A building access control management system and its control method

Abstract

The present application provides a building access control management system and its control method. After obtaining the gating feature harmonic signal sent by the access control card of the personnel accessing the building, the gating feature harmonic signal is sampled and time-frequency cross feature extraction is performed to determine the access control signal fluctuation value set. According to the access control signal fluctuation value set, the access control signal related feature degree corresponding to each access control signal fluctuation value is determined, and an access control signal related feature degree set is formed. After visualizing the access control signal related feature degree set, clustering processing is performed, and then the access control frequency domain modulation factor is determined. After determining the smooth signal sampling set through the access control signal sampling set and extracting the channel analog-to-digital modulus of the smooth signal sampling set to obtain the access control channel analog-to-digital modulus, the gating feature is identified by the access control frequency domain modulation factor and the access control channel analog-to-digital modulus, improving the accuracy of identifying the access control features of the building access control management system.

Description

Technical Field

[0001] This application relates to the technical field of access control management, and more specifically, to an access control management system for buildings and its control method. Background Art

[0002] With the progress of society, people's awareness of security and theft prevention is gradually increasing. Among them, scenarios such as schools, companies, and homes have put forward higher requirements for the applicable scenarios of access control system control technology. An access control management system is a security system used to manage and control access to specific areas or buildings. Such a system is usually used to improve the security of personnel access in building scenarios, ensure that only authorized personnel can enter specific areas within the building, prevent suspicious personnel from invading, and at the same time record and monitor the access records of personnel.

[0003] In the prior art, the biometric information of building access personnel, including fingerprint recognition, iris scanning, face recognition, etc., is often used as access control features. The access control features are used by the access control management system to identify the identity of the access personnel, so as to realize the control of the access control management system. In the case of large environmental factors, it is difficult to completely collect the access control features of building access personnel, resulting in the problem of low accuracy of the access control management system in identifying access control features. Therefore, how to improve the accuracy of the access control management system in identifying access control features in buildings has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides an access control management system for buildings and its control method to solve the technical problem of low accuracy of the access control management system in identifying access control features in the prior art.

[0005] To solve the above technical problems, this application adopts the following technical solutions:

[0006] In a first aspect, this application provides an access control method for buildings, including the following steps:

[0007] Obtain the gating feature harmonic signal sent by the access card of the building access personnel;

[0008] After sampling the gating feature harmonic signal, obtain an access signal sampling set, perform time-frequency cross feature extraction on the access signal sampling set to obtain an access signal fluctuation value set, determine the access signal correlation feature degree corresponding to each access signal fluctuation value in the access signal fluctuation value set, and form an access signal correlation feature degree set;

[0009] Visualize the set of relevant feature degrees of the access control signal to obtain a visible relevant feature degree set, perform feature degree clustering on the visible relevant feature degree set to obtain a set of visible relevant feature degree clusters, determine relevant feature pole values according to the set of visible relevant feature degree clusters, and determine an access control frequency domain modulation factor according to the relevant feature pole values;

[0010] Decompose the access control signal sampling set to obtain an access control feature coefficient set, perform principal component segmentation on the access control feature coefficient set to obtain a smoothing factor, determine a smoothed signal sampling set according to the smoothing factor, and extract an access control channel modulus from the smoothed signal sampling set;

[0011] Perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow access to personnel in buildings according to the recognition result.

[0012] Combined with the first aspect, in some implementation manners of the first aspect, in the process of visualizing the set of relevant feature degrees of the access control signal to obtain a visible relevant feature degree set, each visible relevant feature degree in the visible relevant feature degree set is determined according to the following formula:

[0013]

[0014] where, represents the -th visible relevant feature degree value in the visible relevant feature degree set, represents the -th relevant feature degree of the access control signal in the set of relevant feature degrees of the access control signal, , respectively represent the maximum and minimum values of the relevant feature degrees of the access control signal in the set of relevant feature degrees of the access control signal.

[0015] Combined with the first aspect, in some implementation manners of the first aspect, performing feature degree clustering on the visible relevant feature degree set to obtain a set of visible relevant feature degree clusters specifically includes:

[0016] Obtain a visible relevant feature degree waveform image of the visible relevant feature degree set;

[0017] Determine the critical number of visible relevant feature degree clusters according to the visible relevant feature degree waveform image;

[0018] Cluster each visible relevant feature degree point in the visible relevant feature degree set according to the critical number of visible relevant feature degree clusters to obtain an initial feature cluster set;

[0019] Perform a feature cluster judgment on the set of initial feature clusters. If the initial feature cluster meets the feature cluster judgment condition, then use the set of initial feature clusters as the set of visually relevant feature degree clusters;

[0020] If the initial feature cluster does not meet the feature cluster judgment condition, perform repeated clustering based on the set of initial feature clusters until an initial feature cluster set that meets the feature cluster judgment condition is obtained as the set of visually relevant feature degree clusters.

[0021] Combined with the first aspect, in some implementation manners of the first aspect, determining the relevant feature pole value according to the set of visually relevant feature degree clusters specifically includes:

[0022] Perform a significant feature judgment on each visually relevant feature degree cluster in the set of visually relevant feature degree clusters to obtain significant feature clusters;

[0023] Obtain the local visually relevant feature degree waveform image corresponding to the significant feature cluster, and then extract the feature peak points in the local visually relevant feature degree waveform image to form a feature peak set;

[0024] Obtain the relevant feature pole value according to the feature peak set.

[0025] Combined with the first aspect, in some implementation manners of the first aspect, determining the access control frequency domain modulation factor according to the relevant feature pole value specifically includes:

[0026] Obtain the sampling rate of the gating feature harmonic signal ;

[0027] According to the relevant feature pole value, determine the maximum feature horizontal distance , the maximum feature vertical distance , the adjacent feature horizontal distance and the adjacent feature vertical distance ;

[0028] According to the sampling rate of the gating feature harmonic signal , the maximum feature horizontal distance , the maximum feature vertical distance , the adjacent feature horizontal distance , the adjacent feature vertical distance determine the access control frequency domain modulation factor , the access control frequency domain modulation factor is determined according to the following formula:

[0029]

[0030] where is the access control frequency domain modulation factor, is the maximum characteristic horizontal distance, is the adjacent characteristic horizontal distance, is the maximum characteristic vertical distance, is the adjacent characteristic vertical distance, is the sampling rate of the gated feature harmonic signal.

[0031] Combined with the first aspect, in some implementations of the first aspect, determining the smoothed signal sampling set according to the smoothing factor specifically includes:

[0032] Determine the smoothed access control feature coefficient set according to the smoothing factor;

[0033] Perform feature reconstruction on the smoothed access control feature coefficient set to obtain the smoothed signal sampling set.

[0034] Combined with the first aspect, in some implementations of the first aspect, perform gated feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow the access personnel of the building to pass according to the recognition result, specifically including:

[0035] Obtain the preset access control channel transmission threshold and access control frequency domain modulation threshold;

[0036] Perform threshold judgment on the access control channel modulus and the access control frequency domain modulation factor. If the threshold judgment condition is met, send an open door request to the access control system. If the threshold judgment condition is not met, record the current open door request in the access control system information database of the building and give an alarm.

