An intelligent security door lock method and system combining security monitoring and emergency linkage

Abstract

The application relates to the technical field of security door locks, and relates to an intelligent security door lock method and system combining security monitoring and emergency linkage, which comprises the following steps: confirming a plurality of target sensors based on a security door lock; obtaining an initial data set group based on a multi-source data acquisition instruction and the plurality of target sensors; preprocessing the initial data set group to obtain a target data set group, performing data fusion on the target data set group to obtain a target fusion data set; obtaining a risk level set based on a risk assessment instruction and the target fusion data set, wherein the risk level set comprises a plurality of risk levels, and the plurality of risk levels are respectively a low risk level, a medium risk level and a high risk level; obtaining a plurality of emergency measures based on an emergency management unit and the risk level set; and realizing the intelligent security door lock of the security monitoring and emergency linkage based on the plurality of emergency measures. The application can realize the intelligent combination of the security monitoring and emergency linkage in the security door lock.

Description

Technical Field

[0001] This invention relates to the field of security door lock technology, and in particular to an intelligent security door lock method and system that combines security monitoring and emergency response. Background Technology

[0002] With the continuous development of smart cities and safe communities, smart security door locks, as the first line of defense for security in homes, offices, and critical locations, have been widely used in various scenarios. Correspondingly, achieving accurate and intelligent monitoring and emergency response control of security door locks is indispensable for them to play their normal and effective security role.

[0003] Currently, smart security door locks are mainly unlocked using traditional mechanical keys, or automated management is achieved by setting fixed instructions (such as a single password or static permissions).

[0004] While the above methods can meet basic access control needs, they do not consider the relationship between real-time, multi-dimensional security threats (such as technical unlocking and abnormal loitering) and environmental data when making security decisions. They also do not consider deep linkage between front-end monitoring and back-end emergency response. As a result, when facing complex and dynamic security risks, there are problems such as delayed response, high false alarm rate, and passive protection. Therefore, how to achieve intelligent integration of security monitoring and emergency linkage in security door locks has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a smart security door lock method and a computer-readable storage medium that combine security monitoring and emergency response. Its main purpose is to achieve the intelligent integration of security monitoring and emergency response in security door locks.

[0006] To achieve the above objectives, the present invention provides a smart security door lock method that combines security monitoring and emergency response, comprising: The system receives a security monitoring command and identifies the security monitoring environment based on the command. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. Based on the security door lock, multiple target sensors were identified; Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. Receive risk assessment instructions from the risk assessment unit, and obtain a risk level set based on the risk assessment instructions and the target fusion dataset. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively. Based on the emergency management unit, the door lock controller, video recorder, audible and visual alarm, and communication unit were identified; The risk level is extracted from the risk level set. If the risk level is low, the video recorder is activated to record the entire process, and the security door lock is enhanced using the door lock controller, which is the first emergency measure. If the risk level is medium, the sound and light alarm is used to issue a warning signal, and the warning information is sent through the communication unit, which is the second emergency measure. If the risk level is high, the sound and light alarm is activated to issue an emergency evacuation alarm, and the communication unit is used to send a distress signal to the pre-built emergency center, which is the third emergency measure. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0007] Optionally, the detection of multiple target sensors based on the security door lock includes: Based on the security door lock, a set of monitoring requirements is identified, which includes multiple monitoring requirements, namely, lock cylinder rotation monitoring requirements, abnormal behavior monitoring requirements outside the door, and door vibration monitoring requirements. A sensor type set is obtained based on the monitoring requirement set, wherein the sensor type set includes multiple sensor types, and each sensor type corresponds one-to-one with a monitoring requirement; For each sensor type in the sensor type set, perform the following operation: A candidate sensor set is obtained based on the sensor type, wherein the candidate sensor set includes multiple candidate sensors; For each candidate sensor in the candidate sensor set, perform the following operation: Based on the candidate sensors, a set of performance indicators was identified, which includes multiple performance indicators, namely, accuracy level score, response time score, environmental adaptability score, and power consumption level score. The performance evaluation value is obtained based on the aforementioned performance indicator set, wherein the calculation formula for the performance evaluation value is as follows: in, This represents the performance evaluation value of the candidate sensor. Indicates the accuracy level score. Indicates response time score, Indicates environmental adaptability score, Indicates a power consumption level rating. This represents the weighting coefficient corresponding to the accuracy level score. This represents the weighting coefficient corresponding to the response time score. This represents the weighting coefficient corresponding to the environmental adaptability score. This represents the weighting coefficient corresponding to the power consumption level score; The performance evaluation values ​​are summarized to obtain a performance evaluation value set; The optimal performance evaluation value is obtained based on the performance evaluation value set, wherein the optimal performance evaluation value is the largest performance evaluation value in the performance evaluation value set; The candidate sensor corresponding to the optimal performance evaluation value is taken as the target sensor; By combining the aforementioned target sensors, multiple target sensors are obtained.

