A method, apparatus, device and medium for determining an emotion detection frequency

Abstract

The application discloses a kind of mood detection frequency determination method, device, equipment and medium, it is related to intelligent vehicle technical field, the method includes: obtaining mood detection reference frequency, and the vehicle driving information of total distance that vehicle driver has driven in current time period;According to vehicle driving information, the mood abnormality probability of vehicle driver in future time period is predicted;Wherein, future time period is adjacent to current time period, and located after current time period;According to vehicle driving information and mood abnormality probability in future time period, mood detection reference frequency is adjusted, obtains the mood detection frequency of future time period, to be detected according to mood detection frequency, vehicle driver is carried out mood detection in future time period, the flexibility of mood detection is improved, and the computing resource and electric power resource of vehicle are saved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent vehicles, and in particular to a method and device for determining emotion detection frequency, equipment and a medium. BACKGROUND

[0002] With the development of intelligent vehicles and autonomous driving technology, intelligent management of the vehicle environment and detection of the driver's emotions become particularly important. Currently, the driver's emotion detection method mainly relies on visual information, which analyzes the driver's facial expressions in real time to determine their emotional state.

[0003] This prior art detects the driver's emotional state in real time during the vehicle driving process, continuously occupies high computing resources, and continuously consumes power resources during the vehicle driving process. Although the driver's emotions fluctuate during driving, they remain stable overall. The real-time emotion detection technology in the prior art has the defect of resource waste. SUMMARY

[0004] The present application provides a method and device for determining emotion detection frequency, equipment and a medium to improve the flexibility of emotion detection and save computing resources and power resources of the vehicle.

[0005] In a first aspect, the present application provides a method for determining emotion detection frequency, comprising:

[0006] obtaining an emotion detection reference frequency and vehicle driving information of the total distance driven by the driver in the current time period;

[0007] predicting the probability of emotional abnormalities of the driver in the future time period according to the vehicle driving information; wherein the future time period is adjacent to the current time period and located after the current time period;

[0008] adjusting the emotion detection reference frequency according to the vehicle driving information and the probability of emotional abnormalities in the future time period to obtain the emotion detection frequency in the future time period, and performing emotion detection on the driver in the future time period according to the emotion detection frequency.

[0009] In a second aspect, the present application also provides a device for determining emotion detection frequency, comprising:

[0010] an information acquisition module for obtaining an emotion detection reference frequency and vehicle driving information of the driver in the current time period;

[0011] a probability prediction module for predicting the probability of emotional abnormalities of the driver in the current time period according to the vehicle driving information in the current time period;

[0012] The frequency adjustment module is configured to adjust the emotion detection reference frequency according to the emotion abnormality probability in the current time period and the emotion abnormality probability in the future time period, to obtain an emotion detection frequency in the future time period, and to perform emotion detection on the vehicle driver in the future time period according to the emotion detection frequency; wherein the future time period is adjacent to the current time period and located after the current time period.

[0013] In a third aspect, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory in communication with the at least one processor; wherein

[0016] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for determining the emotion detection frequency according to any of the embodiments of the present application.

[0017] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores computer instructions, and the computer instructions are used to enable the processor to implement the method for determining the emotion detection frequency according to any of the embodiments of the present application when executed.

[0018] The technical scheme of the embodiments of the present application obtains the emotion detection reference frequency, and vehicle driving information of a total driving distance of the vehicle driver in the current time period; predicts the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information; wherein the future time period is adjacent to the current time period and located after the current time period; adjusts the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period, to obtain the emotion detection frequency in the future time period, and performs emotion detection on the vehicle driver in the future time period according to the emotion detection frequency. Compared with the real-time detection technical scheme in the prior art, the embodiments of the present application can predict the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information in the current time period, and flexibly adjust the emotion detection reference probability according to the emotion abnormality probability in the future time period and the vehicle driving information in the current time period. When predicting the emotion abnormality of the vehicle driver, the detection frequency is increased, and when predicting the stable emotion of the vehicle driver, the detection frequency is reduced. By intermittently detecting the emotion of the vehicle driver, the occupation of the computing resource is reduced, and the power resource of the vehicle is also saved.

[0019] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for determining the frequency of emotion detection according to Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of a method for determining the frequency of emotion detection according to Embodiment 2 of the present invention;

[0023] Figure 3 This is a flowchart of a method for determining the frequency of emotion detection according to Embodiment 3 of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of a device for determining the frequency of emotion detection according to Embodiment 4 of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the method for determining the emotion detection frequency according to an embodiment of the present invention. Detailed Implementation

[0026] 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.

[0027] It should be noted that the terms "first" and "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.

[0028] The acquisition, storage, and application of emotion detection benchmark frequencies and vehicle driving information involved in the technical solutions of this invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a method for determining the frequency of emotion detection provided in Embodiment 1 of the present invention. This embodiment can be applied to the situation of determining the frequency of emotion detection of a vehicle driver. The method can be executed by an emotion detection frequency determination device, which can be implemented in hardware and / or software and specifically configured in an electronic device, such as a server.

