CN115035685A - Square security monitoring method and device based on dispersive motor neural network - Google Patents
Square security monitoring method and device based on dispersive motor neural network Download PDFInfo
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Abstract
The invention discloses a method and a device for monitoring square security based on a dispersive motor neural network. Wherein, the method comprises the following steps: acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor; activating a data matrix in a big data platform according to the square map information, and generating historical data corresponding to the square map information in a preset period; substituting the personnel motion parameters as input characteristic vectors into a dispersion motion model, and outputting a dispersion motion result; and when the risk information exists in the dispersion movement result, sending out a security monitoring alarm. The invention solves the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of the behaviors of all personnel in the current image, the action track of dispersive personnel cannot be screened, the security condition of the dispersive personnel can be accurately positioned, and the accuracy and the reliability of the security monitoring process are reduced.
Description
Technical Field
The invention relates to the field of security data processing, in particular to a method and a device for monitoring square security based on a dispersive motor neural network.
Background
Along with the continuous development of intelligent science and technology, people use intelligent equipment more and more among life, work, the study, use intelligent science and technology means, improved the quality of people's life, increased the efficiency of people's study and work.
At present, when security monitoring is performed on a high-pixel camera device, the high-pixel camera device is used to be deployed in a security environment area, whether security hidden dangers exist is judged through original image data collected by the camera device, and a prompt and alarm are performed on security personnel through a direct judgment result.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a plaza security monitoring method and device based on a dispersive motor neural network, and at least solves the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of all personnel behaviors in a current image, cannot screen the action tracks of the dispersive personnel, accurately positions the security situation of the dispersive personnel, and reduces the accuracy and reliability of the security monitoring process.
According to an aspect of the embodiments of the present invention, there is provided a method for monitoring square security based on a dispersive motor neural network, including: acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor; activating a data matrix in a big data platform according to the square map information, and generating historical data corresponding to the square map information in a preset period; substituting the personnel motion parameters as input characteristic vectors into a dispersion motion model, and outputting a dispersion motion result; and when the risk information exists in the dispersion movement result, sending out a security monitoring alarm.
Optionally, the human motion dispersion factor in the human motion parameters is selected by a dispersion screening formula
And calculating, wherein E is a dispersion screening result, s and theta are vector track parameters, and t is motion duration.
Optionally, before substituting the person motion parameter as an input feature vector into the diffusion motion model and outputting a diffusion motion result, the method further includes: and training the diffusion motion model according to the historical data.
Optionally, substituting the person motion parameters as input feature vectors into a diffusion motion model, and outputting a diffusion motion result includes: extracting the personnel movement dispersion factor in the personnel movement parameters; carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; inputting the main diffusion motion factor and the personnel motion track into a feature vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
According to another aspect of the embodiments of the present invention, there is also provided a plaza security monitoring device based on a dispersive motor neural network, including: the acquisition module is used for acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor; the activation module is used for activating a data matrix in a big data platform according to the square map information and generating historical data corresponding to the square map information in a preset period; the output module is used for substituting the personnel motion parameters serving as input characteristic vectors into a diffusion motion model and outputting a diffusion motion result; and the alarm module is used for sending out security monitoring alarm when the risk information exists in the dispersion movement result.
Optionally, the human motion dispersion factor in the human motion parameters is processed by a dispersion screening formula
And calculating to obtain the target object, wherein E is a dispersion screening result, s and theta are vector trajectory parameters, and t is the motion duration.
Optionally, the apparatus further comprises: and the training module is used for training the dispersion movement model according to the historical data.
Optionally, the output module includes: an extraction unit for extracting the person movement dispersion factor in the person movement parameter; the decomposition unit is used for carrying out Gaussian extreme decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; the output unit is used for inputting the main diffusion motion factor and the personnel motion track to a characteristic vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal motion information, analysis information.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a method for monitoring security of a plaza based on a disseminated motor neural network when running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute a method for monitoring the square security based on the dispersive motor neural network.