[0008] Optionally, the step of acquiring an initial dataset group based on the multi-source data acquisition command and multiple target sensors includes: The security area is obtained based on the multi-source data acquisition instructions and the security door lock; The security area is divided using a preset sensing area to obtain multiple effective monitoring areas; For each of the multiple valid monitoring areas, perform the following operations: The target sensor is extracted sequentially from multiple target sensors, and the extracted target sensor is placed in the effective monitoring area to obtain the registered sensor; By combining the registered sensors, a registration sensor set is obtained; For each registration sensor in the registration sensor set, the following operation is performed: The data sampling period is determined based on the registration sensor. By summing the data sampling periods, a sampling period set is obtained; A reference sampling period is obtained based on the sampling period set, wherein the reference sampling period is the largest data sampling period in the sampling period set; Based on the aforementioned reference sampling period and the pre-constructed time-scale synchronizer, the registered sensor set is time-domain aligned to obtain a synchronously acquired sensor set; An initial dataset group is obtained based on the synchronously acquired sensor set, wherein the initial dataset group includes multiple initial datasets, and each initial dataset corresponds one-to-one with a synchronously acquired sensor.

[0009] Optionally, the preprocessing of the initial dataset group to obtain the target dataset group includes: Perform the following operation on each initial dataset in the initial dataset group: A set of statistical features is obtained based on the initial dataset, wherein the set of statistical features includes multiple statistical features, and the statistical features are kurtosis coefficient, skewness coefficient and coefficient of variation. The data quality assessment value is obtained based on the statistical feature set, wherein the calculation formula for the data quality assessment value is as follows: Among them, it means Data quality assessment value, Represents the kurtosis factor. Indicates the skewness coefficient. Indicates the coefficient of variation; Compare the data quality assessment value with a preset quality threshold. If the data quality assessment value is greater than or equal to the quality threshold, then the initial dataset is used as the target dataset. If the data quality assessment value is less than the quality threshold, then initial data is extracted from the initial dataset in sequence, and the extracted initial data is compared with the preset abnormal threshold. If the extracted data is greater than or equal to the abnormal threshold, then the extracted initial data is taken as abnormal data. The abnormal data is summarized to obtain an abnormal dataset. The abnormal dataset is removed from the initial dataset to obtain the target dataset.

[0010] Optionally, obtaining the set of statistical features based on the initial dataset includes: Count the number of initial data points in the initial dataset to obtain the number of data points; The initial data mean is obtained based on the initial dataset, wherein the initial data mean is the arithmetic mean of the initial data in the initial dataset; The initial data standard deviation is obtained based on the initial dataset, wherein the initial data standard deviation is the standard deviation of the initial data in the initial dataset; The kurtosis coefficient is obtained based on the initial dataset, the number of data points, and the initial data mean. The formula for calculating the kurtosis coefficient is as follows: in, Represents the kurtosis factor. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points; The skewness coefficient is obtained based on the initial dataset; The coefficient of variation is obtained based on the initial data mean and initial data standard deviation, wherein the coefficient of variation is the ratio of the initial data standard deviation to the initial data mean; By summing up the kurtosis coefficient, skewness coefficient, and coefficient of variation, a set of statistical characteristics is obtained.