[0031] See Figure 1 The method for determining the frequency of emotion detection shown includes:

[0032] S101. Obtain the baseline frequency for emotion detection, as well as the vehicle driving information for the total distance driven by the driver in the current time period.

[0033] In this embodiment, the emotion detection baseline frequency can be a baseline value for the frequency of emotion detection for the vehicle driver. The current time period can be the time period to which the current real moment belongs. Vehicle driving information can include, but is not limited to, at least one of vehicle control information and external environmental information. It should be noted that the specific value of the emotion detection baseline frequency and the duration of the time period can be set independently by technicians based on real-time needs or practical experience, and this invention does not limit them.

[0034] In the current time period, the vehicle driving information of the total distance driven by the vehicle driver can refer to the total distance driven by the vehicle driver from the moment the vehicle was started until the current time period.

[0035] Optionally, vehicle control information may include, but is not limited to, horn frequency, continuous driving duration, and total driving duration; external environment information may include, but is not limited to, average waiting time at traffic lights and number of times overtaken. In other words, vehicle driving information may include, but is not limited to, horn frequency, average waiting time at traffic lights, continuous driving duration, total driving duration, and number of times overtaken.

[0036] The whistle frequency can be the frequency of the whistle of the vehicle driver. The signal light average waiting time length can be the average time length of the vehicle driver waiting for the signal light to change from the color indicating prohibition of passage to the color indicating permission of passage. The continuous driving time length can be the time length of the continuous driving of the vehicle by the vehicle driver. The total driving time length can be the total driving time length in one vehicle driving process; for example, if the vehicle driver does not stop in one vehicle driving process, the total driving time length is the same as the continuous driving time length; if the vehicle driver stops once in one vehicle driving process, the total driving time length is the sum of the stopping time length and the time length between the two continuous driving time lengths; wherein whether the two adjacent continuous driving time lengths belong to the same vehicle driving process can be determined by the length of the stopping time length between the two adjacent continuous driving time lengths. The number of overtaking can be the number of times that the vehicle driven by the vehicle driver is overtaken by other vehicles.

[0037] In one embodiment, for each time period, vehicle driving information of the vehicle in the time period is collected; if the starting time of the current time period is the vehicle starting time, that is, the current time period is the first time period after the vehicle starts, the vehicle driving information collected in the current time period is determined as the vehicle driving information of the total distance driven by the vehicle driver; if the starting time of the current time period is not the vehicle starting time, that is, there is a historical time period before the current time period, the vehicle driving information of the total distance driven by the vehicle driver in the historical time period and the vehicle driving information collected in the current time period are counted to obtain the vehicle driving information of the total distance driven by the vehicle driver in the current time period; wherein the historical time period is adjacent to the current time period and located before the current time period; the time length of the historical time period is the same as that of the current time period.

[0038] S102, according to the vehicle driving information, predicting the emotional abnormality probability of the vehicle driver in the future time period; wherein the future time period is adjacent to the current time period and located after the current time period.

[0039] In this embodiment, the emotional abnormality probability can refer to the probability that the emotion of the vehicle driver is abnormal, wherein the emotion being abnormal can refer to the occurrence of excessive emotions such as irritability and excitement. The time length of the future time period, the time length of the current time period and the time length of the historical time period are the same. Specifically, a certain algorithm can be used to predict the emotional abnormality probability of the vehicle driver in the future time period according to the vehicle driving information.

[0040] S103, adjusting the emotional detection reference frequency according to the vehicle driving information and the emotional abnormality probability in the future time period to obtain the emotional detection frequency of the future time period, so as to detect the emotion of the vehicle driver in the future time period according to the emotional detection frequency.

[0041] In this embodiment, the emotion detection frequency can be the frequency of emotion detection on the vehicle driver in the future time period. Specifically, a certain algorithm can be used to adjust the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period, to obtain the emotion detection frequency in the future time period, so as to be based on the emotion detection frequency.

[0042] Optionally, after obtaining the emotion detection frequency in the future time period, it further includes: collecting facial images of the vehicle driver in the future time period according to the emotion detection frequency; and performing emotion detection on the vehicle driver according to the facial images.

[0043] For example, if the emotion detection frequency is twice a minute and the length of the future time period is two minutes, the facial images of the vehicle driver are collected every 30 seconds in the future time period; and after each facial image is collected, emotion detection is performed on the vehicle driver according to the facial image. It should be noted that any existing technology can be used to perform emotion detection on the vehicle driver through facial images, and the present application does not limit this.

[0044] It can be understood that by using the above technical solution, the facial images of the vehicle driver can be collected according to the emotion detection frequency, and the vehicle driver can be identified after the facial images are collected, so that the collection frequency of the facial images can be flexibly adjusted, and the occupation of computing power and the occupation of power resources in the vehicle can be further reduced.

