WO2020087848A1 - 一种基于Lora和Capsule的实时行为识别系统及其工作方法 - Google Patents
一种基于Lora和Capsule的实时行为识别系统及其工作方法 Download PDFInfo
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Definitions
- the invention relates to a real-time behavior recognition system based on Lora and Capsule and its working method, belonging to the technical field of artificial intelligence and pattern recognition.
- the behavior recognition system is a system that obtains human behavior information and implements behavior through a reasonable model.
- advanced technologies such as the Internet of Things, artificial intelligence, big data and cloud computing, more and more researchers have begun to pay attention to the research on the direction of behavior recognition.
- Behavior recognition has become a hot research direction in the field of artificial intelligence and pattern recognition research, and the development of wearable devices provides a good opportunity for human behavior recognition.
- human behavior recognition technology has been initially applied in the fields of games, human motion analysis, smart homes, human-computer interaction, and medical diagnosis and monitoring.
- the behavior information required by the behavior recognition system mainly comes from the following two aspects:
- the mainstream behavior recognition technology on the market is mainly to recognize behavior information based on vision.
- both behavior information acquisition methods can achieve real-time recognition of human behavior through corresponding algorithms
- the method based on visual behavior information acquisition has certain drawbacks.
- many behavior information cannot be obtained.
- the current information transmission technology in the behavior recognition system is mainly Bluetooth, ZigBee, WiFi, 3G, 4G.
- the mainstream transmission technology cannot achieve both transmission distance and power consumption. Therefore, to realize real-time behavior recognition, a transmission technology with low power consumption and long transmission distance is required.
- Loa is a radio modem technology released by Semtech. This technology has four categories: Lora-WAN protocol, Lora private protocol, CLASS protocol, and data transparent transmission. Compared with other low-power wide-area Internet of Things technologies, this technology has great power consumption, self-organizing network, etc. Advantage.
- the model of the system for behavior recognition mainly uses algorithms such as machine learning and deep learning.
- mainstream machine learning mainly includes K nearest neighbor algorithm (KNN), support vector machine (SVM), random forest, neural network, etc .
- mainstream deep learning algorithms mainly include deep neural network (DNN) and convolutional neural network (CNN) , Recurrent (recursive) neural network (RNN), deep belief network (DBN), etc.
- Mainstream algorithms are based on behavior information characteristics for behavior recognition, so input behavior information as long as there are certain characteristics will be divided into a certain type of behavior.
- behavior information 1100 and 0011 respectively stand up and sit down
- 1010 and 0101 represent fight behavior
- mainstream algorithms may judge the behavior information as long as there are two 1 and two 0 characteristics of the behavior, so if you enter the behavior information
- the characteristics of 0101, 1100, 0011 or 1010 will be considered as fighting behavior, but in fact 1100 or 0011 represents the standing up or sitting down behavior.
- the existing mainstream algorithms only consider whether the behavior information contains certain features, but do not consider the spatial characteristics of these features, so it will cause misjudgment to a certain extent and reduce the accuracy of behavior recognition.
- Geoffrey Hinton put forward the concept of capsule network in 2017.
- the capsule network can not only identify whether the behavior information has certain characteristics, but also can identify the spatial relationship between each feature.
- the capsule network algorithm model adopted by the invention has a relatively high improvement in the accuracy rate of behavior recognition.
- the present invention proposes a real-time behavior recognition system based on Lora and Capsule.
- the invention also discloses the working method of the above system.
- the intelligent hardware is selected and designed through the device quality (QoD) parameters, and the behavior information is collected through the intelligent device. Then, the behavior information is transmitted through the Lora node. At this time, Lora The base station will receive the behavior information. Since the behavior information at this time is the original behavior information, the system needs to perform uncertainty detection on the original behavior information, and process the incomplete or inconsistent information in the behavior information through context prediction filling, zero-filling, deletion, etc. to improve The credibility of behavioral information. Next, standardize and intercept time-based behavior information. The standardization is to improve the accuracy and generalization ability of the model. The behavior information is intercepted through the sliding window mechanism to normalize the model input.
- QoD device quality
- the system will adjust the preset threshold and model parameters according to the quality of user experience (QoE) and quality of service (QoS) to improve the stability, accuracy, and applicability of the system.
- QoE quality of user experience
- QoS quality of service
- the present invention provides a feasible solution for real-time behavior recognition based on sensors, and makes up for defects caused by behavior recognition based on video.
- Multi-source behavior information lays a foundation for improving the accuracy of behavior recognition.
- the application of deep learning model based on Capsule network in behavior recognition also greatly improves the accuracy of behavior recognition.
- a real-time behavior recognition system based on Lora and Capsule includes a behavior information physical layer, a behavior information access layer, a behavior information platform layer, and a behavior information application layer connected in sequence;
- the physical layer of behavior information is used to: perceive, collect, store, and transmit user behavior information from the environment, and the behavior information includes: acceleration, angular velocity, and heart rate;
- the behavior information access layer is used for networking transmission of the collected behavior information through the low-power wide-area Internet of Things;
- the behavior information platform layer is used to: conduct uncertainty detection, standardization and time series-based interception of behavior information in sequence, train the labeled behavior information collection under the built network architecture model, and continuously optimize the loss value Find the best model at the same time; Uncertainty detection means: processing the incomplete or inconsistent information in the behavior information through context prediction filling, zero-filling, and deleting methods to improve the credibility of the behavior information; standardization is a numerical type The data is normalized to improve the accuracy and generalization ability of the model; time series-based interception refers to the interception of behavior information through the sliding window mechanism to ensure the normalization of the model input and improve the accuracy of the model;
- the behavior information application layer is used to adjust the stability and adaptability of the entire real-time behavior recognition system.
