Driving fatigue music intervention method based on individual differences

Abstract

A method for music intervention in driving fatigue targeting individual differences belongs to the technical field of driving fatigue intervention. The present invention identifies the driver through an identity recognition module and obtains the identity information corresponding to the driver in the system; the fatigue state of the driver is judged through a fatigue identification module; if the driver is in a certain fatigue state, the music intervention module is used to find the best intervention strategy corresponding to the current fatigue state in the best intervention strategy library of the driver in the system, and music intervention is performed on the driver according to this strategy; the fatigue state of the driver after the intervention is judged, and if the driver is still in a fatigue state, the music intervention module is used to intervene in the driver again; the changes in the fatigue intervention strategy model during the intervention process are recorded, and the fatigue intervention strategy model and the best intervention strategy library corresponding to the driver in the system are replaced through a model adjustment module. Interference is performed based on individual differences to fully ensure the effect of music intervention in driving fatigue.

Description

Technical Field

[0001] The present invention belongs to the technical field of driving fatigue intervention, and in particular relates to a driving fatigue music intervention method oriented towards individual differences. Background Art

[0002] Driving fatigue refers to the imbalance of a driver's physiological and psychological functions after prolonged driving, leading to a decline in driving skills. Data indicates that 21% of traffic accidents are caused by fatigued driving, accounting for over 40% of major accidents and 83% of traffic accident fatalities. Therefore, implementing effective measures to reduce driving fatigue is of great practical significance.

[0003] Driver fatigue intervention refers to the use of various technical means to remind, warn, and help drivers stay awake and alert when they become fatigued, thereby reducing the risks posed by fatigued driving to traffic safety. These technical means include but are not limited to sound prompts, vibration prompts, image prompts, and intelligent driving assistance.

[0004] Listening to music is a common driving habit, believed to help drivers pass the time. A study surveying 1,780 British adults found that 68% admitted to listening to music or the radio while driving; nearly half reported humming along to music; 62% felt music relaxed them and made driving more comfortable; and 25% believed it helped them stay alert. Drivers also generally believe that listening to music carries a lower risk of accidents than other distractions, such as talking on a mobile phone. Compared to other interventions, auditory interventions are easier to use, more practical, and have a manageable duration.

[0005] Existing music interventions for driving fatigue often use a uniform music genre and tempo. However, individual drivers may have different preferences and responses to music, and lack of personalized music interventions may not achieve optimal results. Individuals have varying physiological responses to music, making the selection of the appropriate music genre crucial for effective interventions.

[0006] Therefore, a new technical solution is urgently needed in the existing technology to solve this problem. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a driving fatigue music intervention method oriented to individual differences to solve the technical problem that the existing driving fatigue music intervention method has the defect of ignoring personalized needs.

[0008] A method for music intervention for driving fatigue tailored to individual differences is disclosed. The method uses an identity recognition module, a fatigue identification module I, a fatigue identification module II, a music intervention module, a model adjustment module, and a cloud database to identify the driver's fatigue state and select the optimal music intervention strategy to intervene in the driver's fatigue to meet individual differences. The method comprises the following steps, which are performed in sequence:

[0009] Step 1: Identify the driver's ID x through the identity recognition module and obtain the corresponding stored preferred playlist, listening habits, fatigue intervention strategy model library, and optimal intervention strategy library in the cloud database;

[0010] Step 2: The fatigue identification module I identifies the fatigue state of the driver x at a set time interval;

[0011] Step 3: Driver x is identified as being in a fatigue state α, and the music intervention module calls the optimal intervention strategy library P_strategy corresponding to driver x (x) , find the best intervention strategy corresponding to fatigue state α The music intervention module performs music intervention on driver x according to the optimal intervention strategy;

[0012] Step 4: Fatigue Identification Module II identifies the fatigue state of driver x after each intervention and determines whether driver x is awake after the intervention. If driver x is awake, proceed to Step 5. If driver x is still fatigued, the fatigue state of driver x is identified and the current fatigue state of driver x is output. The process returns to Step 3 and music intervention is performed on driver x again through the music intervention module.

[0013] Step 5: The model adjustment module records the fatigue intervention strategy model of driver x after intervention changes, thereby adjusting the fatigue intervention strategy model and the optimal intervention strategy library P_strategy (x) And upload it to the cloud database to update the fatigue intervention strategy model library and the optimal intervention strategy library.