[0045] The technical solution of the embodiment of the present application obtains the emotion detection reference frequency, and the vehicle driving information of the total driving distance of the vehicle driver in the current time period; predicts the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information; wherein the future time period is adjacent to the current time period and is located after the current time period; adjusts the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period, to obtain the emotion detection frequency in the future time period, to perform emotion detection on the vehicle driver in the future time period according to the emotion detection frequency. Compared with the real-time detection technical solution in the prior art, the embodiment of the present application can predict the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information in the current time period, and flexibly adjust the emotion detection reference probability according to the emotion abnormality probability in the future time period and the vehicle driving information in the current time period. When predicting the emotion abnormality of the vehicle driver, the detection frequency is increased, and when predicting the stable emotion of the vehicle driver, the detection frequency is reduced. By intermittently detecting the emotion of the vehicle driver, the occupation of computing power resources is reduced, and the power resources of the vehicle are also saved.

[0046] Embodiment two

[0047] Figure 2 A flowchart of a method for determining an emotion detection frequency is provided for the second embodiment of the present application. The present embodiment optimizes and improves the prediction operation of the emotion abnormality probability based on the technical solutions of the above-mentioned embodiments.

[0048] Further, the "predicting the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information" is refined into "fusing the horn frequency and the number of overtaking to obtain first fusion data; fusing the average waiting time of traffic lights, the continuous driving time and the total driving time to obtain second fusion data; fusing the number of overtaking and the total driving time to obtain third fusion data; fusing the horn frequency, the average waiting time of traffic lights and the number of overtaking to obtain fourth fusion data; fusing the continuous driving time and the total driving time to obtain fifth fusion data; fusing the horn evaluation rate, the average waiting time of traffic lights, the number of overtaking and the continuous driving time to obtain sixth fusion data; and predicting the emotion abnormality probability of the vehicle driver in the future time period according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data through the trained risk prediction model" to perfect the prediction operation of the emotion abnormality probability.

[0049] It should be noted that the parts not described in detail in the embodiments of the present application can refer to the descriptions of the foregoing embodiments.

[0050] Referring to Figure 2 The method for determining an emotion detection frequency comprises the following steps:

[0051] S201, obtaining an emotion detection reference frequency and vehicle driving information of a total driving distance of a vehicle driver in a current time period.

[0052] In the present embodiment, the vehicle driving information can include but is not limited to the horn frequency, the average waiting time of traffic lights, the continuous driving time, the total driving time and the number of overtaking.

[0053] S202, fusing the horn frequency and the number of overtaking to obtain first fusion data.

[0054] Specifically, a certain algorithm is used to fuse the horn frequency and the number of overtaking to obtain the first fusion data. Optionally, fusing the horn frequency and the number of overtaking to obtain the first fusion data includes: logarithmically transforming the number of overtaking to obtain a logarithmic transformation result; determining the product between the logarithmic transformation result and the horn frequency; and exponentially transforming the product to obtain the first fusion data. Illustratively, the first fusion data can be determined by the following formula:

[0055] R1=eh×log(1+o) ;

[0056] Wherein, R1 represents the first fusion data; h represents the horn frequency; o represents the number of overtaking; log(1+o) represents the logarithmic transformation result.

[0057] It can be understood that, by adopting the above technical solution, the number of overtaking is logarithmically transformed to smooth the number of overtaking, avoiding the influence of excessive overtaking on the accuracy of the emotional abnormality probability; the product of the logarithmic transformation result and the horn frequency is exponentially transformed to amplify the combined effect between the horn frequency and the number of overtaking, so that the first fusion data obtained has a strong emotional abnormality feature.

[0058] S203, fusing the signal lamp average waiting time, the continuous driving time and the total driving time to obtain the second fusion data.

[0059] Specifically, the product between the signal lamp average waiting time and the continuous driving time is determined; the ratio between the obtained product and the total driving time is determined, and the square value of the obtained ratio is determined. Exemplarily, the second fusion data can be determined by the following formula:

[0060]

[0061] Wherein, R2 represents the second fusion data; a represents the signal lamp average waiting time; c represents the continuous driving time; t represents the total driving time.

[0062] It can be understood that, by adopting the above technical solution, the product between the signal lamp average waiting time and the continuous driving time is determined; the ratio between the obtained product and the total driving time is determined, and the square value of the obtained ratio is determined, which can amplify the comprehensive effect between the signal lamp average waiting time and the driving time, so that the second fusion data obtained has a strong emotional abnormality feature.

[0063] S204, fusing the number of overtaking and the total driving time to obtain the third fusion data.

[0064] Specifically, the ratio between the number of overtaking and the total driving time is determined; the square root of the obtained ratio is determined; the cubic result of the obtained square root is determined, and the cubic result is determined as the third fusion data; exemplarily, the third fusion data can be determined by the following formula:

[0065]

[0066] Wherein, R3 represents the third fusion data.

[0067] It can be understood that, by using the technical scheme, the square root of the ratio between the overtaken times and the total driving time is first taken and then cubed, so that the initial change of the overtaken times and the total driving time is smoothed through the square root operation, so that the data is more stable; through the cubic operation, the smoothed data is further amplified, so that the characteristics of the emotional abnormalities are more significant.

