Breathing point temperature estimation method, computer equipment and storage medium

Abstract

The invention relates to a breathing point temperature estimation method, computer equipment and a storage medium. The breathing point temperature estimation method comprises the steps that an estimation equation set of the breathing point temperature of a user in a vehicle is established according to the temperature in the vehicle; determining a key coefficient in the estimation equation set according to the sunlight intensity and the air outlet temperature of the vehicle-mounted air conditioner; and performing discrete Kalman filtering circulation on the estimation equation set to obtain an estimated value of the breathing point temperature. According to the method, the breathing point temperature of the user in the vehicle can be accurately estimated by measuring the temperature in the vehicle, combining key factors such as the sunlight intensity and the air outlet temperature of the vehicle-mounted air conditioner and using discrete Kalman filtering circulation.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle air conditioning, and in particular to a breathing point temperature estimation method, a computer device and a storage medium. Background Art

[0002] In the automotive industry, automatic air conditioning has gradually become the standard configuration of automotive air conditioning, and the core algorithm of automatic air conditioning is to adjust the compressor, damper, valve and pump according to the feedback of the driver's breathing point temperature in the car, so as to achieve temperature control in the car. Therefore, the breathing point temperature is the most critical feedback variable, which must be able to reflect the real breathing point temperature of the passengers in the car's cab, that is, the passenger breathing point temperature. The temperature in the car is usually collected by the internal temperature sensor, but it is usually arranged in the leg position of the center console in the car or directly integrated on the car air conditioning panel, which is not the real passenger breathing point temperature required for automatic air conditioning control. This results in that the measured value cannot be directly used for automatic air conditioning control, but must be converted by technical means for use. Therefore, how to obtain the breathing point temperature required for air conditioning control based on the internal temperature acquisition value is one of the key technologies in automatic air conditioning control. Summary of the invention

[0003] The purpose of this application is to provide a breathing point temperature estimation method, computer equipment and storage medium, which can solve the above-mentioned technical problems. By measuring the temperature inside the car, combining key factors such as sunlight intensity and the air outlet temperature of the car air conditioner, and using a discrete Kalman filter loop, the breathing point temperature of the user in the car can be accurately estimated.

[0004] In order to solve the above technical problems, in a first aspect, the present application provides a breathing point temperature estimation method, comprising:

[0005] Establishing an estimation equation group of the breathing point temperature of the user in the car according to the temperature in the car;

[0006] Determining key coefficients in the estimation equation group according to sunlight intensity and air outlet temperature of the vehicle air conditioner;

[0007] A discrete Kalman filter loop is executed based on the estimation equation group to obtain an estimate of the breathing point temperature.

[0008] In one embodiment, the estimation equation group includes a state equation and a measurement equation, the state equation is used to describe the linear change of the breathing point temperature over time, and the measurement equation is used to describe the linear relationship between the in-vehicle temperature and the breathing point temperature.

[0009] In one embodiment, determining the key coefficients in the estimation equation group according to the sunlight intensity and the air outlet temperature of the air conditioner includes at least one of the following:

[0010] Determining a first key coefficient of the state equation based on sunlight intensity and hysteresis effect;

[0011] The second key coefficient of the measurement equation is determined according to the sunlight intensity and the air outlet temperature of the vehicle air conditioner.

[0012] In one implementation, the expression of the first key coefficient is:

[0013] k1(t)=e -αt C s (t-1)+(1-e -αt )C s (t)

[0014] Where k1(t) is the first key coefficient at time t, α is the hysteresis coefficient, and C s (t) is the coefficient corresponding to the sunlight s at time t.

[0015] In one embodiment, the expression of the second key coefficient is:

[0016]

[0017] k2(t)=aξ(t) b +c

[0018] Wherein, k2(t) is the second key coefficient, a, B, and c are all fitting coefficients, ξ(t) is the intermediate variable parameter about the second key coefficient, y(t) is the outlet temperature of the vehicle air conditioner at time t, λ is a parameter between 0 and 1, and s(t) is the sunlight intensity.