[0037] In a second aspect, the present application provides a building access control management system, and the building access control management system includes:

[0038] A gated feature harmonic signal acquisition module, configured to acquire a gated feature harmonic signal sent by an access control card of an access personnel of a building;

[0039] An access control signal related feature degree set determination module, configured to sample the gated feature harmonic signal to obtain an access control signal sampling set, perform time-frequency cross feature extraction on the access control signal sampling set to obtain an access control signal fluctuation value set, determine the access control signal related feature degree corresponding to each access control signal fluctuation value in the access control signal fluctuation value set, and form an access control signal related feature degree set;

[0040] An access control frequency domain modulation factor determination module, configured to perform visualization processing on the access control signal related feature degree set to obtain a visual related feature degree set, perform feature degree clustering on the visual related feature degree set to obtain a visual related feature degree cluster set, determine a related feature pole value according to the visual related feature degree cluster set, and determine an access control frequency domain modulation factor according to the related feature pole value;

[0041] An access control channel modulus determination module is configured to perform access control coefficient decomposition on the access control signal sampling set to obtain an access control feature coefficient set, perform principal component segmentation on the access control feature coefficient set to obtain a smoothing factor, determine a smoothed signal sampling set according to the smoothing factor, and extract the channel modulus of the access control channel from the smoothed signal sampling set;

[0042] An access control system control module is configured to perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow access personnel in the buildings to pass according to the recognition result.

[0043] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned access control method for buildings.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned access control method for buildings is implemented.

[0045] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects:

[0046] In the present application, after receiving the gating feature harmonic signal sent by the access control card of the access personnel in the buildings, the gating feature harmonic signal is sampled and time-frequency cross feature extraction is performed to determine the access control signal fluctuation value set. After time-frequency cross feature extraction, the signal quality and accurate feature extraction are ensured, and the accuracy of the process of identifying the gating feature signal is improved. According to the access control signal fluctuation value set, the access control signal related feature degree corresponding to each access control signal fluctuation value is determined, and an access control signal related feature degree set is formed. After visualizing the access control signal related feature degree set, clustering processing is performed. Visualization and clustering realize efficient feature analysis in the signal recognition process, improve the accuracy of the process of identifying the gating feature signal, and then determine the access control frequency domain modulation factor. After determining the smoothed signal sampling set from the access control signal sampling set, the channel modulus of the access control channel is extracted from the smoothed signal sampling set. The access control channel modulus helps to further improve the understanding and recognition of the signal. The system can more comprehensively consider the possible interference factors in the transmission process of the access control card, increase the accuracy of the system, and perform gating feature recognition by the access control frequency domain modulation factor and the access control channel modulus, improving the accuracy of the access control management system in the buildings to identify special access control features. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is an exemplary flowchart of an access control method for buildings according to some embodiments of the present application;

[0048] Figure 2It is a schematic diagram of exemplary hardware and / or software of a building access control management system shown in some embodiments of the present application;

[0049] Figure 3 It is a schematic diagram of the structure of a computer terminal device for implementing a building access control method shown in some embodiments of the present application. Detailed implementation manners

[0050] The core of the technical solution of the present application is that after receiving the gating feature harmonic signal sent by the access control card of the building access personnel, sampling the gating feature harmonic signal and extracting time-frequency cross features to determine the access control signal fluctuation value set. After the time-frequency cross feature extraction, the signal quality and accurate feature extraction are ensured, and the accuracy of the process of identifying the gating feature signal is improved. According to the access control signal fluctuation value set, determine the access control signal related feature degree corresponding to each access control signal fluctuation value, and form an access control signal related feature degree set. After visualizing the access control signal related feature degree set, perform clustering processing. Visualization and clustering realize efficient analysis of features in the signal recognition process, improve the accuracy of the process of identifying the gating feature signal, and then determine the access control frequency domain modulation factor. After determining the smooth signal sampling set through the access control signal sampling set, perform channel analog-to-digital extraction on the smooth signal sampling set to obtain the access control channel analog-to-digital number. Considering the access control channel analog-to-digital number helps to further improve the understanding and recognition of the signal. The system can more comprehensively consider the possible interference factors in the transmission process of the access control card, increasing the accuracy of the system. Perform gating feature recognition by the access control frequency domain modulation factor and the access control channel analog-to-digital number, improving the accuracy of the building access control management system in identifying access control features.

[0051] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific implementation manners. Refer to Figure 1 , this figure is an exemplary flowchart of a building access control method shown in some embodiments of the present application;

[0052] The building access control method 100 mainly includes the following steps:

[0053] In step 101, obtain the gating feature harmonic signal sent by the access control card of the building access personnel.

[0054] Specifically, the access control card in the hand of the building access personnel can emit the gating feature harmonic signal, and the access control system receives the gating feature harmonic signal and stores it in the access control system information library. The gating feature harmonic signal is used for identifying the identity of the building access personnel, and whether to allow the access personnel to enter is judged according to the identification result.

[0055] It should be noted that the gated feature harmonic signal sent by the visitors to the buildings and halls is an orthogonal signal, and this gated feature harmonic signal carries the feature information of the access control card, that is, the gated feature harmonic signal is composed of two orthogonal signals. One signal is the mapping of the gated feature harmonic signal on the time axis, and the other signal is the mapping of the gated feature harmonic signal with a 90-degree phase shift on the time axis. After analog-to-digital conversion, this gated feature harmonic signal is represented in the form of the sum of the real part and the imaginary part.

[0056] In step 102, after sampling the gated feature harmonic signal, an access control signal sampling set is obtained. By performing time-frequency cross feature extraction on the access control signal sampling set, an access control signal fluctuation value set is obtained, and the access control signal correlation feature degree corresponding to each access control signal fluctuation value in the access control signal fluctuation value set is determined, and an access control signal correlation feature degree set is formed.

[0057] Specifically, in the process of obtaining the access control signal sampling set after sampling the gated feature harmonic signal, it can be achieved by obtaining a sampler with a sampling rate of , inputting the gated feature harmonic signal into the sampler, obtaining access control signal sampling points, converting all access control signal sampling points into a data form through the analog-to-digital converter in the sampler, obtaining an access control signal sampling set with a sampling number of and a sampling period of .

[0058] It should be noted that the analog-to-digital converter is a device that selects a set of discrete data points from a continuous gated feature harmonic signal and then quantifies and converts these data points into a digital form.

[0059] In some embodiments, the access control signal fluctuation value set can be obtained by performing time-frequency cross feature extraction on the access control signal sampling set using the following formula:

[0060]

[0061] where is the access control signal fluctuation value corresponding to the sampling moment, is the access control signal sampling point corresponding to the sampling moment, is the real part of the access control signal sampling point corresponding to the sampling moment, is the imaginary part of the access control signal sampling point corresponding to the sampling moment.