[0068] S205, the horn frequency, the average waiting time of the signal lamp and the overtaken times are fused to obtain fourth fusion data.

[0069] Specifically, the horn frequency, the average waiting time of the signal lamp and the overtaken times are weighted and fused to obtain the fourth fusion data; for example, the fourth fusion data can be determined by the following formula:

[0070] R4 = a x h + b x a + g x o;

[0071] Wherein, R4 represents the fourth fusion data; a represents the weight corresponding to the horn frequency; b represents the weight corresponding to the average waiting time of the signal lamp; g represents the weight corresponding to the overtaken times.

[0072] It should be noted that the weight corresponding to the horn frequency, the weight corresponding to the average waiting time of the signal lamp and the weight corresponding to the overtaken times can be set by the technician according to the actual demand or practical experience, and the present application does not limit this.

[0073] It can be understood that, by using the technical scheme, the horn frequency, the average waiting time of the signal lamp and the overtaken times can be combined by weighting to obtain the fourth fusion data, so that the obtained fourth fusion data can more comprehensively represent the emotional state of the driver.

[0074] S206, the continuous driving time and the total driving time are fused to obtain the fifth fusion data.

[0075] Specifically, the ratio between the continuous driving time and the total driving time is determined; the logarithm of the obtained ratio is determined, and the obtained logarithm result is determined as the fifth fusion data; for example, the fifth fusion data can be determined by the following formula:

[0076]

[0077] Wherein, R5 represents the fifth fusion data.

[0078] It can be understood that, by using the technical scheme, the fifth fusion data obtained by determining the logarithm of the ratio between the continuous driving time and the total driving time can represent the influence of the relative change of the continuous driving time in the total driving time on the driver's emotion, so that the fifth fusion data obtained has a strong emotional abnormality characteristic.

[0079] S207, the horn evaluation rate, the average waiting time of the signal lamp, the number of overtaking and the continuous driving time are fused to obtain the sixth fusion data.

[0080] Specifically, the product between the horn evaluation rate and the average waiting time of the signal lamp is determined; the square value of the obtained product is determined; the product between the number of overtaking and the continuous driving time is determined; the sum between the product between the number of overtaking and the continuous driving time and the square value obtained is determined as the sixth fusion data; for example, the sixth fusion data can be determined by the following formula:

[0081] R6 = (h x a) + o x c; 2

[0082] Wherein, R6 represents the sixth fusion data.

[0083] It can be understood that by using the above technical solution, the horn frequency, the average waiting time of the signal lamp, the number of overtaking and the continuous driving time can be amplified to represent the characteristics of the driver's emotions, so that the sixth fusion data obtained has strong characteristics of emotional abnormalities.

[0084] S208, the trained risk prediction model is used to predict the emotional abnormality probability of the vehicle driver in the current driving event according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data; wherein the future time period is adjacent to the current time period and located after the current time period.

[0085] In this embodiment, the risk prediction model can be a neural network model for predicting the emotional abnormality probability of the vehicle driver, for example, it can be a regression model or a hidden Markov model. Specifically, the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data are input into the trained risk prediction model to obtain the emotional abnormality probability of the vehicle driver in the future time period.

[0086] In one specific embodiment, the risk prediction model can include an input layer, a hidden layer 1, a hidden layer 2, a hidden layer 3 and an output layer; the hidden layer 1 can be a fully connected layer including 64 neurons and a ReLU activation function; the hidden layer 2 can be a fully connected layer including 32 neurons and a ReLU activation function; the hidden layer 3 can be a fully connected layer including 16 neurons and a ReLU activation function; the output layer can include one neuron and a Sigmoid activation function.

[0087] ​S209, adjusting the emotion detection reference frequency according to the vehicle driving information in the current time period and the emotion abnormality probability in the future time period, to obtain an emotion detection frequency in the future time period, so as to perform emotion detection on the vehicle driver in the future time period according to the emotion detection frequency.

[0088] The embodiment of the present application fuses the horn frequency and the number of overtaken vehicles to obtain first fusion data, fuses the average signal light waiting time, the continuous driving time and the total driving time to obtain second fusion data, fuses the number of overtaken vehicles and the total driving time to obtain third fusion data, fuses the horn frequency, the average signal light waiting time and the number of overtaken vehicles to obtain fourth fusion data, fuses the continuous driving time and the total driving time to obtain fifth fusion data, fuses the horn evaluation rate, the average signal light waiting time, the number of overtaken vehicles and the continuous driving time to obtain sixth fusion data, and predicts the emotion abnormality probability of the vehicle driver in the future time period according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data through the trained risk prediction model, so that the vehicle driving information can be fused to obtain the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data with enhanced features, and input to the risk prediction model to obtain the emotion abnormality probability of the vehicle driver, thereby improving the accuracy of the emotion abnormality probability.

