factors after adding the perturbation features;

[0034] A model screening unit, configured to screen out a target fan adjustment model from a plurality of pre-trained fan adjustment models, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices and obtains respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters;

[0035] The fan adjustment unit is used to obtain the fan adjustment parameters output by the target fan adjustment model for the characteristic matrix, and perform fan adjustment based on the obtained fan adjustment parameters.

[0036] In one embodiment, the factor determination unit is specifically used to identify the feature data of each feature factor at different timestamps in the feature matrix; for any feature data, the feature data is used as index data, and query data and attribute data are determined from other feature data, and the index value of the index data is calculated based on the association between the index data and the query data and attribute data; and the main feature factors and secondary feature factors are determined according to the index values ​​of each feature data.

[0037] In one embodiment, the factor determination unit is specifically used to divide the index data, the query data and the attribute data according to the same time span to obtain respective local data; for any first local data of the index data, calculate the similarity between the first local data and each local data of the query data to obtain multiple similarities; use the multiple similarities as weight coefficients to weight each local data of the attribute data, and concatenate the weighted local data to obtain an index feature; input the index feature into an index value calculation model to obtain an index value corresponding to the index feature.

[0038] In one embodiment, the model screening unit trains each of the fan adjustment models in the following manner:

[0039] For any main characteristic factor, obtain a training sample of the main characteristic factor, and set a first label value of a fan adjustment parameter for the training sample;

[0040] On the basis of the training samples, disturbance features are added to the secondary feature factors to generate disturbance samples corresponding to the training samples, and a second label value of the fan adjustment parameter is set for each disturbance sample, wherein when cluster analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different cluster clusters;

[0041] The fan adjustment model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan adjustment model for the training samples and the disturbance samples correspond to the first label value and the second label value respectively;

[0042] The fan adjustment model obtained through training is used as a fan adjustment model of the main characteristic factor.

[0043] The technical solution provided by the present invention can reflect the current driving scene through multi-dimensional data by collecting the object characteristics of the driver and the environmental characteristics in the cabin. For these multi-dimensional data, the main characteristic factors that have a major impact on the fan adjustment and the secondary characteristic factors that have a minor impact on the fan adjustment can be determined. In order to achieve the fine personalized adjustment requirements, under the condition of having the same main characteristic factors, disturbances can be added to the secondary characteristic factors, so as to obtain multiple disturbance data corresponding to the actual multi-dimensional data. Subsequently, a suitable target fan adjustment model is screened out from a large number of fan adjustment models, and the required fan adjustment parameters are output through the target fan adjustment model. In this process, the screened target fan adjustment model should be able to distinguish the actual multi-dimensional data from the generated disturbance data, so that the fan adjustment parameters finally output can be clustered into different clustering clusters. In this way, different driving scenes are calculated to have the same main characteristic factors, but as long as the secondary characteristic factors change, the final fan adjustment parameters may also change, thereby realizing a fine personalized adjustment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A flow chart of a method for controlling operating parameters of a fan for a car seat provided in an embodiment of the present application;

[0046] Figure 2A schematic diagram of a fan operating parameter control system for a car seat provided in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0049] See also Figure 1 , an embodiment of the present application provides a method for controlling operating parameters of a fan for a car seat, the method comprising the following steps.

[0050] S1: Obtain object characteristics of a driver and passenger sitting on a car seat and environmental characteristics in the cabin, wherein the object characteristics are at least used to characterize the driver and passenger's gender, preference, and contact area with the car seat, and the environmental characteristics are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner.

[0051] In this embodiment, the smart cockpit can collect the object characteristics of the driver and the environmental characteristics in the cockpit through the information pre-entered by the driver and the passengers in the car computer and various sensors. In practical applications, the identity of the driver and the passengers can be determined through facial feature recognition or voiceprint recognition, so as to obtain a series of information such as the gender and preferences of the driver and the passengers in the car computer. At the same time, the contact area between the driver and the car seat can be roughly determined through the pressure sensors distributed on the seat. The contact area can be a ratio with a value between 0 and 1. The real-time temperature in the cockpit can be collected through the temperature sensor, and the car computer can also obtain the target adjustment temperature set by the air conditioner. The above information can comprehensively reflect the current fan adjustment scenario, and personalized fan adjustment can be performed based on this information later. Of course, in practical applications, more or less information can be obtained for fan adjustment, depending on the needs of the application scenario.

