Power adapter anti-theft alarm system and method based on Internet of Things

Abstract

The invention discloses a power adapter anti-theft alarm system and method based on the Internet of Things, and relates to the technical field of anti-theft alarm. The system comprises a data acquisition and processing module, an abnormal feature identification module, a deviation analysis module and a real-time monitoring and alarm decision module. The data acquisition and processing module acquires historical data of the power adapter anti-theft alarm device and obtains historical data feature vectors; the abnormal feature recognition module analyzes and recognizes abnormal features of misinformation and missing report events according to historical data; the deviation analysis module compares the deviation between the normal alarm event and the abnormal event and generates a deviation threshold interval; the real-time monitoring and alarm decision-making module judges an alarm event according to the real-time data and a deviation threshold value and sends out a notification in time; according to the invention, the alarm event in the anti-theft alarm system of the power adapter can be effectively identified and processed, and the alarm accuracy is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-theft alarm technology, and in particular to an anti-theft alarm system and method for a power adapter based on the Internet of Things. Background Art

[0002] As the modern business environment attaches great importance to the security of digital products, anti-theft alarm systems have been widely used in shopping malls, electronic product exhibitions and other retail places. In these places, the digital products on display are usually connected to the alarm through the anti-theft alarm sensor line, and the power adapter is used to provide power support for the anti-theft alarm. This type of anti-theft alarm device is mainly used to prevent digital products from being illegally taken away. When the product is removed or the anti-theft alarm line is unplugged, the alarm is triggered to prompt the sales staff to take timely measures.

[0003] Existing power adapter anti-theft alarm devices usually use a connection method in which the alarm sensor line is directly connected to the alarm host, and rely on the power adapter to provide power. However, in some specific situations, for example, due to factors such as power adapter voltage fluctuations and external environmental changes, the existing system may not be able to accurately determine whether the power adapter is supplying power normally, resulting in false alarms or missed alarms. In addition, when the power adapter is disconnected from the alarm, the existing anti-theft alarm system cannot effectively judge and alarm in real time, resulting in some thefts not being discovered in time. Summary of the invention

[0004] The purpose of the present invention is to provide a power adapter anti-theft alarm system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: A power adapter anti-theft alarm method based on the Internet of Things includes the following steps: Step S100. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device, and the historical data is summarized and analyzed to extract historical data features and form a historical data feature vector; Step S200. Analyze the change trend of each dimension of the historical data feature vector according to the historical data feature vector, so as to obtain the corresponding historical data feature trend curve; based on the historical data feature trend curve, identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device; Step S300. Analyze the deviation relationship between the abnormal data characteristics of the power adapter anti-theft alarm device in the event of false alarm and missed alarm and the normal data characteristics of the normal alarm event, so as to obtain the deviation threshold interval between the abnormal data characteristics corresponding to the false alarm and missed alarm event and the normal data characteristics of the normal alarm event; Step S400. Acquire real-time data of the power adapter anti-theft alarm device, analyze the real-time data to obtain real-time data characteristics; analyze the deviation relationship between the real-time data characteristics and the normal data characteristics of normal alarm events, and determine whether an alarm event occurs currently in combination with the deviation threshold interval, and perform corresponding processing based on the determination result.

[0006] The power adapter anti-theft alarm device mentioned in the present invention is common in the prior art, and its specific components are: an alarm sensor line, a power adapter, a sensor monitoring module, and an alarm host; wherein the alarm sensor line is connected between the anti-theft product and the alarm host, and an alarm signal is triggered when the product is illegally removed or the sensor line is unplugged; the power adapter is used to provide the required power support for the alarm, and is usually connected to a power socket to ensure the normal operation of the alarm device; the sensor monitoring module is responsible for real-time monitoring of the working status of the power adapter and the connection status of the alarm sensor line; the alarm host receives signals from the alarm sensor line and the sensor monitoring module, and includes a power adapter anti-theft alarm system, which determines whether to trigger an alarm through the power adapter anti-theft alarm system.

[0007] Furthermore, step S100 includes: S101. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device; the historical data includes various sensor data in the power adapter anti-theft alarm device and alarm event records; the alarm event records include normal alarm events, false alarm events and missed alarm events, wherein the normal alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is an alarm event; the false alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is no alarm event; the missed alarm event refers to the power adapter anti-theft alarm device not sending out an alarm, and after the relevant personnel judge that there is an alarm event; the various sensor data in the historical data are summarized, and the sensor data are preprocessed, and the preprocessing includes denoising, removing outliers, filling missing values ​​and other preprocessing to ensure the integrity and accuracy of the data; for the various sensor data after preprocessing, the dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data of different sensors; S102. For various sensor data synchronized with timestamps, a time window is set, and the sensor data is divided into several data blocks based on the set time window; for the sensor data in each time window, the corresponding statistical features are calculated, and the statistical features include the mean, standard deviation, maximum value, minimum value, peak value and skewness of the sensor data; the mean value is used to reflect the overall level of the sensor data unit, the standard deviation is used to measure the discreteness of the data, the maximum value and the minimum value are used to capture the fluctuation range of the data, and the peak value is used to measure the sharpness of the data distribution; the wavelet transform is used to extract the time-frequency features of the sensor data in each time window, wherein the wavelet transform analyzes the time-frequency characteristics of the sensor data by transforming the scale and displacement, and its formula is: W a,b (t) = (1 / |a|)ψ[(tb) / a]; Among them, W a,b (t) represents the wavelet transform result of the sensor data at a given time point t and a given scale a and displacement b, a represents the scale factor, b represents the translation factor, and t represents the time variable; ψ[(tb) / a] represents the mother wavelet, which is the basic function for wavelet transform. By adjusting a and b, different time-frequency decompositions can be obtained; through wavelet transform, the time-frequency characteristics of the sensor data at different scales and time points are obtained, where these characteristics include energy, frequency band, peak value, skewness, maximum wavelet coefficient, etc.; S103. For each data block of each type of sensor data, the corresponding statistical features and time-frequency features are summarized as historical data features, and normalized to form a historical data feature vector L, and L=[l1,l2,...,ln], where l1 represents the eigenvalue of the first dimension of the sensor data block, l2 represents the eigenvalue of the second dimension of the sensor data block; and so on, ln represents the eigenvalue of the nth dimension of the sensor data block, and n represents the total number of dimensions of the historical data feature vector corresponding to the sensor data block.

