A vehicle-mounted air conditioner associated parameter determination method, device, equipment and storage medium

Abstract

The application discloses a vehicle-mounted air conditioner associated parameter determination method and device, equipment and a storage medium. The method comprises the following steps: obtaining a target data set, wherein the target data set comprises historical target data and labels corresponding to the historical target data, and the historical target data comprises historical target air conditioner data of each vehicle in a vehicle set within a preset time; obtaining the occurrence frequency of each label in the target data set; and filtering the target data set based on the occurrence frequency of the label to obtain a target vehicle-mounted air conditioner associated parameter. According to the technical scheme, any vehicle-mounted air conditioner parameter in the target vehicle-mounted air conditioner associated parameter can be automatically controlled to regulate other associated vehicle-mounted air conditioner parameters, thereby saving the operation of the air conditioner control system by the user and improving the intelligent experience and driving safety of the user.

Description

Technical Field

[0001] The present invention relates to the field of vehicle technology, and in particular to a method, apparatus, device and storage medium for determining vehicle air conditioning related parameters. Background Technology

[0002] In-vehicle air conditioning is an important component of a car, used to regulate the interior temperature and provide a comfortable environment for users. However, most in-vehicle air conditioning control systems rely on manual operation to adjust parameters such as temperature, airflow, and fan mode to meet user needs. Frequent manual operation of the air conditioning system while driving can compromise driving safety; therefore, intelligent air conditioning control systems are gaining popularity.

[0003] Most existing intelligent air conditioning control systems generally control the vehicle's air conditioning system automatically based on user habits, but they do not analyze user usage patterns in detail using historical data, meaning they do not analyze the associated parameters of the vehicle's air conditioning system based on historical user data. Therefore, it is particularly important to learn how to analyze the associated parameters of the vehicle's air conditioning system in detail based on historical user data to achieve intelligent vehicle air conditioning. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for determining vehicle air conditioning related parameters, which solves the problems of low intelligence level and cumbersome user operation caused by the lack of detailed analysis of vehicle air conditioning related parameters based on users' historical usage data.

[0005] According to one aspect of the present invention, a method for determining vehicle air conditioning correlation parameters is provided, comprising:

[0006] Obtain the target dataset, wherein the target dataset includes: historical target data and labels corresponding to the historical target data, wherein the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period;

[0007] Obtain the frequency of occurrence of each label in the target dataset;

[0008] The target dataset is filtered based on the frequency of tag occurrence to obtain the target vehicle air conditioning association parameters.

[0009] According to another aspect of the present invention, a vehicle air conditioning related parameter determination device is provided, the vehicle air conditioning related parameter determination device comprising:

[0010] The first acquisition module is used to acquire a target dataset, wherein the target dataset includes: historical target data and labels corresponding to the historical target data, and the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period;

[0011] The second acquisition module is used to acquire the frequency of occurrence of each label in the target dataset;

[0012] The module is used to filter the target dataset based on the frequency of tag occurrence to obtain the target vehicle air conditioning association parameters.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the vehicle air conditioning associated parameter determination method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the vehicle air conditioning associated parameter determination method according to any embodiment of the present invention.

[0018] This invention addresses the problem of low intelligence and cumbersome user operations caused by the lack of detailed analysis of vehicle air conditioning related parameters based on historical usage data. The invention obtains a target dataset comprising historical target data and corresponding labels, including historical target air conditioning data for each vehicle in a vehicle set within a preset time period. It also obtains the frequency of occurrence of each label in the target dataset and filters the dataset based on the label frequency to obtain target vehicle air conditioning related parameters. This allows for automatic adjustment of other related vehicle air conditioning parameters based on any one of the target vehicle air conditioning related parameters, thereby saving users time and effort in operating the air conditioning control system and improving the user's intelligent experience and driving safety.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for determining vehicle air conditioning associated parameters according to Embodiment 1 of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of a vehicle air conditioning correlation parameter determination device according to Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of the structure of an electronic device according to Embodiment 3 of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0027] Example 1

[0028] Figure 1This is a flowchart of a method for determining vehicle air conditioning associated parameters according to Embodiment 1 of the present invention. This embodiment is applicable to the determination of vehicle air conditioning associated parameters. This method can be executed by the vehicle air conditioning associated parameter determining device in this embodiment of the present invention. This device can be implemented in software and / or hardware, such as... Figure 1 As shown, the method specifically includes the following steps:

[0029] S110, Obtain the target dataset, wherein the target dataset includes: historical target data and the labels corresponding to the historical target data, and the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period.