In the embodiment of the invention, the method for acquiring the square map information and the personnel motion parameters comprises the following steps: a person motion trajectory and a person motion dispersion factor; activating a data matrix in a big data platform according to the square map information, and generating historical data corresponding to the square map information in a preset period; substituting the personnel motion parameters as input characteristic vectors into a dispersion motion model, and outputting a dispersion motion result; when risk information exists in the dispersion movement result, a security monitoring alarm mode is sent out, and the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of behaviors of all personnel in the current image, the dispersion personnel movement track cannot be screened, the security condition of dispersion personnel is accurately positioned, and the accuracy and the reliability of the security monitoring process are reduced are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a method for monitoring security of a square based on a dispersive motor neural network according to an embodiment of the present invention;
fig. 2 is a block diagram of a square security monitoring device based on a dispersive motor neural network according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing a method according to the present invention, according to an embodiment of the present invention;
fig. 4 is a memory unit for holding or carrying program code implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of a method for monitoring plaza security based on a dispersive motor neural network, it is noted that the steps illustrated in the flowchart of the figure may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Example one
Fig. 1 is a flowchart of a monitoring method for plaza security based on a dispersive motor neural network according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, square map information and personnel motion parameters are obtained, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor.
Specifically, in order to solve the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of the behaviors of all people in the current image, the action track of dispersed personnel cannot be screened, the security situation of the dispersed personnel can not be accurately positioned, and the accuracy and reliability of the security monitoring process are reduced, the security result can be judged by judging the dispersion and personnel dispersion motion rules of the square personnel motion, the square map information is firstly acquired through a server, the square map information can be screened and authenticated through a server storage end, the map is optimized in a unified manner through a map editor, the square map information which can be used for judging the square distribution situation and the map parameters is obtained, and meanwhile, the personnel motion parameters need to be acquired through ultrahigh-pixel image acquisition equipment, wherein, above-mentioned personnel motion parameter includes: a person motion trajectory and a person motion dispersion factor.
It should be noted that the staff movement trajectory may be a matching set of the route of the existing staff movement in the security square and the time stamp, that is, P ═ s (t), where s is the staff movement non-linear trajectory, t is the staff movement time stamp, the data set P changes with the change of staff, and P is a dynamic data set.
It should be further noted that the human movement dispersion factor is used for filtering and screening human movement trajectory data through a preset dispersion trajectory screening algorithm on the basis that the human movement trajectories are completely collected, so as to obtain human trajectory parameters with dispersion characteristics, the size and the compactness of the dispersion factor characterize the normal distribution probability of the dispersion, and as an implementation scheme in a preferred embodiment, the human movement dispersion factor optimization and screening method adopts the dispersion factor algorithm to optimize and screen the human movement trajectories, so that the situation that the human movement trajectory data are directly processed in the prior art can be avoided, and the strength and the accuracy degree of square security measures are directly increased.
Optionally, the human motion dispersion factor in the human motion parameters is selected by a dispersion screening formula
And calculating, wherein E is a dispersion screening result, s and theta are vector track parameters, and t is motion duration.
In particular, when transported by personnelWhen the dispersion factor is calculated by the moving track, the dispersion screening formula can be used
The formula is obtained by calculation, and the formula utilizes the combination of a Gaussian argmin operator and a cusp angle to obtain a dispersion factor E, namely E is a dispersion screening result, s and theta are vector trajectory parameters, t is motion duration
And step S104, activating a data matrix in a big data platform according to the square map information, and generating historical data corresponding to the square map information in a preset period.
Specifically, in order to analyze whether security risks exist or not for diffuse motion, the embodiment of the invention can activate and utilize the big data platform according to the square map information, acquire historical data about security events in different big data platforms according to different square map information, and train a model according to the historical data so as to substitute and output subsequent motion parameters.