[0008] Furthermore, for the preprocessed sensor data, the dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data from different sensors. The specific contents of synchronizing the timestamps of the data from different sensors are as follows: For the time series X=(x1,x2,...,xp) and Y=(y1,y2,...,yq) corresponding to different sensor data, x1 represents the timestamp corresponding to the first data point in the time series X, x2 represents the timestamp corresponding to the second data point in the time series X, and so on, xp represents the timestamp corresponding to the pth data point in the time series X; similarly, y1 represents the timestamp corresponding to the first data point in the time series Y, y2 represents the timestamp corresponding to the second data point in the time series Y, and yq represents the timestamp corresponding to the qth data point in the time series Y; calculate the distance between each timestamp in the time series X and the time series Y, and the distance calculation formula is: d(u,v)=|xu-yv|, where d(u,v) represents the distance between xu in the time series X and yv in the time series Y, xu represents the timestamp corresponding to the uth data point in the time series X, and yv represents the timestamp corresponding to the vth data point in the time series Y; Define a cumulative distance matrix D to store the cost of the shortest path, where the element D(u,v) of the cumulative distance matrix D represents the minimum cumulative distance from the u-th point of the time series X to the v-th point of the time series Y, and the calculation formula of the cumulative distance matrix is ​​as follows: D(u,v)=d(u,v)+min[D(u-1,v),D(u,v-1),D(u-1,v-1)]; Among them, D(u-1,v) represents the minimum cumulative distance from the u-1th point of time series X to the vth point of time series Y; D(u,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; D(u-1,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; for the boundary conditions of the cumulative distance matrix, assume that D(0,0)=0, indicating that it is the starting point, and the cumulative distance is zero when there is no data; for other boundary elements: D(u,0)=∑ u k=1 d(k,0),D(0,v)=∑ v k=1 d(0,k); According to the calculated cumulative distance matrix D, starting from D(p,q), select the smallest value from D(u-1,v-1), D(u,v-1) or D(u-1,v) as the next step of the path until backtracking to D(0,0), record the index corresponding to (u,v) selected each time backtracking, and obtain the path of time series X and time series Y with aligned timestamps, thereby completing the timestamp synchronization of time series X and time series Y, and the data after timestamp synchronization is processed accordingly. Among them, if the physical quantity values ​​corresponding to the data points of the time series at different timestamps vary greatly, the data points can be supplemented at the aligned timestamps through interpolation algorithms (such as linear interpolation, spline interpolation, etc.); for noisy sensor data, smoothing methods (such as moving average or Gaussian smoothing) can be used to suppress noise on the synchronized data.

[0009] Further, step S200 includes: S201. Extract and summarize the eigenvalues ​​corresponding to each dimension of the historical data feature vector L of each type of sensor, and draw the corresponding historical data feature trend curve Q in the plane rectangular coordinate system according to the time sequence of the eigenvalues ​​corresponding to each dimension, and the horizontal axis of the historical data feature trend curve Q is the time point, and the vertical axis is the eigenvalue; perform smoothing on each historical data feature trend curve Q, so as to obtain the smoothed historical data feature trend curve Q1, and the corresponding calculation formula is: Q1(t)=α·Q(t)+(1-α)·Q1(t-1); Among them, Q1(t) represents the historical data characteristic value corresponding to time t in the smoothed historical data characteristic trend curve, Q(t) represents the historical data characteristic value corresponding to time t in the original historical data characteristic trend curve Q; α represents the smoothing factor, and its value range is [0,1]; Q1(t-1) represents the historical data characteristic value corresponding to time t-1 in the smoothed historical data characteristic trend curve; S202. For each smoothed historical data characteristic trend curve Q1, calculate the standard deviation σ and the average value μ of the corresponding data point, calculate the coefficient of variation CV according to the standard deviation σ and the average value μ, and CV=σ / μ; calculate the instantaneous rate of change ROC for each data point on the historical data characteristic trend curve Q1, and ROC(t)=[Q1(t)-Q1(t-1)] / Q1(t-1), extract the data points on each historical data characteristic trend curve Q1 to calculate the maximum instantaneous rate of change M_ROC; For the false alarm events in the alarm event records in the historical data, the timestamp t0 of the false alarm events is extracted, and the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t0-△t, t0] is obtained, where △t represents the time window length of the historical data related to the false alarm events; based on the characteristic trend curve Q1, the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC are calculated; similarly, for the missed alarm events in the alarm event records in the historical data, the same analysis as the false alarm events is performed to obtain the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC; the coefficient of variation CV and the maximum instantaneous rate of change M_ROC corresponding to the false alarm events and the missed alarm events are respectively summarized, and the coefficient of variation CV and the maximum instantaneous rate of change M_ROC are used as the abnormal data features F corresponding to the false alarm events and the missed alarm events, and F=(CV,M_ROC), so as to identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device.

[0010] Among them, relying solely on alarm event records to identify false alarms and missed alarms may not be enough to capture all possible abnormal patterns. The alarm record itself is triggered by the device based on the set thresholds and rules, but in actual situations, false alarms and missed alarms may be caused by a variety of complex factors, such as environmental interference, fluctuations or errors in the sensor itself, etc. By combining the coefficient of variation CV and the instantaneous rate of change ROC, more detailed features can be further extracted from the statistical characteristics and change patterns of the data, thereby improving the ability to identify false alarms and missed alarms. The coefficient of variation CV provides information about the stability of data changes. By calculating the ratio of the standard deviation of the data to the mean, the relative volatility of the data can be revealed, helping to distinguish between normal fluctuations and abnormal fluctuations. The instantaneous rate of change ROC describes the rate of change of data at each time point, which helps to capture drastic changes in the data in the short and medium term, and can help identify false alarms caused by sudden changes or missed alarms that are not triggered in time. Through the comprehensive analysis of these two indicators, more accurate judgments can be made on false alarms and missed alarms, avoiding the deviation caused by relying solely on historical records.

[0011] Furthermore, step S300 includes: S301. Extract the timestamp t1 of the normal alarm event according to the alarm event record of the historical data, and obtain the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t1-△t, t1]; obtain the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to the normal alarm event according to the calculation method of the coefficient of variation CV and the maximum instantaneous change rate M_ROC corresponding to the false alarm and missed alarm events; summarize the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to all normal events, calculate the corresponding average value, and thus obtain the normal data feature F1 corresponding to the normal alarm event, and F1=(CV1,M1_ROC); S302. The normal data feature F1 corresponding to the normal alarm event is calculated in turn with the abnormal data feature F corresponding to the false alarm and omission events, and the calculation formula is: R=|F1-F|, so as to obtain the deviation data features R corresponding to the normal alarm event and the false alarm and omission events respectively, and R=(|r_cv|,|r_roc|), wherein |r_cv|=|CV1-CV|, |r_roc|=|M1_ROC-M_ROC|; the deviation data features R corresponding to all normal alarm events and false alarm and omission events are summarized, and the deviation distribution between the normal alarm event and the false alarm and omission events is calculated respectively, so as to obtain the deviation threshold intervals P1 and P2 corresponding to the normal alarm event and the false alarm and omission events, and there is no intersection between the deviation threshold intervals P1 and P2; wherein the deviation threshold interval P1 represents the deviation threshold interval corresponding to the normal alarm event and the false alarm event, and the deviation threshold interval P2 represents the deviation threshold interval corresponding to the normal alarm event and the omission event.