[0030] The target dataset consists of historical target data and corresponding labels. The historical target data includes the historical target air conditioning data of each vehicle in the vehicle set within a preset time period. The preset time period can be set according to actual needs, and each vehicle in the vehicle set can be selected based on human experience and actual needs. The historical target air conditioning data can include data such as air conditioning on / off status, air conditioning temperature, air conditioning mode, air conditioning automatic status, air conditioning air volume, and air conditioning circulation status.

[0031] Specifically, the target dataset can be obtained in the following ways: extract historical target air conditioning data for each vehicle in the vehicle set within a preset time period from the vehicle network data, and label each data point to obtain the corresponding label for the historical target data. Alternatively, the target dataset can be obtained by: acquiring all data for each vehicle in the vehicle set within a preset time period from the vehicle network data, filtering all data according to preset conditions to obtain historical target air conditioning data for each vehicle in the vehicle set within the preset time period, and labeling each data point to obtain the corresponding label for the historical target data. The preset conditions are data where the door is closed and the air conditioning is on.

[0032] S120: Obtain the frequency of occurrence of each label in the target dataset.

[0033] Specifically, the frequency of occurrence of each label in the target dataset can be obtained by using a target association rule algorithm. For example, the target association rule algorithm can be the Apriori algorithm. The Apriori algorithm can be used to mine association rules between the historical target data and the corresponding labels in the target dataset to obtain the frequency of occurrence of each label.

[0034] S130: Filter the target dataset based on the frequency of label occurrence to obtain the target vehicle air conditioning association parameters.

[0035] The target vehicle air conditioning associated parameters include at least two of the following: air conditioning temperature, air conditioning mode, air conditioning automatic status, air conditioning air volume, and air conditioning recirculation status. The air conditioning temperature can be a set range.

[0036] Specifically, the method for filtering the target dataset based on the frequency of label occurrence to obtain the target vehicle air conditioning association parameters can be as follows: obtain the frequency of occurrence of each label in the target dataset, obtain the labels that exceed the preset threshold or have the highest frequency, and filter the target dataset according to the labels that exceed the preset threshold or have the highest frequency to obtain a new dataset. Classify the new dataset according to different air conditioning temperature ranges, repeatedly obtain the frequency of occurrence of each label of all data corresponding to different categories of air conditioning temperature ranges, solidify the labels that exceed the preset threshold or have the highest frequency, and repeatedly filter all data corresponding to different categories of air conditioning temperature ranges according to the solidified labels until the target dataset is completely filtered, and obtain the target vehicle air conditioning association parameters according to the data corresponding to the solidified labels.

[0037] By acquiring a target dataset, which includes historical target data and corresponding labels for the historical target data, and the historical target data includes historical target air conditioning data for each vehicle in a vehicle set within a preset time period; obtaining the frequency of occurrence of each label in the target dataset; and filtering the target dataset based on the frequency of occurrence of the labels to obtain target vehicle air conditioning association parameters, it is possible to analyze the air conditioning usage habits of a large number of users, comprehensively consider the historical target air conditioning data of a large number of users within a preset time period, obtain the user's usage rules for vehicle air conditioning, that is, the target vehicle air conditioning association parameters used, and then obtain other air conditioning parameters based on the air conditioning temperature in the target vehicle air conditioning association parameters, thereby saving users the operation of the air conditioning control system and improving the user's intelligent experience and driving safety.