[0062] It should be noted that the set of access control signal fluctuation values described in this application is a set composed of access control signal fluctuation values corresponding to different sampling times. The access control signal fluctuation value is a comprehensive quantization value of the amplitude fluctuation characteristics of the access control signal in the time domain and frequency domain obtained by performing time-frequency cross feature extraction on the access control signal sampling set, and is used to characterize the signal fluctuation intensity of the gating characteristic harmonic signal at each sampling time. The larger the access control signal fluctuation value, the greater the signal intensity of the gating characteristic harmonic signal at each sampling time.

[0063] In addition, the access control signal fluctuation value described in this application is an element of the set of access control signal fluctuation values. All access control signal fluctuation values form the set of access control signal fluctuation values. The access control signal sampling point is an element of the access control signal sampling set. All access control signal sampling points form the access control signal sampling set. The access control signal sampling point has a uniquely corresponding sampling time, and the sampling time is the corresponding time for sampling the access control signal sampling point.

[0064] In some other embodiments, the process of performing time-frequency cross feature extraction on the sampled gating characteristic harmonic signal can also obtain the modulus value of the sampling point as the access control signal fluctuation value by taking the modulus of the access control signal sampling set. It should be noted that time-frequency cross feature extraction can extract the cross feature information in the time domain and frequency domain of the access control signal sampling set, so as to strengthen the characteristics of important information variables in the access control signal sampling set and increase the feature detail degree in the access control signal, so as to facilitate the determination of the fluctuation value of the gating characteristic harmonic signal.

[0065] In some embodiments, determining the access control signal related feature degrees corresponding to each access control signal fluctuation value in the set of access control signal fluctuation values and forming a set of access control signal related feature degrees can be implemented by the following steps:

[0066] Take the first access control signal fluctuation value in the set of access control signal fluctuation values as the selected access control signal fluctuation value, and determine the access control signal fundamental frequency value according to the selected access control signal fluctuation value;

[0067] Perform inverse mapping on the access control signal fundamental frequency value to obtain the access control signal related feature degree corresponding to the selected access control signal fluctuation value;

[0068] Take the other access control signal fluctuation values in the set of access control signal fluctuation values as the selected access control signal fluctuation values in turn, and repeat the above steps to obtain the access control signal related feature degrees corresponding to the other access control signal fluctuation values respectively, and form a set of access control signal related feature degrees.

[0069] In some embodiments, the first access control signal fluctuation value in the set of access control signal fluctuation values is used as the selected access control signal fluctuation value. The access control signal fundamental frequency value can be determined according to the selected access control signal fluctuation value by using the following formula:

[0070]

[0071] where is the access control signal fundamental frequency value corresponding to the selected access control signal fluctuation value, is the number of access control signal fluctuation values in the set of access control signal fluctuation values, is the base of the natural logarithm, is the imaginary unit, is the selected access control signal fluctuation value.

[0072] It should be noted that the access control signal fundamental frequency value is a quantization value of the smoothness of the selected access control signal fluctuation value relative to the remaining access control signal fluctuation values in the set of access control signal fluctuation values.

[0073] In some embodiments, the access control signal correlation feature degree corresponding to the selected access control signal fluctuation value can be obtained by inverse mapping the access control signal fundamental frequency value by using the following formula:

[0074]

[0075] where represents the access control signal correlation feature degree corresponding to the selected access control signal fluctuation value, is the access control signal fundamental frequency value corresponding to the selected access control signal fluctuation value, is the number of access control signal fluctuation values in the set of access control signal fluctuation values, is the base of the natural logarithm, is the imaginary unit, represents the access control signal fundamental frequency value at the sampling time lagging sampling times starting from the sampling time of the selected access control signal fluctuation value.

[0076] It should be noted that the access control signal correlation feature degree in this application is a quantization value of the correlation degree between the selected access control signal fluctuation value and the access control signal fluctuation values at other sampling times, and is used to represent the correlation degree between the selected access control signal fluctuation value and the access control signal fluctuation values at other sampling times. The correlation degree refers to the similarity degree of the periodicity, trend and other important features of the access control signal fluctuation values at different sampling times. The larger the access control signal correlation feature degree corresponding to the selected access control signal fluctuation value, the greater the correlation degree between the selected access control signal fluctuation value and the access control signal fluctuation values at other sampling times, thereby improving the accuracy of identifying access control features in the access control system of buildings.

[0077] In specific implementation, other access control signal fluctuation values in the access control signal fluctuation value set are sequentially used as the selected access control signal fluctuation values, and access control signal-related feature degrees corresponding to the other access control signal fluctuation values are obtained, and an access control signal-related feature degree set is formed.

[0078] It should be noted that the access control signal-related feature degree in this application reflects the degree of change correlation of the access control signal fluctuation value at different sampling moments. By mapping the access control signal fundamental frequency value to the time domain through the reflection, the access control signal-related feature degree of the selected access control signal fluctuation value and other access control signal fluctuation values can be reflected by the access control signal fundamental frequency value, so as to perform further extraction and analysis of the access control frequency domain modulation factor.

[0079] In step 103, the access control signal-related feature degree set is visually processed to obtain a visually related feature degree set, the visually related feature degree set is feature-degree clustered to obtain a visually related feature degree cluster set, relevant feature pole values are determined according to the visually related feature degree cluster set, and an access control frequency domain modulation factor is determined according to the relevant feature pole values.

[0080] In some embodiments, visually processing the access control signal-related feature degree set to obtain a visually related feature degree set can be implemented by the following formula:

[0081]

[0082] where represents the th visually related feature degree value in the visually related feature degree set, represents the th access control signal-related feature degree in the access control signal-related feature degree set, , respectively represent the maximum and minimum values of the access control signal-related feature degrees in the access control signal-related feature degree set.

[0083] The above-mentioned visually related feature degree set is a set composed of visually related feature degrees corresponding to different sampling moments. The visually related feature degree is a visualized value obtained by mapping the access control signal-related feature degree at the corresponding sampling moment within a preset interval, making the change situation of the access control signal-related feature degree set clearer, thereby improving the accuracy of the access control feature recognition of the access control system in buildings.

[0084] It should be noted that in this application, the visualization process maps the variables of the access control signal-related feature degrees within a preset interval, so that the change situation of the access control signal fluctuation value degrees is clearer, facilitating the observation of the changes in the access control signal-related feature degrees. Specifically, when implementing, the visual-related feature degree set can be determined through the visualization process for subsequent analysis and processing.