[0012] Furthermore, step S400 includes: S401. Acquire the real-time data of the power adapter anti-theft alarm device, analyze the real-time data with reference to the analysis method of historical data, and obtain the real-time data feature vector S; analyze the real-time data feature vector S according to the analysis process of the historical data feature vector L, and obtain the real-time data feature trend curve Q2; based on the real-time data feature trend curve Q2, obtain the current corresponding coefficient of variation CV2 and the maximum instantaneous rate of change M2_ROC, and form the real-time data feature F2, and F2=(CV2,M2_ROC); S402. Perform deviation analysis on the normal data feature F1 and the real-time data feature F2 corresponding to the normal alarm event, so as to calculate the corresponding real-time deviation data feature R1, and compare the real-time deviation data feature R1 with the deviation threshold intervals P1 and P2 respectively; if the real-time deviation data feature R1 belongs neither to the deviation threshold interval P1 nor to the deviation threshold interval P2, it indicates that the current event is a normal event; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P1, it indicates that the current event is a false alarm event, then the notification information of the false alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P2, it indicates that the current event is a missed alarm event, then the notification information of the missed alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing.

[0013] A power adapter anti-theft alarm system based on the Internet of Things, comprising: a data acquisition and processing module, an abnormal feature recognition module, a deviation analysis module, and a real-time monitoring and alarm decision module; The data collection and processing module collects data from the power adapter anti-theft alarm device through the Internet of Things technology, obtains historical data of the power adapter anti-theft alarm device, summarizes and analyzes the historical data, thereby extracting historical data features and forming a historical data feature vector; The abnormal feature recognition module analyzes the change trend of each dimension of the historical data feature vector according to the historical data feature vector, thereby obtaining the corresponding historical data feature trend curve; based on the historical data feature trend curve, the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device are identified; The deviation analysis module analyzes the deviation relationship between the abnormal data features of the power adapter anti-theft alarm device when false alarms and missed alarms occur and the normal data features of normal alarm events, thereby obtaining a deviation threshold interval between the abnormal data features corresponding to the false alarms and missed alarms and the normal data features of normal alarm events; The real-time monitoring and alarm decision module obtains the real-time data of the power adapter anti-theft alarm device, analyzes the real-time data, and obtains the real-time data characteristics; analyzes the deviation relationship between the real-time data characteristics and the normal data characteristics of normal alarm events, and combines the deviation threshold interval to determine whether an alarm event has occurred, and performs corresponding processing based on the judgment result.

[0014] Furthermore, the data acquisition and processing module includes a data acquisition unit, a data preprocessing unit and a feature extraction unit; The data acquisition unit obtains various sensor data and alarm event records from the power adapter anti-theft alarm device through the Internet of Things technology, including normal alarm events, false alarm events and missed alarm events; the data preprocessing unit preprocesses the collected historical data, including cleaning, denoising and timestamp synchronization of the sensor data; the feature extraction unit extracts statistical features and time-frequency features from the processed data and converts them into historical data feature vectors.

[0015] Furthermore, the abnormal feature recognition module includes a historical data analysis unit, an abnormal pattern recognition unit, and a feature matching unit; The historical data analysis unit analyzes the change trend of each dimension according to the historical data feature vector to obtain the historical data feature trend curve; the abnormal pattern recognition unit identifies the abnormal data features of the power adapter anti-theft alarm device when false alarms and missed alarms occur in combination with the historical data feature trends, thereby distinguishing normal alarms from abnormal alarms; the feature matching unit identifies the feature patterns of false alarms and missed alarms by comparing with the normal data features of normal alarm events, and extracts the corresponding abnormal data features; The deviation analysis module includes a deviation calculation unit and a deviation threshold interval generation unit; The deviation calculation unit calculates the deviation relationship between them based on the historical data characteristics of normal alarm events and the abnormal characteristics of false alarm and missed alarm events, and extracts the deviation data characteristics; the deviation threshold interval generation unit generates the deviation threshold interval of false alarm and missed alarm events based on the calculated deviation data characteristics.

[0016] Furthermore, the real-time monitoring and alarm decision module includes a real-time data acquisition and analysis unit, an alarm judgment unit, and an alarm decision unit; The real-time data acquisition and analysis unit acquires the real-time data of the power adapter anti-theft alarm device, analyzes the real-time data characteristics and generates a real-time data feature vector; the alarm judgment unit performs deviation analysis on the real-time data characteristics and the characteristics of normal alarm events, and judges the current alarm event type; the alarm decision unit outputs an alarm notification based on the alarm judgment result, and performs relevant processing based on false alarms or missed alarms.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention can effectively identify false alarm and missed alarm events by analyzing the historical data of the power adapter anti-theft alarm device, extracting data features and constructing historical data feature vectors; by quantifying the abnormal data features of false alarm and missed alarm, it can more accurately determine whether the alarm event is true, significantly reducing the probability of false alarm and missed alarm. By adopting the DTW algorithm to synchronize the timestamps of the data of different sensors, it can effectively handle the inconsistency of the sensor data caused by time differences or fluctuations, making the data analysis more accurate; in addition, the smoothing and interpolation processing of the noise data further improves the data quality and the accuracy of the analysis. The present invention can make instant judgments and alarm processing when an abnormality occurs in the power adapter anti-theft alarm device through the analysis of real-time data, combined with the characteristic trend and deviation threshold interval of historical data; compared with the traditional system, it can detect the state changes of the power adapter and the alarm sensor line in real time, especially in the case of power disconnection or voltage fluctuation, it can accurately identify the abnormality and timely alarm, avoiding the problem of missed alarm or delayed response. The present invention can identify regular changes in historical data by calculating statistical features such as smoothing the characteristic trend of historical data, coefficient of variation CV and instantaneous rate of change ROC, and timely discover anomalies through changes in data trends; this method based on data trend analysis enables the system to dynamically adapt to various environmental changes and avoid the defects of relying solely on fixed thresholds and rule judgments. By extracting historical data features and performing deviation analysis on false alarm and missed alarm events, the present invention can establish a deviation threshold interval for false alarm and missed alarm events, and compare it with real-time data to accurately determine the nature of the alarm event; this division of the deviation threshold interval can not only distinguish normal alarm events, but also accurately distinguish false alarm and missed alarm events, avoiding the misjudgment problem that is easy to occur in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 The present invention is a schematic diagram of a module of a power adapter anti-theft alarm system based on the Internet of Things. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 , the present invention provides a technical solution: A power adapter anti-theft alarm system based on the Internet of Things, comprising: a data acquisition and processing module, an abnormal feature recognition module, a deviation analysis module, and a real-time monitoring and alarm decision module; The data collection and processing module collects data from the power adapter anti-theft alarm device through the Internet of Things technology, obtains historical data of the power adapter anti-theft alarm device, summarizes and analyzes the historical data, thereby extracting historical data features and forming a historical data feature vector; The abnormal feature recognition module analyzes the change trend of each dimension of the historical data feature vector according to the historical data feature vector, thereby obtaining the corresponding historical data feature trend curve; based on the historical data feature trend curve, the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device are identified; The deviation analysis module analyzes the deviation relationship between the abnormal data features of the power adapter anti-theft alarm device when false alarms and missed alarms occur and the normal data features of normal alarm events, thereby obtaining a deviation threshold interval between the abnormal data features corresponding to the false alarms and missed alarms and the normal data features of normal alarm events; The real-time monitoring and alarm decision module obtains the real-time data of the power adapter anti-theft alarm device, analyzes the real-time data, and obtains the real-time data characteristics; analyzes the deviation relationship between the real-time data characteristics and the normal data characteristics of normal alarm events, and combines the deviation threshold interval to determine whether an alarm event has occurred, and performs corresponding processing based on the judgment result.

