Operation control method for anti-theft safety door

Abstract

The invention discloses an operation control method for an anti-theft safety door, and relates to the technical field of anti-theft door control. The method comprises the steps that when the safety door is in a closed state, the operation state of the safety door is collected through a sensor, and the operation state of the safety door comprises an unlocking monitoring behavior and / or a load monitoring behavior; performing anomaly analysis on the unlocking monitoring behavior and / or the monitoring load behavior to generate a safety door intrusion coefficient; when the safety door intrusion coefficient is smaller than an intrusion coefficient threshold value, activating an image collector, collecting a target portrait in front of the door, verifying the target portrait in front of the door through the authority portrait database, and generating an identity verification result; and when the identity verification result is not passed, or the safety door intrusion coefficient is greater than or equal to the intrusion coefficient threshold value, carrying out acousto-optic early warning. The technical problem that in the prior art, the computing power cost is high due to the fact that portrait verification is frequently carried out on a safety door is solved, and the technical effects that the portrait verification frequency is reduced, and the portrait verification computing power occupancy rate is reduced are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of anti-theft door control, and in particular to an operation control method for an anti-theft safety door. Background Art

[0002] With the continuous advancement of science and technology and the increasing demand for social security, anti-theft security doors, as an important part of modern security protection, have been widely used in various public places and important facilities. Anti-theft security doors can not only effectively prevent unauthorized illegal intrusions, but also quickly and effectively authenticate the identity of people entering and leaving, thereby ensuring the safety and order in the venue. Traditional anti-theft security doors usually use fingerprint recognition, portrait verification and other methods to identify illegal intruders. However, since each verification requires complex data processing and comparison, frequent fingerprint recognition and portrait verification will result in relatively high computing costs, especially in environments with high traffic. This frequent verification will further increase the burden on the system. Summary of the invention

[0003] The embodiment of the present application provides an operation control method for an anti-theft security door, which solves the technical problem in the prior art that the security door frequently performs portrait verification, resulting in high computing power costs.

[0004] In view of the above problems, an embodiment of the present application provides an operation control method for an anti-theft security door.

[0005] The embodiment of the present application provides an operation control method for an anti-theft security door, the method comprising: When the safety door is in a closed state, the operating state of the safety door is collected through a sensor, wherein the operating state of the safety door includes unlocking monitoring behavior and / or monitoring load behavior; Performing abnormal analysis on the unlocking monitoring behavior and / or the monitoring load behavior to generate a safety door intrusion coefficient; When the intrusion coefficient of the security door is less than the intrusion coefficient threshold, the image collector is activated to capture the target person portrait in front of the door, and the target person portrait in front of the door is verified through the authorized portrait library to generate an identity verification result; When the identity verification result is failed, or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, an audible and visual warning is performed.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: When the security door is in a closed state, the operating status of the security door is collected through sensors, wherein the operating status of the security door includes unlocking monitoring behavior and / or monitoring load behavior. Then, the unlocking monitoring behavior and / or monitoring load behavior are analyzed for abnormalities to generate a security door intrusion coefficient. When the security door intrusion coefficient is less than the intrusion coefficient threshold, the image collector is activated to collect the target portrait in front of the door, and the target portrait in front of the door is verified through the permission portrait library to generate an identity verification result; when the identity verification result is failed, or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, an audible and visual warning is issued. The technical problem of high computing power cost caused by frequent portrait verification of security doors in the prior art is solved, and the technical effect of reducing the frequency of portrait verification and reducing the computing power occupancy rate of portrait verification is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A schematic flow chart of an operation control method for an anti-theft security door provided in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart for performing abnormal analysis in an operation control method for an anti-theft security door provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The embodiment of the present application solves the technical problem in the prior art that high computing power cost is caused when the security door frequently performs portrait verification by providing an operation control method for the anti-theft security door.