[0052] In this embodiment, the driver's preference can be used to represent the driver's preference for cold or warm, and the preference is represented by a numerical value in a specified interval, wherein a smaller numerical value represents a greater preference for cold, and a higher numerical value represents a greater preference for warm. For example, the numerical value in the specified interval can be a numerical value between 0 and 1.

[0053] S2: constructing a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determining primary feature factors and secondary feature factors in the feature matrix.

[0054] In this embodiment, the object features and environmental features can be collected according to a preset sampling period, and the object features and environmental features with the same timestamp can be spliced ​​into a timestamp sequence. For example, at time t1, various data in the object features and environmental features can be collected, and the collected data can be arranged in a predetermined order to form a timestamp sequence at time t1. Each different moment can have its own timestamp sequence. Subsequently, each of the timestamp sequences is arranged in sequence according to the time sequence relationship to form a feature matrix for controlling the fan operating parameters. In this feature matrix, different rows represent timestamp sequences at different moments.

[0055] In this embodiment, after the feature matrix is ​​generated, the feature matrix can be analyzed to determine the main feature factors and secondary feature factors therein. Specifically, the feature data of each feature factor at different timestamps can be identified in the feature matrix. Among them, the feature factor can refer to the gender, preference, contact area, actual temperature, target adjustment temperature, etc. in step S1. For any feature data, the feature data can be used as index data, and the query data and attribute data can be determined from other feature data. Subsequently, the index value of the index data can be calculated based on the association between the index data and the query data and the attribute data. Finally, according to the index value of each feature data, the main feature factor and the secondary feature factor can be determined. For example, the feature factor corresponding to the feature data with the highest index value can be used as the main feature factor, and the other feature factors except the main feature factor can be used as the secondary feature factor. Of course, the feature factors with index values ​​less than a certain threshold value can also be filtered out, and these filtered out feature factors are neither the main feature factors nor the secondary feature factors.

[0056] In this embodiment, after the selected feature data is used as index data, query data and attribute data can be determined in descending order of relevance to the feature data. For example, if the feature data of the contact area is currently used as index data, then gender and preference can be used as query data, and actual temperature and target adjustment temperature can be used as attribute data.

[0057] In one embodiment, when calculating the index value of the index data, the index data, the query data, and the attribute data can be divided according to the same time span (for example, with the time span t as the span) to obtain respective local data. For example, the index data, the query data, and the attribute data all contain data with N timestamps. Now they are divided according to the time span t as the span. Assuming that the time span t contains m timestamps, these three types of data can be divided to obtain N / m local data.

[0058] When calculating the index value, for any first local data of the index data, the similarity between the first local data and each local data of the query data can be calculated to obtain multiple similarities. Since the local data obtained by division are all in the form of vectors, the similarity between two vectors can be obtained by calculating the Euclidean distance between the vectors. Among them, the smaller the distance, the higher the similarity.

[0059] After calculating each similarity, each similarity can be used as a weight coefficient (normalization is required in some scenarios) to weight each local data of the attribute data, and then concatenate the weighted local data to obtain the index feature. For example, the current similarities are k1, k2, and k3, and the local data of the attribute data are D1, D2, and D3. Then k1, k2, and k3 can be used as weight coefficients of D1, D2, and D3, respectively, and the three pieces of data can be concatenated after weighting to obtain the index feature.

[0060] Subsequently, the index feature is input into the index value calculation model to obtain the index value corresponding to the index feature. The index value calculation model is obtained based on a large number of sample training, which can be a large number of index feature samples, each of which will be marked with a standard index value, so as to perform supervised training.

[0061] S3: adding different disturbance features to the secondary characteristic factors while keeping the primary characteristic factors unchanged, and constructing a plurality of disturbance matrices according to the primary characteristic factors and the secondary characteristic factors after adding the disturbance features.

[0062] In this embodiment, in order to achieve a fine personalized fan adjustment process, the secondary characteristic factors can be disturbed. In this way, even if different fan adjustment scenarios have the same main characteristic factors, different fan adjustment parameters will be generated as long as the disturbance of the secondary characteristic factors is large enough.