[0021] The data acquisition and processing module includes a data acquisition unit, a data preprocessing unit and a feature extraction unit; The data acquisition unit obtains various sensor data and alarm event records from the power adapter anti-theft alarm device through the Internet of Things technology, including normal alarm events, false alarm events and missed alarm events; the data preprocessing unit preprocesses the collected historical data, including cleaning, denoising and timestamp synchronization of the sensor data; the feature extraction unit extracts statistical features and time-frequency features from the processed data and converts them into historical data feature vectors.

[0022] The abnormal feature recognition module includes a historical data analysis unit, an abnormal pattern recognition unit and a feature matching unit; The historical data analysis unit analyzes the change trend of each dimension according to the historical data feature vector to obtain the historical data feature trend curve; the abnormal pattern recognition unit identifies the abnormal data features of the power adapter anti-theft alarm device when false alarms and missed alarms occur in combination with the historical data feature trends, thereby distinguishing normal alarms from abnormal alarms; the feature matching unit identifies the feature patterns of false alarms and missed alarms by comparing with the normal data features of normal alarm events, and extracts the corresponding abnormal data features; The deviation analysis module includes a deviation calculation unit and a deviation threshold interval generation unit; The deviation calculation unit calculates the deviation relationship between them based on the historical data characteristics of normal alarm events and the abnormal characteristics of false alarm and missed alarm events, and extracts the deviation data characteristics; the deviation threshold interval generation unit generates the deviation threshold interval of false alarm and missed alarm events based on the calculated deviation data characteristics.

[0023] The real-time monitoring and alarm decision module includes a real-time data acquisition and analysis unit, an alarm judgment unit, and an alarm decision unit; The real-time data acquisition and analysis unit acquires the real-time data of the power adapter anti-theft alarm device, analyzes the real-time data characteristics and generates a real-time data feature vector; the alarm judgment unit performs deviation analysis on the real-time data characteristics and the characteristics of normal alarm events, and judges the current alarm event type; the alarm decision unit outputs an alarm notification based on the alarm judgment result, and performs relevant processing based on false alarms or missed alarms.

[0024] A power adapter anti-theft alarm method based on the Internet of Things includes the following steps: Step S100. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device, and the historical data is summarized and analyzed to extract historical data features and form a historical data feature vector; Step S200. Analyze the change trend of each dimension of the historical data feature vector according to the historical data feature vector, so as to obtain the corresponding historical data feature trend curve; based on the historical data feature trend curve, identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device; Step S300. Analyze the deviation relationship between the abnormal data characteristics of the power adapter anti-theft alarm device in the event of false alarm and missed alarm and the normal data characteristics of the normal alarm event, so as to obtain the deviation threshold interval between the abnormal data characteristics corresponding to the false alarm and missed alarm event and the normal data characteristics of the normal alarm event; Step S400. Acquire real-time data of the power adapter anti-theft alarm device, analyze the real-time data to obtain real-time data characteristics; analyze the deviation relationship between the real-time data characteristics and the normal data characteristics of normal alarm events, and determine whether an alarm event occurs currently in combination with the deviation threshold interval, and perform corresponding processing based on the determination result.

[0025] The power adapter anti-theft alarm device mentioned in the present invention is common in the prior art, and its specific components are: an alarm sensor line, a power adapter, a sensor monitoring module, and an alarm host; wherein the alarm sensor line is connected between the anti-theft product and the alarm host, and an alarm signal is triggered when the product is illegally removed or the sensor line is unplugged; the power adapter is used to provide the required power support for the alarm, and is usually connected to a power socket to ensure the normal operation of the alarm device; the sensor monitoring module is responsible for real-time monitoring of the working status of the power adapter and the connection status of the alarm sensor line; the alarm host receives signals from the alarm sensor line and the sensor monitoring module, and includes a power adapter anti-theft alarm system, which determines whether to trigger an alarm through the power adapter anti-theft alarm system.

[0026] Step S100 includes: S101. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device; the historical data includes various sensor data in the power adapter anti-theft alarm device and alarm event records; the alarm event records include normal alarm events, false alarm events and missed alarm events, wherein the normal alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is an alarm event; the false alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is no alarm event; the missed alarm event refers to the power adapter anti-theft alarm device not sending out an alarm, and after the relevant personnel judge that there is an alarm event; the various sensor data in the historical data are summarized, and the sensor data are preprocessed, and the preprocessing includes denoising, removing outliers, filling missing values ​​and other preprocessing to ensure the integrity and accuracy of the data; for the various sensor data after preprocessing, the dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data of different sensors; S102. For various sensor data synchronized with timestamps, a time window is set, and the sensor data is divided into several data blocks based on the set time window; for the sensor data in each time window, the corresponding statistical features are calculated, and the statistical features include the mean, standard deviation, maximum value, minimum value, peak value and skewness of the sensor data; the mean value is used to reflect the overall level of the sensor data unit, the standard deviation is used to measure the discreteness of the data, the maximum value and the minimum value are used to capture the fluctuation range of the data, and the peak value is used to measure the sharpness of the data distribution; the wavelet transform is used to extract the time-frequency features of the sensor data in each time window, wherein the wavelet transform analyzes the time-frequency characteristics of the sensor data by transforming the scale and displacement, and its formula is: W a,b (t) = (1 / |a|)ψ[(tb) / a]; Among them, W a,b (t) represents the wavelet transform result of the sensor data at a given time point t and a given scale a and displacement b, a represents the scale factor, b represents the translation factor, and t represents the time variable; ψ[(tb) / a] represents the mother wavelet, which is the basic function for wavelet transform. By adjusting a and b, different time-frequency decompositions can be obtained; through wavelet transform, the time-frequency characteristics of the sensor data at different scales and time points are obtained, where these characteristics include energy, frequency band, peak value, skewness, maximum wavelet coefficient, etc.; S103. For each data block of each type of sensor data, the corresponding statistical features and time-frequency features are summarized as historical data features, and normalized to form a historical data feature vector L, and L=[l1,l2,...,ln], where l1 represents the eigenvalue of the first dimension of the sensor data block, l2 represents the eigenvalue of the second dimension of the sensor data block; and so on, ln represents the eigenvalue of the nth dimension of the sensor data block, and n represents the total number of dimensions of the historical data feature vector corresponding to the sensor data block.