[0019] In one embodiment, performing a discrete Kalman filter loop based on the estimation equation set includes:

[0020] Constructing a Kalman filter, wherein the Kalman filter includes a time update equation and a measurement update equation;

[0021] Execute a time updating step according to the time updating equation;

[0022] Performing a measurement update step according to the measurement update equation;

[0023] The time updating step and the measurement updating step are repeatedly and alternately executed until the Kalman filter converges.

[0024] In one embodiment, the step of performing a time update according to the time update equation comprises:

[0025] The estimated breathing point temperature and the error covariance at the current moment are predicted based on the estimated breathing point temperature at the previous moment.

[0026] In one embodiment, the step of performing the measurement update step according to the measurement update equation comprises:

[0027] The Kalman gain is obtained according to the error covariance at the current moment;

[0028] The breathing point temperature estimation value and the error covariance at the current moment are corrected according to the vehicle interior temperature at the next moment and the Kalman gain.

[0029] In a second aspect, the present application also provides a computer device, including: a processor, a memory, and a network interface;

[0030] The processor is connected to the memory and the network interface, wherein the network interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the breathing point temperature estimation method as described above.

[0031] In a third aspect, the present application also provides a computer-readable storage medium, in which a computer program is stored. The computer program is suitable for being loaded and executed by a processor so that a computer device having the processor executes the breathing point temperature estimation method as described above.

[0032] The breathing point temperature estimation method, computer device and storage medium of the present application The breathing point temperature estimation method comprises: establishing an estimation equation group of the breathing point temperature of the user in the car according to the temperature in the car; determining the key coefficients in the estimation equation group according to the sunlight intensity and the air outlet temperature of the car air conditioner; executing a discrete Kalman filter cycle on the estimation equation group to obtain an estimated value of the breathing point temperature. The present application can accurately estimate the breathing point temperature of the user in the car by measuring the temperature in the car, combining key factors such as the sunlight intensity and the air outlet temperature of the car air conditioner, and using a discrete Kalman filter cycle.

[0033] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specifically cites the preferred embodiments and describes them in detail with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a flowchart of a breathing point temperature estimation method according to an embodiment of the present application.

[0035] Figure 2 It is a schematic diagram of a fitting curve of the second key coefficient shown in an embodiment of the present application.

[0036] Figure 3It is a specific flow chart of estimating breathing point temperature according to the discrete calculation method of the Kalman filter shown in the embodiment of the present application.

[0037] Figure 4 It is a schematic diagram comparing the temperature result of the breathing point temperature estimation method according to the embodiment of the present application with the temperature result of the existing method and the actual temperature.

[0038] Figure 5 This is one of the schematic diagrams comparing the errors of the breathing point temperature estimation method according to the embodiment of the present application and the existing method.

[0039] Figure 6 This is the second schematic diagram of the error comparison between the breathing point temperature estimation method according to the embodiment of the present application and the existing method.

[0040] Figure 7 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined application purpose, the specific implementation methods, methods, steps, structures, features and effects of the breathing point temperature estimation method, computer equipment and storage medium proposed in the present application are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0042] The aforementioned and other technical contents, features and effects of the present application will be clearly presented in the following detailed description of the preferred embodiments with reference to the drawings. Through the description of the specific implementation methods, the technical means and effects adopted by the present application to achieve the predetermined purpose can be more deeply and specifically understood. However, the drawings are only for reference and explanation purposes and are not used to limit the present application.

[0043] Please refer to Figure 1 In order to quickly and accurately calculate the temperature of the breathing point of the personnel in the cab, the embodiment of the present application provides a breathing point temperature estimation method, including:

[0044] Step S1, establishing a group of estimation equations for the breathing point temperature of the user in the vehicle according to the temperature in the vehicle.