[0063] Specifically, the added disturbance features can be realized through disturbance weights. For different secondary feature factors, the disturbance weights can be obtained based on big data analysis. The disturbance weights selected here need to represent sufficient interference to the secondary feature factors, so as to obtain a disturbance matrix that is different from the feature matrix.

[0064] S4: Filter out a target fan adjustment model from the multiple fan adjustment models obtained in advance, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices to obtain respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters.

[0065] In this embodiment, in order to obtain accurate fan adjustment parameters, different sets of fan adjustment models can be selected for different main characteristic factors. In other words, for any main characteristic factor, multiple different fan adjustment models can be provided, and these fan adjustment models can significantly distinguish the secondary characteristic factors, so that different fan adjustment parameters can be output when the secondary characteristic factors are greatly disturbed.

[0066] Specifically, each fan adjustment model corresponding to the main characteristic factors is trained in the following way:

[0067] For any main characteristic factor, a training sample of the main characteristic factor is obtained, and a first label value of a fan adjustment parameter is set for the training sample. It should be noted that the first label values ​​of the fan adjustment parameters corresponding to the training samples obtained here are the same, that is, although the specific values ​​of the secondary characteristic factors in the training samples may be different, the difference is small and will not affect the fan adjustment parameters. Subsequently, based on the training samples, disturbance features are added to the secondary characteristic factors to generate disturbance samples corresponding to the training samples, and the second label values ​​of the fan adjustment parameters are set for each disturbance sample. It should be noted that the disturbance samples here are samples that are essentially different from the training samples, so the added second label value is different from the first label value, and in practical applications, as the number of disturbance samples increases, there may be multiple values ​​of the second label value. When clustering analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different clusters, thereby reflecting the essential difference between the training samples and the disturbance samples.

[0068] The fan regulation model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan regulation model for the training samples and the disturbance samples correspond to the first label value and the second label value, respectively. Finally, the trained fan regulation model is used as a fan regulation model of the main characteristic factor. In this way, for training samples and disturbance samples with significant differences, the results output by the fan regulation model will also be significantly different (located in different clusters), and as the number of disturbance samples continues to increase, the number of trained fan regulation models will also continue to increase.

[0069] After training and obtaining multiple fan adjustment models corresponding to the main characteristic factors, in actual application, you can try to use each fan adjustment model to process the feature matrix and each disturbance matrix. If it is found that a certain fan adjustment model processes the feature matrix and each disturbance matrix and obtains their respective fan adjustment parameters, if a cluster analysis is performed on each fan adjustment parameter, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are in different cluster clusters, then it means that the fan adjustment model has been selected correctly. At this time, the output result of the feature matrix can be used for the screened fan adjustment model.

[0070] S5: Obtain fan adjustment parameters output by the target fan adjustment model for the feature matrix, and perform fan adjustment based on the obtained fan adjustment parameters.

[0071] The technical solution provided by the present invention can reflect the current driving scene through multi-dimensional data by collecting the object characteristics of the driver and the environmental characteristics in the cabin. For these multi-dimensional data, the main characteristic factors that have a major impact on the fan adjustment and the secondary characteristic factors that have a minor impact on the fan adjustment can be determined. In order to achieve the fine personalized adjustment requirements, under the condition of having the same main characteristic factors, disturbances can be added to the secondary characteristic factors, so as to obtain multiple disturbance data corresponding to the actual multi-dimensional data. Subsequently, a suitable target fan adjustment model is screened out from a large number of fan adjustment models, and the required fan adjustment parameters are output through the target fan adjustment model. In this process, the screened target fan adjustment model should be able to distinguish the actual multi-dimensional data from the generated disturbance data, so that the fan adjustment parameters finally output can be clustered into different clustering clusters. In this way, different driving scenes are calculated to have the same main characteristic factors, but as long as the secondary characteristic factors change, the final fan adjustment parameters may also change, thereby realizing a fine personalized adjustment process.