[0038] Optionally, obtain the target dataset, including:

[0039] Obtain an initial dataset, filter the data in the initial dataset to obtain a first dataset, wherein the initial dataset includes: historical initial driving data and historical initial air conditioning data of each vehicle in the vehicle set within a preset time period;

[0040] The historical target data in the first dataset is labeled to obtain the labels corresponding to the historical target data.

[0041] Generate a target dataset based on historical target data and the corresponding labels.

[0042] The initial dataset includes historical initial driving data and historical initial air conditioning data for each vehicle in the vehicle set within a preset time period. The historical initial driving data includes data such as the timestamp corresponding to the GPS time, instrument mileage, GPS vehicle speed, instrument vehicle speed, door opening and closing, and atmospheric temperature. The historical initial air conditioning data includes all air conditioning data for each vehicle in the vehicle set within the preset time period.

[0043] The first dataset is the data obtained after filtering the data in the initial dataset, including the historical target air conditioning data of each vehicle in the vehicle set within a preset time period.

[0044] Specifically, the method for obtaining the initial dataset and filtering the data in the initial dataset to obtain the first dataset can be as follows: obtain the historical initial driving data and historical initial air conditioning data of each vehicle in the vehicle set within a preset time period from the vehicle network data, and filter the initial dataset based on the data of the door opening and closing status in the historical initial driving data and the data of the air conditioning use status being on in the historical initial air conditioning data to obtain the first dataset. The first dataset includes the historical target air conditioning data of each vehicle in the vehicle set within the preset time period.

[0045] Specifically, the method to label the historical target data in the first dataset and obtain the corresponding labels for the historical target data can be as follows: label each historical target data item in the first dataset and obtain the corresponding label for the historical target data.

[0046] Specifically, the method for generating the target dataset based on historical target data and the corresponding labels of historical target data can be as follows: substitute the labels corresponding to the historical target data back into the historical target data so that the historical target data and the corresponding labels of historical target data can generate the target dataset.

[0047] For example, the first dataset could be recorded using a record table, as shown in Table 1:

[0048] Table 1

[0049] air conditioning temperature Air conditioning mode Air conditioner in automatic mode Air conditioning air volume Air conditioning circulation status 15 0 0 8 1 16 0 0 5 1 16 0 0 0 1 17 0 0 1 1 18 0 0 5 1 20 0 0 7 1 19 0 0 0 1 18 0 0 5 1

[0050] It should be noted that, for ease of subsequent calculations, the air conditioning temperature can be divided into different set temperature ranges, and then these different temperature ranges can be labeled. For example, the specific labeling process is shown in Table 2:

[0051] Table 2

[0052]

[0053]

[0054] The labels are as follows: A1 for air conditioning temperature range [15-20°C], A2 for air conditioning temperature range [21-25°C], A3 for air conditioning temperature range [26-30°C], and A4 for air conditioning temperature range [31-35°C]. In the air conditioning mode, 0 indicates face blowing mode (label B1), 1 indicates face and foot blowing mode (label B2), 2 indicates foot blowing mode (label B3), 3 indicates foot blowing and defrosting mode (label B4), and 4 indicates defrosting mode (label B5). In the air conditioning automatic status, 0 indicates off state (label C1), and 1 indicates on state (label C2). The air conditioning fan speed data indicates the air conditioning fan speed level, which can be represented by labels D1-D14. In the air conditioning circulation status, 0 indicates internal circulation (label E1), and 1 indicates external circulation (label E2).

[0055] After tagging, the tags are substituted back into Table 1, as shown in Table 3:

[0056] Table 3

[0057] air conditioning temperature Air conditioning mode Air conditioner in automatic mode Air conditioning air volume Air conditioning circulation status A1 B1 C1 D9 E2 A1 B1 C1 D6 E2 A1 B1 C1 D1 E2 A1 B1 C1 D2 E2 A1 B1 C1 D6 E2 A1 B1 C1 D8 E2 A1 B1 C1 D1 E2 A1 B1 C1 D6 E2

[0058] Therefore, a target dataset can be generated based on historical target data and the corresponding labels.