[0027] For the pre-processed sensor data, the dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data from different sensors. The specific contents of synchronizing the timestamps of the data from different sensors are as follows: For the time series X=(x1,x2,...,xp) and Y=(y1,y2,...,yq) corresponding to different sensor data, x1 represents the timestamp corresponding to the first data point in the time series X, x2 represents the timestamp corresponding to the second data point in the time series X, and so on, xp represents the timestamp corresponding to the pth data point in the time series X; similarly, y1 represents the timestamp corresponding to the first data point in the time series Y, y2 represents the timestamp corresponding to the second data point in the time series Y, and yq represents the timestamp corresponding to the qth data point in the time series Y; calculate the distance between each timestamp in the time series X and the time series Y, and the distance calculation formula is: d(u,v)=|xu-yv|, where d(u,v) represents the distance between xu in the time series X and yv in the time series Y, xu represents the timestamp corresponding to the uth data point in the time series X, and yv represents the timestamp corresponding to the vth data point in the time series Y; Define a cumulative distance matrix D to store the cost of the shortest path, where the element D(u,v) of the cumulative distance matrix D represents the minimum cumulative distance from the u-th point of the time series X to the v-th point of the time series Y, and the calculation formula of the cumulative distance matrix is ​​as follows: D(u,v)=d(u,v)+min[D(u-1,v),D(u,v-1),D(u-1,v-1)]; Among them, D(u-1,v) represents the minimum cumulative distance from the u-1th point of time series X to the vth point of time series Y; D(u,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; D(u-1,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; for the boundary conditions of the cumulative distance matrix, assume that D(0,0)=0, indicating that it is the starting point, and the cumulative distance is zero when there is no data; for other boundary elements: D(u,0)=∑ u k=1 d(k,0),D(0,v)=∑ v k=1 d(0,k); According to the calculated cumulative distance matrix D, starting from D(p,q), select the smallest value from D(u-1,v-1), D(u,v-1) or D(u-1,v) as the next step of the path until backtracking to D(0,0), record the index corresponding to (u,v) selected each time backtracking, and obtain the path of time series X and time series Y with aligned timestamps, thereby completing the timestamp synchronization of time series X and time series Y, and the data after timestamp synchronization is processed accordingly. Among them, if the physical quantity values ​​corresponding to the data points of the time series at different timestamps vary greatly, the data points can be supplemented at the aligned timestamps through interpolation algorithms (such as linear interpolation, spline interpolation, etc.); for noisy sensor data, smoothing methods (such as moving average or Gaussian smoothing) can be used to suppress noise on the synchronized data.

[0028] In this embodiment, it is assumed that the optimal path corresponding to the time series X=(x1,x2,...,xp) and Y=(y1,y2,...,yq) is: (u1,v1)→(u2,v2)→...→(uk,vk), then each pair of indexes (ui,vi) is the aligned timestamps in the two time series. If p>q, it means that the length of the time series X is greater than the length of the time series Y. The situation where the lengths of the two time series are not equal can be handled by interpolation, resampling, dynamic time warping or filling missing values, and the appropriate method can be selected for alignment according to the actual problem.

[0029] Step S200 includes: S201. Extract and summarize the eigenvalues ​​corresponding to each dimension of the historical data feature vector L of each type of sensor, and draw the corresponding historical data feature trend curve Q in the plane rectangular coordinate system according to the time sequence of the eigenvalues ​​corresponding to each dimension, and the horizontal axis of the historical data feature trend curve Q is the time point, and the vertical axis is the eigenvalue; perform smoothing on each historical data feature trend curve Q, so as to obtain the smoothed historical data feature trend curve Q1, and the corresponding calculation formula is: Q1(t)=α·Q(t)+(1-α)·Q1(t-1); Among them, Q1(t) represents the historical data characteristic value corresponding to time t in the smoothed historical data characteristic trend curve, Q(t) represents the historical data characteristic value corresponding to time t in the original historical data characteristic trend curve Q; α represents the smoothing factor, and its value range is [0,1]; Q1(t-1) represents the historical data characteristic value corresponding to time t-1 in the smoothed historical data characteristic trend curve; S202. For each smoothed historical data characteristic trend curve Q1, calculate the standard deviation σ and the average value μ of the corresponding data point, calculate the coefficient of variation CV according to the standard deviation σ and the average value μ, and CV=σ / μ; calculate the instantaneous rate of change ROC for each data point on the historical data characteristic trend curve Q1, and ROC(t)=[Q1(t)-Q1(t-1)] / Q1(t-1), extract the data points on each historical data characteristic trend curve Q1 to calculate the maximum instantaneous rate of change M_ROC; For the false alarm events in the alarm event records in the historical data, the timestamp t0 of the false alarm events is extracted, and the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t0-△t, t0] is obtained, where △t represents the time window length of the historical data related to the false alarm events; based on the characteristic trend curve Q1, the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC are calculated; similarly, for the missed alarm events in the alarm event records in the historical data, the same analysis as the false alarm events is performed to obtain the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC; the coefficient of variation CV and the maximum instantaneous rate of change M_ROC corresponding to the false alarm events and the missed alarm events are respectively summarized, and the coefficient of variation CV and the maximum instantaneous rate of change M_ROC are used as the abnormal data features F corresponding to the false alarm events and the missed alarm events, and F=(CV,M_ROC), so as to identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device.