[0010] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0011] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0012] Embodiment 1 like Figure 1 As shown, the embodiment of the present application provides an operation control method for an anti-theft security door, wherein the method includes: When the safety door is in a closed state, the operating state of the safety door is collected through a sensor, wherein the operating state of the safety door includes unlocking monitoring behavior and / or monitoring load behavior; When the safety door is in the closed state, the sensor will collect the operating status of the safety door, which includes unlocking monitoring behavior and / or monitoring load behavior. Unlocking monitoring behavior means that the system uses sensors to monitor in real time whether the safety door is unlocked or whether there is any attempt to unlock it. Monitoring load behavior means monitoring whether the safety door is subjected to abnormal external pressure or impact.

[0013] Furthermore, the operating status of the safety door is collected through sensors, including: When the electronic password lock is in the unlock verification state, collect the unlock attempt password list and add it to the unlock monitoring behavior; The pressure vector timing information is collected through the pressure sensor and added into the monitoring load behavior.

[0014] Furthermore, the pressure sensor is deployed on the inner layer of the outer surface of the anti-theft security door.

[0015] When the electronic password lock is in the unlock verification state, the system will collect the unlock attempt password list and add it to the unlock monitoring behavior. The unlock attempt password list records all the passwords that are attempted to unlock. The pressure sensor is deployed on the inner layer of the outer surface of the anti-theft security door to ensure that the sensor can accurately sense the pressure applied to the security door from the outside while avoiding interference from external environmental factors. The pressure vector timing information is collected through the pressure sensor and added to the monitoring load behavior. The pressure vector timing information reflects the pressure changes on the security door, including the size and direction of the pressure and the time series of the change.

[0016] Performing abnormal analysis on the unlocking monitoring behavior and / or the monitoring load behavior to generate a safety door intrusion coefficient; An abnormal analysis is performed on the unlocking monitoring behavior and / or monitoring load behavior to generate a safety door intrusion coefficient, which reflects the intrusion risk or threat level currently faced by the safety door.

[0017] Furthermore, if Figure 2 As shown, the unlocking monitoring behavior and / or the monitoring load behavior are analyzed abnormally to generate a safety door intrusion coefficient, including: For the unlocking monitoring behavior, a deviation check is performed based on the unlocking reference password to generate a target abnormality probability; The monitored load behavior is processed by a behavior classifier to obtain a behavior classification result; When the target abnormal probability is greater than or equal to the abnormal probability threshold, and / or the behavior classification result belongs to the intrusion behavior type, the security door intrusion coefficient is set to be greater than or equal to the intrusion coefficient threshold; When the target abnormal probability is less than the abnormal probability threshold, and the behavior classification result does not belong to the intrusion behavior type, the security door intrusion coefficient is set to be less than the intrusion coefficient threshold.

[0018] The unlock monitoring behavior is verified for deviation according to the preset unlocking reference password. The unlocking reference password is a legal and correct password used for comparison with the password for attempted unlocking. By comparing the degree of deviation between each attempted password and the reference password, a target abnormality probability is calculated. The target abnormality probability reflects the degree of inconsistency between the attempted password and the correct password, thereby indicating the potential risk of illegal intrusion. The monitoring load behavior is processed by the behavior classifier to obtain the behavior classification result. The behavior classifier is a pre-trained model that can classify the pressure changes on the security door according to the pressure vector time series information collected by the pressure sensor, and determine whether these pressure changes belong to normal operation, misoperation or potential intrusion behavior. The security door intrusion coefficient is updated according to the target abnormality probability and the behavior classification result. If the target abnormality probability is greater than or equal to the preset abnormality probability threshold, or the behavior classification result belongs to the intrusion behavior type, the system will consider that there is a high intrusion risk at present, and therefore the security door intrusion coefficient will be set to be greater than or equal to the intrusion coefficient threshold. If the target abnormality probability is less than the abnormality probability threshold, and the behavior classification result does not belong to the intrusion behavior type, the system will consider that the current security risk is low, and therefore the security door intrusion coefficient will be set to be less than the intrusion coefficient threshold. Through detailed anomaly analysis, the system can more accurately determine whether the unlocking monitoring behavior and monitoring load behavior are abnormal, thereby generating an appropriate safety door intrusion coefficient.