[0011] Optionally, the skewness coefficient is obtained based on the initial dataset, and its calculation formula is as follows: in, Indicates the skewness coefficient. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points.

[0012] Optionally, the step of fusing the target dataset group to obtain the target fused dataset includes: Perform the following operation on each target dataset in the target dataset group: Obtain the maximum and minimum measured values ​​of the synchronous acquisition sensors corresponding to the target dataset; Extract the target data sequentially from the target dataset, and perform the following operations on the extracted target data: The extracted target data is normalized using the maximum and minimum measured values ​​to obtain normalized data. The normalized data are then aggregated to obtain the normalized dataset; The target data type is obtained based on the normalized dataset and its corresponding synchronous acquisition sensor; The normalized datasets are then aggregated to obtain a normalized dataset group; By summarizing the target data types, a target data type set is obtained; An initial weight set is obtained based on the target data type set and the pre-built expert database. The initial weight set includes multiple initial weights, and each initial weight corresponds one-to-one with a normalized dataset. Perform the following operation on each normalized dataset in the normalized dataset group: Normalized data is extracted sequentially from the normalized dataset to obtain the data to be fused; By summarizing the data to be merged, a dataset to be merged is obtained; The target fused data is obtained by weighted summation of the dataset to be fused using the initial weight set. The target fusion data are then aggregated to obtain the target fusion dataset.

[0013] Optionally, obtaining the risk level set based on the risk assessment instruction and the target fusion dataset includes: A risk threshold set is obtained based on the risk assessment instruction, wherein the risk threshold set includes multiple risk thresholds, and the risk thresholds are low risk thresholds and high risk thresholds, respectively. The target fusion mean is obtained based on the target fusion dataset, wherein the target fusion mean is the mean of the target fusion data in the target fusion dataset; The target fusion extreme value is obtained based on the target fusion dataset, wherein the target fusion extreme value is the largest target fusion data in the target fusion dataset; The target fusion variance is obtained based on the target fusion dataset, wherein the target fusion variance is the variance of the target fusion data in the target fusion dataset; The risk assessment value is obtained based on the target fusion mean, target fusion extreme value, and target fusion variance. By comparing the risk assessment value with the risk threshold set, if the risk assessment value is less than the low risk threshold, the risk level is determined to be low risk level. If the risk assessment value is greater than or equal to the low risk threshold and less than the high risk threshold, the risk level is determined to be medium risk. If the risk assessment value is greater than or equal to the high-risk threshold, the risk level is determined to be high-risk. By summarizing the low-risk, medium-risk, and high-risk levels, a risk level set is obtained.

[0014] Optionally, the intelligent security door lock that realizes security monitoring and emergency response based on the first emergency measure, the second emergency measure, and the third emergency measure includes: Obtain multiple reference monitoring dataset groups; Multiple reference fusion datasets are obtained based on the aforementioned multiple reference monitoring dataset groups; Multiple reference risk levels are obtained based on the aforementioned multiple reference fusion datasets; By summarizing the first, second, and third emergency measures, multiple emergency measures are obtained. Based on the multiple reference risk levels and multiple emergency measures, a set of emergency measures to be trained is obtained; A model is built by training a reference fusion dataset and a set of emergency measures to be trained using a pre-built machine learning method, resulting in an intelligent emergency decision-making model. An intelligent security door lock that achieves security monitoring and emergency response based on the aforementioned intelligent emergency decision-making model and security door lock.