[0089] Embodiment three

[0090] Figure 3 A flowchart of the method for determining the emotion detection frequency provided by the third embodiment of the present application, the third embodiment of the present application optimizes and improves the distribution operation of the candidate tower crane on the basis of the technical solutions of the above-mentioned embodiments.

[0091] Further, the "adjusting the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period to obtain the emotion detection frequency in the future time period" is refined as "performing logarithmic transformation on the horn frequency to obtain transformed data, fusing the average signal light waiting time and the total driving time to obtain seventh fusion data, fusing the number of overtaken vehicles and the continuous driving time to obtain eighth fusion data, and adjusting the emotion reference frequency according to the emotion abnormality probability, the transformed data, the seventh fusion data and the eighth fusion data to obtain the emotion detection frequency in the future time period", so as to perfect the distribution operation of the candidate tower crane.

[0092] It should be noted that the parts not described in detail in the embodiments of the present application can be referred to the descriptions of the foregoing embodiments.

[0093] Referring to Figure 3 The method for determining the emotion detection frequency shown in the above-mentioned embodiments of the present application, comprising:

[0094] S301, obtain an emotion detection reference frequency, and vehicle driving information of a total driving distance of a vehicle driver in a current time period.

[0095] S302, predict an emotion abnormality probability of the vehicle driver in a future time period according to the vehicle driving information; the future time period is adjacent to the current time period and located after the current time period.

[0096] S303, perform logarithmic transformation on the horn frequency to obtain transformed data.

[0097] Exemplarily, the transformed data can be determined by the following formula:

[0098] S = log (1+h) ;

[0099] Wherein, S represents the transformed data.

[0100] It can be understood that, by adopting the above technical solution, the logarithmic transformation is performed on the horn frequency, so that the horn frequency is compressed, the horn frequency tends to be flat to the emotion detection frequency, and the extreme value of the horn frequency is avoided to cause the emotion detection frequency to fluctuate sharply.

[0101] S304, fuse the average signal light waiting time and the total driving time to obtain seventh fusion data.

[0102] Specifically, a ratio between the average signal light waiting time and the total driving time is determined; a square value of the obtained ratio is determined as the seventh fusion data; exemplarily, the seventh fusion data can be determined by the following formula:

[0103]

[0104] Wherein, R7 represents the seventh fusion data.

[0105] It can be understood that, by adopting the above technical solution, the ratio between the average signal light waiting time and the total driving time is determined; the square value of the obtained ratio is determined as the seventh fusion data, the average signal light waiting time can be smoothed by the total driving time, and the influence of the signal light waiting time on the emotion abnormality is amplified by the square transformation, so that the matching rate of the emotion detection frequency and the emotion state of the vehicle driver is improved.

[0106] S305, fuse the number of overtaken vehicles and the continuous driving time to obtain eighth fusion data.

[0107] Specifically, a ratio between the number of overtaken vehicles and the continuous driving time is determined; an exponential transformation is performed on the obtained ratio to obtain the eighth fusion data; exemplarily, the eighth fusion data can be determined by the following formula:

[0108]

[0109] wherein, R8 represents the eighth fusion data.

[0110] It can be understood that by using the technical solution, the ratio between the number of overtaking and the continuous driving duration is determined; the obtained ratio is subjected to exponential transformation to obtain the eighth fusion data, which can determine the overtaking frequency within the continuous driving duration and perform exponential transformation, thereby amplifying the influence of the overtaking frequency on the determination of the emotion detection frequency and improving the matching rate of the emotion detection frequency and the emotional state of the vehicle driver.

[0111] S306, adjusting the emotion reference frequency according to the emotion abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain the emotion detection frequency of the future time period, so as to perform emotion detection on the vehicle driver in the future time period according to the emotion detection frequency.

[0112] Specifically, a certain algorithm is used to adjust the emotion reference frequency according to the emotion abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain the emotion detection frequency of the future time period.

[0113] Optionally, adjusting the emotion reference frequency according to the emotion abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain the emotion detection frequency of the future time period comprises: weighting and fusing the emotion abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain a weighted fusion result; and adjusting the emotion reference probability according to the weighted fusion result to obtain the emotion detection frequency of the future time period.

[0114] Specifically, the weighted fusion result is added to the emotion reference detection frequency to obtain the emotion detection frequency corresponding to the future time period. Exemplarily, the emotion detection frequency can be determined by the following formula:

[0115] F=f+Q1×P+Q2×S+Q3×R7+Q4×R8;

[0116] wherein, F represents the emotion detection frequency; f represents the emotion detection reference probability; Q1 represents the weight corresponding to the emotion abnormality probability; P represents the emotion abnormality probability; Q2 represents the weight corresponding to the transformation data; Q3 represents the weight corresponding to the seventh fusion data; and Q4 represents the weight corresponding to the eighth fusion data.

[0117] It should be noted that the weight corresponding to the transformation data, the weight corresponding to the seventh fusion data and the weight corresponding to the eighth fusion data can be set by the technician according to actual needs or practical experience, and the present application does not limit this.