[0059] By obtaining an initial dataset, filtering the data in the initial dataset to obtain the first dataset, and labeling the historical target data in the first dataset to obtain the corresponding labels, the target dataset is generated based on the historical target data and the corresponding labels. This facilitates the analysis of historical target data and improves analysis efficiency.

[0060] Optionally, the data in the initial dataset can be filtered to obtain the first dataset, which includes:

[0061] Obtain the vehicle identification number and the timestamp corresponding to each data point from the initial dataset;

[0062] The initial dataset is filtered based on the vehicle identification number and the timestamp corresponding to each data point to obtain the first dataset.

[0063] Among them, the Vehicle Identification Number (VIN) is unique and can be used to identify vehicles. The timestamp corresponding to each data point can be the timestamp corresponding to the GPS time obtained from the historical initial driving data of each vehicle within a preset time period.

[0064] Specifically, the method to obtain the vehicle identification code and the timestamp corresponding to each data in the initial dataset can be: obtain the vehicle identification code in the initial dataset and the timestamp corresponding to the GPS time in the historical initial driving data in the initial dataset.

[0065] Specifically, the initial dataset is filtered based on the vehicle identification number (VIN) and the timestamp corresponding to each data point to obtain the first dataset. The method is as follows: First, the number of VINs is obtained. If the number of VINs exceeds a preset threshold, it indicates sufficient data diversity. Then, the initial dataset is filtered based on the VIN and the timestamp corresponding to each data point. If the number of VINs is less than or equal to the preset threshold, historical initial driving data and historical initial air conditioning data for other vehicles are obtained. If the number of VINs exceeds the preset threshold, the initial dataset is filtered based on the door open / closed status data and the air conditioning status (on) data from the historical initial driving data. Simultaneously, to facilitate subsequent data analysis, the timestamp corresponding to each data point is converted to a string format. Duplicates in the filtered initial dataset are removed according to the VIN and the timestamp. The initial dataset after removing duplicates is split according to the VIN and sorted in ascending order based on the timestamp to obtain the time-series data for each vehicle. The first dataset is obtained based on the time-series data of each vehicle in the vehicle set.

[0066] By obtaining the vehicle identification number (VIN) and the timestamp corresponding to each data point in the initial dataset, and then filtering the initial dataset based on the VIN and the timestamp, the first dataset is obtained. This ensures the diversity and validity of historical target data in the target dataset, thereby improving the accuracy of obtaining the target vehicle air conditioning correlation parameters.

[0067] Optionally, the target dataset is filtered based on the frequency of label occurrence to obtain target vehicle air conditioning association parameters, including:

[0068] Tags whose frequency of occurrence exceeds a preset threshold are identified as the first tag;

[0069] The target dataset is filtered based on the first label to obtain a second dataset associated with the first label;

[0070] The target vehicle air conditioner association parameters are obtained from the second dataset associated with the first label.

[0071] The preset threshold can be set according to actual needs. The first label is the label in the target dataset that is greater than the preset threshold. The second dataset is the dataset associated with the first label, obtained by filtering the target dataset based on the first label.

[0072] Specifically, the method for determining a label whose frequency of occurrence exceeds a preset threshold as the first label can be as follows: if there is a label whose frequency of occurrence exceeds the preset threshold, then that label is determined as the first label. For example, it could be to obtain the frequency of occurrence of each label in the target dataset; if the frequency of occurrence of labels C1 and E2 exceeds the preset threshold of 0.6, then labels C1 and E2 are determined as the first labels, the automatic air conditioner status is fixed as C1, and the air conditioner circulation status is fixed as E2.

[0073] Specifically, the method of filtering the target dataset based on the first label to obtain the second dataset associated with the first label can be as follows: filter the target dataset based on the first label, that is, fix the first label, filter the data associated with the first label in the target dataset, and generate the second dataset based on the data associated with the first label.