[0085] In some embodiments, to perform feature degree clustering on the visual-related feature degree set to obtain a visual-related feature degree cluster set, the following steps can be adopted:

[0086] Obtain the visual-related feature degree waveform image of the visual-related feature degree set;

[0087] Determine the critical visual-related feature degree cluster number according to the visual-related feature degree waveform image;

[0088] Cluster each visual-related feature degree point in the visual-related feature degree set according to the critical visual-related feature degree cluster number to obtain an initial feature cluster set;

[0089] Perform feature cluster judgment on the initial feature cluster set. If the initial feature cluster meets the feature cluster judgment condition, then use the initial feature cluster set as the visual-related feature degree cluster set;

[0090] If the initial feature cluster does not meet the feature cluster judgment condition, cluster again and perform feature cluster judgment until an initial feature cluster set that meets the feature cluster judgment condition is obtained as the visual-related feature degree cluster set.

[0091] In some embodiments, in the process of obtaining the visual-related feature degree waveform image of the visual-related feature degree set, the visual-related feature degree set can be fitted by using the Lagrange interpolation method commonly used in sequence fitting, so as to obtain the visual-related feature degree waveform image. It should be noted that the Lagrange interpolation method can obtain a visual-related feature degree waveform image that conforms to the change law of the selected visual-related feature degree point corresponding to the access control signal-related feature degree, thereby realizing the fitting process to obtain the visual-related feature degree waveform image.

[0092] It should be noted that in this application, the visual-related feature degree points are the points where the elements in the visual-related feature degree set are mapped on the coordinate system, and all the visual-related feature degree points form the visual-related feature degree waveform image.

[0093] In some embodiments, in the process of determining the number of critical visible correlation feature degree clusters based on the visible correlation feature degree waveform image, first, a range of the number of visible correlation feature degree clusters is preset, and traversal is performed within the range of the number of visible correlation feature degree clusters to obtain the sum of squared errors corresponding to all the numbers of visible correlation feature degree clusters within the range of the number of visible correlation feature degree clusters. Taking the sum of squared errors as the ordinate and the number of visible correlation feature degree clusters as the abscissa, the abscissa corresponding to the first inflection point of the image according to the time sequence is used as the number of critical visible correlation feature degree clusters. For example, if the preset range of the number of visible correlation feature degree clusters is from 1 to 100, when the number of visible correlation feature degree clusters is selected as 5, 5 visible correlation feature degree points are randomly selected in the feature waveform image, and these 5 visible correlation feature degree points are used as the center points. The distances from all visible correlation feature degree points to the center points are determined, and clustering is performed according to the shortest distances from all visible correlation feature degree points to the center points. For example, in specific implementation, a visible correlation feature degree point can be randomly selected in the feature waveform image, and the distances from this visible correlation feature degree point to the 5 center points are determined. The above steps are repeated to traverse the visible correlation feature degree points in the feature waveform image until the distances from all visible correlation feature degree points to the 5 center points are determined. All visible correlation feature degree points with the shortest distance to this center point are used as 1 visible correlation feature degree cluster, and then 5 visible correlation feature degree clusters are obtained. The squares of the distances from the visible correlation feature degree points within each cluster to the center point are determined, and after adding up the squares of the distances from all visible correlation feature degree points within all visible correlation feature degree clusters to their respective center points, the sum of squared errors corresponding to this number of visible correlation feature degree clusters is obtained. Taking the sum of squared errors as the ordinate and the number of visible correlation feature degree clusters as the abscissa, the abscissa corresponding to the first inflection point of the image according to the time sequence is used as the number of critical visible correlation feature degree clusters.

[0094] In some embodiments, in the process of clustering each visual correlation feature degree point in the visual correlation feature degree set according to the number of critical visual correlation feature degree clusters to obtain an initial feature cluster set, first, randomly select the same number of initial singular points corresponding to the number of critical visual correlation feature degree clusters on the visual correlation feature degree point waveform image, obtain the distances between all visual correlation feature degree points and each initial singular point, and obtain the initial feature cluster set according to the distances between all visual correlation feature degree points and each initial singular point. For example, in specific implementation, first randomly obtain the same number of initial singular points as the number of critical visual correlation feature degree clusters on the visual correlation feature degree point waveform image, then obtain a visual correlation feature degree point according to the time sequence, determine the distances between this visual correlation feature degree point and all initial singular points, and classify this visual correlation feature degree point into the cluster of the initial singular point with the shortest distance to this visual correlation feature degree point. Traverse all visual correlation feature degree points on the visual correlation feature degree point waveform image, so as to classify other visual correlation feature degree points on the visual correlation feature degree point waveform image into the corresponding clusters of the initial singular points, and obtain the initial feature cluster set.

[0095] In some embodiments, when performing feature cluster judgment on the initial feature cluster set and using the initial feature cluster set as the visual correlation feature degree cluster set if the initial feature cluster set meets the feature cluster judgment conditions, first, the coordinate average values of the visual correlation feature degree points in each initial feature cluster can be determined respectively, and the coordinate average values of each initial feature cluster are respectively used as the coordinates of a transition singular point in the transition singular point set to obtain the transition singular point set. When each initial feature cluster in the initial feature cluster set meets the feature cluster judgment conditions, the initial feature cluster set is used as the visual correlation feature degree cluster set.

[0096] It should be noted that in this application, the initial feature cluster is a point set composed of an initial singular point and all visual correlation feature degree points classified into the cluster of this initial singular point; in this embodiment, the initial singular point set has a uniquely corresponding initial feature cluster set, the initial feature cluster set has a uniquely corresponding transition singular point set, and the transition singular point set has a uniquely corresponding new initial feature cluster set. In specific implementation, whether the coordinates of the initial singular point and the transition singular point corresponding to the initial feature cluster are the same on the visual correlation feature degree waveform image can be used as the feature cluster judgment condition.

[0097] In some embodiments, the horizontal and vertical coordinates of all visual correlation feature degree points in the initial feature cluster can be obtained, the average value of all horizontal coordinates and the average value of vertical coordinates are determined, and the average values of the horizontal and vertical coordinates are used as the horizontal and vertical coordinates of the transition singular point to obtain the coordinates of the transition singular point.

[0098] In specific implementation, if the initial feature cluster does not meet the feature cluster judgment condition, clustering is performed again and the feature cluster judgment is carried out until an initial feature cluster set that meets the feature cluster judgment condition is obtained as the visible correlation feature degree cluster set. During this process, when the coordinates of the initial singular points corresponding to each initial feature cluster in the initial feature cluster set are different from the coordinates of the transition singular points corresponding to this initial feature cluster on the visible correlation feature degree waveform image, the set of transition singular points corresponding to the initial feature cluster set is used as the new initial singular points, and repeated clustering is performed according to the new initial singular points until the coordinates of the initial singular points corresponding to each initial feature cluster in the initial feature cluster set obtained after repeated clustering are the same as the coordinates of the transition singular points corresponding to this initial feature cluster respectively, and the new initial feature cluster set is used as the visible correlation feature degree cluster set.

[0099] It should be noted that in this application, the visible correlation feature degree cluster set is a set composed of different visible correlation feature degree clusters. The visible correlation feature degree cluster is a cluster set obtained by clustering each visible correlation feature degree in the visible correlation feature degree set according to distance, and is used to clarify the quantization value structure of the correlation degree of the visible correlation feature degree set, thereby improving the accuracy of the building access control system in identifying access control features.