[0118] It can be understood that, by using the technical scheme, the emotion detection reference probability can be adjusted by weighted combination of the emotion abnormality probability, the transformed data, the seventh fused data and the eighth fused data, so as to improve the matching rate of the emotion detection frequency and the emotion state of the vehicle driver.

[0119] The embodiment of the present application performs logarithmic transformation on the horn frequency to obtain transformed data, fuses the average waiting time of the signal lamp and the total driving time to obtain seventh fused data, fuses the number of overtaking and the continuous driving time to obtain eighth fused data, and adjusts the emotion reference frequency according to the emotion abnormality probability, the transformed data, the seventh fused data and the eighth fused data to obtain the emotion detection frequency of the future time period, so that the vehicle driver is detected for emotion at a higher emotion detection frequency when the vehicle driver is in an emotion abnormality, and the vehicle driver is detected for emotion at a lower emotion detection probability when the vehicle driver is in a stable emotion state, thereby improving the matching degree of the emotion detection frequency and the emotion state of the vehicle driver.

[0120] Embodiment Four

[0121] Figure 4 A structural schematic diagram of an emotion detection frequency determination device provided by the fourth embodiment of the present application. The embodiment can be applicable to the case of determining the emotion detection frequency of the vehicle driver, the device can perform the emotion detection frequency determination method, the emotion detection frequency determination device can be realized in the form of hardware and / or software, and the device can be configured in an electronic device.

[0122] Referring to Figure 4 The emotion detection frequency determination device shown in the figure comprises an information acquisition module 401, a probability prediction module 402 and a frequency adjustment module 403, wherein,

[0123] The information acquisition module 401 is configured to acquire an emotion detection reference frequency and vehicle driving information of a total driving distance of the vehicle driver in a current time period.

[0124] The probability prediction module 402 is configured to predict an emotion abnormality probability of the vehicle driver in a future time period according to the vehicle driving information, wherein the future time period is adjacent to the current time period and located after the current time period.

[0125] The frequency adjustment module 403 is configured to adjust the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period to obtain an emotion detection frequency of the future time period, so as to detect the vehicle driver for emotion according to the emotion detection frequency in the future time period.

[0126] The embodiment of the present application obtains the emotion detection reference frequency and vehicle driving information of the total driving distance of the vehicle driver in the current time period through the information acquisition module; the probability prediction module predicts the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information; wherein the future time period is adjacent to the current time period and is located after the current time period; the frequency adjustment module adjusts the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period to obtain the emotion detection frequency in the future time period, so as to detect the emotion of the vehicle driver in the future time period according to the emotion detection frequency. Compared with the real-time detection technical solution in the prior art, the embodiment of the present application can predict the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information in the current time period, and flexibly adjust the emotion detection reference probability according to the emotion abnormality probability in the future time period and the vehicle driving information in the current time period. When predicting the emotion abnormality of the vehicle driver, the detection frequency is increased, and when predicting the stable emotion of the vehicle driver, the detection frequency is reduced. By intermittently detecting the emotion of the vehicle driver, the occupation of the computing resource is reduced, and the power resource of the vehicle is also saved.

[0127] Optionally, the vehicle driving information includes the horn frequency, the average waiting time of the signal lamp, the continuous driving time, the total driving time and the number of overtaking.

[0128] Optionally, the probability prediction module 402 includes:

[0129] The first fusion unit is configured to fuse the horn frequency and the number of overtaking to obtain first fusion data;

[0130] The second fusion unit is configured to fuse the average waiting time of the signal lamp, the continuous driving time and the total driving time to obtain second fusion data;

[0131] The third fusion unit is configured to fuse the number of overtaking and the total driving time to obtain third fusion data;

[0132] The fourth fusion unit is configured to fuse the horn frequency, the average waiting time of the signal lamp and the number of overtaking to obtain fourth fusion data;

[0133] The fifth fusion unit is configured to fuse the continuous driving time and the total driving time to obtain fifth fusion data;

[0134] The sixth fusion unit is configured to fuse the horn frequency, the average waiting time of the signal lamp, the number of overtaking and the continuous driving time to obtain sixth fusion data;

[0135] The probability prediction unit is configured to predict, by using the trained risk prediction model, an emotional abnormality probability of the vehicle driver in a future time period according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data.

[0136] Optionally, the first fusion unit is specifically configured to:

[0137] perform logarithmic transformation on the number of overtaking times to obtain a logarithmic transformation result;

[0138] determine a product between the logarithmic transformation result and the horn frequency;

[0139] perform exponential transformation on the product to obtain the first fusion data.

[0140] Optionally, the frequency adjustment module 402 comprises:

[0141] a transformation unit configured to perform logarithmic transformation on the horn frequency to obtain transformation data;

[0142] a seventh fusion unit configured to fuse the average waiting time of the signal lamp and the total driving time to obtain seventh fusion data;

[0143] an eighth fusion unit configured to fuse the number of overtaking times and the continuous driving time to obtain eighth fusion data;

[0144] a frequency adjustment unit configured to adjust the emotional reference frequency according to the emotional abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain the emotional detection frequency in the future time period.