It should be noted that the big data platform may be a diversified data mixing server for security monitoring, the feature input parameters and the feature output parameters of the security event may be extracted from the server, and the vectorization factor is used as a parameter for training the neural network model to obtain a complete and mature neural network model for judgment of diffuse motion in a user preset period.
And S106, substituting the personnel motion parameters serving as input characteristic vectors into a diffusion motion model, and outputting a diffusion motion result.
Specifically, after the personnel movement parameters are obtained, in order to further judge whether the result of the personnel dispersion movement has a risk that an alarm needs to be given, the personnel movement parameters are required to be substituted into the dispersion movement model, and the dispersion risk which may occur is output through a model output algorithm, so that a final security judgment result is obtained.
Optionally, before substituting the person motion parameter as an input feature vector into the diffusion motion model and outputting a diffusion motion result, the method further includes: and training the diffusion motion model according to the historical data.
Specifically, the dispersion motion model trained by using the personnel motion parameters as the feature vectors may be a DNN deep neural network model, which continuously simulates and trains input-output relationships using different input parameters, and uses the relationships as a complete process algorithm as a judgment standard for obtaining security dispersion motion output data.
Optionally, substituting the person motion parameters as input feature vectors into a diffusion motion model, and outputting a diffusion motion result includes: extracting the personnel movement dispersion factor in the personnel movement parameters; carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; inputting the main diffusion motion factor and the personnel motion track into a feature vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
Specifically, the personnel motion parameters are used as input characteristic vectors to be substituted into a dispersion motion model, the output dispersion motion result comprises that personnel motion dispersion factors in the personnel motion parameters need to be extracted at first, the personnel motion dispersion factors are used as important parameters for dispersion decomposition analysis, further the personnel motion dispersion factors are subjected to Gaussian extreme value decomposition, and the utilization of the parameters is realized
Obtaining a main dispersion motion factor suitable for a normal vector by a quadratic polynomial, and finally inputting the main dispersion motion factor and the personnel motion trail into a characteristic vector input end of the dispersion motion model to obtain a dispersion motion result, wherein the dispersion motion result comprises: normal motion information, analysis information.
And S108, when the risk information exists in the dispersion movement result, sending out a security monitoring alarm.
Specifically, after the dispersion movement result is completed, whether security risk information, such as dangerous data of suspicious people and abnormal trajectory high altitude objects, exists in the dispersion movement result needs to be analyzed, the risk information is sent to a security system, and a security monitoring alarm is sent out.
By the embodiment, the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of behaviors of all people in the current image, action tracks of dispersive people cannot be screened, security conditions of the dispersive people are accurately positioned, and accuracy and reliability of a security monitoring process are reduced are solved.
Example two
Fig. 2 is a block diagram of a square security monitoring device based on a dispersive motor neural network according to an embodiment of the present invention, and as shown in fig. 2, the device includes:
an obtaining module 20, configured to obtain square map information and personnel motion parameters, where the personnel motion parameters include: a person motion trajectory and a person motion dispersion factor.
Specifically, in order to solve the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of all people's behaviors in the current image, cannot screen the action tracks of dispersed personnel, accurately positions the security situation of the dispersed personnel, and reduces the accuracy and reliability of the security monitoring process, the security result is judged by judging the dispersion and personnel dispersion movement laws of the square personnel movement, the square map information is firstly acquired through a server, the square map information can be screened and authenticated through a server storage end, the map is optimized in a unified manner through a map editor, the square map information which can be used for judging the square distribution situation and map parameters is obtained, and meanwhile, the personnel movement parameters need to be acquired through ultrahigh-pixel image acquisition equipment, wherein, the above-mentioned personnel motion parameter includes: a person motion trajectory and a person motion dispersion factor.
It should be noted that the staff movement trajectory may be a matching set of a route of the existing staff movement in the security square and a timestamp, that is, P ═ s, t, where s is a staff movement non-linear trajectory, t is a staff movement timestamp, the data set P changes with the change of the staff, and P is a dynamic data set.