[0014] In the step 1, the identity recognition module identifies the identity ID of driver x. If the cloud database does not store the identity information of driver x, the driver x is reminded to upload the preferred song list and set the listening habits. The cloud database assigns the identity ID of driver x to x (x∈N + ), and the initial fatigue intervention strategy model stored in the cloud database is used as the fatigue intervention strategy model of the driver x, and the initial optimal intervention strategy library is used as the optimal intervention strategy library of the driver x.

[0015] In step 2, the fatigue identification module I identifies the fatigue state of the driver x:

[0016] Driver x's driving load is obtained through the cloud, and driver x's fatigue state is divided into active fatigue and passive fatigue. Driver x's facial information is also obtained through the cloud, and these two fatigue states are further divided into three levels based on set thresholds: mild fatigue, moderate fatigue, and severe fatigue. If driver x is determined to be awake, it is determined again after T1 minutes whether driver x is in a fatigue state. If driver x is determined to be in a fatigue state, the fatigue identification module I determines which fatigue state α driver x is in and outputs it.

[0017] The specific steps of performing music intervention on driver x in step 3 are as follows:

[0018] Step 1: Driver x is identified as a fatigue state α, and the optimal intervention strategy library P_strategy corresponding to driver x is called (x) , find the best intervention strategy corresponding to fatigue state α Perform music intervention on driver x according to the optimal intervention strategy;

[0019] Among them, the optimal intervention strategy library is divided into two types, one is the initial optimal intervention strategy library, and the other is the optimal intervention strategy library after adjustment in step five;

[0020] The fatigue intervention strategy model library is composed of fatigue intervention strategy models, and β represents each fatigue intervention strategy corresponding to the fatigue intervention strategy model. The fatigue intervention strategy is a combination of music rhythm, loudness and duration, that is, rhythm×loudness×duration;

[0021] The initial fatigue intervention strategy model with fatigue state as α and fatigue intervention strategy as β As follows:

[0022]

[0023] in, is the change in the initial pre-intervention normalized fatigue composite value over time t; is the change of the normalized fatigue comprehensive value after the intervention of the initial setting over time t, where α is the driving fatigue state, β represents the fatigue intervention strategy, and (0) represents the initial setting, that is, the driver ID is 0; are the initial model parameters; the moment for the musical intervention to begin; The moment when the driver enters the awake state from the fatigue state after the music intervention;

[0024] The fatigue intervention strategy model formula is used to calculate the optimal intervention strategy β under each fatigue state α;

[0025] The initial fatigue intervention strategy library consists of the initial optimal fatigue intervention strategy under each fatigue state, which is expressed as:

[0026]

[0027] Among them, P_strategy (0) The initial fatigue intervention strategy library; strategy1 (0) and β i1 The fatigue intervention strategy for the first fatigue state, namely mild active fatigue; strategy2 (0) and β i2 The fatigue intervention strategy for the first fatigue state, namely moderate active fatigue; strategy3 (0) and β i3 The fatigue intervention strategy for the first fatigue state, namely severe active fatigue; strategy 4 (0) and β i4 The fatigue intervention strategy for the first fatigue state, namely mild passive fatigue; strategy5 (0) and β i5 The fatigue intervention strategy for the first fatigue state, namely moderate passive fatigue; strategy6 (0) and β i6 The fatigue intervention strategy for the first fatigue state, i.e., severe passive fatigue; u (1) ,u (2) ,u (3) ,u (4) ,u (5) ,u (6) ∈{fast tempo, medium tempo, slow tempo}, v (1) ,v (2) ,v (3) ,v (4) ,v (5) ,v (6) ∈{high volume, medium volume, low volume}, q (1) ,q (2) ,q (3) ,q (4) ,q (5) ,q (6) ∈{10min,20min,30min,40min,50min,60min};

[0028] Driver x (x=1,2,3…) uses this method for the first time, then the fatigue intervention strategy model F corresponding to driver x is (x) (t) and the optimal intervention strategy library P_strategy (x)All used the initial fatigue intervention strategy model and the initial optimal intervention strategy library;

[0029] Step 2: Based on the optimal intervention strategy described in Step 1, find songs with a rhythm that matches this strategy in the driver x's preferred playlist and perform driver fatigue intervention in the music intervention module based on the loudness and duration settings. Specifically:

[0030] Fatigue identification module I determines that the fatigue state of driver x is α, and the optimal intervention strategy library P_strategy corresponding to driver x is (x) Find the optimal intervention strategy corresponding to fatigue state α in strategy α =β→rhythm u, loudness v, duration q;

[0031] In driver x's preferred playlist, find songs with rhythm u, arrange the order of songs based on driver x's listening habits, and use loudness v and duration q to perform music intervention on driver x.