[0015] To achieve the above objectives, the present invention also provides an intelligent security door lock system that combines security monitoring and emergency response, comprising: The monitoring environment confirmation module is used to receive security monitoring instructions and confirm the security monitoring environment based on the security monitoring instructions. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. The data acquisition and processing module is used to identify multiple target sensors based on the security door lock; Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. The risk assessment module is used to receive risk assessment instructions from the risk assessment unit, and obtain a risk level set based on the risk assessment instructions and the target fusion dataset. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively. The emergency management module is used to identify the door lock controller, video recorder, audible and visual alarm, and communication unit based on the emergency management unit. The risk level is extracted from the risk level set. If the risk level is low, the video recorder is activated to record the entire process, and the security door lock is enhanced using the door lock controller, which is the first emergency measure. If the risk level is medium, the sound and light alarm is used to issue a warning signal, and the warning information is sent through the communication unit, which is the second emergency measure. If the risk level is high, the sound and light alarm is activated to issue an emergency evacuation alarm, and the communication unit is used to send a distress signal to the pre-built emergency center, which is the third emergency measure. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0016] To address the above problems, the present invention also provides an electronic device, the electronic device comprising: A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the aforementioned intelligent security door lock method that combines security monitoring and emergency response.

[0017] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned intelligent security door lock method combining security monitoring and emergency response.

[0018] To address the problems described in the background art, this invention receives a security monitoring command and, based on the command, identifies the security monitoring environment. This environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system comprises a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. Therefore, before intelligently combining security monitoring and emergency response in a security door lock, this invention considers the operating conditions of the security door lock under different environments or circumstances. Thus, it identifies the security monitoring system and the security door lock to be monitored. Based on the security door lock, it identifies multiple target sensors, receives a multi-source data acquisition command from the multi-source data acquisition unit, and obtains an initial dataset based on the command and the multiple target sensors. This invention also considers the different sampling performance and sampling periods of different sensors when monitoring the security door lock. Therefore, by evaluating sensor performance, the subsequently collected target dataset is more accurate, laying the foundation for subsequent risk assessment. The initial dataset is preprocessed to obtain the target dataset. The invention also considers preprocessing the initial dataset group and fusing it with the target dataset group to obtain the target fused dataset. By performing risk assessment on the target fused dataset, the problem of inaccurate risk assessment due to data from a single sensor can be avoided, thus improving the accuracy of security monitoring. The invention receives risk assessment instructions from the risk assessment unit and obtains a risk level set based on these instructions and the target fused dataset. This risk level set includes multiple risk levels, namely low, medium, and high risk. Therefore, the invention also considers the actual situation of the area where the security door lock is located, dividing the risk level into three levels, and considers different emergency measures for different risk levels. Furthermore, the invention receives emergency management instructions from the emergency management unit and obtains multiple emergency measures based on these instructions and the risk level set. Based on these multiple emergency measures, the invention achieves intelligent security door lock integration of security monitoring and emergency response. Therefore, the invention can realize the intelligent integration of security monitoring and emergency response in security door locks. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an embodiment of the intelligent security door lock method combining security monitoring and emergency response provided by the present invention. Figure 2 This is a functional block diagram of an intelligent security door lock system that combines security monitoring and emergency response, provided in an embodiment of the present invention. Figure 3This is a schematic diagram of the structure of an electronic device that implements the intelligent security door lock method combining security monitoring and emergency response, according to an embodiment of the present invention.

[0020] Explanation of reference numerals in the attached figures: 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] This application provides a method for intelligent security door locks that combines security monitoring and emergency response. The executing entity of this method includes, but is not limited to, at least one electronic device that can be configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0024] Reference Figure 1 The diagram shown is a flowchart illustrating a smart security door lock method combining security monitoring and emergency response according to an embodiment of the present invention. In this embodiment, the smart security door lock method combining security monitoring and emergency response includes: S1. Receive a security monitoring instruction and confirm the security monitoring environment based on the security monitoring instruction. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit.