It should be further noted that the human movement dispersion factor is used for filtering and screening human movement trajectory data through a preset dispersion trajectory screening algorithm on the basis that the human movement trajectories are completely collected, so as to obtain human trajectory parameters with dispersion characteristics, the size and the compactness of the dispersion factor characterize the normal distribution probability of the dispersion, and as an implementation scheme in a preferred embodiment, the human movement dispersion factor optimization and screening method adopts the dispersion factor algorithm to optimize and screen the human movement trajectories, so that the situation that the human movement trajectory data are directly processed in the prior art can be avoided, and the strength and the accuracy degree of square security measures are directly increased.
Optionally, the human motion dispersion factor in the human motion parameters is selected by a dispersion screening formula
And calculating, wherein E is a dispersion screening result, s and theta are vector track parameters, and t is motion duration.
Specifically, when the dispersion factor is calculated through the movement track of the person, a dispersion screening formula can be used
The formula is obtained by calculation, and the formula utilizes the combination of a Gaussian argmin operator and a cusp angle to obtain a dispersion factor E, namely E is a dispersion screening result, s and theta are vector trajectory parameters, and t is motion duration
And the activation module 22 is used for activating a data matrix in the big data platform according to the square map information and generating historical data corresponding to the square map information in a preset period.
Specifically, in order to analyze whether security risks exist or not for diffuse motion, the embodiment of the invention can activate and utilize the big data platform according to the square map information, acquire historical data about security events in different big data platforms according to different square map information, and train a model according to the historical data so as to substitute and output subsequent motion parameters.
It should be noted that the big data platform may be a diversified data mixing server for security monitoring, the feature input parameters and the feature output parameters of the security event may be extracted from the server, and the vectorization factor is used as a parameter for training the neural network model to obtain a complete and mature neural network model for judgment of diffuse motion in a user preset period.
And the output module 24 is used for substituting the personnel motion parameters serving as input feature vectors into the diffusion motion model and outputting a diffusion motion result.
Specifically, after the personnel movement parameters are obtained, in order to further judge whether the result of the personnel dispersion movement has a risk that an alarm needs to be given or not, the personnel movement parameters need to be substituted into the dispersion movement model, and the dispersion risk which possibly occurs is output through a model output algorithm, so that the final security judgment result is obtained.
Optionally, the apparatus further comprises: and the training module is used for training the dispersion movement model according to the historical data.
Specifically, the dispersion motion model trained by using the personnel motion parameters as the feature vectors may be a DNN deep neural network model, which continuously simulates and trains input-output relationships using different input parameters, and uses the relationships as a complete process algorithm as a judgment standard for obtaining security dispersion motion output data.
Optionally, the output module includes: an extraction unit, configured to extract the person motion dispersion factor from the person motion parameters; the decomposition unit is used for carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; the output unit is used for inputting the main diffusion motion factor and the personnel motion track to a characteristic vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
Specifically, the personnel motion parameters are used as input characteristic vectors to be substituted into a dispersion motion model, the output dispersion motion result comprises that personnel motion dispersion factors in the personnel motion parameters need to be extracted at first, the personnel motion dispersion factors are used as important parameters for dispersion decomposition analysis, further the personnel motion dispersion factors are subjected to Gaussian extreme value decomposition, and the utilization of the parameters is realized
Obtaining a prime dispersion motion factor suitable for a normal vector by a quadratic polynomial, and finally inputting the prime dispersion motion factor and the personnel motion trajectory into a characteristic vector input end of the dispersion motion model to obtain a dispersion motion result, wherein the dispersion motion result comprises: normal movement information, analysis information.
And the alarm module 28 is used for sending out a security monitoring alarm when the risk information exists in the dispersion movement result.
Specifically, after the dispersion movement result is completed, whether security risk information, such as dangerous data of suspicious people and abnormal trajectory high altitude objects, exists in the dispersion movement result needs to be analyzed, the risk information is sent to a security system, and a security monitoring alarm is sent out.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a method for monitoring security of a plaza based on a disseminated motor neural network when running.