[0145] Optionally, the frequency adjustment unit is specifically configured to:

[0146] perform weighted fusion on the emotional abnormality probability, the transformation data, the seventh fusion data and the eighth fusion data to obtain a weighted fusion result;

[0147] adjust the emotional reference probability according to the weighted fusion result to obtain the emotional detection frequency in the future time period.

[0148] Optionally, the device further comprises:

[0149] an image acquisition module configured to acquire facial images of the vehicle driver in the future time period according to the emotional detection frequency;

[0150] an emotional detection module configured to perform emotional detection on the vehicle driver according to the facial images.

[0151] The emotion detection frequency determination apparatus provided by the embodiments of the present application can execute the emotion detection frequency determination method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of executing the emotion detection frequency determination method.

[0152] Embodiment five

[0153] Figure 5 A structural schematic diagram of an electronic device 500 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0154] As shown in Figure 5 The electronic device 500 includes at least one processor 501 and a memory, such as a read-only memory (ROM) 502, a random access memory (RAM) 503, etc., connected to the at least one processor 501 in communication, where the memory stores computer programs executable by the at least one processor. The processor 501 can perform various appropriate actions and processes according to the computer programs stored in the read-only memory (ROM) 502 or loaded into the random access memory (RAM) 503 from the storage unit 508. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0155] A plurality of components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a magnetic disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0156] The processor 501 can be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Some examples of the processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The processor 501 performs various methods and processes described above, such as the emotion detection frequency determination method.

[0157] In some embodiments, the emotion detection frequency determination method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded onto the RAM 503 and executed by the processor 501, one or more steps of the emotion detection frequency determination method described above can be performed. Alternatively, in other embodiments, the processor 501 can be configured to perform the emotion detection frequency determination method by any other appropriate means, such as by means of firmware.

[0158] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0159] Computer programs used to implement the methods of the application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable emotion detection frequency determination device to produce a machine, such that the computer program, when executed, enables the machine to perform functions specified by the flowchart and / or block diagram block or blocks. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0160] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0161] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; 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 acoustic, speech, or tactile input.

[0162] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0163] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS (Virtual Private Server) service.

[0164] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, and this is not limited herein.

[0165] The above detailed description does not constitute a limitation on the protection scope of the present application. 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 replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of determining an emotion detection frequency, characterized by, The method comprises: obtaining an emotion detection reference frequency, and vehicle driving information of a total driving distance of a vehicle driver in a current time period; predicting an emotion abnormality probability of the vehicle driver in a future time period according to the vehicle driving information, wherein the future time period is adjacent to and located after the current time period; adjusting the emotion detection reference frequency according to the vehicle driving information and the emotion abnormality probability in the future time period to obtain an emotion detection frequency of the future time period, so as to perform emotion detection on the vehicle driver in the future time period according to the emotion detection frequency; the vehicle driving information comprises a horn frequency, an average signal light waiting time, a continuous driving time, a total driving time and a number of times of being overtaken; the prediction of the emotion abnormality probability of the vehicle driver in the future time period according to the vehicle driving information comprises: fusing the horn frequency and the number of times of being overtaken to obtain first fusion data; fusing the average signal light waiting time, the continuous driving time and the total driving time to obtain second fusion data; fusing the number of times of being overtaken and the total driving time to obtain third fusion data; fusing the horn frequency, the average signal light waiting time and the number of times of being overtaken to obtain fourth fusion data; fusing the continuous driving time and the total driving time to obtain fifth fusion data; fusing the horn frequency, the average signal light waiting time, the number of times of being overtaken and the continuous driving time to obtain sixth fusion data; predicting the emotion abnormality probability of the vehicle driver in the future time period according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data and the sixth fusion data through a trained risk prediction model; the fusion of the horn frequency and the number of times of being overtaken to obtain the first fusion data comprises: logarithmic transformation is performed on the number of times of being overtaken to obtain a logarithmic transformation result; a product between the logarithmic transformation result and the horn frequency is determined; exponential transformation is performed on the product to obtain the first fusion data; The signal lamp average waiting time, the continuous driving time and the total driving time are fused to obtain second fusion data, including: determining the product between the signal lamp average waiting time and the continuous driving time, determining the ratio between the obtained product and the total driving time, and determining the square value of the obtained ratio as the second fusion data; the overtaking frequency and the total driving time are fused to obtain third fusion data, including: determining the ratio between the overtaking frequency and the total driving time; determining the square root of the obtained ratio; determining the cubic result of the obtained square root, and determining the cubic result as the third fusion data; the horn frequency, the signal lamp average waiting time and the overtaking frequency are fused to obtain fourth fusion data, including: performing weighted fusion on the horn frequency, the signal lamp average waiting time and the overtaking frequency, and obtaining the fourth fusion data; the continuous driving time and the total driving time are fused to obtain fifth fusion data, including: determining the ratio between the continuous driving time and the total driving time; determining the logarithm of the obtained ratio, and determining the logarithm result as the fifth fusion data; the horn evaluation rate, the signal lamp average waiting time, the overtaking frequency and the continuous driving time are fused to obtain sixth fusion data, including: determining the product between the horn evaluation rate and the signal lamp average waiting time; determining the square value of the obtained product; determining the product between the overtaking frequency and the continuous driving time; and determining the sum between the product between the overtaking frequency and the continuous driving time and the square value as the sixth fusion data.