[0025] It should be explained that the security monitoring command is issued by personnel who want to achieve intelligent integration of security monitoring and emergency response in security door locks. The security monitoring environment refers to the necessary environment for achieving intelligent integration of security monitoring and emergency response in security door locks. The security monitoring system refers to the software or app used to achieve intelligent integration of security monitoring and emergency response in security door locks. A security door lock refers to an intelligent door lock device that achieves comprehensive proactive protection by intelligently integrating security monitoring and emergency response. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. For the specific application of these units, please refer to subsequent embodiments. The main purpose of this invention is to improve the intelligence of integrating security monitoring and emergency response.

[0026] For example, Xiao Zhang, as the security personnel responsible for a certain smart security door lock, in order to achieve the intelligent integration of security monitoring and emergency response in the security door lock and avoid problems such as security vulnerabilities or property losses due to untimely detection or delayed response to abnormal situations, issues the aforementioned security monitoring command and confirm the security monitoring environment.

[0027] S2. Based on the security door lock, multiple target sensors are identified.

[0028] Furthermore, the multiple target sensors identified based on the security door lock include: Based on the security door lock, a set of monitoring requirements is identified, which includes multiple monitoring requirements, namely, lock cylinder rotation monitoring requirements, abnormal behavior monitoring requirements outside the door, and door vibration monitoring requirements. A sensor type set is obtained based on the monitoring requirement set, wherein the sensor type set includes multiple sensor types, and each sensor type corresponds one-to-one with a monitoring requirement; For each sensor type in the sensor type set, perform the following operation: A candidate sensor set is obtained based on the sensor type, wherein the candidate sensor set includes multiple candidate sensors; For each candidate sensor in the candidate sensor set, perform the following operation: Based on the candidate sensors, a set of performance indicators was identified, which includes multiple performance indicators, namely, accuracy level score, response time score, environmental adaptability score, and power consumption level score. The performance evaluation value is obtained based on the aforementioned performance indicator set, wherein the calculation formula for the performance evaluation value is as follows: in, This represents the performance evaluation value of the candidate sensor. Indicates the accuracy level score. Indicates response time score, Indicates environmental adaptability score, Indicates a power consumption level rating. This represents the weighting coefficient corresponding to the accuracy level score. This represents the weighting coefficient corresponding to the response time score. This represents the weighting coefficient corresponding to the environmental adaptability score. This represents the weighting coefficient corresponding to the power consumption level score; The performance evaluation values ​​are summarized to obtain a performance evaluation value set; The optimal performance evaluation value is obtained based on the performance evaluation value set, wherein the optimal performance evaluation value is the largest performance evaluation value in the performance evaluation value set; The candidate sensor corresponding to the optimal performance evaluation value is taken as the target sensor; By combining the aforementioned target sensors, multiple target sensors are obtained.

[0029] It is clear that identifying the monitoring requirement set based on the aforementioned security door lock refers to identifying the objects that need to be monitored based on the security requirements of the security door lock. For example, if the security requirement of the security door lock is to prevent unauthorized lock picking, the monitoring requirement set identified based on this security requirement includes monitoring abnormal pressure applied to the lock cylinder area, monitoring continuous vibration or impact of the door, and monitoring prying tools at specific angles. Lock cylinder rotation monitoring requirement refers to monitoring the rotation of the lock cylinder; abnormal behavior monitoring requirement refers to monitoring abnormal behavior outside the door, such as multiple suspicious individuals loitering outside the door; and door vibration monitoring requirement refers to monitoring the vibration of the door where the security door lock is located.

[0030] Understandably, obtaining a sensor type set based on the monitoring requirement set means identifying sensor types that can meet the monitoring requirements based on the monitoring requirements in the monitoring requirement set. For example, if the monitoring requirement is door vibration monitoring, then the sensor type is identified as vibration type. Sensor type refers to the type of sensor. Obtaining a candidate sensor set based on the sensor type means identifying multiple sensors produced by different manufacturers based on the sensor type, and the sensor type is the aforementioned sensor type. Candidate sensor refers to a sensor whose type is the sensor type; for example, if the sensor type is vibration type, then the candidate sensor is a vibration sensor.