[0019] Furthermore, the unlock monitoring behavior is subjected to deviation verification based on the unlock reference password to generate a target abnormal probability, including: Extracting the unlock attempt password list according to the unlock monitoring behavior; Traversing the unlock attempt password list, calculating deviations with the unlock reference password, and generating a first deviation distance list; Performing pairwise deviation calculation on the unlock attempt password list to generate a second deviation distance list; An abnormal unlocking analysis is performed according to the first deviation distance list and the second deviation distance list to generate the target abnormality probability.

[0020] Optionally, based on the unlock monitoring behavior, extract the unlock attempt password list, which contains all the passwords that were attempted to unlock. Traverse the unlock attempt password list, calculate the deviation between each attempted password and the unlock reference password, and generate a first deviation distance list. The unlock reference password is a preset correct password, which is used to compare with the attempted password. The first deviation distance list reflects the degree of deviation between each attempted password and the correct password. Perform pairwise deviation calculations on the unlock attempt password list, that is, compare the deviations between any two attempted passwords in the list, and generate a second deviation distance list, which contains the deviation information between the attempted passwords. Perform abnormal unlocking analysis based on the first deviation distance list and the second deviation distance list to generate a target abnormality probability.

[0021] Furthermore, traversing the unlock attempt password list, calculating the deviation from the unlock reference password, and generating a first deviation distance list includes: Construct the deviation calculation function: ; ; ; in, The i-th unlock attempt password representing the unlock attempt password list, The symbol representing the jth digit of the password for the i-th unlock attempt, Characterizes the unlocking base password, represents the jth digit symbol of the unlocking base password, M represents the number of digits of the password constraint, Characterize the deviation distance between the i-th unlock attempt password and the unlock reference password; According to the deviation calculation function, the unlock attempt password list is traversed, and deviation calculation is performed with the unlock reference password to generate the first deviation distance list.

[0022] The deviation calculation function uses the Euclidean distance formula to calculate the deviation between two passwords. In the deviation calculation function, The i-th unlock attempt password representing the unlock attempt password list The symbol representing the jth digit of the password for the i-th unlock attempt, Unlock the base password. represents the jth digit symbol of the unlocking base password, M represents the number of digits of the password constraint, Characterize the deviation distance between the i-th unlock attempt password and the unlock reference password. According to the deviation calculation function, traverse the unlock attempt password list, calculate the deviation with the unlock reference password, and generate a first deviation distance list.

[0023] Further, performing abnormal unlocking analysis according to the first deviation distance list and the second deviation distance list to generate the target abnormal probability includes: Calculating the variance of the first deviation distance list to generate a first dispersion coefficient; Calculating the mean of the first deviation distance list to generate a first distance coefficient; Calculating the variance of the second deviation distance list to generate a second dispersion coefficient; Calculating the mean of the second deviation distance list to generate a second distance coefficient; When the first discrete coefficient is less than a first discrete coefficient threshold, and the second discrete coefficients are both less than a second discrete coefficient threshold, and the first distance coefficient is less than a first distance coefficient threshold, and the second distance coefficient is less than a second distance coefficient threshold, the target abnormal probability is set to 0; Otherwise, the target abnormal probability is set to 1.

[0024] The variance and mean of the first deviation distance list are calculated respectively to generate the first discrete coefficient and the first distance coefficient. The first discrete coefficient reflects the discrete degree of the deviation distribution between the unlock attempt password and the reference password, and the first distance coefficient reflects the average deviation between the unlock attempt password and the reference password. The variance and mean of the second deviation distance list are calculated to generate the second discrete coefficient and the second distance coefficient. When the first discrete coefficient is less than the first discrete coefficient threshold, and the second discrete coefficient is less than the second discrete coefficient threshold, and the first distance coefficient is less than the first distance coefficient threshold, and the second distance coefficient is less than the second distance coefficient threshold, that is, when all four conditions are met at the same time, it is considered that the unlock attempt password as a whole does not deviate much from the unlock reference password, and there is no abnormal unlocking behavior, so the target abnormal probability is set to 0. If any one of the conditions is not met, it is considered that there is abnormal unlocking behavior, and the target abnormal probability is set to 1.