[0074] Specifically, the method for obtaining the target vehicle air conditioner association parameters based on the second dataset associated with the first label can be as follows: use the target association rule algorithm to obtain the frequency of occurrence of each label in the second dataset, solidify the label with the highest frequency, repeatedly filter the second dataset based on the solidified label, until the second dataset is completely filtered, and obtain the target vehicle air conditioner association parameters based on the data corresponding to the solidified label.

[0075] Labels whose frequency of occurrence exceeds a preset threshold are identified as first labels; the target dataset is filtered based on the first label to obtain a second dataset associated with the first label; the target vehicle air conditioning association parameters are obtained based on the second dataset associated with the first label. This method can obtain air conditioning data that most users frequently use, remove air conditioning data with extremely low usage, and improve the efficiency and accuracy of obtaining the target vehicle air conditioning association parameters.

[0076] Optionally, the target vehicle air conditioning association parameters are obtained based on the second dataset associated with the first label, including:

[0077] The data in the second dataset are classified according to different air conditioning temperature ranges to obtain the historical target data and the labels corresponding to the historical target data for different air conditioning temperature ranges.

[0078] The frequency of occurrence of historical target data and corresponding tags for each air conditioning temperature range is statistically analyzed to obtain the target tag with the highest frequency of occurrence.

[0079] Target vehicle air conditioning associated parameters are generated based on the historical target data corresponding to each air conditioning temperature range and the target label of each air conditioning temperature range.

[0080] Different air conditioning temperature ranges can be set according to actual needs. For example, different air conditioning temperature ranges can be set as [15~20], [21~25], [26~30] and [31~35].

[0081] Specifically, the method for classifying the data in the second dataset according to different air conditioning temperature ranges to obtain the historical target data and the corresponding labels for each air conditioning temperature range can be as follows: classify the data in the second dataset according to the set different air conditioning temperature ranges to obtain the historical target data and the corresponding labels for each air conditioning temperature range.

[0082] Specifically, the method for obtaining the target label with the highest frequency of occurrence by statistically analyzing the historical target data and the corresponding labels for each air conditioning temperature range can be as follows: Calculate the frequency of occurrence of the historical target data and the corresponding labels for each air conditioning temperature range, obtain the label with the highest frequency for each air conditioning temperature range, and solidify the label with the highest frequency. Based on the solidified label, continue filtering the historical target data and the corresponding labels for each air conditioning temperature range, repeating this process to obtain new target labels until the entire second dataset has been filtered. It should be noted that if multiple labels have the same and highest frequency in the same historical target data, then multiple target labels can exist in the same historical target data. It should also be noted that another method for obtaining target labels is: statistically analyze the frequency of occurrence of the historical target data and the corresponding labels for each air conditioning temperature range, and determine the labels with a frequency greater than a preset frequency threshold as target labels.

[0083] Specifically, the method for generating target vehicle air conditioning association parameters based on the historical target data corresponding to the target label of each air conditioning temperature range can be as follows: associate each air conditioning temperature range with the target label obtained in each air conditioning temperature range and the historical target data corresponding to the target label, thereby generating target vehicle air conditioning association parameters under different air conditioning temperature ranges.

[0084] For example, the target vehicle air conditioning associated parameters are shown in Table 4:

[0085] Table 4

[0086]

[0087]

[0088] By classifying the data in the second dataset according to different air conditioning temperature ranges, historical target data and corresponding labels for each air conditioning temperature range are obtained. The frequency of occurrence of the historical target data and corresponding labels for each air conditioning temperature range is statistically analyzed to obtain the target label with the highest frequency. Target vehicle air conditioning association parameters are generated based on the historical target data corresponding to each air conditioning temperature range and its target label. This allows for separate analysis of data within different air conditioning temperature ranges, revealing typical common mainstream user preferences. This results in target vehicle air conditioning association parameters for different air conditioning temperature ranges, enabling users to automatically adjust other parameters of the vehicle air conditioning system based on the air conditioning temperature range specified in the target vehicle air conditioning association parameters. This reduces user intervention in the air conditioning control system, enhancing the user's intelligent experience and driving safety.