- the present invention proposes a relatively optimized system in four aspects of transmission technology, information processing, behavior recognition and behavior application, which makes up for the shortcomings of poor real-time behavior recognition on the market and the inability to use it in specific areas.
- the aspect has also been further improved, so that the system has stability.
- the behavior information physical layer is a behavior information collection module, and the behavior information collection module includes a sensor module and several intelligent hardware modules; the sensor module includes several different types of sensors, and the intelligent hardware module Several different types of sensors are connected respectively, and the intelligent hardware module is used to control the sensors to perceive different types of user behavior information and store the sensed behavior information.
- the behavior information collection module selects sub-modules and designs smart devices through the QoD parameters, application scenarios, and user requirements of each sub-module.
- QoD parameters mainly include the sampling frequency, service life, and accuracy of the sensor module.
- the intelligent hardware module adopts Lora to transmit behavior information.
- Transmission technology refers to a technology that makes full use of the transmission capabilities of different channels to form a complete transmission system, so that information can be reliably transmitted. With the progress of society and the development of wireless technology, the convenience of wireless transmission is further amplified under the premise that the packet loss rate is not high.
- the mainstream wireless technologies are mainly WiFi, Bluetooth, ZigBee, 3G, 4G, etc.
- Each wireless technology is in an indispensable situation in terms of transmission distance and power consumption, but in order to achieve real-time behavior recognition, the transmission of behavior information requires a Wireless technology with low power consumption and long transmission distance.
- Low power consumption local area network (LPWAN) is the main technology to solve the current situation, so the present invention uses Lora to transmit behavior information.
- LPWAN low power consumption local area network
- the behavior information access layer is a behavior information transmission module, and the behavior information transmission module includes a behavior information transmission module and a behavior information reception module; the behavior information transmission module is connected to the intelligent hardware module for Yu sends behavior information to the behavior information receiving module.
- the information sending module is a Lora node
- the behavior information receiving module is a Lora base station.
- the behavior information platform layer is a behavior information preprocessing module
- the behavior information preprocessing module includes a behavior information detection module, a behavior information uncertainty elimination module, a behavior information processing module, and a network architecture that are sequentially connected Module
- the behavior information detection module includes an inconsistency detection / quantization unit and an incompleteness detection / quantization unit;
- the behavior information uncertainty elimination module includes an inconsistency elimination unit and an incompleteness elimination unit;
- the behavior information processing module includes a behavior information standardization unit and a behavior information sliding window unit connected in sequence;
- the network architecture module includes a convolution layer unit, a Capsule layer one unit, a Capsule layer two unit, and a fully connected layer unit connected in sequence;
- the behavior information receiving module that is, the gateway is connected to the behavior information detection module
- the behavior information received by the behavior information receiving module that is, the original behavior information is input to the behavior information detection module, and the original behavior information is uncertain by the inconsistency detection / quantization unit and the incompleteness detection / quantization unit Detection, the inconsistency detection / quantization unit detects whether different types of behavior information at the same time have objections, and the incompleteness detection / quantization unit detects whether the perceived behavior information at the same time is missing;
- the incompleteness elimination unit and the inconsistency elimination unit are used to eliminate the uncertainty, and the incompleteness elimination unit passes the loss of the perceived behavioral information at the same time.
- the deletion method, the zero-filling method, and the context prediction filling method are used for processing.
- the inconsistency elimination unit processes inconsistent information through voting, hardware QoD optimal principles, DS (Dempster-Shafer) evidence theory, and fuzzy sets.
- the behavior information standardization unit Enters the behavior information standardization unit; if it is found that there is no uncertainty in the behavior information, directly enter the behavior information standardization unit; through the behavior information standardization unit and the behavior information sliding window unit for processing, the behavior information
- the standardization unit is processed by a normalization and normalization method to improve recognition accuracy and applicability;
- the behavior information sliding window unit performs time series-based interception of behavior information by adjusting the size of the sliding window and the sliding manner of the sliding window;
- the convolutional layer unit extracts features from the behavior information and converts the characteristic scalar to vector
- the Capsule layer is a unit It is used to convert the input behavior information into behavior information with spatial characteristics
- the Capsule layer two units process the behavior information through a dynamic routing protocol
- the fully connected layer unit transforms the behavior information characteristics into ordered one-dimensional characteristics
- the main work of the network architecture module is to make recognition based on behavioral information.
- the proposition of machine learning can truly appreciate the power of artificial intelligence.
- the proposition of deep learning has a considerable aspect in the recognition rate progress.
- the focus of machine learning models and deep learning models is whether the input information contains some feature values.
- the network architecture based on Capsule adopted in the present invention not only pays attention to the characteristics of the behavior information, but also adds the spatial relationship of the characteristics of the behavior information to improve the accuracy of behavior recognition.
- the behavior preprocessing module mainly improves the credibility of behavior information by preprocessing the behavior information. Compared with some systems that directly perform behavior recognition on the original behavior information, the present invention will greatly improve the stability and accuracy after the behavior information preprocessing.
- the invention mainly performs uncertainty analysis on the original information, and performs corresponding processing on the category and degree of the uncertainty of the behavior information.
- the present invention provides a normalization method and a normalization method. The behavior information is intercepted based on time series by adjusting the size of the sliding window and the sliding mode of the sliding window.