[0072] See also Figure 2 The present application also provides a fan operating parameter control system for a car seat, the system comprising:

[0073] a feature acquisition unit, configured to acquire object features of a driver and passenger sitting on a car seat and environmental features in the cabin, wherein the object features are used to characterize at least the gender, preference, and contact area of ​​the driver and passenger with the car seat, and the environmental features are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner;

[0074] A factor determination unit, configured to construct a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determine main feature factors and secondary feature factors in the feature matrix;

[0075] A perturbation unit, used for adding different perturbation features to the secondary characteristic factors while keeping the primary characteristic factors unchanged, and constructing a plurality of perturbation matrices according to the primary characteristic factors and the secondary characteristic factors after adding the perturbation features;

[0076] A model screening unit, configured to screen out a target fan adjustment model from a plurality of pre-trained fan adjustment models, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices and obtains respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters;

[0077] The fan adjustment unit is used to obtain the fan adjustment parameters output by the target fan adjustment model for the characteristic matrix, and perform fan adjustment based on the obtained fan adjustment parameters.

[0078] In one embodiment, the factor determination unit is specifically used to identify the feature data of each feature factor at different timestamps in the feature matrix; for any feature data, the feature data is used as index data, and query data and attribute data are determined from other feature data, and the index value of the index data is calculated based on the association between the index data and the query data and attribute data; and the main feature factors and secondary feature factors are determined according to the index values ​​of each feature data.

[0079] In one embodiment, the factor determination unit is specifically used to divide the index data, the query data and the attribute data according to the same time span to obtain respective local data; for any first local data of the index data, calculate the similarity between the first local data and each local data of the query data to obtain multiple similarities; use the multiple similarities as weight coefficients to weight each local data of the attribute data, and concatenate the weighted local data to obtain an index feature; input the index feature into an index value calculation model to obtain an index value corresponding to the index feature.

[0080] In one embodiment, the model screening unit trains each of the fan adjustment models in the following manner:

[0081] For any main characteristic factor, obtain a training sample of the main characteristic factor, and set a first label value of a fan adjustment parameter for the training sample;

[0082] On the basis of the training samples, disturbance features are added to the secondary feature factors to generate disturbance samples corresponding to the training samples, and a second label value of the fan adjustment parameter is set for each disturbance sample, wherein when cluster analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different cluster clusters;

[0083] The fan adjustment model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan adjustment model for the training samples and the disturbance samples correspond to the first label value and the second label value respectively;

[0084] The fan adjustment model obtained through training is used as a fan adjustment model of the main characteristic factor.

[0085] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0086] The unit in this embodiment refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0087] See also Figure 3 , Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Figure 3As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 3 A processor 10 is taken as an example.

[0088] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0089] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0090] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0091] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0092] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0093] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or is implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0094] The systems, devices, and units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0095] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0096] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and devices. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0097] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices and apparatuses according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0098] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0101] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0102] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0103] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for controlling operating parameters of a fan for a car seat, characterized in that: The method comprises: Acquire object features of a driver and passenger sitting on a car seat and environmental features in the cabin, wherein the object features are used to characterize at least the gender, preference, and contact area of ​​the driver and passenger with the car seat, and the environmental features are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner; Constructing a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determining main feature factors and secondary feature factors in the feature matrix; While keeping the main characteristic factor unchanged, adding different disturbance features to the secondary characteristic factor, and constructing a plurality of disturbance matrices according to the main characteristic factor and the secondary characteristic factor after adding the disturbance features; A target fan adjustment model is selected from the plurality of fan adjustment models obtained by pre-training, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices and obtains respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters; The fan adjustment parameters output by the target fan adjustment model for the characteristic matrix are obtained, and the fan adjustment is performed based on the obtained fan adjustment parameters.

2. The method according to claim 1, characterized in that The preference is used to characterize the driver's and passenger's preference for cold or warm. The preference is represented by a numerical value in a specified interval, wherein a smaller numerical value indicates a greater preference for cold, and a higher numerical value indicates a greater preference for warm.

3. The method according to claim 1, characterized in that Constructing a feature matrix for controlling fan operating parameters based on the object features and the environment features includes: The object features and environmental features are collected according to a preset sampling period, and the object features and environmental features with the same timestamp are spliced ​​into a timestamp sequence; The time stamp sequences are arranged in sequence according to a time sequence relationship to form a feature matrix for controlling the fan operating parameters.

4. The method according to claim 1 or 3, characterized in that: Determining the main characteristic factors and the secondary characteristic factors in the characteristic matrix includes: Identifying feature data of each feature factor at different time stamps in the feature matrix; For any feature data, the feature data is used as index data, and query data and attribute data are determined from other feature data, and an index value of the index data is calculated based on the association between the index data and the query data and the attribute data; According to the index value of each characteristic data, the main characteristic factor and the secondary characteristic factor are determined.