[0045] In this embodiment, the in-car temperature refers to the overall ambient temperature in the car, which can be collected by an internal temperature sensor. The breathing point temperature refers to the temperature of the passenger's head, especially the temperature of the area near the nostrils or mouth during breathing. This temperature can reflect the changes in the core temperature of the human body and the impact of environmental factors on the human body. The present application introduces the in-car temperature into the estimation equation group to estimate the breathing point temperature of the user in the car.

[0046] In some embodiments, the estimation equation group includes a state equation and a measurement equation, the state equation is used to describe the linear change of the breathing point temperature over time, and the measurement equation is used to describe the linear relationship between the temperature inside the vehicle and the breathing point temperature.

[0047] As an implementation in this embodiment, the state equation of the breathing point temperature of the passenger's face in the vehicle can be expressed by a generalized function expression as follows:

[0048] x k =f(x k-1 ,u k-1 , w k-1 ) (1)

[0049] In formula (1), x k represents the breathing point temperature at time k, x k-1 represents the breathing point temperature at time k-1, u k-1 represents the control variable at time k-1, w k-1 represents the process noise at time k-1, representing uncertainty in the model and unmodeled dynamics.

[0050] The independent variable in the measurement equation is the breathing point temperature, and the dependent variable is the in-vehicle temperature, which can be collected by the interior temperature sensor arranged in the cab. The relationship between the in-vehicle temperature and the breathing point temperature is as follows:

[0051] z k =h(x k )+v k (2)

[0052] In formula (2), z k represents the temperature inside the car at time k, v k represents the measurement noise.

[0053] Due to the complexity of the vehicle use environment, the passenger breathing point temperature is affected by multiple factors such as sunlight intensity, ambient temperature, vehicle speed, etc., and the function is actually a nonlinear function. However, the nonlinear relationship between these variables is not clear, which makes it difficult to directly derive the above theoretical mathematical formula, and thus the Kalman filter method cannot be used. However, it is found from a large number of engineering tests that the breathing point temperature of passengers in the car is actually a slowly changing process. Therefore, it can be considered that the breathing point temperature is an approximate linear change in a short period of time, and the nonlinear equation is linearized using the linear superposition principle.

[0054] Using the principle of linear superposition, the nonlinear equation is linearized. Specifically, the nonlinear function can be locally linearized first. Near each time point, the system is assumed to be linear. For example, near time t, the first-order Taylor expansion can be used to approximate the nonlinear function. Then, the nonlinear function is piecewise linearized. The entire process is divided into multiple small time periods, and the system is assumed to be linear in each time period. Then, a linear model is established to describe the relationship between these variables.

[0055] In this way, formula (1) can be simplified to the following formula (3):

[0056] x k =f′(x k-1 )+u k-1 +w k-1 (3)

[0057] In formula (3), f′(x k-1 ) is a linear function.

[0058] Due to the slow nature of temperature change, f′ can be further simplified into the following form:

[0059] f′(x k-1 )=k1x k-1 (4)

[0060] In formula (4), k1 represents an undetermined coefficient.

[0061] Finally, the state equation of the passenger breathing point temperature can be simplified as follows:

[0062] x k =k1x k-1 +u k-1 +w k-1 (5)

[0063] Similarly, formula (2) can also be simplified to the following formula (6):

[0064] z k =k2x k +v k (6)

[0065] In formula (6), k2 represents the coefficient to be determined.

[0066] Step S2, determining key coefficients in the estimation equation group according to the sunlight intensity and the air outlet temperature of the vehicle air conditioner.

[0067] In this embodiment, the key coefficient refers to a parameter in the state equation and the measurement equation that is used to describe the influence of different factors (such as sunlight intensity and the air outlet temperature of the vehicle air conditioner) on the system state.