- the behavior information application layer includes a behavior information threshold setting module and a behavior application layer adjustment module
- the behavior application layer adjustment module includes a behavior recognition unit, a user feedback unit, and an error correction unit connected in sequence
- the behavior information threshold setting module is used to adjust the threshold in the behavior information uncertainty elimination module, so as to determine whether there is uncertainty in the monitoring data, and adjust the uncertainty processing module to select the data processing method;
- the behavior recognition unit is used to do the current behavior Real-time identification;
- the user feedback unit adjusts the preset threshold and the parameters of the network architecture module according to different scenarios and user needs, which improves the applicability of the system to a certain extent;
- the error correction unit continuously adjusts the network architecture module, Keep the network architecture module in an optimal state all the time.
- the error correction unit adjusts parameters when the recognition error rate is high, and the user feedback unit adjusts system parameters to adapt to different scenarios.
- Step S01 Sensors perceive behavior information
- the QoD parameters of the sensor include: sampling frequency, service life, and accuracy. For example: for users who require key monitoring, high sampling frequency can be used. , High-precision sensors for behavior information perception, for ordinary users can use ordinary sampling frequency, common accuracy sensors for behavior information perception; sensors sense different types of user behavior information;
- Step S02 Design the intelligent hardware module
- Step S03 Sending behavior information
- Step S04 Reception of behavior information
- the Lora base station is used to receive the behavior information; the behavior information sending device uses a Lora node, and the corresponding behavior information is received using the Lora base station.
- Step S05 Uncertainty detection of behavior information
- step S05 is executed.
- step S06 the original behavior information refers to different types of behavior information of the user perceived by the sensor in step S01;
- Step S06 Elimination of uncertainty in behavior information
- the incompleteness elimination unit selects different methods to process the behavior information through the threshold of the uncertainty detection of the behavior information.
- the accuracy of the behavior information is 85% -90%
- the context prediction filling method is used to process the behavior information.
- the correct rate of behavior information is 90% -95%
- the method of supplementing 0 is used for the behavior information
- the correct rate of behavior information is 95% -100%
- the method of deletion is used for the behavior information;
- the inconsistency elimination unit processes inconsistent information, and the processing methods include voting and election, hardware QoD optimal principles, D-S (Dempster-Shafer) evidence theory, and fuzzy sets; improving the credibility of the original behavior information;
- Step S07 Processing of behavior information
- Standardize behavior information with high credibility through behavior information standardization unit uses different standardization methods for different types of data, including: one-hot coding (one-hot coding) for data of categorical characteristics ) Standardization; for data with numeric features, normalization is used for standardization; for data with ordered features, ordered numeric encoding is used for standardization; standardization allows the system to have good scalability.
- the user preset parameters include: the size of the sliding window and the sliding mode of the window, the behavior information after the standardized processing is subjected to sliding window processing through the behavior information sliding window unit, so that the behavior information becomes the input network architecture Information block of the module;
- Step S08 Behavior information network architecture
- the parameters set by the user mainly include: the status of the input data, size, volume
- a series of parameters such as the size and number of multi-layer cores are used to train the labeled behavior information through several iterations.
- the model parameters and the dynamic routing protocol in the unit of the Capsule layer are continuously optimized by reducing the loss function, and finally recognized High rate network architecture model;
- Step S09 Recognition of behavior information
- Step S10 Error detection
- step S11 If an error is found, perform step S11; otherwise, perform step S12;
- the error correction unit adjusts the behavior information threshold range and the corresponding parameters of the behavior information processing module; the behavior information threshold range includes the threshold range of uncertainty detection, and the corresponding parameters of the behavior information processing module include the sliding window of the behavior information sliding window unit Size and sliding mode of the window; when there are many recognition errors, the threshold range of behavior information is appropriately increased, and the size of the sliding window and the sliding mode of the window are reduced.
- Step S12 User feedback detection
- step S13 is executed.
- Step S13 User feedback
- the user feedback unit makes feedback adjustments to the behavior information threshold range and the corresponding parameters of the behavior information processing module.
- step S08 in the step S08,
- the network architecture module includes a convolution layer unit, a Capsule layer one unit, a Capsule layer two unit, and a fully connected layer unit connected in sequence;
- Batch_Size refers to the number of behavior information that runs in the network architecture module at a time
- Window_Size refers to the length of each input network architecture module
- X i refers to each piece of information after behavior information undergoes uncertainty, standardization, and sliding window processing based on time series
- W ij refers to the weight parameter of the convolutional layer unit, and the initial value defaults to generation truncation Normally distributed random numbers
- b j refers to the offset parameter of the convolutional layer unit, the initial value defaults to 0.0;
- n the number of convolution kernels
- Y j is the output of the convolution layer
- the output information size is: It needs to ensure that the result of the fraction in the previous formula is a positive integer.
- the output at this time is a vector behavior information, which meets the input requirements of the Capsule network;
- W jl refers to the weight parameter of a unit in the Capsule layer, and the initial value defaults to generating a random number with truncated normal distribution
- b l refers to the offset parameter of a unit in the Capsule layer, the initial value is 0.0 by default;
- the squsah () function is a new nonlinear function, similar to the previous common nonlinear functions such as tanh (), relu (), etc.