[0032] Adjust the fatigue intervention strategy model in step 5 and the optimal intervention strategy library P_strategy (x) The specific method is:

[0033] Adjusting the fatigue intervention strategy model involves adjusting the model parameters θ(a, b, c, k, l, m, n) using stochastic gradient descent (SGD). The steps are as follows:

[0034] Set the initial fatigue intervention strategy model parameters θ(a,b,c,k,l,m,n)=θ (0) ;

[0035] The first step is to define the loss function:

[0036]

[0037] Where, f i is the normalized fatigue comprehensive value of the driver recorded after the intervention, where N is the total number of samples, i∈[1,N]; F i (t i ; θ) is the normalized comprehensive fatigue value of the driver stored in the current cloud database;

[0038] The second step is to get the complete gradient:

[0039]

[0040] Simplify the expression and get the gradient vector

[0041]

[0042] Update parameters:

[0043]

[0044] θ new is the updated model parameter, η is the learning rate, which is used to control the step size of each parameter update;

[0045] Get new model parameter values ​​through stochastic gradient descent SGD Replace the original model parameter values ​​to obtain a new fatigue intervention strategy model Recalculate and adjust the optimal intervention strategy library P_strategy for driver x (x,new) ;

[0046] The third step is to integrate the new fatigue intervention strategy model and the optimal intervention strategy library P_strategy (x,new) The data is stored in the cloud database to replace the original model and data, and corresponds to the ID of driver x for easy retrieval next time.

[0047] Through the above design scheme, the present invention can bring the following beneficial effects:

[0048] The present invention identifies the driver through an identity recognition module and obtains the identity information corresponding to the driver in the system; determines the fatigue state of the driver through a fatigue recognition module; if the driver is in a certain fatigue state, searches for the best intervention strategy corresponding to the current fatigue state in the best intervention strategy library of the driver in the system through a music intervention module, and performs music intervention on the driver according to this strategy; determines the fatigue state of the driver after the intervention, and if the driver is still in the fatigue state, intervenes in the driver again through the music intervention module; records the changes of the fatigue intervention strategy model during the intervention process, and replaces the fatigue intervention strategy model and the best intervention strategy library corresponding to the driver in the system through a model adjustment module, thereby realizing optimal driving fatigue music intervention oriented to individual differences.

[0049] The present invention performs music intervention according to individual differences of drivers, fully guarantees the effectiveness and pertinence of music intervention for driving fatigue, and has important practical significance for alleviating driving fatigue. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0051] Figure 1 This is a flow chart of a method for music intervention for driving fatigue targeting individual differences according to the present invention;

[0052] Figure 2This is a flow chart of the identity recognition module in a driving fatigue music intervention method for individual differences according to the present invention;

[0053] Figure 3 This is a flow chart of the fatigue identification module I in the driving fatigue music intervention method for individual differences of the present invention;

[0054] Figure 4 This is a flow chart of a music intervention module in a driving fatigue music intervention method for individual differences according to the present invention;

[0055] Figure 5 This is a flow chart of fatigue identification module II in a driving fatigue music intervention method for individual differences of the present invention;

[0056] Figure 6 This is a flow chart of a model adjustment module in a driving fatigue music intervention method for individual differences according to the present invention;

[0057] Figure 7 This is a flow chart of the gradient descent method in the model adjustment module of a driving fatigue music intervention method for individual differences in the present invention. DETAILED DESCRIPTION

[0058] A method for music intervention in driving fatigue tailored to individual differences comprises the following steps: a driver facial recognition module is provided on an identity recognition module for obtaining facial information and uploading it to a cloud database, the identity recognition module identifies the driver and obtains the identity information corresponding to the driver in the cloud database; the fatigue recognition module obtains the facial information of the driver x through the cloud and determines the fatigue state of the driver; if the driver is in a certain fatigue state, the music intervention module searches for the best intervention strategy corresponding to the current fatigue state in the best intervention strategy library of the driver, performs music intervention on the driver according to the strategy, and determines the fatigue state of the driver after the intervention again; if the driver is still in the fatigue state, the music intervention module intervenes on the driver again; and the changes in the fatigue intervention strategy model during the intervention process are recorded, and the fatigue intervention strategy model and the best intervention strategy library corresponding to the driver in the cloud database are replaced by the model adjustment module.