[0068] In some embodiments, the key coefficients in the estimation equation group are determined according to the sunlight intensity and the air outlet temperature of the air conditioner, including at least one of the following:

[0069] Determine the first critical coefficient of the state equation based on sunlight intensity and hysteresis effect;

[0070] The second key coefficient of the measurement equation is determined according to the sunlight intensity and the air outlet temperature of the vehicle air conditioner.

[0071] In some embodiments, according to the analysis of actual test data, the sunlight intensity has a greater impact on the breathing point temperature. Considering the hysteresis effect of the temperature inside the car, the expression of the first key coefficient can be obtained as follows:

[0072] k1(t)=e -αt C s (t-1)+(1-e -αt )C s (t) (7)

[0073] Where k1(t) is the first key coefficient at time t, α is the hysteresis coefficient, and C s (t) is the coefficient corresponding to the sunlight s at time t. s The (t) value is different, and it can be reversely approximated using the Kalman filter calculation based on the actual measured value.

[0074] It should be noted that the hysteresis effect refers to the fact that in the in-car environment, the change in temperature is affected by many factors, including the external ambient temperature, sunlight intensity, the operating status of the vehicle air conditioner, etc. These factors cause the change in the temperature inside the car to be not instantaneous, but there is a certain delay and lag. For example, the materials inside the vehicle (such as seats, dashboards, windows, etc.) have thermal inertia and absorb and release heat slowly. When the external temperature changes, it takes some time for the materials inside the car to reach a new equilibrium temperature. The flow rate of the air inside the car will also affect the change in temperature. For example, when the air conditioner is turned on, it takes a certain amount of time for the cold air to be evenly distributed throughout the car. The accuracy of the in-car temperature estimation can be improved by reasonable modeling and processing of the hysteresis effect.

[0075] In some embodiments, for the measurement point z k , the outlet temperature y(t) and sunlight s(t) have the greatest impact on it. By designing an intermediate variable parameter ξ(t), the function of the coefficient k2(t) about the variable ξ(t) can be obtained, that is, k2(t) = g(ξ(t)). According to the actual collected data, the corresponding k2(t) value and the calculated value ξ(t) can be calculated, so that the expression of g(ξ(t)) can be obtained by fitting. According to the actual data analysis, the exponential fitting formula is more suitable. Therefore, the expression of the second key coefficient can be obtained as follows:

[0076]

[0077] k2(t)=aξ(t) b +c (9)

[0078] Wherein, k2(t) is the second key coefficient, a, B, and c are all fitting coefficients, ξ(t) is the intermediate variable parameter about the second key coefficient, y(t) is the outlet temperature of the vehicle air conditioner at time t, λ is a parameter between 0 and 1, and s(t) is the sunlight intensity.

[0079] Please refer to Figure 2 , provides an example of modeling a certain vehicle model using the breathing point temperature estimation method of an embodiment of the present application. Figure 2 The horizontal axis is the value of the intermediate variable ξ(t), and the vertical axis is the value of the second key coefficient k2(t). The data in the blue point set are obtained by collecting test data, and the red line is the fitting curve for the blue point set. In this way, the expression of the second key coefficient can be obtained as follows:

[0080] k2(t)=-0.000122ξ(t) 0.0112 +1.137 (10)

[0081] Based on the expressions of the first key coefficient and the second key coefficient, the first key coefficient and the second key coefficient can be calculated. Then, the specific expression of the estimation equation group is obtained according to the first key coefficient and the second key coefficient. On this basis, the breathing point temperature can be estimated according to the discrete calculation method of the Kalman filter.

[0082] Step S3, executing a discrete Kalman filter loop based on the estimation equations to obtain an estimated value of the breathing point temperature.

[0083] In this embodiment, a discrete Kalman filter loop is executed based on the estimation equations, including:

[0084] Construct a Kalman filter, which includes a time update equation and a measurement update equation;

[0085] Perform a time update step according to a time update equation;

[0086] performing a measurement update step according to a measurement update equation;

[0087] Repeat the alternating time update step and measurement update step until the Kalman filter converges.