[0031] It should be explained that the performance index set identified based on the candidate sensors refers to extracting multiple performance indicators from the sensor performance test reports provided by the corresponding manufacturers of the candidate sensors. These multiple performance indicators are accuracy level score, response time score, environmental adaptability score, and power consumption level score. The accuracy level score refers to the score of the accuracy of the candidate sensor's monitoring data. The response time score refers to the score of the candidate sensor's response time. The environmental adaptability score refers to the score of the candidate sensor's adaptability to its environment. The power consumption level score refers to the score of the candidate sensor's power consumption when monitoring data. Performance indicators are metrics used to evaluate the performance of candidate sensors. Performance evaluation values ​​are values ​​used to measure the overall performance of candidate sensors. The optimal performance evaluation value is the highest performance evaluation value in the performance evaluation value set. The target sensor is the candidate sensor corresponding to the optimal performance evaluation value.

[0032] S3. Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors.

[0033] It should be explained that the multi-source data acquisition unit is a functional module in the security monitoring system used to collect multiple environmental data around the security door lock. Optionally, multiple target sensors can be used as the multi-source data acquisition module.

[0034] Furthermore, the step of acquiring an initial dataset group based on the multi-source data acquisition command and multiple target sensors includes: The security area is obtained based on the multi-source data acquisition instructions and the security door lock; The security area is divided using a preset sensing area to obtain multiple effective monitoring areas; For each of the multiple valid monitoring areas, perform the following operations: The target sensor is extracted sequentially from multiple target sensors, and the extracted target sensor is placed in the effective monitoring area to obtain the registered sensor; By combining the registered sensors, a registration sensor set is obtained; For each registration sensor in the registration sensor set, the following operation is performed: The data sampling period is determined based on the registration sensor. By summing the data sampling periods, a sampling period set is obtained; A reference sampling period is obtained based on the sampling period set, wherein the reference sampling period is the largest data sampling period in the sampling period set; Based on the aforementioned reference sampling period and the pre-constructed time-scale synchronizer, the registered sensor set is time-domain aligned to obtain a synchronously acquired sensor set; An initial dataset group is obtained based on the synchronously acquired sensor set, wherein the initial dataset group includes multiple initial datasets, and each initial dataset corresponds one-to-one with a synchronously acquired sensor.

[0035] It is clear that the multi-source data acquisition command refers to the operation command issued by the multi-source data acquisition unit to begin collecting environmental data around the security door lock. Based on the multi-source data acquisition command and the security door lock acquiring the security area, this means determining the range of environmental data to be collected around the security door lock before starting environmental data collection; for example, collecting environmental data only within a 1-square-meter area directly in front of the security door lock. The security area refers to the area where the security door lock needs to perform security monitoring. Dividing the security area using a preset sensing area means dividing the security area into areas where each target sensor can accurately collect environmental data based on the sensing area of ​​the target sensor. The sensing area refers to the range within which the target sensor can collect environmental data. The effective monitoring area refers to the range within which the target sensor can effectively monitor the environmental data around the security door lock. The registered sensor refers to the target sensor placed within the effective monitoring area.

[0036] It should be explained that determining the data sampling period based on the registration sensor refers to recording the time interval between two consecutive data acquisitions by the registration sensor, thereby determining the data sampling period. For example, if the time corresponding to the first data detected by the registration sensor is 2, and the time corresponding to the second data is 5, then the data sampling period is 3. The data sampling period refers to the time interval between two consecutive data acquisitions by the registration sensor. Obtaining the reference sampling period based on the sampling period set refers to determining the largest data sampling period in the sampling period set. The reference sampling period refers to the largest data sampling period in the sampling period set. Time-domain alignment of the registration sensor set based on the reference sampling period and the pre-built time-scale synchronizer means setting the data sampling period of all registration sensors in the registration sensor set as the reference sampling period, and using the time-scale synchronizer to provide a unified clock source for the registration sensors, so that each registration sensor acquires data at the same time. Optionally, a real-time clock can be used as the time-scale synchronizer. The synchronous acquisition sensor set refers to the registration sensor set that acquires data with the reference sampling period and the same clock source. Obtaining an initial dataset based on the aforementioned synchronously acquired sensor set refers to collecting environmental data around the security door lock using the synchronously acquired sensors within the same sensor set. The initial dataset refers to the set of data monitored by the synchronously acquired sensors.