[0059] To make the objects, features, and advantages of the present invention more apparent and understandable, the technical solutions of the present invention are described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the present invention is not limited to the following embodiments, and specific implementation methods can be determined based on the technical solutions of the present invention and actual conditions. To avoid obscuring the essence of the present invention, well-known methods, processes, and procedures are not described in detail.

[0060] like Figure 1As shown, a driving fatigue music intervention method oriented to individual differences includes the following steps:

[0061] Step 1: Identify the driver's ID x through the identity recognition module, and obtain the corresponding preferred song list, listening habits, fatigue intervention strategy model and optimal intervention strategy library from the cloud database.

[0062] If the cloud database does not store the identity information of the driver x, then the driver x is reminded to upload the preferred playlist and set the listening habits, and the identity ID of the driver x is assigned to the cloud database as x (x∈N + ) and use the initial fatigue intervention strategy model as the fatigue intervention strategy model of the driver x, and the initial optimal intervention strategy library as the optimal intervention strategy library of the driver x.

[0063] Step 2: The fatigue identification module I identifies the fatigue status of driver x every T1 minutes.

[0064] The normalized fatigue comprehensive value of driver x is calculated every T1 minute as the normalized fatigue comprehensive value Fb of driver x before intervention (x) , record Fb (x) Curve Fb changing with time t (x) (t).

[0065]

[0066] Among them, Fb (x) (t) is the normalized fatigue comprehensive value change curve before the intervention of driver x, Fb (x) The larger the value of (t), the more fatigued the driver x is; t is the driving time of driver x; a, b, and c are model parameters determined in step five.

[0067] By Facebook (x)The (t) curve determines whether driver x is fatigued. If driver x is awake, fatigue is determined again after T1 minutes. If driver x is fatigued, fatigue identification module I determines which fatigue state α driver x is in and outputs it. Driver x's driving load is obtained from the cloud. If driver x's driving load is greater than or equal to threshold z1, driver x is considered actively fatigued. If driver x's driving load is less than threshold z, driver x is considered passively fatigued. The driving load is determined by information such as traffic flow and vehicle movement. These two fatigue states are further classified into three levels based on driver x's facial information and set thresholds: mild fatigue, moderate fatigue, and severe fatigue. This is prior art and not the focus of the present invention, so it will not be elaborated on in detail. Let the driving fatigue state be α, then α∈(1,2,3,4,5,6). Where 1 = mild active fatigue, 2 = moderate active fatigue, 3 = severe active fatigue, 4 = mild passive fatigue, 5 = moderate passive fatigue, and 6 = severe passive fatigue.

[0068] Step 3: When driver x is identified as a fatigue state α, the optimal intervention strategy library P_strategy corresponding to driver x is called (x) , find the best intervention strategy corresponding to fatigue state α Perform music intervention on driver x according to the optimal intervention strategy.

[0069] There are two types of optimal intervention strategy libraries: one is the initial optimal intervention strategy library. The other is the optimal intervention strategy library adjusted in step 5. When the cloud database does not store the information of driver x, the ID of driver x is set to x, and the optimal intervention strategy library corresponding to driver x is set to the initial optimal intervention strategy library P_strategy (x) =P_strategy (0) For the initial setting, the default ID of driver x is 0; and after the adjustment in step 5, the optimal intervention strategy library P_strategy (x) There will be differences between different drivers.

[0070] The fatigue intervention strategy model library consists of fatigue intervention strategies. A fatigue intervention strategy is a combination of music tempo, loudness, and duration: tempo × loudness × duration. The tempo of music is determined by the beats per minute (BPM) of the song: fast music has a BPM greater than 120, medium music has a BPM between 70 and 120, and slow music has a BPM less than 70. Loudness is considered high if it's between 70 and 80 dB, medium if it's between 60 and 70 dB, and low if it's between 50 and 60 dB. The music intervention duration is the duration of the music, which can be 10 minutes, 20 minutes, 30 minutes, 40 minutes, 50 minutes, or 60 minutes. When β represents a fatigue intervention strategy, β∈{1,2,3,...,54}, which is 3 (tempo) × 3 (loudness) × 6 (duration). Each β has a corresponding fatigue intervention strategy model.

