An experience-aware anomaly processing system and a method for an experience-aware anomaly processing system are provided. The experience-aware anomaly processing system comprises an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An experience-aware anomaly processing system comprising one or more processors, memory, and one or more program units stored in the memory and to be executed by the one or more processors, the one or more program units comprising: an anomaly detection module configured to receive geographic location data with corresponding time information of a target object, and analyze target object behavior based on the geographic location data with corresponding time information of the target object; a user feedback module configured to receive user feedback from a user and model user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and a decision module configured to receive user setting from the user, and make a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting. 2. The experience-aware anomaly processing system according to claim 1, further includes: a data preprocessing module configured to receive raw geographic location data with corresponding time information of the target object from the target object, preprocess the raw geographic location data with corresponding time information of the target object to get the geographic location data with corresponding time information of the target object, and send the geographic location data with corresponding time information of the target object to anomaly detection module. 3. The experience-aware anomaly processing system according to claim 2, further includes: a training module configured to receive the geographic location data with corresponding time information of the target object from the data preprocessing module, and learn a model corresponding to the geographic location data with corresponding time information of the target object based on training data stored in a database. 4. The experience-aware anomaly processing system according to claim 3, wherein: the training data stored in the database includes normal geographic location data with corresponding normal time information of the target object. 5. The experience-aware anomaly processing system according to claim 1, wherein the anomaly detection module is further configured to: generate an anomaly probability P(D(xt, yt)) of the target object at time t based on n-gram model, wherein P  ( D  ( x t , y t ) ) = C  ( { D  ( x t - n , y t - n ) , D  ( x t - n + 1 , y t - n + 1 ) , …  , D  ( x t , y t ) } ) C  ( { D  ( x t - n , y t - n ) , D  ( x t - n + 1 , y t - n + 1 ) , …  , D  ( x t - 1 , y t - 1 ) } ) , where D(xt, yt) is the target object's location, xt is the x-coordinate at time t, yt is they-coordinate at time t, {D(xt−n, yt−n), D(xt−n+1, yt−n30 a) . . . , D(xt, yt)} is a sequence of the geographic location data of the target object, C({D (xt−n, yt−n), D(xt−n+1, yt−n+1) . . . , D(xt, yt)}) is the number of elements in {D(xt−n, yt−n), D(xt−n+1, yt−n+1) . . . , D(xtyt)}, and n is a positive interger larger than 1. 6. The experience-aware anomaly processing system according to claim 5, wherein the user feedback module is further configured to generate a threshold function G(θt), where θt is a threshold at time t to make the detection decision: the threshold function G(θt) at time t is G(θt )=γθt, where γ is a security factor set by the user; and the threshold θt at time t is calculated by minimizing a predicted anomaly probability P(θt|DATA) based on history data, wherein the predicted anomaly probability P  ( θ t | DATA ) = P  ( DATA | θ t )  P  ( θ t ) ρ = P  ( θ t | DATA ) ∝ P  ( DATA | θ t )  P  ( θ t ) , where ρ is a constant value, DATA={data1, data2, . . . , datat} is the history data containing target object's previous locations and user's previous feedbacks, datat=(D(xt, yt), (αt,βt)), D(xt, yt) is target object's location, (at, βt) is user's feedback at time t, αt is a count of target object's abnormal behavior observed by the user by time t, and βt is a count of target object's normal behavior observed by the user by time t. 7. The experience-aware anomaly processing system according to claim 6, wherein: P(θt) is a Beta distribution, and P  ( θ t ) = ( 1 - θ t ) β t - 1  θ t α t B  ( α t , β t ) ; B(αt, βt) is a Beta function, and B  ( α t , β t ) = ( α t - 1 ) !  ( β t - 1 ) ! ( α t + β t - 1 ) ! ; and P(DATA|θt) is a Bernoulli distribution, and P(DATA|θt)∝θtz(1−θt)N−z; where N is a total number of data and z represents a time of the target object's anomaly behavior observed by the experience-aware anomaly processing system. 8. The experience-aware anomaly processing system according to claim 7, wherein: P  ( θ t | DATA ) ∝ ( α t + β t - 1 ) ! ( α t - 1 ) !  ( β t - 1 ) !  θ t z + α t  ( 1 - θ t ) N - z + β t - 1 9. The experience-aware anomaly processing system according to claim 8, wherein: the threshold function G(θt) is adaptive based on the user's feedback. 