[0030] Among them, relying solely on alarm event records to identify false alarms and missed alarms may not be enough to capture all possible abnormal patterns. The alarm record itself is triggered by the device based on the set thresholds and rules, but in actual situations, false alarms and missed alarms may be caused by a variety of complex factors, such as environmental interference, fluctuations or errors in the sensor itself, etc. By combining the coefficient of variation CV and the instantaneous rate of change ROC, more detailed features can be further extracted from the statistical characteristics and change patterns of the data, thereby improving the ability to identify false alarms and missed alarms. The coefficient of variation CV provides information about the stability of data changes. By calculating the ratio of the standard deviation of the data to the mean, the relative volatility of the data can be revealed, helping to distinguish between normal fluctuations and abnormal fluctuations. The instantaneous rate of change ROC describes the rate of change of data at each time point, which helps to capture drastic changes in the data in the short and medium term, and can help identify false alarms caused by sudden changes or missed alarms that are not triggered in time. Through the comprehensive analysis of these two indicators, more accurate judgments can be made on false alarms and missed alarms, avoiding the deviation caused by relying solely on historical records.

[0031] In the present embodiment, since the alarm events in the historical data records are usually triggered based on some significant thresholds, false alarms and missed alarms sometimes do not occur at these significant threshold points. Actual false alarm and missed alarm events may only be slight fluctuations in the data or some trends that have not been fully analyzed. Through the dual determination of CV and ROC, it is possible to identify those slight fluctuations and trend changes near the thresholds, which is very important for capturing potential false alarm or missed alarm events. For example, some alarm events may occur during the stable period of the data, rather than during the sudden and violent fluctuation period, which cannot be effectively captured only through historical alarm records. However, in combination with CV and ROC, potential deviations can be identified within the normal fluctuation range of the data, thereby effectively identifying missed alarms.

[0032] Step S300 includes: S301. Extract the timestamp t1 of the normal alarm event according to the alarm event record of the historical data, and obtain the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t1-△t, t1]; obtain the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to the normal alarm event according to the calculation method of the coefficient of variation CV and the maximum instantaneous change rate M_ROC corresponding to the false alarm and missed alarm events; summarize the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to all normal events, calculate the corresponding average value, and thus obtain the normal data feature F1 corresponding to the normal alarm event, and F1=(CV1,M1_ROC); S302. The normal data feature F1 corresponding to the normal alarm event is calculated in turn with the abnormal data feature F corresponding to the false alarm and omission events, and the calculation formula is: R=|F1-F|, so as to obtain the deviation data features R corresponding to the normal alarm event and the false alarm and omission events respectively, and R=(|r_cv|,|r_roc|), wherein |r_cv|=|CV1-CV|, |r_roc|=|M1_ROC-M_ROC|; the deviation data features R corresponding to all normal alarm events and false alarm and omission events are summarized, and the deviation distribution between the normal alarm event and the false alarm and omission events is calculated respectively, so as to obtain the deviation threshold intervals P1 and P2 corresponding to the normal alarm event and the false alarm and omission events, and there is no intersection between the deviation threshold intervals P1 and P2; wherein the deviation threshold interval P1 represents the deviation threshold interval corresponding to the normal alarm event and the false alarm event, and the deviation threshold interval P2 represents the deviation threshold interval corresponding to the normal alarm event and the omission event.

[0033] In this embodiment, the deviation threshold interval P1 corresponding to the normal alarm event and the false alarm event is taken as an example. Since R=(|r_cv|,|r_roc|), the deviation threshold interval P1 includes two parts, one is the deviation threshold interval corresponding to the coefficient of variation, and the other is the deviation threshold interval corresponding to the maximum instantaneous rate of change. The mean and standard deviation are used to calculate the deviation distribution between the normal alarm event and the false alarm event. The deviation threshold interval corresponding to the coefficient of variation is expressed as: [μ_|r_cv|-k1·σ_|r_cv|,μ_|r_cv|+k1·σ_|r_cv|]; the deviation threshold interval corresponding to the maximum instantaneous change rate is expressed as: [μ_|r_roc|-k2·σ_|r_roc|,μ_|r_roc|+k2·σ_|r_roc|], where k1 and k2 both represent adjustment factors, which are used to control the width of the error range.

[0034] Step S400 includes: S401. Acquire the real-time data of the power adapter anti-theft alarm device, analyze the real-time data with reference to the analysis method of historical data, and obtain the real-time data feature vector S; analyze the real-time data feature vector S according to the analysis process of the historical data feature vector L, and obtain the real-time data feature trend curve Q2; based on the real-time data feature trend curve Q2, obtain the current corresponding coefficient of variation CV2 and the maximum instantaneous rate of change M2_ROC, and form the real-time data feature F2, and F2=(CV2,M2_ROC); S402. Perform deviation analysis on the normal data feature F1 and the real-time data feature F2 corresponding to the normal alarm event, so as to calculate the corresponding real-time deviation data feature R1, and compare the real-time deviation data feature R1 with the deviation threshold intervals P1 and P2 respectively; if the real-time deviation data feature R1 belongs neither to the deviation threshold interval P1 nor to the deviation threshold interval P2, it indicates that the current event is a normal event; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P1, it indicates that the current event is a false alarm event, then the notification information of the false alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P2, it indicates that the current event is a missed alarm event, then the notification information of the missed alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power adapter anti-theft alarm method based on the Internet of Things, characterized in that: The method comprises the following steps: Step S100. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device, and the historical data is summarized and analyzed to extract historical data features and form a historical data feature vector; Step S200. Analyze the change trend of each dimension of the historical data feature vector according to the historical data feature vector, so as to obtain the corresponding historical data feature trend curve; based on the historical data feature trend curve, identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device; Step S300. Analyze the deviation relationship between the abnormal data characteristics of the power adapter anti-theft alarm device in the event of false alarm and missed alarm and the normal data characteristics of the normal alarm event, so as to obtain the deviation threshold interval between the abnormal data characteristics corresponding to the false alarm and missed alarm event and the normal data characteristics of the normal alarm event; Step S400. Acquire real-time data of the power adapter anti-theft alarm device, analyze the real-time data to obtain real-time data characteristics; analyze the deviation relationship between the real-time data characteristics and the normal data characteristics of normal alarm events, and determine whether an alarm event occurs currently in combination with the deviation threshold interval, and perform corresponding processing based on the determination result.