[0100] In some embodiments, determining the relevant feature pole value according to the visible correlation feature degree cluster set can be implemented by the following steps:

[0101] Perform a significant feature judgment on each visible correlation feature degree cluster in the visible correlation feature degree cluster set to obtain a significant feature cluster;

[0102] Obtain the local visible correlation feature degree waveform image corresponding to this significant feature cluster, and then extract the feature peak points in the local visible correlation feature degree waveform image to form a feature peak set;

[0103] Obtain the relevant feature pole value according to the feature peak set.

[0104] In some embodiments, the number of zero-crossing points of the first derivative in the visible correlation feature degree cluster can be used as the significant judgment basis, that is, the visible correlation feature degree cluster with the most zero-crossing points of the first derivative is used as the significant feature cluster.

[0105] In some embodiments, during the process of obtaining the local visible correlation feature degree waveform image corresponding to this significant feature cluster and then extracting the feature peak points in the local visible correlation feature degree waveform image to form a feature peak set, first obtain the local visible correlation feature degree waveform image of the significant feature cluster, and then select all the feature peaks in the local visible correlation feature degree waveform image of the significant feature cluster; the peak values of all the feature peaks in the local visible correlation feature degree waveform image of the significant feature cluster are used as the feature peak set.

[0106] It should be noted that the local visible correlation feature degree waveform image corresponding to the significant feature cluster is a curve image obtained by fitting all the visible correlation feature degree values in the significant feature cluster. All the feature peaks in the local visible correlation feature degree waveform image of the significant feature cluster are selected by obtaining the visible correlation feature degree values at the zero-crossing points of the first derivative on the local visible correlation feature degree waveform image as the feature peaks.

[0107] When specifically implemented, in the process of obtaining the relevant feature pole value according to the set of feature peak values, first traverse the set of feature peak values to obtain the maximum feature peak value, and use this maximum feature peak value as the relevant feature pole value.

[0108] It should be noted that in this application, the relevant feature pole value is the peak value corresponding to the maximum feature peak in the relevant feature degree waveform image, which is obtained after feature degree clustering and feature peak screening according to the visible correlation feature degree set. Therefore, the relevant feature pole value has the highest frequency characteristic, which is used to determine the access control frequency domain modulation factor. The frequency characteristic is a quantization factor for measuring the correlation degree between the peak value and the main frequency. When the frequency characteristic of the peak value is higher, the adaptability between the peak value and the main frequency is higher. The main frequency is the frequency with the largest amplitude in the spectrum of the gating feature harmonic signal. Furthermore, the access control frequency domain modulation factor can be determined according to the relevant feature pole value, and the gating feature is identified through the access control frequency domain modulation factor, thereby improving the accuracy of identifying the access control feature of the access control management system in buildings.

[0109] In some embodiments, determining the access control frequency domain modulation factor according to the relevant feature pole value can be implemented through the following steps:

[0110] Obtain the sampling rate of the gating feature harmonic signal ;

[0111] According to the relevant feature pole value, determine the maximum feature horizontal distance , the maximum feature vertical distance , the adjacent feature horizontal distance and the adjacent feature vertical distance ;

[0112] According to the sampling rate of the gating feature harmonic signal, the maximum feature horizontal distance , the maximum feature vertical distance , the adjacent feature horizontal distance , the adjacent feature vertical distance determine the access control frequency domain modulation factor , and the access control frequency domain modulation factor is determined according to the following formula:

[0113]

[0114] wherein is the access control frequency domain modulation factor, is the maximum characteristic horizontal distance, is the adjacent characteristic horizontal distance, is the maximum characteristic vertical distance, is the adjacent characteristic vertical distance, is the sampling rate of the gating feature harmonic signal.

[0115] In specific implementation, the horizontal coordinate distance of the visible correlation feature degree point corresponding to the relevant feature pole value is used as the maximum characteristic horizontal distance; the horizontal coordinate distance of the visible correlation feature degree point of the left adjacent feature peak of the feature peak corresponding to the relevant feature pole value is used as the adjacent characteristic horizontal distance; the vertical coordinate distance of the visible correlation feature degree point corresponding to the relevant feature pole value is used as the maximum characteristic vertical distance; the vertical coordinate distance of the visible correlation feature degree point of the left adjacent feature peak of the feature peak corresponding to the relevant feature pole value is used as the adjacent characteristic vertical distance.

[0116] It should be noted that the access control frequency domain modulation factor in this application is the frequency value of the main frequency of the gating feature harmonic signal, which is used as the identification feature of the gating feature harmonic signal, so as to improve the accuracy of identifying the access control features of the access control management system in buildings.

[0117] In step 104, the access control signal sampling set is subjected to access control coefficient decomposition to obtain an access control feature coefficient set, the access control feature coefficient set is subjected to principal component segmentation to obtain a smoothing factor, a smoothing signal sampling set is determined according to the smoothing factor, and access control channel modulus is obtained by extracting the channel modulus of the smoothing signal sampling set.

[0118] In some embodiments, the access control coefficient decomposition of the access control signal sampling set to obtain the access control feature coefficient set can be implemented by the following steps:

[0119] Determine the characteristic decomposition resolution corresponding to each sampling moment of the access control signal sampling set;

[0120] Determine the characteristic decomposition function;

[0121] According to the characteristic decomposition function and the characteristic decomposition resolution corresponding to each sampling moment of the access control signal sampling set, perform access control coefficient decomposition on the access control signal sampling set to obtain an access control feature coefficient set.

[0122] It should be noted that the feature decomposition function can be preset according to existing functions. In other embodiments, it can be preset by other methods, which is not limited here. Specifically, when the feature decomposition function performs feature decomposition at each sampling moment of the access control signal sampling set, each sampling moment has a uniquely corresponding feature decomposition resolution.

[0123] In some embodiments, the feature decomposition function can be expressed by the following formula:

[0124]

[0125] Where is the feature decomposition function, is the base of the natural logarithm, represents the th sampling moment of the access control signal sampling set, is the sampling period of the access control signal sampling set.

[0126] It should be noted that the feature decomposition resolution corresponding to each sampling moment of the access control signal sampling set is a preset parameter. The feature decomposition resolution realizes the feature decomposition of the access control signal sampling points in the access control signal sampling set by the feature decomposition function at multi-scale frequencies. For example, when the feature decomposition resolution is 1, the feature decomposition function realizes the low-frequency feature decomposition of the access control signal sampling points in the access control signal sampling set; when the feature decomposition resolution is 0.1, the feature decomposition function realizes the high-frequency feature decomposition of the access control signal sampling points in the access control signal sampling set.