2. The method of claim 1, wherein, The emotion detection reference frequency is adjusted according to the vehicle driving information and the emotion abnormality probability in the future time period to obtain an emotion detection frequency in the future time period, including: The horn frequency is logarithmically transformed to obtain transformed data; The signal lamp average waiting time and the total driving time are fused to obtain seventh fusion data; The overtaking frequency and the continuous driving time are fused to obtain eighth fusion data; The emotion detection reference frequency is adjusted according to the emotion abnormality probability, the transformed data, the seventh fusion data and the eighth fusion data to obtain an emotion detection frequency in the future time period.

3. The method of claim 2, wherein, The emotion detection reference frequency is adjusted according to the emotion abnormality probability, the transformed data, the seventh fusion data and the eighth fusion data to obtain an emotion detection frequency in the future time period, including: The emotion abnormality probability, the transformed data, the seventh fusion data and the eighth fusion data are weightedly fused to obtain a weighted fusion result; The emotion detection reference frequency is adjusted according to the weighted fusion result to obtain an emotion detection frequency in the future time period.

4. The method of claim 1, wherein, After obtaining the emotion detection frequency in the future time period, further including: A face image of the vehicle driver is collected in the future time period according to the emotion detection frequency; An emotion of the vehicle driver is detected according to the face image.

5. An apparatus for determining an emotion detection frequency, the apparatus comprising: a processor configured to: receive a plurality of emotion detection frequencies; and determine a mean of the plurality of emotion detection frequencies. The device includes: The information acquisition module is configured to acquire a mood detection reference frequency and vehicle driving information of a total driving distance of a vehicle driver in a current time period; The probability prediction module is configured to predict an emotional abnormality probability of the vehicle driver in a future time period according to the vehicle driving information, wherein the future time period is adjacent to and located after the current time period; The frequency adjustment module is configured to adjust the mood detection reference frequency according to the vehicle driving information and the emotional abnormality probability in the future time period to obtain a mood detection frequency of the future time period, so as to perform mood detection on the vehicle driver in the future time period according to the mood detection frequency. The vehicle driving information includes a horn frequency, an average signal light waiting time, a continuous driving time, a total driving time, and a number of times of being overtaken. The probability prediction module includes: A first fusion unit configured to fuse the horn frequency and the number of times of being overtaken to obtain first fusion data; A second fusion unit configured to fuse the average signal light waiting time, the continuous driving time, and the total driving time to obtain second fusion data; A third fusion unit configured to fuse the number of times of being overtaken and the total driving time to obtain third fusion data; A fourth fusion unit configured to fuse the horn frequency, the average signal light waiting time, and the number of times of being overtaken to obtain fourth fusion data; A fifth fusion unit configured to fuse the continuous driving time and the total driving time to obtain fifth fusion data; A sixth fusion unit configured to fuse the horn frequency, the average signal light waiting time, the number of times of being overtaken, and the continuous driving time to obtain sixth fusion data; The probability prediction unit is configured to predict an emotional abnormality probability of the vehicle driver in the future time period according to the first fusion data, the second fusion data, the third fusion data, the fourth fusion data, the fifth fusion data, and the sixth fusion data through a trained risk prediction model. The first fusion unit is specifically configured to: perform logarithmic transformation on the number of times of being overtaken to obtain a logarithmic transformation result; determine a product between the logarithmic transformation result and the horn frequency; perform exponential transformation on the product to obtain the first fusion data; The second fusion unit is specifically configured to: determine a product between the average signal light waiting time and the continuous driving time, determine a ratio between the determined product and the total driving time, and determine a square value of the determined ratio as the second fusion data; The third fusion unit is specifically configured to: determine a ratio between the number of times of being overtaken and the total driving time, determine a square root of the determined ratio, and determine a cubic result of the determined square root as the third fusion data; The fourth fusion unit is specifically configured to: perform weighted fusion on the horn frequency, the average signal light waiting time, and the number of times of being overtaken to obtain the fourth fusion data; The fifth fusion unit is specifically configured to: determine a ratio between the continuous driving time and the total driving time, determine a logarithm of the determined ratio, and determine the logarithm result as the fifth fusion data; The sixth fusion unit is specifically configured to: Determine a product between the horn evaluation rate and the average waiting time of the signal light; determine a square value of the obtained product; determine a product between the number of overtaking and the continuous driving duration; and determine a sum between the product between the number of overtaking and the continuous driving duration and the square value as the sixth fused data.

6. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for determining the emotion detection frequency according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the method for determining the emotion detection frequency according to any one of claims 1-4 when executed.

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