5. The method according to claim 4, characterized in that Calculating the index value of the index data includes: According to the same time span, the index data, the query data and the attribute data are divided respectively to obtain respective local data; For any first local data of the index data, calculating the similarity between the first local data and each local data of the query data to obtain multiple similarities; Using the multiple similarities as weight coefficients, weighting each local data of the attribute data, and concatenating the weighted local data to obtain an index feature; The index feature is input into an index value calculation model to obtain an index value corresponding to the index feature.

6. The method according to claim 1, characterized in that Each of the fan adjustment models is trained in the following manner: For any main characteristic factor, obtain a training sample of the main characteristic factor, and set a first label value of a fan adjustment parameter for the training sample; On the basis of the training samples, disturbance features are added to the secondary feature factors to generate disturbance samples corresponding to the training samples, and a second label value of the fan adjustment parameter is set for each disturbance sample, wherein when cluster analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different cluster clusters; The fan adjustment model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan adjustment model for the training samples and the disturbance samples correspond to the first label value and the second label value respectively; The fan adjustment model obtained through training is used as a fan adjustment model of the main characteristic factor.

7. A fan operating parameter control system for a car seat, characterized in that: The system comprises: a feature acquisition unit, configured to acquire object features of a driver and passenger sitting on a car seat and environmental features in the cabin, wherein the object features are used to characterize at least the gender, preference, and contact area of ​​the driver and passenger with the car seat, and the environmental features are used to characterize the current actual temperature in the cabin and the target adjustment temperature of the air conditioner; A factor determination unit, configured to construct a feature matrix for controlling fan operating parameters based on the object features and the environment features, and determine main feature factors and secondary feature factors in the feature matrix; A perturbation unit, used for adding different perturbation features to the secondary characteristic factors while keeping the primary characteristic factors unchanged, and constructing a plurality of perturbation matrices according to the primary characteristic factors and the secondary characteristic factors after adding the perturbation features; A model screening unit, configured to screen out a target fan adjustment model from a plurality of pre-trained fan adjustment models, wherein after the target fan adjustment model processes the feature matrix and each of the disturbance matrices and obtains respective fan adjustment parameters, if cluster analysis is performed on each of the fan adjustment parameters, the fan adjustment parameters corresponding to the feature matrix and the fan adjustment parameters corresponding to the disturbance matrix are located in different cluster clusters; The fan adjustment unit is used to obtain the fan adjustment parameters output by the target fan adjustment model for the characteristic matrix, and perform fan adjustment based on the obtained fan adjustment parameters.

8. The system according to claim 7, characterized in that The factor determination unit is specifically used to identify the feature data of each feature factor at different timestamps in the feature matrix; for any feature data, use the feature data as index data, and determine query data and attribute data from other feature data, and calculate the index value of the index data based on the association between the index data and the query data and attribute data; determine the main feature factor and the secondary feature factor according to the index value of each feature data.

9. The system according to claim 8, characterized in that The factor determination unit is specifically used to divide the index data, the query data and the attribute data according to the same time span, respectively, to obtain respective local data; for any first local data of the index data, calculate the similarity between the first local data and each local data of the query data, to obtain multiple similarities; use the multiple similarities as weight coefficients, weight each local data of the attribute data, and splice the weighted local data to obtain index features; The index feature is input into an index value calculation model to obtain an index value corresponding to the index feature.

10. The system according to claim 7, characterized in that The model screening unit trains each of the fan adjustment models in the following manner: For any main characteristic factor, obtain a training sample of the main characteristic factor, and set a first label value of a fan adjustment parameter for the training sample; On the basis of the training samples, disturbance features are added to the secondary feature factors to generate disturbance samples corresponding to the training samples, and a second label value of the fan adjustment parameter is set for each disturbance sample, wherein when cluster analysis is performed on the first label value and the second label value, the first label value and the second label value are located in different cluster clusters; The fan adjustment model is trained using the training samples and the disturbance samples, so that the prediction results output by the trained fan adjustment model for the training samples and the disturbance samples correspond to the first label value and the second label value respectively; The fan adjustment model obtained through training is used as a fan adjustment model of the main characteristic factor.

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