Specifically, the method comprises the following steps: acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor; activating a data matrix in a big data platform according to the square map information, and generating a corresponding square in a preset periodHistorical data of graph information; substituting the personnel motion parameters as input characteristic vectors into a dispersion motion model, and outputting a dispersion motion result; and when the risk information exists in the dispersion movement result, sending out a security monitoring alarm. Optionally, the human motion dispersion factor in the human motion parameters is selected by a dispersion screening formula
And calculating to obtain the target object, wherein E is a dispersion screening result, s and theta are vector trajectory parameters, and t is the motion duration. Optionally, before substituting the person motion parameter as an input feature vector into the diffusion motion model and outputting a diffusion motion result, the method further includes: and training the diffusion motion model according to the historical data. Optionally, substituting the person motion parameters as input feature vectors into a diffusion motion model, and outputting a diffusion motion result includes: extracting the personnel movement dispersion factor in the personnel movement parameters; carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; inputting the main dispersion motion factor and the personnel motion track into a feature vector input end of the dispersion motion model to obtain a dispersion motion result, wherein the dispersion motion result comprises the following steps: normal movement information, analysis information.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is used for storing computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute a method for monitoring the square security based on the dispersive motor neural network.
Specifically, the method comprises the following steps: acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor; according to the square map informationGenerating historical data corresponding to the square map information in a preset period by using a data matrix in a big living data platform; substituting the personnel motion parameters serving as input feature vectors into a diffusion motion model, and outputting a diffusion motion result; and when the risk information exists in the dispersion movement result, sending out a security monitoring alarm. Optionally, the human motion dispersion factor in the human motion parameters is selected by a dispersion screening formula
And calculating, wherein E is a dispersion screening result, s and theta are vector track parameters, and t is motion duration. Optionally, before substituting the person motion parameter as an input feature vector into the diffusion motion model and outputting a diffusion motion result, the method further includes: and training the diffusion motion model according to the historical data. Optionally, substituting the person motion parameters as input feature vectors into a diffusion motion model, and outputting a diffusion motion result includes: extracting the personnel movement dispersion factor in the personnel movement parameters; carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector; inputting the main diffusion motion factor and the personnel motion track into a feature vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
By the embodiment, the technical problems that the security monitoring method in the prior art is only limited to image acquisition and image recognition of behaviors of all people in the current image, action tracks of dispersive people cannot be screened, security conditions of the dispersive people are accurately positioned, and accuracy and reliability of a security monitoring process are reduced are solved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, fig. 3 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to realize communication connections between the elements. The memory 33 may comprise a high speed RAM memory, and may also include a non-volatile memory NVM, such as at least one disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented by, for example, a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through a wired or wireless connection.
Optionally, the input device 30 may include a variety of input devices, for example, at least one of a user-oriented user interface, a device-oriented device interface, a software programmable interface, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware plug-in interface (e.g., a USB interface, a serial port, etc.) for data transmission between devices; optionally, the user-facing user interface may be, for example, a user-facing control key, a voice input device for receiving voice input, and a touch sensing device (e.g., a touch screen with a touch sensing function, a touch pad, etc.) for receiving user touch input; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, such as an input pin interface or an input interface of a chip; optionally, the transceiver may be a radio frequency transceiver chip with a communication function, a baseband processing chip, a transceiver antenna, and the like. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, a sound, or other output device.
In this embodiment, the processor of the terminal device includes a module for executing the functions of the modules of the data processing apparatus in each device, and specific functions and technical effects may refer to the foregoing embodiments, which are not described herein again.
Fig. 4 is a schematic diagram of a hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of fig. 3 in an implementation process. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above-described embodiment.