[0025] Furthermore, the monitored load behavior is processed by a behavior classifier to obtain a behavior classification result, including: Configure a pressure vector time series record data set and a behavior classification identification result, wherein the behavior classification identification result includes an intrusion behavior identification and a non-intrusion behavior identification; According to the behavior classification identification result and the pressure vector time series record data set, a random forest is trained, and when the proportion of accurate classification times for a consecutive preset number of times is greater than or equal to a convergence proportion threshold, the behavior classifier is generated; The pressure vector time series information of the monitored load behavior is processed by a behavior classifier to obtain the behavior classification result.

[0026] The behavior classification identification results include intrusion behavior identification and non-intrusion behavior identification. The pressure vector time series record data set is subjected to behavior classification identification, that is, the data in the pressure vector time series record data set is divided into data with intrusion behavior identification and data with non-intrusion behavior identification based on historical experience. The behavior classification identification results are used as labels and the pressure vector time series record data set is used as features to train the random forest model. During the training process, the number of accurate classifications for each iteration is recorded. When the proportion of accurate classifications for a preset number of consecutive times is greater than or equal to the convergence proportion threshold, the model is considered to have converged and a behavior classifier is generated. The pressure vector time series information of the monitored load behavior is input into the behavior classifier for processing to obtain the behavior classification results, thereby reflecting whether the current behavior is an intrusion behavior or a non-intrusion behavior.

[0027] When the intrusion coefficient of the security door is less than the intrusion coefficient threshold, the image collector is activated to capture the target person portrait in front of the door, and the target person portrait in front of the door is verified through the authorized portrait library to generate an identity verification result; The calculated security door intrusion coefficient is compared with the preset intrusion coefficient threshold. When the security door intrusion coefficient is less than the intrusion coefficient threshold, the system activates an image collector, such as a camera, to capture the target portrait in front of the security door. After capturing the target portrait, the system verifies it through the permission portrait library, which contains facial images and related permission information of people allowed to enter the security area. The captured target portrait is compared with the images in the permission portrait library to generate an identity verification result. The identity verification result reflects whether the target portrait has the permission to open the door, thereby realizing effective monitoring and management of potential intrusion behaviors in front of the security door.

[0028] When the identity verification result is failed, or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, an audible and visual warning is performed.

[0029] The system judges the identity verification result. If the identity of the target person does not match the information in the authorized portrait library, the identity verification result is failed. At the same time, the system will also check whether the security door intrusion coefficient is greater than or equal to the preset intrusion coefficient threshold. If the intrusion coefficient is greater than or equal to the intrusion coefficient threshold, it also indicates that an intrusion may have occurred in front of the security door. Whether the identity verification result is failed or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, the system will immediately trigger an audible and visual warning. An audible and visual warning refers to a high-decibel alarm sounded by a siren or speaker to attract the attention of people around and remind them that an intrusion may have occurred; a warning message is displayed through flashing lights or LED displays to visually remind people to pay attention to safety.

[0030] In summary, the embodiments of the present application have at least the following technical effects: When the security door is in a closed state, the operating status of the security door is collected through sensors, wherein the operating status of the security door includes unlocking monitoring behavior and / or monitoring load behavior. Then, the unlocking monitoring behavior and / or monitoring load behavior are analyzed for abnormalities to generate a security door intrusion coefficient. When the security door intrusion coefficient is less than the intrusion coefficient threshold, the image collector is activated to collect the target portrait in front of the door, and the target portrait in front of the door is verified through the permission portrait library to generate an identity verification result; when the identity verification result is failed, or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, an audible and visual warning is issued. The technical problem of high computing power cost caused by frequent portrait verification of security doors in the prior art is solved, and the technical effect of reducing the frequency of portrait verification and reducing the computing power occupancy rate of portrait verification is achieved.

[0031] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0032] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0033] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An operation control method for an anti-theft safety door, characterized in that: The method comprises: When the safety door is in a closed state, the operating state of the safety door is collected through a sensor, wherein the operating state of the safety door includes unlocking monitoring behavior and / or load monitoring behavior; Performing abnormal analysis on the unlocking monitoring behavior and / or the monitoring load behavior to generate a safety door intrusion coefficient; When the intrusion coefficient of the security door is less than the intrusion coefficient threshold, the image collector is activated to capture the target person portrait in front of the door, and the target person portrait in front of the door is verified through the authorized portrait library to generate an identity verification result; When the identity verification result is failed, or the security door intrusion coefficient is greater than or equal to the intrusion coefficient threshold, an audible and visual warning is performed.