- the squsah () function is a nonlinear process for vector information; other nonlinear functions are mainly Processing of scalar information;
- the size of the information output after one unit in the Capsule layer is:
- b ik refers to the dynamic routing weights of the i-th neuron in the first unit of the Capsule layer and the k-th neuron in the second unit of the Capsule layer;
- b ij refers to the dynamic routing weights of the i-th neuron in the first unit of the Capsule layer and the j-th neuron in the second unit of the Capsule layer;
- S j refers to the behavior information characteristics output by the second unit of the Capsule layer after the dynamic routing protocol.
- the size of the information output by the second unit of the Capsule layer is: Batch_Size ⁇ Num_Output ⁇ Vec_Lenv ⁇ 1;
- the size of the information output after passing through the fully connected layer unit is:
- Real-time human behavior recognition has high requirements for transmission media and accuracy.
- the present invention achieves low power consumption and long-distance transmission of behavior information; at the same time, it also has certain advantages in the accuracy of behavior recognition.
- the user feedback unit (QoE) and error correction unit (QoS) are used to adjust the parameters in the system to improve the system's adaptability and provide users with personalized and intelligent services.
- the adjustable parameters include: the threshold of the inconsistency detection / quantization unit and the threshold of the incompleteness detection / quantization unit, the standardization method in the behavior information standardization unit and the sliding window size and sliding method in the behavior information sliding window unit, Parameters such as the number of iterations, the learning rate, and the number of training iterations in the network architecture module.
- the behavior recognition system After adding behavior information based on sensors to a single information source based on visual behavior information, the behavior recognition system will be more perfect; at the same time, compared with mainstream model algorithms for behavior recognition, the present invention has further improved accuracy. It also has good stability in real-time.
- FIG. 1 is a schematic diagram of the main module structure framework and connection relationship of the real-time behavior recognition system based on Lora and Capsule of the present invention.
- FIG. 2 is a schematic diagram of the module composition and connection relationship of the real-time behavior recognition system based on Lora and Capsule of the present invention.
- FIG. 3 is a schematic diagram of the workflow of the real-time behavior recognition system based on Lora and Capsule of the present invention.
- FIG. 4 is a schematic diagram of behavior recognition of a real-time behavior recognition system based on Lora and Capsule of the present invention.
- FIG. 5 is a schematic diagram of the working principle of the unit of the Capsule layer and the unit of the Capsule layer in the behavior recognition of the real-time behavior recognition system based on Lora and Capsule of the present invention.
- a real-time behavior recognition system based on Lora and Capsule includes a behavior information physical layer, a behavior information access layer, a behavior information platform layer, and a behavior information application layer connected in sequence;
- the physical layer of behavioral information is used to: perceive, collect, store, and transmit user's behavioral information from the environment.
- the behavioral information includes: acceleration, angular velocity, and heart rate;
- the behavior information access layer is used to: transmit the collected behavior information through Lora technology in the low-power wide-area Internet of Things;
- the behavior information platform layer is used to: conduct uncertainty detection, standardization and time series-based interception of behavior information in sequence, train the labeled behavior information collection under the built network architecture model, while continuously optimizing the loss value Find the best model;
- Uncertainty detection refers to: processing incomplete or inconsistent information in the behavior information through context prediction filling, zero-filling, and deletion methods to improve the credibility of the behavior information; standardization is performed on numeric data Normalization processing to improve the accuracy and generalization ability of the model;
- Time series-based interception refers to the interception of behavior information through the sliding window mechanism to ensure the normalization of the model input and improve the accuracy of the model;
- the behavior information application layer is used to: adjust the stability and adaptability of the entire real-time behavior recognition system.
- the present invention proposes a relatively optimized system in four aspects of transmission technology, information processing, behavior recognition and behavior application, which makes up for the shortcomings of poor real-time behavior recognition on the market and the inability to use it in specific areas.
- the accuracy rate of behavior recognition The aspect has also been further improved, so that the system has stability.
- the physical layer of behavior information is the behavior information collection module.
- the behavior information collection module includes a sensor module and several intelligent hardware modules; the sensor module includes several different types of sensors, and the intelligent hardware modules are connected to several different types of sensors, respectively, and the intelligent hardware modules are used for control
- the sensor perceives different types of user behavior information, and stores the sensed behavior information.
- the behavior information collection module selects sub-modules and designs smart devices through the QoD parameters, application scenarios, and user requirements of each sub-module.
- QoD parameters mainly include the sampling frequency, service life, and accuracy of the sensor module.
- the intelligent hardware module adopts Lora to transmit behavior information.
- Transmission technology refers to a technology that makes full use of the transmission capabilities of different channels to form a complete transmission system, so that information can be reliably transmitted. With the progress of society and the development of wireless technology, the convenience of wireless transmission is further amplified under the premise that the packet loss rate is not high.
- the mainstream wireless technologies are mainly WiFi, Bluetooth, ZigBee, 3G, 4G, etc.
- Each wireless technology is in an indispensable situation in terms of transmission distance and power consumption, but in order to achieve real-time behavior recognition, the transmission of behavior information requires a Wireless technology with low power consumption and long transmission distance.
- Low power consumption local area network (LPWAN) is the main technology to solve the current situation, so the present invention uses Lora to transmit behavior information.
- LPWAN low power consumption local area network
- the behavior information access layer is a behavior information transmission module.
- the behavior information transmission module includes a behavior information transmission module and a behavior information reception module; the behavior information transmission module is connected to the intelligent hardware module, and is used to send behavior information to the behavior information reception module.
- the information sending module is a Lora node
- the behavior information receiving module is a Lora base station.
- the present invention selects Lora nodes and Lora base stations as transmission media.
- the behavior information platform layer is the behavior information preprocessing module.