[0037] S4. Preprocess the initial dataset group to obtain the target dataset group, and perform data fusion on the target dataset group to obtain the target fused dataset.

[0038] It should be explained that, since the initial dataset acquired by the sensor may contain noise and outliers, in order to improve the data quality of the initial dataset, the preprocessing of the initial dataset to obtain the target dataset includes: Perform the following operation on each initial dataset in the initial dataset group: A set of statistical features is obtained based on the initial dataset, wherein the set of statistical features includes multiple statistical features, and the statistical features are kurtosis coefficient, skewness coefficient and coefficient of variation. The data quality assessment value is obtained based on the statistical feature set, wherein the calculation formula for the data quality assessment value is as follows: Among them, it means Data quality assessment value, Represents the kurtosis factor. Indicates the skewness coefficient. Indicates the coefficient of variation; Compare the data quality assessment value with a preset quality threshold. If the data quality assessment value is greater than or equal to the quality threshold, then the initial dataset is used as the target dataset. If the data quality assessment value is less than the quality threshold, then initial data is extracted from the initial dataset in sequence, and the extracted initial data is compared with the preset abnormal threshold. If the extracted data is greater than or equal to the abnormal threshold, then the extracted initial data is taken as abnormal data, the abnormal data is summarized to obtain an abnormal dataset, and the abnormal dataset is removed from the initial dataset to obtain the target dataset.

[0039] Furthermore, obtaining the set of statistical features based on the initial dataset includes: Count the number of initial data points in the initial dataset to obtain the number of data points; The initial data mean is obtained based on the initial dataset, wherein the initial data mean is the arithmetic mean of the initial data in the initial dataset; The initial data standard deviation is obtained based on the initial dataset, wherein the initial data standard deviation is the standard deviation of the initial data in the initial dataset; The kurtosis coefficient is obtained based on the initial dataset, the number of data points, and the initial data mean. The formula for calculating the kurtosis coefficient is as follows: in, Represents the kurtosis factor. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points; The skewness coefficient is obtained based on the initial dataset; The coefficient of variation is obtained based on the initial data mean and initial data standard deviation, wherein the coefficient of variation is the ratio of the initial data standard deviation to the initial data mean; By summing up the kurtosis coefficient, skewness coefficient, and coefficient of variation, a set of statistical characteristics is obtained.

[0040] Furthermore, the skewness coefficient obtained based on the initial dataset is calculated using the following formula: in, Indicates the skewness coefficient. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points.

[0041] In essence, the number of data points refers to the quantity of initial data in the initial dataset. For example, if the initial dataset is (3, 5, 6, 8), then the number of data points is 4. The initial data mean is the arithmetic mean of the initial data in the initial dataset. The initial data standard deviation is the standard deviation of the initial data in the initial dataset. The kurtosis coefficient is a relative statistic that measures the steepness or flatness of the initial data distribution in the initial dataset. The skewness coefficient is a statistic that measures the direction and degree of asymmetry in the initial data distribution in the initial dataset. The coefficient of variation is a relative indicator that measures the dispersion of the initial data in the initial dataset. Obtaining the coefficient of variation based on the initial data mean and standard deviation involves calculating the ratio of the initial data mean to the initial data standard deviation, using this ratio as the coefficient of variation. For example, if the initial dataset is {1.2, 1.5, 1.1, 1.6, 1.3}, then the initial data mean is 1.34, the initial data standard deviation is approximately 0.19, and the coefficient of variation is approximately 0.14. The set of statistical characteristics refers to the collection of kurtosis, skewness, and coefficient of variation.