The memory 42 is configured to store various types of data to support operations at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, and so forth. The memory 42 may include a Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, the processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The specific components included in the terminal device are set according to actual requirements, which is not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. Processing component 40 may include one or more processors 41 to execute instructions to perform all or a portion of the steps of the above-described method. Further, processing component 40 may include one or more modules that facilitate interaction between processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate interaction between the multimedia component 45 and the processing component 40.
The power supply component 44 provides power to the various components of the terminal device. The power components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device.
The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a voice recognition mode. The received audio signal may further be stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 also includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing component 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: a volume button, a start button, and a lock button.
The sensor assembly 48 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor assembly 48 may detect the open/closed status of the terminal device, the relative positioning of the components, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot for inserting a SIM card therein, so that the terminal device can log on to a GPRS network and establish communication with the server via the internet.
From the above, the communication component 43, the audio component 46, the input/output interface 47 and the sensor component 48 referred to in the embodiment of fig. 4 can be implemented as the input device in the embodiment of fig. 3.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A plaza security monitoring method based on a dispersive motor neural network is characterized by comprising the following steps:
acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor;
activating a data matrix in a big data platform according to the square map information, and generating historical data corresponding to the square map information in a preset period;
substituting the personnel motion parameters as input characteristic vectors into a dispersion motion model, and outputting a dispersion motion result;
and when the risk information exists in the dispersion movement result, sending out a security monitoring alarm.
2. The method of claim 1, wherein the human motion dispersion factor in the human motion parameters is formulated by dispersion screening
And calculating to obtain the target object, wherein E is a dispersion screening result, s and theta are vector trajectory parameters, and t is the motion duration.
3. The method according to claim 1, wherein before the step of inputting the human motion parameters into a diffusion motion model as input feature vectors and outputting a diffusion motion result, the method further comprises the following steps:
and training the diffusion motion model according to the historical data.
4. The method of claim 1, wherein the step of substituting the human motion parameters into a diffusion motion model as input feature vectors and outputting the diffusion motion result comprises the steps of:
extracting the human motion dispersion factor in the human motion parameters;
carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector;
inputting the main diffusion motion factor and the personnel motion track into a feature vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
5. The utility model provides a square security monitoring devices based on disseminated motor neural network which characterized in that includes:
the acquisition module is used for acquiring square map information and personnel motion parameters, wherein the personnel motion parameters comprise: a person motion trajectory and a person motion dispersion factor;
the activation module is used for activating a data matrix in a big data platform according to the square map information and generating historical data corresponding to the square map information in a preset period;
the output module is used for substituting the personnel motion parameters serving as input characteristic vectors into a diffusion motion model and outputting a diffusion motion result;
and the alarm module is used for sending out security monitoring alarm when the risk information exists in the dispersion movement result.
6. The apparatus of claim 5, wherein the human motion dispersion factor in the human motion parameters is determined by a dispersion screening formula
And calculating to obtain the target object, wherein E is a dispersion screening result, s and theta are vector trajectory parameters, and t is the motion duration.
7. The apparatus of claim 5, further comprising:
and the training module is used for training the dispersion movement model according to the historical data.
8. The apparatus of claim 5, wherein the output module comprises:
an extraction unit, configured to extract the person motion dispersion factor from the person motion parameters;
the decomposition unit is used for carrying out Gaussian extreme value decomposition on the personnel motion dispersion factor to obtain a main dispersion motion factor suitable for a normal vector;
the output unit is used for inputting the main diffusion motion factor and the personnel motion track to a characteristic vector input end of the diffusion motion model to obtain a diffusion motion result, wherein the diffusion motion result comprises: normal movement information, analysis information.
9. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168344A (en) * | 2023-02-21 | 2023-05-26 | 航天正通汇智(北京)科技股份有限公司 | Security monitoring method and device based on array computing vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116168344A (en) * | 2023-02-21 | 2023-05-26 | 航天正通汇智(北京)科技股份有限公司 | Security monitoring method and device based on array computing vision |
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