[0071] The initial fatigue intervention strategy model with fatigue state as α and fatigue intervention strategy as β Depend on and composition:

[0072]

[0073] in, It is obtained from formula (1), that is, the change of the initial normalized fatigue composite value before intervention with time t; is the change of the normalized fatigue comprehensive value over time t after the initial intervention. Where α is the driving fatigue state, α∈(1,2,3,4,5,6). β represents the fatigue intervention strategy, β∈{1,2,3,...,54}. (0) represents the initial setting, that is, the driver ID is 0. are the initial model parameters, where is related to the fatigue level of driver 0 before intervention in fatigue state α; Indicates the rate at which the comprehensive fatigue value of driver 0 changes over time; represents the fatigue growth rate constant of driver 0 in the process of reaching fatigue state α; It represents the rate of fatigue reduction after the intervention strategy β is implemented for driver 0 in fatigue state α; It represents the intensity of the intervention effect of intervention strategy β when driver 0 is in fatigue state α; It represents the recovery rate of the comprehensive fatigue value over time after the intervention strategy β is implemented for driver 0 in fatigue state α; When driver 0 is in fatigue state α, the rate constant of fatigue recovery after intervention strategy β is implemented; the moment for the musical intervention to begin; The moment when the driver enters the awake state from the fatigue state after the music intervention. The parameters corresponding to different fatigue states Different fatigue intervention strategies correspond to as well as It's also different.

[0074] Assume that the initial intervention effect value corresponding to the βth fatigue intervention strategy under the αth fatigue state for:

[0075]

[0076] in, is the cumulative fatigue value before intervention when fatigue state is α, is the cumulative fatigue value after intervention strategy β is used when fatigue state is α, For curve The horizontal coordinate of the inflection point.

[0077] The initial optimal intervention effect under the αth fatigue state is:

[0078]

[0079] That is, the initial optimal intervention effect under the αth fatigue state is the maximum of the initial intervention effect values ​​corresponding to all fatigue intervention strategies under the αth fatigue state. Where α is the driving fatigue state, α∈(1,2,3,4,5,6). β represents the fatigue intervention strategy, β∈{1,2,3,…,54}.

[0080] In formula (5) For driver 0, the best intervention effect is achieved by using the βth fatigue intervention strategy to deal with the αth fatigue state among all fatigue intervention strategies. This effect is expressed as To measure.

[0081] The initial optimal intervention effect library P_effect (0) for:

[0082]

[0083] The initial optimal intervention effect was obtained in the αth fatigue state Then the fatigue intervention strategy β used for the initial optimal intervention effect is the initial optimal fatigue intervention strategy under the αth fatigue state:

[0084]

[0085] is the initial optimal fatigue intervention strategy under the αth fatigue state;

[0086] u∈{fast tempo, medium tempo, slow tempo}, v∈{high volume, medium volume, low volume}, q∈{10min,20min,30min,40min,50min,60min}.

[0087] The initial fatigue intervention strategy library consists of the initial optimal fatigue intervention strategy under each fatigue state. That is,

[0088]

[0089] Among them, P_strategy (0) The initial fatigue intervention strategy library; strategy1 (0) and β i1 The fatigue intervention strategy for the first fatigue state, namely mild active fatigue; strategy2 (0) and β i2 The fatigue intervention strategy for the first fatigue state, namely moderate active fatigue; strategy3 (0) and β i3 The fatigue intervention strategy for the first fatigue state, namely severe active fatigue; strategy 4 (0) and β i4 The fatigue intervention strategy for the first fatigue state, namely mild passive fatigue; strategy5 (0) and β i5 The fatigue intervention strategy for the first fatigue state, namely moderate passive fatigue; strategy6 (0) and β i6 The fatigue intervention strategy for the first fatigue state, i.e., severe passive fatigue; u (1) ,u (2) ,u (3) ,u (4) ,u (5) ,u (6) ∈{fast tempo, medium tempo, slow tempo}, v (1) ,v (2) ,v (3) ,v (4) ,v (5) ,v (6) ∈{high volume, medium volume, low volume}, q (1) ,q (2) ,q (3) ,q (4) ,q (5) ,q (6) ∈{10min,20min,30min,40min,50min,60min};

[0090] After completing the above settings, start using it. The first step is that when driver x (x=1,2,3…) uses the method or system for the first time, the fatigue intervention strategy model F corresponding to driver x is (x) (t), the best intervention effect library P_effect (x) and the optimal intervention strategy library P_strategy (x) All use the initial model or library, i.e.