[0089] The technical solution of this embodiment obtains a target dataset, which includes historical target data and corresponding labels for the historical target data. The historical target data includes historical target air conditioning data for each vehicle in a vehicle set within a preset time period. The frequency of occurrence of each label in the target dataset is obtained. Based on the frequency of occurrence of the labels, the target dataset is filtered to obtain target vehicle air conditioning associated parameters. This solves the problem of low intelligence level of vehicle air conditioning and cumbersome user operation caused by the lack of detailed analysis of vehicle air conditioning associated parameters based on users' historical usage data. It can automatically adjust other associated vehicle air conditioning parameters based on any one of the target vehicle air conditioning associated parameters, thereby saving users from operating the air conditioning control system and improving the user's intelligent experience and driving safety.

[0090] Example 2

[0091] Figure 2 This is a schematic diagram of a vehicle air conditioning related parameter determination device according to Embodiment 2 of the present invention. This embodiment is applicable to situations involving the determination of vehicle air conditioning related parameters. The device can be implemented using software and / or hardware, and can be integrated into any device that provides the function of determining vehicle air conditioning related parameters, such as… Figure 2 As shown, the vehicle air conditioning associated parameter determination device specifically includes: a first acquisition module 210, a second acquisition module 220, and a obtaining module 230.

[0092] The first acquisition module 210 is used to acquire a target dataset, wherein the target dataset includes: historical target data and labels corresponding to the historical target data, and the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period;

[0093] The second acquisition module 220 is used to acquire the frequency of occurrence of each label in the target dataset;

[0094] The module 230 is used to filter the target dataset based on the frequency of tag occurrence to obtain the target vehicle air conditioning association parameters.

[0095] Optionally, the first acquisition module is specifically used for:

[0096] Obtain an initial dataset, filter the data in the initial dataset to obtain a first dataset, wherein the initial dataset includes: historical initial driving data and historical initial air conditioning data of each vehicle in the vehicle set within a preset time period;

[0097] The historical target data in the first dataset is labeled to obtain the labels corresponding to the historical target data.

[0098] Generate a target dataset based on historical target data and the corresponding labels.

[0099] Optionally, the first acquisition module is specifically used for:

[0100] Obtain the vehicle identification number and the timestamp corresponding to each data point from the initial dataset;

[0101] The initial dataset is filtered based on the vehicle identification number and the timestamp corresponding to each data point to obtain the first dataset.

[0102] Optionally, the obtaining module is specifically used for:

[0103] Tags whose frequency of occurrence exceeds a preset threshold are identified as the first tag;

[0104] The target dataset is filtered based on the first label to obtain a second dataset associated with the first label;

[0105] The target vehicle air conditioner association parameters are obtained from the second dataset associated with the first label.

[0106] Optionally, the obtaining module is specifically used for:

[0107] The data in the second dataset are classified according to different air conditioning temperature ranges to obtain the historical target data and the labels corresponding to the historical target data for different air conditioning temperature ranges.

[0108] The frequency of occurrence of historical target data and corresponding tags for each air conditioning temperature range is statistically analyzed to obtain the target tag with the highest frequency of occurrence.

[0109] Target vehicle air conditioning associated parameters are generated based on the historical target data corresponding to each air conditioning temperature range and the target label of each air conditioning temperature range.

[0110] Optionally, the target vehicle air conditioning associated parameters include at least two of the following: air conditioning temperature, air conditioning mode, air conditioning automatic status, air conditioning air volume, and air conditioning circulation status.

[0111] The above-described products can perform the methods provided in any embodiment of the present invention, and have the corresponding functional modules and beneficial effects for performing the methods.

[0112] Example 3

[0113] Figure 3 This is a schematic diagram of an electronic device according to Embodiment 3 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0114] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0115] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0116] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method for determining vehicle air conditioning associated parameters.