- the behavior information preprocessing module includes a behavior information detection module, a behavior information uncertainty elimination module, a behavior information processing module, and a network architecture module that are connected in sequence;
- Behavior information detection module includes inconsistency detection / quantization unit and incompleteness detection / quantization unit; behavior information uncertainty elimination module includes inconsistency elimination unit and incompleteness elimination unit; behavior information processing module includes behavior information standardization connected in sequence Unit, behavior information sliding window unit; network architecture module includes convolution layer unit, Capsule layer one unit, Capsule layer two unit, fully connected layer unit connected in sequence;
- the behavior information receiving module is the gateway connection behavior information detection module
- the behavior information received by the behavior information receiving module is the original behavior information input to the behavior information detection module, and the inconsistency detection / quantization unit and the incompleteness detection / quantization unit are used to detect the uncertainty of the original behavior information.
- the quantization unit detects whether there is any objection to different types of behavior information at the same time, and the incompleteness detection / quantization unit detects whether the perceived behavior information at the same time is missing;
- the incompleteness elimination unit and the inconsistency elimination unit are used to eliminate the uncertainty.
- Method 0, context prediction filling method, inconsistency elimination unit processes inconsistent information through voting, hardware QoD optimal principle, DS (Dempster-Shafer) evidence theory, fuzzy set, and enters the behavior information standardization unit;
- QoC index refers to the index used to describe the quality of behavior information, including completeness, credibility, and update; if there is no uncertainty in behavior information, directly enter the behavior information standardization unit; through the behavior information standardization unit and behavior
- the information sliding window unit is processed, and the behavior information standardization unit is processed through normalization and normalization methods to improve recognition accuracy and applicability; the behavior information sliding window unit performs behavior information adjustment by adjusting the size of the sliding window and the sliding method of the sliding window Interception based on time series;
- Input the processed behavior information into the trained network architecture model, and realize the behavior recognition through the network architecture model; the convolutional layer unit extracts features from the behavior information and converts the characteristic scalar to vector.
- a unit in the Capsule layer is used to Convert the input behavior information into behavior information with spatial characteristics; the second unit of the Capsule layer processes the behavior information through a dynamic routing protocol; the fully connected layer unit converts the behavior information features into ordered one-dimensional features, and finally passes the Softmax classifier Calculate all the features and recognize the current behavior.
- the main work of the network architecture module is to make recognition based on behavioral information.
- the proposition of machine learning can truly appreciate the power of artificial intelligence.
- the proposition of deep learning has a considerable aspect in the recognition rate progress.
- the focus of machine learning models and deep learning models is whether the input information contains some feature values.
- the network architecture based on Capsule adopted in the present invention not only pays attention to the characteristics of the behavior information, but also adds the spatial relationship of the characteristics of the behavior information to improve the accuracy of behavior recognition.
- the behavior preprocessing module mainly improves the credibility of behavior information by preprocessing the behavior information. Compared with some systems that directly perform behavior recognition on the original behavior information, the present invention will greatly improve the stability and accuracy after the behavior information preprocessing.
- the invention mainly performs uncertainty analysis on the original information, and performs corresponding processing on the category and degree of the uncertainty of the behavior information.
- the present invention provides a normalization method and a normalization method. The behavior information is intercepted based on time series by adjusting the size of the sliding window and the sliding mode of the sliding window.
- the behavior information application layer includes a behavior information threshold setting module and a behavior application layer adjustment module.
- the behavior application layer adjustment module includes a behavior recognition unit, a user feedback unit, and an error correction unit connected in sequence;
- the behavior information threshold setting module is used to adjust the threshold in the behavior information uncertainty elimination module, so as to determine whether there is uncertainty in the monitoring data, and adjust the uncertainty processing module to select the data processing method;
- the behavior recognition unit is used to do the current behavior Real-time identification;
- the user feedback unit adjusts the preset threshold and the parameters of the network architecture module according to different scenarios and user needs, which improves the applicability of the system to a certain extent;
- the error correction unit continuously adjusts the network architecture module, Keep the network architecture module in an optimal state all the time.
- the behavior recognition unit mainly conducts behavior recognition based on the behavior information and a reasonable model; the QoS index value refers to the quality of service, generates corresponding adjustment information according to the service quality, and then feeds back to the behavior information pre-processing module; obtains the QoE of the user for the entire application service
- the index value and feedback information are generated and transmitted to the behavior information preprocessing module; the QoE index value is a user rating index used to express the user's satisfaction with the application service, and the main function is to adjust the preset QoC index value.
- Step S01 Obtain QoD parameters
- the main QoD parameters include: sensor accuracy, sampling interval and bracelet material.
- the accuracy of the sensor is 0.94, 0.80, 0.88, and the sampling interval is 0.02s-1s.
- the material is mainly composed of rubber, alloy and other materials.
- Step S02 Design collection equipment
- the design of behavioral information collection equipment can use materials with high sampling frequency, high recognition accuracy, and non-destructive materials.
- the design can be considered to use materials with low sampling frequency, common identification and slightly lower cost for design.
- Step S03 Sending behavior information
- the Lora node is used to send the behavior information
- Step S04 Reception of behavior information
- the Lora base station is used to receive the behavior information; the behavior information sending device uses a Lora node, and the corresponding behavior information is received using the Lora base station.