[0091]

[0092] In the second step, based on the optimal intervention strategy described in the first step, songs with a rhythm that matches this strategy are found in the driver x's preferred playlist, and fatigue intervention is performed on driver x in the music intervention module based on the loudness and duration settings.

[0093] When the fatigue identification module I determines that the fatigue state of driver x is α, the music intervention module selects the optimal intervention strategy library P_strategy corresponding to driver x. (x) Find the optimal intervention strategy corresponding to fatigue state α in strategy α =β→rhythm u, loudness v, duration q.

[0094] In driver x's preferred playlist, find songs with rhythm u, arrange the order of songs based on driver x's listening habits, and use loudness v and duration q to perform music intervention on the driver.

[0095] During the entire music intervention process, the model adjustment module records the fatigue intervention strategy model of driver x changes.

[0096] Step 4: Identify the fatigue state of driver x after the intervention in fatigue identification module II.

[0097] Fatigue Identification Module II identifies driver x's fatigue state after the intervention and determines whether driver x is awake after the intervention. If driver x is awake, proceed to step five. If driver x is still fatigued, driver x's fatigue state is further identified and the current fatigue state of driver x is output. The music intervention module then performs music intervention on driver x again.

[0098] Step 5: The model adjustment module records the fatigue intervention strategy model of driver x after each intervention changes, thereby adjusting and the optimal intervention strategy library P_strategy (x) .

[0099] Adjusting the fatigue intervention strategy model F(t) involves adjusting the model parameters θ(a, b, c, k, l, m, n). Stochastic Gradient Descent (SGD) is used.

[0100] Gradient descent is an optimization algorithm based on gradient descent. It iteratively updates parameters along the negative gradient of the error function to minimize the sum of the squared errors between the fitted function and the actual data. This algorithm is applicable to various nonlinear fitting problems.

[0101] The basic idea of ​​stochastic gradient descent is to use the gradient of only one example to update the model parameters at each iteration. Since only the gradient of one example needs to be calculated at a time, stochastic gradient descent is highly efficient on large datasets. When using SGD, the goal is to minimize the loss function, which measures the difference between the model's predictions and the actual values.

[0102] When using stochastic gradient descent, adjusting F(t;θ) involves the following steps:

[0103] The initial parameters are the parameters of the initial fatigue intervention strategy model θ(a,b,c,k,l,m,n)=θ (0) .

[0104] The first step is to define the loss function:

[0105]

[0106] Among them, f i is the normalized fatigue comprehensive value of the driver recorded after intervention; where N is the total number of samples, i∈[1,N]; F i (t i ; θ) is the normalized comprehensive fatigue value of the driver stored in the current cloud database;

[0107] In the second step, for each sample i, perform the following operations until in, The gradient is the vector of partial derivatives of the loss function R with respect to each parameter θ, and threshold is the threshold:

[0108] Compute the gradient:

[0109]

[0110] Among them, R i is the loss of a single sample, R i The gradient with respect to the parameters θ.

[0111] First, calculate the single sample error term:

[0112] e i =(f i -F(t i ;θ)) 2 ;

[0113] Then, calculate the single sample error term e i The gradient of the loss function (f i -F(t i ;θ)) 2 Derivative with respect to θ:

[0114]

[0115] where θ j ∈θ(a,b,c,k,l,m,n),j∈{1,2,3...7} are the parameters of the model.

[0116] Sum the gradients of all samples to get the loss function for each parameter θ j The total gradient of:

[0117]

[0118] Full gradient:

[0119]

[0120] Simplify the expression and get the gradient vector

[0121]

[0122] Finally, update the parameters.

[0123]

[0124] θ new is the updated model parameter. η is the learning rate, which is used to control the step size of each parameter update.

[0125] Get new model parameter values ​​through SGD Replace the original model parameter values ​​to obtain a new fatigue intervention strategy model Recalculate the optimal intervention effect under the αth fatigue state Thus adjusting the optimal intervention effect library P_effect of driver x (x,new) and the optimal intervention strategy library P_strategy (x,new) .