10. The experience-aware anomaly processing system according to claim 8, wherein the decision module is further configured to generate an anomaly value at time t, donate as Vt, through comparing the anomaly probabilities P(D(xt, yt)) at time t and the threshold function G(θt) at time t, wherein: V t = { 1 , if   P  ( D  ( x t , y t ) ) > G  ( θ t ) 0 , if   P  ( D  ( x t , y t ) ) ≤ G  ( θ t ) when Vt=1, the decision module considers the target object's behavior is abnormal and send a notification to the user, and when Vt=0, the decision module considers the target object's behavior is normal and does not send a notification to the user. 11. A method for an experience-aware anomaly processing system, comprising: receiving geographic location data with corresponding time information of a target object; analyzing target object behavior based on the received geographic location data with corresponding time information of the target object; receiving user feedback and user setting from a user; modeling user feedback behavior when the user receives an alarm message indicating the target object is abnormal; and making a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting. 12. The method for an experience-aware anomaly processing system according to claim 11, further including: receiving raw geographic location data with corresponding time information of the target object from the target object; and preprocessing the raw geographic location data with corresponding time information of the target object to get the geographic location data with corresponding time information of the target object. 13. The method for an experience-aware anomaly processing system according to claim 12, further including: receiving the geographic location data with corresponding time information of the target object from the data preprocessing module; and learning a model corresponding to the geographic location data with corresponding time information of the target object based on training data stored in a database. 14. The method for an experience-aware anomaly processing system according to claim 13, wherein: the training data stored in the database includes normal geographic location data with corresponding normal time information of the target object. 15. The method for an experience-aware anomaly processing system according to claim 11, wherein analyzing target object behavior based on the received geographic location data with corresponding time information of the target object further includes: generating an anomaly probability P(D(xt, yt)) of the target object at time t based on n-gram model, wherein P  ( D  ( x t , y t ) ) = C  ( { D  ( x t - n , y t - n ) , D  ( x t - n + 1 , y t - n + 1 ) , …  , D  ( x t , y t ) } ) C  ( { D  ( x t - n , y t - n ) , D  ( x t - n + 1 , y t - n + 1 ) , …  , D  ( x t - 1 , y t - 1 ) } ) , where D(xt, yt) is the target object's location, xt is the x-coordinate at time t, yt is they-coordinate at time t, {D(xt−n, yt−n), D(xt−n+1, yt−n+1) . . . , D(xt, yt)} is a sequence of the geographic location data of the target object, C({D(xt−n, yt−n), D(xt−n+1, yt−n+1) . . . , D(xt, yt)}) is the number of elements in {D(xt−n, yt−n), D(xt−n+1, yt−n+1) . . . , D(xt, yt)}, and n is a positive interger larger than 1. 16. The method for an experience-aware anomaly processing system according to claim 15, wherein modeling user feedback behavior when the user receives an alarm message indicating the target object is abnormal further includes: generating a threshold function G(θt), where θt is a threshold at time t to make the detection decision, wherein the threshold function G(θt) at time t is G(θt)=γθt, where γ is a security factor set by the user; and the threshold θt at time t is calculated by minimizing a predicted anomaly probability P(θt|DATA) based on history data, wherein the predicted anomaly probability P  ( θ t | DATA ) = P  ( DATA | θ t )  P  ( θ t ) ρ = P  ( θ t | DATA ) ∝ P  ( DATA | θ t )  P  ( θ t ) , where ρ is a constant value, DATA={data1, data2, . . . , datat} is the history data containing target object's previous locations and user's previous feedbacks, datat=(D(xt, yt), (αt, βt)), D(xt, yt) is target object's location, (αt, βt) is user's feedback at time t, αt is a count of target object's abnormal behavior observed by the user by time t, and βt is a count of target object's normal behavior observed by the user by time t. 17. The method for an experience-aware anomaly processing system according to claim 16, wherein: P(θt) is subject to beta distribution, and P  ( θ t ) = ( 1 - θ t ) β t - 1  θ t α t B  ( α t , β t ) ; B(αt, βt) is a beta function, and B  ( α t , β t ) = ( α t - 1 ) !  ( β t - 1 ) ! ( α t + β t - 1 ) ! ; and P(DATA|θt) is subject to Bernoulli distribution, and P(DATA|θt)∝θtz(1−θt)N−z; where N is a total number of data and z represents a time of the target object's anomaly behavior observed by the experience-aware anomaly processing system. 18. The method for an experience-aware anomaly processing system according to claim 17, wherein: P  ( θ t | DATA ) ∝ ( α t + β t - 1 ) ! ( α t - 1 ) !  ( β t - 1 ) !  θ t z + α t  ( 1 - θ t ) N - z + β t - 1 19. The method for an experience-aware anomaly processing system according to claim 18, wherein: the threshold function G(θt) is adaptive based on the user's feedback. 20. The method for an experience-aware anomaly processing system according to claim 18, wherein making a detection decision through fusing target object behavior information corresponding to the target object behavior, user feedback behavior information corresponding to the user feedback behavior, and the user setting further includes: generating an anomaly value at time t, donate as Vt, through comparing the anomaly probabilities P(D(xt, yt)) at time t and the threshold function G(θt) at time t, V t = { 1 , if   P  ( D  ( x t , y t ) ) > G  ( θ t ) 0 , if   P  ( D  ( x t , y t ) ) ≤ G  ( θ t ) , when Vt=1, the decision module considers the target object's behavior is abnormal and send a notification to the user, and when Vt=0, the decision module considers the target object's behavior is normal and does not send a notification to the user.