- Step S05 Uncertainty detection of behavior information
- the incompleteness detection / quantization unit sets the threshold to 0.85, that is, 85% of the behavior information received per second is missing, and the original behavior information is considered to be incomplete;
- the inconsistency detection / quantization unit sets the threshold to 0.8, that is, when the similarity of the behavior information received per second is lower than 0.8, it is determined that there is inconsistency in the original behavior information of the group.
- step S07 Perform inconsistency detection / quantization and incompleteness detection / quantization on the original behavior information according to the set behavior information threshold range to analyze the original behavior information and find that the original behavior information has inconsistencies, incompleteness and other uncertainties, perform the steps S06, otherwise execute step S07.
- Step S06 Elimination of uncertainty in behavior information
- the system can delete the incomplete behavior information, or fill in 0 with incomplete information, or fill in the incomplete behavior information according to the prediction of context information.
- the system defaults to choose according to the context Information prediction will fill in incomplete behavior information;
- the system can modify the inconsistent information according to the voting and election principles, or modify the inconsistent information according to the QoD optimal principle of information acquisition hardware, or use the DS evidence theory to find the inconsistent information. If the credibility is changed, the system chooses to modify the inconsistent information according to the voting and election principles by default.
- Step S07 Processing of behavior information
- the standardization unit of behavior information mainly standardizes the same type of behavior information.
- the standardization that can be used in the system is mainly the normalization method or the normalization method.
- the default standardization of the system is the normalization method;
- the behavior information sliding window unit mainly intercepts behavior information based on time series.
- the system mainly provides two types of parameters: the size of the sliding window and the sliding mode.
- the size of the sliding window is 40, 60, 80, 100, and the sliding mode is mainly based on For half-time series sliding and sliding based on all time series, the default sliding window size of the system is 80, and the sliding mode is based on half time series sliding.
- Step S08 Behavior information network architecture
- the loss function is continuously optimized to optimize the module parameters and the dynamic routing protocol in the Capsule layer unit, and finally the module with a higher recognition rate is obtained.
- the training set can select the behavior information of all people, or can select the behavior information of a certain individual for the training module, and then conduct behavior recognition for a certain individual. Because the program requires huge behavior information and needs large resources to support, it is recommended to use it only for some heavyweight criminals.
- the system selects the criminal behavior information database of the entire prison area as the training module by default.
- the specific implementation process of the module used in this example is as follows:
- X i refers to each piece of information after behavior information undergoes uncertainty, standardization, and sliding window processing based on time series
- W ij refers to the weight parameter of the convolutional layer unit, and the initial value defaults to generation truncation Normally distributed random numbers
- b j refers to the offset parameter of the convolutional layer unit, the initial value defaults to 0.0;
- n the number of convolution kernels
- Y j is the output of the convolution layer
- the size of the output information is: 5 ⁇ 1 ⁇ 40 ⁇ 256; it needs to ensure that the result of the score in the previous formula is a positive integer.
- the output at this time is a vector behavior information, which meets the input requirements of the Capsule network;
- W jl refers to the weight parameter of a unit in the Capsule layer, and the initial value defaults to generating a random number with truncated normal distribution
- b l refers to the offset parameter of a unit in the Capsule layer, the initial value is 0.0 by default;
- the squsah () function is a new nonlinear function, similar to the previous common nonlinear functions such as tanh (), relu (), etc.
- the squsah () function is a nonlinear process for vector information; other nonlinear functions are mainly Processing of scalar information;
- the size of the information output after one unit in the Capsule layer is: 5 ⁇ 320 ⁇ 8 ⁇ 1;
- b ik refers to the dynamic routing weights of the i-th neuron in the first unit of the Capsule layer and the k-th neuron in the second unit of the Capsule layer;
- b ij refers to the dynamic routing weights of the i-th neuron in the first unit of the Capsule layer and the j-th neuron in the second unit of the Capsule layer;
- S j refers to the behavior information characteristics output by the second unit of the Capsule layer after the dynamic routing protocol.
- the size of the information output after processing by the two units of the Capsule layer is: 5 ⁇ 12 ⁇ 16 ⁇ 1;
- the size of the information output after passing through the fully connected layer unit is: 5 ⁇ 192 ⁇ 1;
- the system adjustable parameters mainly include dynamic routing iterations, learning rate, and training iterations.
- the dynamic routing iterations are set to 1-10; the learning rate is set to 0.1, 0.01, and 0.001; and the training iterations are set to 1 —50.
- the system default parameters are 5, 0.01, and 40 in order.
- Step S09 Recognition of behavior information
- Step S10 Error detection
- step S11 If an error is found, perform step S11; otherwise, perform step S12;
- the error correction unit adjusts the behavior information threshold range and the corresponding parameters of the behavior information processing module;
- the behavior information threshold range includes the threshold range of uncertainty detection, and the corresponding parameters of the behavior information processing module include the sliding window of the behavior information sliding window unit Size and window sliding mode; when there are many recognition errors, the threshold range of behavior information is appropriately increased, and the size of the sliding window and the sliding interval of the window are reduced;
- Step S12 User feedback detection
- step S13 is executed.
- Step S13 User feedback
- Parameters that can be adjusted include: the threshold of the inconsistency detection / quantization unit and the threshold of the incompleteness detection / quantization unit in the original behavior information detection module, the standardization method and behavior information slip in the behavior information standardization unit in the behavior information processing module The size and manner of sliding windows in the window unit, the number of iterations, the learning rate, and the number of training iterations in the network architecture module.