[0126] The third step is to adjust the model and P_effect (x) 、P_strategy(x) The data and models are stored in the cloud database to replace the original data and models, and correspond to the driver's ID for easy retrieval next time.

[0127]

[0128] The adjusted model is in line with the individual differences of drivers and can achieve better intervention effects.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions and ideas of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary technicians in this field should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A method for individual-specific driver fatigue intervention using music. This method uses an identity recognition module, fatigue identification module I, fatigue identification module II, a music intervention module, a model adjustment module, and a cloud database to identify the driver's fatigue state and select the optimal music intervention strategy to address individual differences. The method is characterized by: The following steps are included and are performed in sequence: Step 1: Identify the driver's ID x through the identity recognition module and obtain the corresponding stored preferred playlist, listening habits, fatigue intervention strategy model library, and optimal intervention strategy library in the cloud database; Step 2: The fatigue identification module I identifies the fatigue state of the driver x at a set time interval; Step 3: Driver x is identified as being in a fatigue state α, and the music intervention module calls the optimal intervention strategy library P_strategy corresponding to driver x (x) , find the best intervention strategy corresponding to fatigue state α The music intervention module performs music intervention on driver x according to the optimal intervention strategy; The specific steps of performing music intervention on driver x are as follows: Step 1: Driver x is identified as a fatigue state α, and the optimal intervention strategy library P_strategy corresponding to driver x is called (x) , find the best intervention strategy corresponding to fatigue state α Perform music intervention on driver x according to the optimal intervention strategy; Among them, the optimal intervention strategy library is divided into two types, one is the initial optimal intervention strategy library, and the other is the optimal intervention strategy library after adjustment in step five; The fatigue intervention strategy model library is composed of fatigue intervention strategy models, and β represents each fatigue intervention strategy corresponding to the fatigue intervention strategy model. The fatigue intervention strategy is a combination of music rhythm, loudness and duration, that is, rhythm×loudness×duration; The initial fatigue intervention strategy model with fatigue state as α and fatigue intervention strategy as β As follows: in, is the change in the initial pre-intervention normalized fatigue composite value over time t; is the change of the normalized fatigue comprehensive value after the intervention of the initial setting over time t, where α is the driving fatigue state, β represents the fatigue intervention strategy, and (0) represents the initial setting, that is, the driver ID is 0; are the initial model parameters, where is related to the fatigue level of driver 0 before intervention in fatigue state α; Indicates the rate at which the comprehensive fatigue value of driver 0 changes over time; represents the fatigue growth rate constant of driver 0 in the process of reaching fatigue state α; It represents the rate of fatigue reduction after the intervention strategy β is implemented for driver 0 in fatigue state α; It represents the intensity of the intervention effect of intervention strategy β when driver 0 is in fatigue state α; It represents the recovery rate of the comprehensive fatigue value over time after the intervention strategy β is implemented for driver 0 in fatigue state α; When driver 0 is in fatigue state α, the rate constant of fatigue recovery after intervention strategy β is implemented; the moment for the musical intervention to begin; The moment when the driver enters the awake state from the fatigue state after the music intervention; The fatigue intervention strategy model formula is used to calculate the optimal intervention strategy β under each fatigue state α; The initial fatigue intervention strategy library consists of the initial optimal fatigue intervention strategy under each fatigue state, which is expressed as: Among them, P_strategy (0) The initial fatigue intervention strategy library; strategy1 (0) and β i1 The fatigue intervention strategy for the first fatigue state, namely mild active fatigue; strategy2 (0) and β i2 The fatigue intervention strategy for the second fatigue state, namely moderate active fatigue; strategy3 (0) and β i3 The fatigue intervention strategy for the third fatigue state, severe active fatigue; strategy 4 (0) and β i4 The fatigue intervention strategy for the fourth fatigue state, namely mild passive fatigue; strategy5 (0) and β i5 The fatigue intervention strategy for the fifth fatigue state, namely moderate passive fatigue; strategy6 (0) and β i6 The fatigue intervention strategy for the sixth fatigue state, namely severe passive fatigue; u (1) ,u (2) ,u (3) ,u (4) ,u (5) ,u (6) ∈{fast tempo, medium tempo, slow tempo}, v (1) ,v (2) ,v (3) ,v (4) ,v (5) ,v (6) ∈{high volume, medium volume, low volume}, q (1) ,q (2) ,q (3) ,q (4) ,q (5) ,q (6) ∈{10min,20min,30min,40min,50min,60min}; Driver x (x=1,2,3...) uses this method for the first time, then the fatigue intervention strategy model F corresponding to driver x (x) (t) and the optimal intervention strategy library P_strategy (x) All used the initial fatigue intervention strategy model and the initial optimal intervention strategy library; Step 2: Based on the optimal intervention strategy described in Step 1, find songs with a rhythm that matches this strategy in the driver x's preferred playlist and perform driver fatigue intervention in the music intervention module based on the loudness and duration settings. Specifically: Fatigue identification module I determines that the fatigue state of driver x is α, and the optimal intervention strategy library P_strategy corresponding to driver x is (x) Find the optimal intervention strategy corresponding to fatigue state α in strategy α =β→rhythm u, loudness v, duration q; In driver x's preferred playlist, find songs with rhythm u, arrange the order of the songs based on driver x's listening habits, and perform music intervention on driver x using loudness v and duration q; Step 4: Fatigue Identification Module II identifies the fatigue state of driver x after each intervention and determines whether driver x is awake after the intervention. If driver x is awake, proceed to Step 5. If driver x is still fatigued, the fatigue state of driver x is identified and the current fatigue state of driver x is output. The process returns to Step 3 and music intervention is performed on driver x again through the music intervention module. Step 5: The model adjustment module records the fatigue intervention strategy model of driver x after intervention changes, thereby adjusting the fatigue intervention strategy model and the optimal intervention strategy library P_strategy (x) And upload it to the cloud database to update the fatigue intervention strategy model library and the optimal intervention strategy library.