2. The method according to claim 1, characterized in that The operating status of the safety door is collected through sensors, including: When the electronic password lock is in the unlock verification state, collect the unlock attempt password list and add it to the unlock monitoring behavior; The pressure vector timing information is collected through the pressure sensor and added into the monitoring load behavior.

3. The method according to claim 2, characterized in that The pressure sensor is deployed on the inner layer of the outer surface of the anti-theft safety door.

4. The method according to claim 2, characterized in that An abnormal analysis is performed on the unlocking monitoring behavior and / or the monitoring load behavior to generate a safety door intrusion coefficient, including: For the unlocking monitoring behavior, a deviation check is performed based on the unlocking reference password to generate a target abnormality probability; The monitored load behavior is processed by a behavior classifier to obtain a behavior classification result; When the target abnormal probability is greater than or equal to the abnormal probability threshold, and / or the behavior classification result belongs to the intrusion behavior type, the security door intrusion coefficient is set to be greater than or equal to the intrusion coefficient threshold; When the target abnormal probability is less than the abnormal probability threshold, and the behavior classification result does not belong to the intrusion behavior type, the security door intrusion coefficient is set to be less than the intrusion coefficient threshold.

5. The method according to claim 4, characterized in that The unlock monitoring behavior is subjected to deviation verification based on the unlock reference password to generate a target abnormal probability, including: Extracting the unlock attempt password list according to the unlock monitoring behavior; Traversing the unlock attempt password list, calculating deviations with the unlock reference password, and generating a first deviation distance list; Performing pairwise deviation calculation on the unlock attempt password list to generate a second deviation distance list; An abnormal unlocking analysis is performed according to the first deviation distance list and the second deviation distance list to generate the target abnormality probability.

6. The method according to claim 5, characterized in that Traversing the unlock attempt password list, calculating the deviation from the unlock reference password, and generating a first deviation distance list, including: Construct the deviation calculation function: ； ； ； in, The i-th unlock attempt password representing the unlock attempt password list, The symbol representing the jth digit of the password for the i-th unlock attempt, Characterizes the unlocking base password, represents the jth digit symbol of the unlocking base password, M represents the number of digits of the password constraint, Characterize the deviation distance between the i-th unlock attempt password and the unlock reference password; According to the deviation calculation function, the unlock attempt password list is traversed, and deviation calculation is performed with the unlock reference password to generate the first deviation distance list.

7. The method according to claim 5, characterized in that Performing abnormal unlocking analysis according to the first deviation distance list and the second deviation distance list to generate the target abnormality probability includes: Calculating the variance of the first deviation distance list to generate a first dispersion coefficient; Calculating the mean of the first deviation distance list to generate a first distance coefficient; Calculating the variance of the second deviation distance list to generate a second dispersion coefficient; Calculating the mean of the second deviation distance list to generate a second distance coefficient; When the first discrete coefficient is less than a first discrete coefficient threshold, and the second discrete coefficients are both less than a second discrete coefficient threshold, and the first distance coefficient is less than a first distance coefficient threshold, and the second distance coefficient is less than a second distance coefficient threshold, the target abnormal probability is set to 0; Otherwise, the target abnormal probability is set to 1.

8. The method according to claim 4, characterized in that The monitored load behavior is processed by a behavior classifier to obtain a behavior classification result, including: Configure a pressure vector time series record data set and a behavior classification identification result, wherein the behavior classification identification result includes an intrusion behavior identification and a non-intrusion behavior identification; According to the behavior classification identification result and the pressure vector time series record data set, a random forest is trained, and when the proportion of accurate classification times for a consecutive preset number of times is greater than or equal to a convergence proportion threshold, the behavior classifier is generated; The pressure vector time series information of the monitored load behavior is processed by a behavior classifier to obtain the behavior classification result.

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