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Abstract
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- 一种基于Lora和Capsule的实时行为识别系统,其特征在于,包括依次连接的行为信息物理层、行为信息接入层、行为信息平台层、行为信息应用层;所述行为信息物理层用于:从环境中感知、采集、存储、传输用户的行为信息,行为信息包括:加速度、角速度、心率;所述行为信息接入层用于:将采集的行为信息通过低功耗广域物联网进行组网传输;所述行为信息平台层用于:对行为信息依次进行不确定性检测、标准化和基于时间序列的截取、在搭建的网络架构模型下将带有标签的行为信息集合进行训练,在不断优化损失值的同时找到最佳模型;不确定性检测是指:将行为信息中不完备或不一致的信息通过上下文预测填充、补0、删除方法进行处理,提高行为信息的可信度;标准化是对数值型数据进行归一化处理;基于时间序列的截取是指通过滑动窗口机制进行行为信息的截取;所述行为信息应用层用于:调节整个实时行为识别系统稳定性和自适应性。
- 根据权利要求1所述的一种基于Lora和Capsule的实时行为识别系统,其特征在于,所述行为信息物理层即行为信息采集模块,所述行为信息采集模块包括传感器模块和若干个智能硬件模块;所述传感器模块包括若干不同类型的传感器,所述智能硬件模块分别连接若干不同类型的传感器,所述智能硬件模块用于控制传感器感知用户不同类型的行为信息,并把感知到的行为信息进行存储。
- 根据权利要求2所述的一种基于Lora和Capsule的实时行为识别系统,其特征在于,所述行为信息接入层即行为信息传输模块,所述行为信息传输模块包括行为信息发送模块和行为信息接收模块;所述行为信息发送模块连接所述智能硬件模块,用于将行为信息发送至所述行为信息接收模块。
- 根据权利要求3所述的一种基于Lora和Capsule的实时行为识别系统,其特征在于,所述信息发送模块为Lora节点,所述行为信息接收模块为Lora基站。
- 根据权利要求3所述的一种基于Lora和Capsule的实时行为识别系统,其特征在于,所述行为信息平台层即行为信息预处理模块,所述行为信息预处理模块包括依次连接的行为信息检测模块、行为信息不确定性消除模块、行为信息处理模块、网络架构模块;所述行为信息检测模块包括不一致性检测/量化单元和不完备性检测/量化单元;所述行为信息不确定性消除模块包括不一致性消除单元和不完整性消除单元;所述行为信息处理模块包括依次连接的行为信息标准化单元、行为信息滑窗单元;所述网络架构模块包括依次连接的卷积层单元、Capsule层一单元、Capsule层二单元、全连接 层单元;所述行为信息接收模块即网关连接所述行为信息检测模块;所述行为信息接收模块接收到的行为信息即原始行为信息输入到所述行为信息检测模块,通过所述不一致性检测/量化单元和所述不完备性检测/量化单元对原始行为信息进行不确定性的检测,所述不一致性检测/量化单元检测同一时刻不同类型的行为信息是否存在异议,所述不完备性检测/量化单元检测同一时刻感知的行为信息是否存在丢失;如果发现行为信息具有不确定性,则通过所述不完整性消除单元和所述不一致性消除单元进行不确定性的消除,所述不完整性消除单元对同一时刻感知行为信息存在的丢失情况通过删除法、补0法、上下文预测填充法进行处理,所述不一致性消除单元对不一致信息通过投票选举、硬件的QoD最优原则、D-S证据论、模糊集的方式进行处理,进入所述行为信息标准化单元;如果发现行为信息不存在不确定性,则直接进入所述行为信息标准化单元;通过所述行为信息标准化单元和所述行为信息滑窗单元进行处理,所述行为信息标准化单元通过规范化、归一化方法进行处理,提高识别准确率和适用性;所述行为信息滑窗单元通过调节滑动窗口的大小和滑动窗口的滑动方式对行为信息进行基于时间序列的截取;将处理完毕的行为信息输入到训练好的网络架构模型中,通过网络架构模型实现行为识别;所述卷积层单元对行为信息提取特征,进行特征标量到矢量的转换,所述Capsule层一单元用于将输入的行为信息转换成具有空间特性的行为信息;所述Capsule层二单元通过动态路由协议对行为信息进行处理;所述全连接层单元将行为信息特征转变为有序的一维特征,最后通过Softmax分类器将所有特征进行运算,识别出当前的行为。
- 根据权利要求5所述的一种基于Lora和Capsule的实时行为识别系统,其特征在于,所述行为信息应用层包括行为信息阈值设置模块、行为应用层调整模块,所述行为应用层调整模块包括依次连接的行为识别单元、用户反馈单元、错误修正单元;行为信息阈值设置模块用于调节行为信息不确定性消除模块中的阈值,从而对监测数据是否存在不确定性,并调整不确定性处理模块选择数据处理方式;行为识别单元用于对当前行为做实时识别;所述用户反馈单元根据不同的场景和用户需求对预设的阈值和网络架构模块的参数进行调整,错误修正单元不断的调整网络架构模块,让网络架构模块一直处于最优状态。