2. According to the method for anti-theft alarm of a power adapter based on the Internet of Things in claim 1, it is characterized in that: The step S100 includes: S101. Through the Internet of Things technology, data is collected on the power adapter anti-theft alarm device to obtain historical data of the power adapter anti-theft alarm device; the historical data includes various sensor data in the power adapter anti-theft alarm device and alarm event records; the alarm event records include normal alarm events, false alarm events and missed alarm events, wherein the normal alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is an alarm event; the false alarm event refers to the power adapter anti-theft alarm device sending out an alarm, and after the relevant personnel judge that there is no alarm event; the missed alarm event refers to the power adapter anti-theft alarm device not sending out an alarm, and after the relevant personnel judge that there is an alarm event; the various sensor data in the historical data are summarized and the sensor data are pre-processed; for the various sensor data after pre-processing, the dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data of different sensors; S102. For various sensor data synchronized with timestamps, a time window is set, and the sensor data is divided into several data blocks based on the set time window; for the sensor data in each time window, the corresponding statistical features are calculated, and the statistical features include the mean, standard deviation, maximum value, minimum value, peak value and skewness of the sensor data; the wavelet transform is used to extract the time-frequency features of the sensor data in each time window, wherein the wavelet transform analyzes the time-frequency characteristics of the sensor data by transforming the scale and displacement, and the formula is: W a,b (t)=(1 / |a|)ψ[(t-b) / a]; Among them, W a,b (t) represents the wavelet transform result of the sensor data at a given time point t and a given scale a and displacement b, a represents the scale factor, b represents the translation factor, and t represents the time variable; ψ[(tb) / a] represents the mother wavelet; through wavelet transform, the time-frequency characteristics of the sensor data at different scales and time points are obtained; S103. For each data block of each type of sensor data, the corresponding statistical features and time-frequency features are summarized as historical data features, and normalized to form a historical data feature vector L, and L=[l1,l2,...,ln], where l1 represents the eigenvalue of the first dimension of the sensor data block, l2 represents the eigenvalue of the second dimension of the sensor data block; and so on, ln represents the eigenvalue of the nth dimension of the sensor data block, and n represents the total number of dimensions of the historical data feature vector corresponding to the sensor data block.

3. The power adapter anti-theft alarm method based on the Internet of Things according to claim 2 is characterized in that: The dynamic time warping (DTW) algorithm is used to synchronize the timestamps of the data of different sensors after preprocessing. The specific contents of synchronizing the timestamps of the data of different sensors are as follows: For the time series X=(x1,x2,...,xp) and Y=(y1,y2,...,yq) corresponding to different sensor data, x1 represents the timestamp corresponding to the first data point in the time series X, x2 represents the timestamp corresponding to the second data point in the time series X, and so on, xp represents the timestamp corresponding to the pth data point in the time series X; similarly, y1 represents the timestamp corresponding to the first data point in the time series Y, y2 represents the timestamp corresponding to the second data point in the time series Y, and yq represents the timestamp corresponding to the qth data point in the time series Y; calculate the distance between each timestamp in the time series X and the time series Y, and the distance calculation formula is: d(u,v)=|xu-yv|, where d(u,v) represents the distance between xu in the time series X and yv in the time series Y, xu represents the timestamp corresponding to the uth data point in the time series X, and yv represents the timestamp corresponding to the vth data point in the time series Y; Define a cumulative distance matrix D to store the cost of the shortest path, where the element D(u,v) of the cumulative distance matrix D represents the minimum cumulative distance from the u-th point of the time series X to the v-th point of the time series Y, and the calculation formula of the cumulative distance matrix is ​​as follows: D(u,v)=d(u,v)+min[D(u-1,v),D(u,v-1),D(u-1,v-1)]; Among them, D(u-1,v) represents the minimum cumulative distance from the u-1th point of time series X to the vth point of time series Y; D(u,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; D(u-1,v-1) represents the minimum cumulative distance from the u-1th point of time series X to the v-1th point of time series Y; for the boundary conditions of the cumulative distance matrix, assume that D(0,0)=0, indicating that it is the starting point, and the cumulative distance is zero when there is no data; for other boundary elements: D(u,0)=∑ u k=1 d(k,0),D(0,v)=∑ v k=1 d(0,k); According to the calculated cumulative distance matrix D, starting from D(p,q), select the smallest value from D(u-1,v-1), D(u,v-1) or D(u-1,v) as the next step of the path until backtracking to D(0,0), record the index corresponding to (u,v) selected each time backtracking, and obtain the path with aligned timestamps in time series X and time series Y, thereby completing the timestamp synchronization of time series X and time series Y, and the data after timestamp synchronization is processed accordingly.

4. The power adapter anti-theft alarm method based on the Internet of Things according to claim 2 is characterized in that: The step S200 includes: S201. Extract and summarize the eigenvalues ​​corresponding to each dimension of the historical data feature vector L of each type of sensor, and draw the corresponding historical data feature trend curve Q in the plane rectangular coordinate system according to the time sequence of the eigenvalues ​​corresponding to each dimension, and the horizontal axis of the historical data feature trend curve Q is the time point, and the vertical axis is the eigenvalue; perform smoothing on each historical data feature trend curve Q, so as to obtain the smoothed historical data feature trend curve Q1, and the corresponding calculation formula is: Q1(t)=α·Q(t)+(1-α)·Q1(t-1); Among them, Q1(t) represents the historical data characteristic value corresponding to time t in the smoothed historical data characteristic trend curve, Q(t) represents the historical data characteristic value corresponding to time t in the original historical data characteristic trend curve Q; α represents the smoothing factor, and its value range is [0,1]; Q1(t-1) represents the historical data characteristic value corresponding to time t-1 in the smoothed historical data characteristic trend curve; S202. For each smoothed historical data characteristic trend curve Q1, calculate the standard deviation σ and the average value μ of the corresponding data point, calculate the coefficient of variation CV according to the standard deviation σ and the average value μ, and CV=σ / μ; calculate the instantaneous rate of change ROC for each data point on the historical data characteristic trend curve Q1, and ROC(t)=[Q1(t)-Q1(t-1)] / Q1(t-1), extract the data points on each historical data characteristic trend curve Q1 to calculate the maximum instantaneous rate of change M_ROC; For the false alarm events in the alarm event records in the historical data, the timestamp t0 of the false alarm events is extracted, and the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t0-△t, t0] is obtained, where △t represents the time window length of the historical data related to the false alarm events; based on the characteristic trend curve Q1, the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC are calculated; similarly, for the missed alarm events in the alarm event records in the historical data, the same analysis as the false alarm events is performed to obtain the corresponding coefficient of variation CV and the maximum instantaneous rate of change M_ROC; the coefficient of variation CV and the maximum instantaneous rate of change M_ROC corresponding to the false alarm events and the missed alarm events are respectively summarized, and the coefficient of variation CV and the maximum instantaneous rate of change M_ROC are used as the abnormal data features F corresponding to the false alarm events and the missed alarm events, and F=(CV,M_ROC), so as to identify the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device.