[0088] It should be noted that the Kalman filter is a recursive algorithm used to estimate the state of a linear dynamic system. The Kalman filter cycle is a process of gradually improving the state estimate by alternating between the time update step and the measurement update step. Specifically, the breathing point temperature estimate and the initial error covariance are first initialized, and the process noise covariance Q and the measurement noise covariance R are set. Next, the time update step is executed to predict the breathing point temperature at the current moment according to the state equation, and the prediction error covariance is calculated according to the predicted state. Next, the measurement update step is executed to calculate the Kalman gain according to the measurement equation, and the predicted state is corrected using the Kalman gain to obtain a more accurate estimate, and the update error covariance is updated. Repeat the alternating execution of the time update step and the measurement update step at each time step until the Kalman filter converges or reaches a predetermined stop condition.

[0089] The convergence of the Kalman filter means that as data processing proceeds, the state estimate of the filter gradually approaches the true state, and the estimation error gradually decreases. The signs of the convergence of the Kalman filter include but are not limited to the estimation error tending to be stable or fluctuating within a small range, and / or the error covariance matrix tending to be stable or decreasing.

[0090] In some embodiments, performing the time updating step according to the time updating equation includes:

[0091] The estimated breathing point temperature and the error covariance at the current moment are predicted based on the estimated breathing point temperature at the previous moment.

[0092] As an implementation of this embodiment, the expression of the time update equation includes:

[0093] Extrapolate state variables forward:

[0094]

[0095] Extrapolate the error covariance forward:

[0096]

[0097] In formulas (11) and (12), x k represents the estimated value of the breathing point temperature at time k, x k-1 represents the estimated value of the breathing point temperature at time k-1, that is, the initial temperature estimate, represents the error covariance at time k, P k-1 represents the error covariance at time k-1, that is, the initial error covariance.

[0098] In some embodiments, performing the measurement update step according to the measurement update equation includes:

[0099] The Kalman gain is obtained according to the error covariance at the current moment;

[0100] The estimated breathing point temperature and error covariance at the current moment are corrected according to the in-vehicle temperature at the next moment and the Kalman gain.

[0101] As an implementation of this embodiment, the expression of the measurement update equation includes:

[0102] Calculate the Kalman gain:

[0103]

[0104] From the observed variable z k Update estimates:

[0105]

[0106] Update the error covariance:

[0107]

[0108] In formulas (13) to (15), K k represents the Kalman gain.

[0109] The above formula allows a discrete Kalman filter loop to be executed based on the estimation equations, thereby gradually improving the estimate of the breathing point temperature. This process includes constructing a Kalman filter, executing a time update step and a measurement update step, and performing these steps alternately until the filter converges. By combining actual measurement data and mathematical models, a more accurate estimate of the breathing point temperature can be provided.

[0110] Optionally, the following is a specific implementation of the breathing point temperature estimation method of the embodiment of the present application. Figure 3 , shows the process of estimating the breathing point temperature using the discrete calculation method of the Kalman filter. First, according to the old estimate The state propagation equation (see formula (11)) is constructed by using the state transfer matrix k1 (i.e., the first key coefficient) to predict the state at the next moment. At the same time, based on the old covariance k1P k-1 and system noise Q, predict the covariance matrix of the next moment (See formula (12)). Next, the Kalman gain K is calculated based on the prediction covariance matrix, the observation noise R and the observation matrix k2 (i.e., the second key coefficient): k (See formula (13)). Finally, according to the observed variable z k , observation matrix k2 and Kalman gain K k Update old estimates Get a new estimate (See formula (14)). At the same time, according to the observation matrix k2 and the Kalman gain K k Update the old covariance matrix Get the new covariance matrix P k (See formula (15)). In the above process, the Kalman filter first predicts the new state and covariance based on the previous estimate and covariance. Then, the Kalman gain is calculated based on the difference between the actual observation and the predicted value, and the state estimate and covariance are corrected accordingly. Finally, the updated state and covariance are passed to the next time step and the process continues to iterate.