[0127] Specifically, when the feature decomposition resolution realizes the feature decomposition of the access control signal sampling points in the access control signal sampling set by the feature decomposition function at multi-scale frequencies, when the change of the access control signal sampling point at a sampling moment of the access control signal sampling set relative to the access control signal sampling point at the previous sampling moment is relatively significant, a smaller feature decomposition resolution is preset to perform high-frequency feature decomposition on the access control signal sampling point. When the change of the access control signal sampling point at a sampling moment of the access control signal sampling set relative to the access control signal sampling point at the previous sampling moment is not significant, a larger feature decomposition resolution is preset to perform low-frequency feature decomposition on the access control signal sampling point. It should be noted that presetting different feature decomposition resolutions for each sampling moment of the access control signal sampling set can make adaptive adjustments to the access control signal sampling points at different sampling moments during feature decomposition. By presetting the feature decomposition resolution corresponding to each access control signal sampling point, it is convenient for the feature decomposition function to better capture the feature details in the access control signal sampling point.

[0128] In some embodiments, during the process of determining the feature decomposition resolution corresponding to each sampling moment of the access control signal sampling set, the The eigendecomposition resolution corresponding to each sampling moment can be achieved by the following steps:

[0129] Get the eigendecomposition correction coefficients ;

[0130] Get the access control signal sampling set in Access control signal sampling points at sampling moments ;

[0131] Get the sampling period of the access control signal sampling set ;

[0132] The correction coefficients are decomposed according to the eigenvalues , the access control signal sampling set is in the first Access control signal sampling points at sampling moments , the sampling period of the door access signal sampling set Determine the The eigendecomposition resolution corresponding to the sampling time , No. The eigendecomposition resolution corresponding to the sampling time Determined according to the following formula:

[0133]

[0134] in is the access control signal sampling point at the previous sampling moment, The number of sampled signals in the access control signal sampling set.

[0135] In some embodiments, the eigendecomposition resolution corresponding to other sampling moments of the access control signal sampling set can be determined in the same manner. It should be noted that the eigendecomposition correction coefficient is a constant used to balance the effects of frequency and sampling point change rate on eigendecomposition resolution. Its specific value is determined through experimentation. In other embodiments, other constants may be used to balance the effects of frequency and change rate on eigendecomposition resolution. This is not limited here. The sampling point change rate is a measure of the degree of change in the access control signal sampling point at one sampling moment in the access control signal sampling set relative to the access control signal sampling point at the previous sampling moment.

[0136] In some embodiments, performing access control coefficient decomposition on the access control signal sample set according to the eigendecomposition function and the eigendecomposition resolution corresponding to each sampling moment of the access control signal sample set to obtain the access control feature coefficient set can be implemented using the following formula:

[0137]

[0138] in is the access control feature coefficient corresponding to the th sampling moment of the access control signal sampling set, is the access control signal sampling point corresponding to the th sampling moment of the access control signal sampling set, is the feature decomposition function, is the number of sampling signals of the access control signal sampling set, is the th sampling moment of the access control signal sampling set corresponding to the feature decomposition resolution.

[0139] It should be noted that in this application, the access control feature coefficient set is a set of access control feature coefficients corresponding to each sampling moment after the access control coefficient decomposition of the access control signal sampling set. The access control feature coefficient represents the amplitude value of the gating feature harmonic signal at the corresponding sampling moment, and is used to provide the amplitude information of the access control signal sampling set at different sampling moments, so as to improve the accuracy of the access control management system in identifying access control features in buildings.

[0140] In some embodiments, the following formula can be used to implement the principal component segmentation of the access control feature coefficient set to obtain the smoothing factor:

[0141]

[0142] where is the smoothing factor, is the access control feature coefficient corresponding to the th sampling moment of the access control signal sampling set, is the average value of the access control feature coefficient set, is the number of sampling signals of the access control signal sampling set.

[0143] It should be noted that in this application, the smoothing factor is a characteristic value reflecting the dispersion degree of the access control feature coefficient set, and is used to traverse all the access control feature coefficients in the access control feature coefficient set. When the difference between the access control feature coefficient and the average value of the access control feature coefficient set is less than the smoothing factor, the access control feature coefficient is discarded. When the difference between the access control feature coefficient and the average value of the access control feature coefficient set is greater than the smoothing factor, the access control feature coefficient is retained, and all the retained access control feature coefficients are used as the smoothed access control feature coefficient set, so as to improve the accuracy of the access control management system in identifying access control features in buildings.

[0144] In some embodiments, the following steps can be used to determine the smoothed signal sampling set according to the smoothing factor:

[0145] Determine the smoothed access control feature coefficient set according to the smoothing factor;

[0146] Reconstruct the features of the smoothed access control feature coefficient set to obtain the smoothed signal sampling set.

[0147] Specifically, when determining the smoothed access control feature coefficient set according to the smoothing factor, first obtain an access control feature coefficient. Discard the access control feature coefficient when the difference between the access control feature coefficient and the average value of the access control feature coefficient set is less than the smoothing factor, and retain the access control feature coefficient when the difference between the access control feature coefficient and the average value of the access control feature coefficient set is greater than the smoothing factor; traverse all the access control feature coefficients in the access control feature coefficient set, and use all the retained access control feature coefficients as the smoothed access control feature coefficient set, which is expressed as It should be noted that The value range of is [1, K] and .

[0148] It should be noted that the smoothed access control feature coefficient set is a subset of the access control feature coefficient set, and all the smoothed access control feature coefficients in the smoothed access control feature coefficient set respectively correspond to a unique feature decomposition resolution.

[0149] In some embodiments, reconstructing the features of the smoothed access control feature coefficient set to obtain the smoothed signal sampling set can be implemented by the following formula:

[0150]

[0151] Where Is the smoothed signal feature corresponding to the th sampling moment of the access control signal sampling set, where Is the value of the feature decomposition function corresponding to the th sampling moment of the access control signal sampling set, Is the smoothed access control feature coefficient corresponding to the th sampling moment of the access control signal sampling set, Is the feature decomposition resolution corresponding to the th sampling moment of the access control signal sampling set.

[0152] It should be noted that in this application, the smoothed signal sampling set is a set of sampling points obtained by removing the sampling points with too high amplitudes from the access control signal sampling set, which is used to smooth the access control signal sampling set to obtain a clearer smoothed signal sampling set and reduce the data complexity. The smoothing is achieved by reducing the sampling points with larger signal amplitudes. In other embodiments, other methods can be used to achieve smoothing, which is not limited here.

[0153] In some embodiments, extracting the channel modulus of the access control from the smoothed signal sampling set is achieved through the following steps:

[0154] Obtain the smoothed signal feature corresponding to the th sampling moment ;

[0155] Obtain the average value of the smoothed signal sampling set ;

[0156] Obtain the variance of the smoothed signal sampling set ;

[0157] Obtain the number of elements in the smoothed signal sampling set ;

[0158] Obtain the eigen - decomposition resolution corresponding to the th sampling moment of the access control signal sampling set ;

[0159] According to the smoothed signal feature corresponding to the th sampling moment , the average value of the smoothed signal sampling set , the variance of the smoothed signal sampling set , the number of elements in the smoothed signal sampling set , the eigen - decomposition resolution corresponding to the th sampling moment of the access control signal sampling set Determine the access control channel modulus , the access control channel modulus is determined according to the following formula:

[0160]

[0161] where is the number of elements in the smoothed signal sampling set.