5. The power adapter anti-theft alarm method based on the Internet of Things according to claim 4 is characterized in that: The step S300 includes: S301. Extract the timestamp t1 of the normal alarm event according to the alarm event record of the historical data, and obtain the characteristic trend curve Q1 of each dimension of the historical data feature vector that overlaps with the time period [t1-△t, t1]; obtain the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to the normal alarm event according to the calculation method of the coefficient of variation CV and the maximum instantaneous change rate M_ROC corresponding to the false alarm and missed alarm events; summarize the coefficient of variation CV1 and the maximum instantaneous change rate M1_ROC corresponding to all normal events, calculate the corresponding average value, and thus obtain the normal data feature F1 corresponding to the normal alarm event, and F1=(CV1,M1_ROC); S302. The normal data feature F1 corresponding to the normal alarm event is calculated in turn with the abnormal data feature F corresponding to the false alarm and omission events, and the calculation formula is: R=|F1-F|, so as to obtain the deviation data features R corresponding to the normal alarm event and the false alarm and omission events respectively, and R=(|r_cv|,|r_roc|), wherein |r_cv|=|CV1-CV|, |r_roc|=|M1_ROC-M_ROC|; the deviation data features R corresponding to all normal alarm events and false alarm and omission events are summarized, and the deviation distribution between the normal alarm event and the false alarm and omission events is calculated respectively, so as to obtain the deviation threshold intervals P1 and P2 corresponding to the normal alarm event and the false alarm and omission events, and there is no intersection between the deviation threshold intervals P1 and P2; wherein the deviation threshold interval P1 represents the deviation threshold interval corresponding to the normal alarm event and the false alarm event, and the deviation threshold interval P2 represents the deviation threshold interval corresponding to the normal alarm event and the omission event.

6. The power adapter anti-theft alarm method based on the Internet of Things according to claim 5, characterized in that: The step S400 includes: S401. Acquire the real-time data of the power adapter anti-theft alarm device, analyze the real-time data with reference to the analysis method of historical data, and obtain the real-time data feature vector S; analyze the real-time data feature vector S according to the analysis process of the historical data feature vector L, and obtain the real-time data feature trend curve Q2; based on the real-time data feature trend curve Q2, obtain the current corresponding coefficient of variation CV2 and the maximum instantaneous rate of change M2_ROC, and form the real-time data feature F2, and F2=(CV2,M2_ROC); S402. Perform deviation analysis on the normal data feature F1 and the real-time data feature F2 corresponding to the normal alarm event, so as to calculate the corresponding real-time deviation data feature R1, and compare the real-time deviation data feature R1 with the deviation threshold intervals P1 and P2 respectively; if the real-time deviation data feature R1 belongs neither to the deviation threshold interval P1 nor to the deviation threshold interval P2, it indicates that the current event is a normal event; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P1, it indicates that the current event is a false alarm event, then the notification information of the false alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing; if the real-time deviation data feature R1 only belongs to the deviation threshold interval P2, it indicates that the current event is a missed alarm event, then the notification information of the missed alarm event is output to the relevant personnel, and the relevant personnel perform corresponding processing.

7. A power adapter anti-theft alarm system based on the Internet of Things, applied to a power adapter anti-theft alarm method based on the Internet of Things as claimed in any one of claims 1 to 6, characterized in that: The system includes: a data acquisition and processing module, an abnormal feature recognition module, a deviation analysis module, and a real-time monitoring and alarm decision module; The data collection and processing module collects data from the power adapter anti-theft alarm device through the Internet of Things technology, obtains historical data of the power adapter anti-theft alarm device, summarizes and analyzes the historical data, thereby extracting historical data features and forming a historical data feature vector; The abnormal feature recognition module analyzes the change trend of each dimension of the historical data feature vector according to the historical data feature vector, thereby obtaining a corresponding historical data feature trend curve; based on the historical data feature trend curve, the abnormal data features corresponding to the false alarm and missed alarm events of the power adapter anti-theft alarm device are identified; The deviation analysis module analyzes the deviation relationship between the abnormal data features of the power adapter anti-theft alarm device when false alarms and missed alarms occur and the normal data features of normal alarm events, thereby obtaining a deviation threshold interval between the abnormal data features corresponding to the false alarms and missed alarms and the normal data features of normal alarm events; The real-time monitoring and alarm decision module obtains real-time data from the power adapter anti-theft alarm device, analyzes the real-time data, and obtains real-time data features; analyzes the deviation relationship between the real-time data features and the normal data features of normal alarm events, and determines whether an alarm event occurs currently in combination with the deviation threshold interval, and performs corresponding processing based on the judgment result.

8. The power adapter anti-theft alarm system based on the Internet of Things according to claim 7, characterized in that: The data acquisition and processing module includes a data acquisition unit, a data preprocessing unit and a feature extraction unit; The data acquisition unit obtains various sensor data and alarm event records from the power adapter anti-theft alarm device through the Internet of Things technology, including normal alarm events, false alarm events and missed alarm events; the data preprocessing unit preprocesses the collected historical data, including cleaning, denoising and timestamp synchronization of the sensor data; the feature extraction unit extracts statistical features and time-frequency features from the processed data and converts them into historical data feature vectors.

9. The power adapter anti-theft alarm system based on the Internet of Things according to claim 7, characterized in that: The abnormal feature recognition module includes a historical data analysis unit, an abnormal pattern recognition unit and a feature matching unit; The historical data analysis unit analyzes the change trend of each dimension according to the historical data feature vector to obtain the historical data feature trend curve; the abnormal pattern recognition unit identifies the abnormal data characteristics of the power adapter anti-theft alarm device when false alarms and missed alarms occur, thereby distinguishing normal alarms from abnormal alarms; The feature matching unit identifies the feature patterns of false alarm and missed alarm events by comparing with the normal data features of normal alarm events, and extracts the corresponding abnormal data features; The deviation analysis module includes a deviation calculation unit and a deviation threshold interval generation unit; The deviation calculation unit calculates the deviation relationship between the historical data characteristics of normal alarm events and the abnormal characteristics of false alarm and missed alarm events, and extracts the deviation data characteristics; The deviation threshold interval generating unit generates deviation threshold intervals for false positive and false negative events according to the calculated deviation data features.

10. The power adapter anti-theft alarm system based on the Internet of Things according to claim 7, characterized in that: The real-time monitoring and alarm decision module includes a real-time data acquisition and analysis unit, an alarm judgment unit and an alarm decision unit; The real-time data acquisition and analysis unit acquires the real-time data of the power adapter anti-theft alarm device, analyzes the real-time data features and generates a real-time data feature vector; the alarm judgment unit performs deviation analysis on the real-time data features and the features of normal alarm events, and judges the current alarm event type; the alarm decision unit outputs an alarm notification based on the alarm judgment result, and performs relevant processing based on the false alarm or missed alarm situation.

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