[0111] Please refer to Figure 4 , is a schematic diagram comparing the temperature results of the breathing point temperature estimation method shown in the embodiment of the present application with the temperature results of the existing method and the actual temperature. Figure 4 As shown, the blue dots represent the breathing point temperature calculated by the embodiment of the present application, the green dots represent the breathing point temperature actually collected, and the red dots represent the breathing point temperature calculated by the calibration method. It can be seen that the breathing point temperature calculated by the embodiment of the present application has a higher coincidence rate with the breathing point temperature actually collected, and the use of the breathing point temperature estimation method of the present application can significantly improve the estimation accuracy.

[0112] In order to see the difference clearly, the errors obtained by the method of the embodiment of the present application and the calibration method of the prior art can be compared on the same curve, such as Figure 5 As shown, blue represents the error of the breathing point temperature obtained by the method of the embodiment of the present application, and red represents the error of the breathing point temperature obtained by the calibration method. It can be seen that the error of the breathing point temperature obtained by the breathing point temperature estimation method of the present application is within 0.5° in most cases. The error of the calibration method is higher than that of the method of the embodiment of the present application. It should be noted that in the initial stage, the error value calculated by the method of the embodiment of the present application is slightly higher than the calibration method, but this has no effect, because the use requirement of the automobile air conditioner is stability in the later stage, that is, the smaller the error in the later stage, the better, and a slightly larger error in the early stage has no effect.

[0113] Furthermore, if Figure 6 As shown, the calculation errors of the two methods can also be classified and counted to achieve a clearer comparison. First, the errors are divided into three groups: (1) The first group is the absolute value of the error within 0.5°; (2) The second group is the absolute value of the error between 0.5° and 1°; (3) The third group is the absolute value of the error above 1°. Obviously, the larger the proportion value of the first group, the higher the accuracy of the calculated value of the passenger's breathing point temperature.

[0114] Then, we statistically analyze the error distribution obtained by the method of the embodiment of the present application and the calibration method in the prior art to obtain the result. Figure 6(a) is the error obtained by using the calibration method of the prior art, Figure 6 (b) is the error obtained by the method of the embodiment of the present application. It can be seen from the figure that the data ratio of the first group reaches more than 85% by the method of the embodiment of the present application, which is much higher than the 63% ratio of the calibration method. In addition, in the method of the embodiment of the present application, the ratio of the third group is also much lower than the third group error of the calibration method, indicating that the ratio of the calculated value with large deviation in the embodiment of the present application is much smaller than that in the calibration method, while the ratio of the calculated value with small deviation is much higher than that in the calibration method.

[0115] The embodiment of the present application applies the Kalman filter concept to the calculation of the breathing point temperature of the car, so that the temperature estimation gets rid of the industry status quo of relying heavily on road calibration for a long time, and provides strong theoretical support for the temperature calculation, rather than relying on calibration trial and error. Secondly, a calculation equation for the breathing point temperature in the car is established, and a method of combining theoretical calculation and data fitting is adopted to make the theoretical formula more universal and adaptable to different models. Finally, the algorithm based on discrete recursion can quickly converge the breathing point temperature, greatly shorten the calibration time, save manpower and material resources, and is of particular practical value to car companies.

[0116] The breathing point temperature estimation method of the embodiment of the present application establishes an estimation equation group of the breathing point temperature of the user in the car according to the temperature in the car; determines the key coefficients in the estimation equation group according to the sunlight intensity and the air outlet temperature of the car air conditioner; and executes a discrete Kalman filter cycle on the estimation equation group to obtain an estimated value of the breathing point temperature. The present application can accurately estimate the breathing point temperature of the user in the car by measuring the temperature in the car, combining key factors such as the sunlight intensity and the air outlet temperature of the car air conditioner, and using a discrete Kalman filter cycle.