[0162] It should be noted that the access control channel modulus is obtained by extracting the statistical characteristics of the smoothed signal sampling set during the transmission process. In other embodiments, other methods can be used to achieve this, which is not limited here.

[0163] It should be noted that when sampling the gating feature harmonic signal, different modulation methods and modulation orders will generate different signal characteristics, including spectral characteristics and waveform shapes. By analyzing these characteristics of the received signal, the modulation method and the access control channel modulus of the gating feature harmonic signal during sampling can be inversely transformed. In this application, the access control channel modulus is a quantization value reflecting the spectral characteristics of the gating feature harmonic signal, which is used for the access control system to identify the characteristics of the gating feature harmonic signal, thereby improving the accuracy of the access control feature recognition in the building access control system.

[0164] In step 105, perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow the personnel accessing the building to pass according to the recognition result.

[0165] In some embodiments, performing gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determining whether to allow the personnel accessing the building to pass according to the recognition result can be implemented by the following steps:

[0166] Obtain a preset access control channel transmission threshold and an access control frequency domain modulation threshold;

[0167] Perform threshold judgment on the access control channel modulus and the access control frequency domain modulation factor. If the threshold judgment condition is met, send an open door request to the access control system. If the threshold judgment condition is not met, record the current open door request in the access control system information database of the building and give an alarm.

[0168] Specifically, during the process of performing threshold judgment on the access control channel modulus and the access control frequency domain modulation factor, when the access control channel modulus is less than or equal to the set access control channel transmission threshold and the access control frequency domain modulation factor is less than or equal to the set access control frequency domain modulation threshold, send an open door request to the access control system;

[0169] When the access control channel modulus is greater than the set access control channel transmission threshold, or the access control frequency domain modulation factor is greater than the set access control frequency domain modulation threshold, record the current open door request in the access control system information database of the building and give an alarm.

[0170] It should be noted that the access control channel transmission threshold and the access control frequency domain modulation threshold are preset according to the access control requirements of the users in the building. In other embodiments, other thresholds can be preset to implement the recognition of the access control channel modulus and the access control frequency domain modulation factor, which is not limited here.

[0171] It should be noted that in this application, the access control frequency domain modulation factor is determined according to the main frequency of the gating feature harmonic signal sent by the access control card in the accesser's hand, and is used to compare with the access control frequency domain modulation threshold, so as to identify the identity information of the accesser. Since the main frequency is unique, the accuracy of the access control management system in the building for recognizing the gating feature harmonic signal can be improved by recognizing the main frequency. The access control channel modulus is determined according to the statistical characteristics of the gating feature harmonic signal sent by the access control card in the accesser's hand, and is used to compare with the access control channel transmission threshold, so as to judge the identity information of the accesser.

[0172] By identifying multiple characteristic factors of the gated feature harmonic signal, the interference of external interference on the identification of the access control feature signal can be reduced, thereby enhancing the robustness of the access control management system, and ultimately improving the accuracy of the access control management system in identifying the gated feature harmonic signal in buildings. In other embodiments, the identification of the gated feature harmonic signal can be achieved by determining other characteristic factors, which is not limited here.

[0173] In addition, on the other hand of the present application, in some embodiments, the present application provides an access control management system for buildings. Refer to Figure 2 , which is a schematic diagram of exemplary hardware and / or software of the access control management system for buildings according to some embodiments of the present application. The access control management system 200 for buildings includes: a gated feature harmonic signal acquisition module 201, an access control signal related feature degree set determination module 202, an access control frequency domain modulation factor determination module 203, an access control channel modulus determination module 204, and an access control system control module 205, which are described as follows:

[0174] The gated feature harmonic signal acquisition module 201 is mainly used in the present application to acquire the gated feature harmonic signal sent by the access control card of the building access personnel.

[0175] The access control signal related feature degree set determination module 202 is mainly used in the present application to obtain an access control signal sampling set after sampling the gated feature harmonic signal, perform time-frequency cross feature extraction on the access control signal sampling set to obtain an access control signal fluctuation value set, determine the access control signal related feature degree corresponding to each access control signal fluctuation value in the access control signal fluctuation value set, and form an access control signal related feature degree set.

[0176] The access control frequency domain modulation factor determination module 203 is mainly used in the present application to perform visualization processing on the access control signal related feature degree set to obtain a visual related feature degree set, perform feature degree clustering on the visual related feature degree set to obtain a visual related feature degree cluster set, determine relevant feature pole values according to the visual related feature degree cluster set, and determine the access control frequency domain modulation factor according to the relevant feature pole values.

[0177] The access control channel modulus determination module 204 is mainly used in the present application to perform access control coefficient decomposition on the access control signal sampling set to obtain an access control feature coefficient set, perform principal component segmentation on the access control feature coefficient set to obtain a smoothing factor, determine a smoothed signal sampling set according to the smoothing factor, and perform channel modulus extraction on the smoothed signal sampling set to obtain the access control channel modulus.

[0178] The access control module 205. In this application, the access control system control module 205 is mainly used to perform gating feature recognition on the access frequency domain modulation factor and the access channel modulus, and determine whether to allow the personnel accessing the building to pass according to the recognition result.

[0179] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned access control method for buildings.

[0180] In some embodiments, refer to Figure 3 , this figure is a schematic structural diagram of a computer device for implementing the access control method for buildings according to some embodiments of this application. The access control method for buildings in the above embodiments can be implemented by Figure 3 the computer device shown, which includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0181] The processor 301 can be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more for controlling the execution of the access control method for buildings in this application.

[0182] The communication bus 302 may include a path for transmitting information between the above components.

[0183] The memory 303 can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 303 can exist independently and be connected to the processor 301 through the communication bus 302. The memory 303 can also be integrated with the processor 301.

[0184] Among them, the memory 303 is used to store the program code for executing the solution of this application and is controlled by the processor 301 to execute. The processor 301 is used to execute the program code stored in the memory 303. The program code may include one or more software modules. The determination of the access control signal-related feature degree set in the above embodiments can be implemented by one or more software modules in the processor 301 and the program code in the memory 303.

[0185] The communication interface 304 uses any device such as a transceiver for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0186] In a specific implementation, as an embodiment, the computer device may include multiple processors, and each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, the processor may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0187] The above computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of this application do not limit the type of the computer device.

[0188] In addition, this application also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned access control method for buildings and halls is implemented.