- 权利要求6所述的基于Lora和Capsule的实时行为识别系统的工作方法,其特征在于,包括步骤如下:步骤S01:传感器感知行为信息根据行为识别所需要的原始行为信息、传感器的QoD参数筛选不同厂家、不同类型的传感器, 传感器的QoD参数包括:采样频率、使用寿命、精度,传感器感知用户不同类型的行为信息;步骤S02:设计智能硬件模块根据方案要求选择合适的智能硬件模块,通过智能硬件模块控制各个传感器,并采集行为识别系统所需的行为信息;步骤S03:行为信息的发送采用Lora节点进行行为信息的发送;步骤S04:行为信息的接收采用Lora基站接收行为信息;步骤S05:行为信息的不确定性检测设定行为信息阈值范围,当原始行为信息存在不一致、不完备时,执行步骤S05,否则,执行步骤S06;原始行为信息是指步骤S01传感器感知的用户不同类型的行为信息;步骤S06:行为信息的不确定性消除不完备性消除单元通过行为信息不确定性检测的阈值,选用不同的方法对行为信息进行处理,当行为信息的正确率为85%-90%时,针对行为信息采用上下文预测填充法进行处理,当行为信息的正确率为90%-95%时,针对行为信息采用补0法进行处理,当行为信息的正确率为95%-100%时,针对行为信息采用删除法进行处理;不一致性消除单元对不一致信息进行处理,处理方法包括投票选举、硬件的QoD最优原则、D-S证据论、模糊集;提高原始行为信息的可信度;步骤S07:行为信息的处理通过行为信息标准化单元对可信度较高的行为信息进行标准化;行为信息的标准化针对不同类型的数据使用不同的标准化方式,包括:针对类别型特征的数据,采用独热编码标准化;针对数值型特征的数据,采用归一化处理标准化;针对有序型特征的数据,采用有序型数值编码标准化;参照用户预设的参数,用户预设的参数包括:滑动窗口的大小和窗口的滑动方式,通过行为信息滑窗单元对标准化处理后的行为信息进行滑动窗口处理,使行为信息变为输入网络架构模块的信息块;步骤S08:行为信息网络架构通过卷积层单元、Capsule层一单元,Capsule层二单元、全连接层单元构建一个四层的网络架构模型,参照用户设置的参数,通过若干次迭代对具有标签的行为信息进行训练,训练过程中通过降低损失函数来不断优化模型参数以及Capsule层单元中的动态路由协议,最终得到识别率高的网络架构模型;步骤S09:行为信息的识别将实时采集的行为信息输入到已经训练好的网络架构模型中进行当前行为的实时识别;步骤S10:错误检测判断当前行为识别是否发生错误,如发现存在错误,则执行步骤S11,否则执行步骤S12;步骤S11:错误修正错误修正单元对行为信息阈值范围、行为信息处理模块的相应参数进行调整;行为信息阈值范围包括不确定性检测的阈值范围,行为信息处理模块的相应参数包括行为信息滑窗单元中的滑动窗口的大小和窗口的滑动方式;步骤S12:用户反馈检测判断系统是否存在用户反馈信息,如存在反馈信息,则执行步骤S13;步骤S13:用户反馈用户反馈单元对对行为信息阈值范围、行为信息处理模块的相应参数进行反馈调整。
- 根据权利要求7所述的基于Lora和Capsule的实时行为识别系统的工作方法,其特征在于,所述步骤S08,所述网络架构模块包括依次连接的卷积层单元、Capsule层一单元、Capsule层二单元、全连接层单元;设置卷积层单元中卷积核个数为N 1,每个卷积核大小为1×Nuclear_Size 1,步长为L 1;设置Capsule层一单元中卷积核个数为N 2,每个卷积核大小为1×Nuclear_Size 2,步长为L 2;设置Capsule层二单元的输出长度为Num_Output维行为信息,每个维度采用Vec_Lenv个行为信息特征;设置全连接层单元中输出长度为Output_Length;包括步骤如下:(1)输入一个Batch_Size×1×Window_Size×3大小的行为信息,Batch_Size是指一次在网络架构模块中运行的行为信息的个数,Window_Size是指每次输入网络架构模块的长度;(2)当Batch_Size×1×Window_Size×3大小的行为信息经过卷积层单元之后,通过式(Ⅰ)将输入的行为信息由标量转换成矢量:式(Ⅰ)中,X i是指行为信息经过不确定性、标准化、基于时间序列的滑动窗口处理之后的每个信息;W ij是指卷积层单元的权重参数,初始值默认为生成截断正态分布的随机数;b j是指卷积层单元的偏置参数,初始值默认为0.0;n表示卷积核的个数;Y j是表示卷积层输出;式(Ⅱ)中,W jl是指Capsule层一单元的权重参数,初始值默认为生成截断正态分布的随机数;b l是指Capsule层一单元的偏置参数,初始值默认为0.0;squsah()函数是一种新的非线性函数;经过Capsule层一单元后输出的信息大小为:(4)将具有空间特性的行为信息输入到Capsule层二单元,通过动态路由协议即式(Ⅲ)、(Ⅳ)将行为信息进行处理;式(Ⅲ)、(Ⅳ)中,b ik是指Capsule层一单元中第i个神经元和Capsule层二单元中第k个神经元的动态路由权重;b ij是指Capsule层一单元中第i个神经元和Capsule层二单元中第j个神经元的动态路由权重;S j是指Capsule层二单元经过动态路由协议之后输出的行为信息特征;Capsule层二单元处理后输出的信息大小为:Batch_Size×Num_Output×Vec_Lenv×1;(5)通过全连接层单元将行为信息由矢量转换成标量;经过全连接层单元后输出的信息大小为:Batch_Size×Output_Length×1;(6)加入Softmax分类器,通过Softmax分类器进行行为信息的分类识别;通过将信息大小为Batch_Size×Output_Length×1的行为信息特征经过分类器进行各个行为概率的求解,找出对应的各类概率数值最大的行为,网络架构模块最终的识别结果即概率数值最大的行为。
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