2. The driving fatigue music intervention method for individual differences according to claim 1 is characterized by: In the step 1, the identity recognition module identifies the identity ID of driver x. If the cloud database does not store the identity information of driver x, the driver x is reminded to upload the preferred song list and set the listening habits. The cloud database assigns the identity ID of driver x to x (x∈N + ), and the initial fatigue intervention strategy model stored in the cloud database is used as the fatigue intervention strategy model of the driver x, and the initial optimal intervention strategy library is used as the optimal intervention strategy library of the driver x.

3. The driving fatigue music intervention method for individual differences according to claim 1 is characterized by: In step 2, the fatigue identification module I identifies the fatigue state of the driver x: Driver x's driving load is obtained through the cloud, and driver x's fatigue state is divided into active fatigue and passive fatigue. Driver x's facial information is also obtained through the cloud, and these two fatigue states are further divided into three levels based on set thresholds: mild fatigue, moderate fatigue, and severe fatigue. If driver x is determined to be awake, it is determined again after T1 minutes whether driver x is in a fatigue state. If driver x is determined to be in a fatigue state, the fatigue identification module I determines which fatigue state α driver x is in and outputs it.

4. The driving fatigue music intervention method for individual differences according to claim 1 is characterized by: Adjust the fatigue intervention strategy model in step 5 and the optimal intervention strategy library P_strategy (x) The specific method is: Adjusting the fatigue intervention strategy model involves adjusting the model parameters θ(a, b, c, k, l, m, n) using stochastic gradient descent (SGD). The steps are as follows: Set the initial fatigue intervention strategy model parameters θ(a,b,c,k,l,m,n)=θ (0) ; The first step is to define the loss function: Where, f i is the normalized fatigue comprehensive value of the driver recorded after the intervention, where N is the total number of samples, i∈[1,N]; F i (t i ; θ) is the normalized comprehensive fatigue value of the driver stored in the current cloud database; The second step is to get the complete gradient: Simplify the expression and get the gradient vector Update parameters: θ new is the updated model parameter, η is the learning rate, which is used to control the step size of each parameter update; Get new model parameter values ​​through stochastic gradient descent SGD Replace the original model parameter values ​​to obtain a new fatigue intervention strategy model Recalculate and adjust the optimal intervention strategy library P_strategy for driver x (x ,new) ; The third step is to integrate the new fatigue intervention strategy model and the optimal intervention strategy library P_strategy (x,new) The data is stored in the cloud database to replace the original model and data, and corresponds to the ID of driver x for easy retrieval next time.

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