[0117] Based on the same inventive concept as the above embodiments, the embodiments of the present invention further provide an electronic device, such as Figure 7 As shown, the electronic device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 7 The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Figure 7 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the above method is implemented.

[0118] The electronic device may also include: at least one network interface 312. The various components in the electronic device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 313 is not described in detail. Figure 7 Various buses are labeled as bus system 313.

[0119] The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present invention is intended to include but is not limited to these and any other suitable types of memories.

[0120] The memory 311 in the embodiment of the present invention is used to store various types of data to support the operation of the electronic device. Examples of these data include: any computer program used to operate on the electronic device, such as an operating system and an application; contact data; phone book data; messages; pictures; videos, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application program may include various applications, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements the method of the embodiment of the present invention may be included in the application program.

[0121] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); or various devices including one or any combination of the above memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is run by the processor, the above method or system is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to the description of the above embodiment, which will not be repeated here.

[0122] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0124] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A breathing point temperature estimation method, characterized in that: include: Establishing an estimation equation group of the breathing point temperature of the user in the car according to the temperature in the car; Determining key coefficients in the estimation equation group according to sunlight intensity and air outlet temperature of the vehicle air conditioner; A discrete Kalman filter loop is executed based on the estimation equation group to obtain an estimate of the breathing point temperature.

2. The method according to claim 1, characterized in that The estimation equation group includes a state equation and a measurement equation, wherein the state equation is used to describe the linear change of the breathing point temperature over time, and the measurement equation is used to describe the linear relationship between the in-vehicle temperature and the breathing point temperature.

3. The method according to claim 2, characterized in that The key coefficients in the estimation equation group are determined according to the sunlight intensity and the air outlet temperature of the air conditioner, including at least one of the following: Determining a first key coefficient of the state equation based on sunlight intensity and hysteresis effect; The second key coefficient of the measurement equation is determined according to the sunlight intensity and the air outlet temperature of the vehicle air conditioner.

4. The method according to claim 3, characterized in that The expression of the first key coefficient is: k1(t)=e -αt C s (t-1)+(1-e -αt )C s (t) Where k1(t) is the first key coefficient at time t, α is the hysteresis coefficient, and C s (t) is the coefficient corresponding to the sunlight s at time t.

5. The method according to claim 3, characterized in that: The expression of the second key coefficient is: k2(t)=aξ(t) b +c Wherein, k2(t) is the second key coefficient, a, B, and c are all fitting coefficients, ξ(t) is the intermediate variable parameter about the second key coefficient, y(t) is the outlet temperature of the vehicle air conditioner at time t, λ is a parameter between 0 and 1, and s(t) is the sunlight intensity.

6. The method according to claim 2, characterized in that The performing of a discrete Kalman filter loop based on the estimation equation set comprises: Constructing a Kalman filter, wherein the Kalman filter includes a time update equation and a measurement update equation; Execute a time updating step according to the time updating equation; Performing a measurement update step according to the measurement update equation; The time updating step and the measurement updating step are repeatedly and alternately executed until the Kalman filter converges.

7. The method according to claim 6, characterized in that The step of performing a time update according to the time update equation comprises: The estimated breathing point temperature and the error covariance at the current moment are predicted based on the estimated breathing point temperature at the previous moment.

8. The method according to claim 6, characterized in that The step of performing the measurement update according to the measurement update equation comprises: The Kalman gain is obtained according to the error covariance at the current moment; The breathing point temperature estimation value and the error covariance at the current moment are corrected according to the vehicle interior temperature at the next moment and the Kalman gain.

9. A computer device, characterized in that: include: processor and memory and network interface; The processor is connected to the memory and the network interface, wherein the network interface is used to provide a data communication function, the memory is used to store a computer program, and the processor is used to call the computer program so that the computer device executes the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded and executed by a processor, so that a computer device having the processor executes the method according to any one of claims 1 to 8.

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