[0117] In some embodiments, the vehicle air conditioning associated parameter determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle air conditioning associated parameter determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle air conditioning associated parameter determination method by any other suitable means (e.g., by means of firmware).

[0118] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0121] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0122] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0123] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0124] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining vehicle air conditioning correlation parameters, characterized in that, include: Obtain the target dataset, wherein the target dataset includes: historical target data and labels corresponding to the historical target data, wherein the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period; Obtain the frequency of occurrence of each label in the target dataset; The target dataset is filtered based on the frequency of tag occurrence to obtain the target vehicle air conditioning association parameters, including: Tags whose frequency of occurrence exceeds a preset threshold are identified as the first tag; The target dataset is filtered based on the first label to obtain a second dataset associated with the first label; The target vehicle air conditioner association parameters are obtained from the second dataset associated with the first label, including: The data in the second dataset are classified according to different air conditioning temperature ranges to obtain the historical target data and the labels corresponding to the historical target data for different air conditioning temperature ranges. The frequency of occurrence of historical target data and corresponding tags for each air conditioning temperature range is statistically analyzed to obtain the target tag with the highest frequency of occurrence. Target vehicle air conditioning associated parameters are generated based on the historical target data corresponding to each air conditioning temperature range and the target label of each air conditioning temperature range.

2. The method according to claim 1, characterized in that, Obtain the target dataset, including: Obtain an initial dataset, filter the data in the initial dataset to obtain a first dataset, wherein the initial dataset includes: historical initial driving data and historical initial air conditioning data of each vehicle in the vehicle set within a preset time period; The historical target data in the first dataset is labeled to obtain the labels corresponding to the historical target data. Generate a target dataset based on historical target data and the corresponding labels.

3. The method according to claim 1, characterized in that, The data in the initial dataset is filtered to obtain the first dataset, which includes: Obtain the vehicle identification number and the timestamp corresponding to each data point from the initial dataset; The initial dataset is filtered based on the vehicle identification number and the timestamp corresponding to each data point to obtain the first dataset.

4. The method according to claim 1, characterized in that, The target vehicle air conditioning associated parameters include at least two of the following: air conditioning temperature, air conditioning mode, air conditioning automatic status, air conditioning air volume, and air conditioning circulation status.

5. A device for determining vehicle air conditioning related parameters, characterized in that, include: The first acquisition module is used to acquire a target dataset, wherein the target dataset includes: historical target data and labels corresponding to the historical target data, and the historical target data includes: historical target air conditioning data of each vehicle in the vehicle set within a preset time period; The second acquisition module is used to acquire the frequency of occurrence of each label in the target dataset; The module is used to filter the target dataset based on the frequency of tag occurrence to obtain the target vehicle air conditioning association parameters; The obtaining module is specifically used to determine labels whose occurrence frequency is greater than a preset threshold as first labels; to filter the target dataset according to the first labels to obtain a second dataset associated with the first labels; and to obtain target vehicle air conditioning association parameters according to the second dataset associated with the first labels, including: classifying the data in the second dataset according to different air conditioning temperature ranges to obtain historical target data and labels corresponding to different air conditioning temperature ranges; statistically analyzing the occurrence frequency of historical target data and labels corresponding to historical target data for each air conditioning temperature range to obtain the target label with the highest occurrence frequency; and generating target vehicle air conditioning association parameters according to historical target data corresponding to each air conditioning temperature range and the target label for each air conditioning temperature range.

6. The apparatus according to claim 5, characterized in that, The first acquisition module is specifically used for: Obtain an initial dataset, filter the data in the initial dataset to obtain a first dataset, wherein the initial dataset includes: historical initial driving data and historical initial air conditioning data of each vehicle in the vehicle set within a preset time period; The historical target data in the first dataset is labeled to obtain the labels corresponding to the historical target data. Generate a target dataset based on historical target data and the corresponding labels.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle air conditioning associated parameter determination method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for determining vehicle air conditioning associated parameters as described in any one of claims 1-4.

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