[0189] In summary, for an access control management system and its control method for halls and buildings disclosed in the embodiments of the present application, after receiving the gating feature harmonic signal sent by the access card of the visitor to the hall or building, the gating feature harmonic signal is sampled and time-frequency cross-feature extraction is performed to determine the access control signal fluctuation value set. After time-frequency cross-feature extraction, the signal quality and accurate feature extraction are ensured, and the accuracy of the process of identifying the gating feature signal is improved. According to the access control signal fluctuation value set, the access control signal correlation feature degree corresponding to each access control signal fluctuation value is determined, and an access control signal correlation feature degree set is formed. After visualizing the access control signal correlation feature degree set, clustering processing is performed. Visualization and clustering enable efficient analysis of features in the signal recognition process, improving the accuracy of the process of identifying the gating feature signal. Furthermore, the access control frequency domain modulation factor is determined. After determining the smooth signal sampling set through the access control signal sampling set, channel analog-to-digital extraction is performed on the smooth signal sampling set to obtain the access control channel analog-to-digital modulus. Considering the access control channel analog-to-digital modulus helps to further improve the understanding and recognition of the signal. The system can more comprehensively consider the possible interference factors in the transmission process of the access card, increasing the accuracy of the system. Gating feature recognition is performed using the access control frequency domain modulation factor and the access control channel analog-to-digital modulus, improving the accuracy of the access control management system for halls and buildings in identifying access control features.

[0190] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0191] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A method for access control of halls and buildings, characterized in that, The steps are as follows: Obtain the gating feature harmonic signal sent by the access control card of the personnel accessing the building; After sampling the gating feature harmonic signal, obtain the access control signal sampling set, perform time-frequency cross feature extraction on the access control signal sampling set to obtain the access control signal fluctuation value set, determine the access control signal correlation feature degree corresponding to each access control signal fluctuation value in the access control signal fluctuation value set, and form the access control signal correlation feature degree set; Perform visualization processing on the access control signal correlation feature degree set to obtain the visual correlation feature degree set, perform feature degree clustering on the visual correlation feature degree set to obtain the visual correlation feature degree cluster set, determine the correlation feature pole value according to the visual correlation feature degree cluster set, and determine the access control frequency domain modulation factor according to the correlation feature pole value; Perform access control coefficient decomposition on the access control signal sampling set to obtain the access control feature coefficient set, perform principal component segmentation on the access control feature coefficient set to obtain the smoothing factor, determine the smoothing signal sampling set according to the smoothing factor, and perform channel modulus extraction on the smoothing signal sampling set to obtain the access control channel modulus; Perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and judge whether to allow the personnel accessing the building to pass according to the recognition result.

2. The method according to claim 1, wherein Each visual correlation feature degree in the visual correlation feature degree set is determined according to the following formula: Among them, represents the th visual correlation feature degree value in the visual correlation feature degree set, represents the th access control signal related feature degree in the access control signal related feature degree set, , respectively represent the maximum and minimum values of the access control signal related feature degrees in the access control signal related feature degree set.

3. The method according to claim 1, characterized in that, Performing feature degree clustering on the visual correlation feature degree set to obtain the visual correlation feature degree cluster set specifically includes: Obtain the visual correlation feature degree waveform image of the visual correlation feature degree set; Determine the critical visual correlation feature degree cluster number according to the visual correlation feature degree waveform image; Cluster each visual correlation feature degree point in the visual correlation feature degree set according to the critical visual correlation feature degree cluster number to obtain the initial feature cluster set; Perform feature cluster judgment on the initial feature cluster set. If the initial feature cluster meets the feature cluster judgment condition, use the initial feature cluster set as the visual correlation feature degree cluster set; If the initial feature cluster does not meet the feature cluster judgment condition, perform repeated clustering according to the initial feature cluster set until an initial feature cluster set that meets the feature cluster judgment condition is obtained as the visual correlation feature degree cluster set.

4. The method according to claim 1, wherein Determining the correlation feature pole value according to the visual correlation feature degree cluster set specifically includes: Perform significant feature judgment on each visual correlation feature degree cluster in the visual correlation feature degree cluster set to obtain the significant feature cluster; Obtain the local visual correlation feature degree waveform image corresponding to the significant feature cluster, and then extract the feature peak points in the local visual correlation feature degree waveform image to form the feature peak set; Obtain the correlation feature pole value according to the feature peak set.

5. The method according to claim 1, wherein Determining the access control frequency domain modulation factor according to the correlation feature pole value specifically includes: Sampling rate for obtaining gated feature harmonic signals ; Determine the maximum characteristic horizontal distance according to the relevant characteristic pole values , the maximum characteristic vertical distance , the adjacent characteristic horizontal distance and the adjacent characteristic vertical distance ; According to the sampling rate of the gated characteristic harmonic signal 、the maximum characteristic horizontal distance 、the maximum characteristic vertical distance 、the adjacent characteristic horizontal distance 、the adjacent characteristic vertical distance Determine the access control frequency domain modulation factor, where the access control frequency domain modulation factor is determined according to the following formula: Among them, is the access control frequency domain modulation factor.

6. The method according to claim 1, characterized in that, Determining the smoothing signal sampling set according to the smoothing factor specifically includes: Determine the smoothing access control feature coefficient set according to the smoothing factor; Perform feature reconstruction on the smoothing access control feature coefficient set to obtain the smoothing signal sampling set.

7. The method according to claim 1, characterized in that, Perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow access to personnel in the building based on the recognition result, specifically including: Obtain a preset access control channel transmission threshold and an access control frequency domain modulation threshold; Perform threshold judgment on the access control channel modulus and the access control frequency domain modulation factor. If the threshold judgment condition is met, send an open door request to the access control system. If the threshold judgment condition is not met, record the current open door request in the access control system information database of the building and issue an alarm.

8. An access control management system for buildings, characterized in that, Include: A gating feature harmonic signal acquisition module, configured to acquire a gating feature harmonic signal sent by the access control card of the access personnel in the building; An access control signal correlation feature degree set determination module, configured to sample the gating feature harmonic signal to obtain an access control signal sampling set, perform time-frequency cross feature extraction on the access control signal sampling set to obtain an access control signal fluctuation value set, determine the access control signal correlation feature degree corresponding to each access control signal fluctuation value in the access control signal fluctuation value set, and form an access control signal correlation feature degree set; An access control frequency domain modulation factor determination module, configured to perform visualization processing on the access control signal correlation feature degree set to obtain a visual correlation feature degree set, perform feature degree clustering on the visual correlation feature degree set to obtain a visual correlation feature degree cluster set, determine a correlation feature pole value according to the visual correlation feature degree cluster set, and determine an access control frequency domain modulation factor according to the correlation feature pole value; An access control channel modulus determination module, configured to perform access control coefficient decomposition on the access control signal sampling set to obtain an access control feature coefficient set, perform principal component segmentation on the access control feature coefficient set to obtain a smoothing factor, determine a smoothed signal sampling set according to the smoothing factor, and perform channel modulus extraction on the smoothed signal sampling set to obtain an access control channel modulus; An access control system control module, configured to perform gating feature recognition on the access control frequency domain modulation factor and the access control channel modulus, and determine whether to allow access to personnel in the building based on the recognition result.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the building access control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the building access control method according to any one of claims 1 to 7 are implemented.

Images