[0042] Understandably, the data quality assessment value refers to the value used to evaluate the quality of the initial data in the initial dataset. If the data quality assessment value is greater than or equal to a preset quality threshold, it indicates that the quality of the initial dataset collected by the synchronous acquisition sensor is qualified and can be considered as valid synchronous data, which can be used for subsequent data fusion, and thus the initial dataset is used as the target dataset. If the data quality assessment value is less than the quality threshold, it indicates that the quality of the initial data in the initial dataset is unqualified. Therefore, initial data is extracted sequentially from the initial dataset, and the extracted initial data is compared with a preset anomaly threshold. If the extracted initial data is greater than or equal to the anomaly threshold, the extracted initial data is considered anomaly data. The anomaly data is summarized to obtain an anomaly dataset. The anomaly dataset is then removed from the initial dataset to obtain the target dataset. Anomaly data refers to initial data that is greater than the anomaly threshold and is considered an anomaly. The target dataset refers to data that does not contain anomaly data and is of qualified quality.

[0043] Understandably, since the data collected by different sensors have different dimensions and ranges, in order to facilitate subsequent data analysis and processing, the target dataset group is fused to obtain a target fused dataset, which includes: Perform the following operation on each target dataset in the target dataset group: Obtain the maximum and minimum measured values ​​of the synchronous acquisition sensors corresponding to the target dataset; Extract the target data sequentially from the target dataset, and perform the following operations on the extracted target data: The extracted target data is normalized using the maximum and minimum measured values ​​to obtain normalized data. The normalized data are then aggregated to obtain the normalized dataset; The target data type is obtained based on the normalized dataset and its corresponding synchronous acquisition sensor; The normalized datasets are then aggregated to obtain a normalized dataset group; By summarizing the target data types, a target data type set is obtained; An initial weight set is obtained based on the target data type set and the pre-built expert database. The initial weight set includes multiple initial weights, and each initial weight corresponds one-to-one with a normalized dataset. Perform the following operation on each normalized dataset in the normalized dataset group: Normalized data is extracted sequentially from the normalized dataset to obtain the data to be fused; By summarizing the data to be merged, a dataset to be merged is obtained; The target fused data is obtained by weighted summation of the dataset to be fused using the initial weight set. The target fusion data are then aggregated to obtain the target fusion dataset.

[0044] It is clear that the maximum measured value refers to the maximum value that the synchronous acquisition sensor can measure. The minimum target data refers to the minimum value that the synchronous acquisition sensor can measure. Normalizing the extracted target data using the maximum and minimum measured values ​​means performing a minimum-maximum normalization operation on the target data using the maximum and minimum measured values. Normalized data refers to the data obtained by normalizing the target data. For example, if the target dataset is (8, 12, 13, 14, 16), and the synchronous acquisition sensor corresponding to the target dataset is a vibration sensor with a maximum and minimum measured values ​​of 100 and 0 respectively, then if the target data is 12, the normalized data is 0.12, and the normalized dataset is (0.08, 0.12, 0.13, 0.14, 0.16).

[0045] It should be explained that obtaining the target data type based on the normalized dataset and its corresponding synchronous acquisition sensor means determining the data type of the normalized dataset according to the type of synchronous acquisition sensor corresponding to the normalized dataset. For example, if the sensor corresponding to the normalized dataset is a vibration sensor, then the target data type is vibration data. Obtaining the initial weight set based on the target data type set and the pre-built expert database means confirming the proportion and weight of each target data type in the security monitoring of the security door lock in the expert database. Optionally, an existing weight standard determined by expert scoring can be used as the expert database. For example, in the security monitoring corresponding to the security door lock, the weight of vibration data is 0.6, the weight of the number of abnormal personnel outside is 0.2, and the weight of the number of times the lock cylinder rotates in the security door lock is 0.2.

[0046] It is clear that the data to be fused refers to the normalized data extracted sequentially from the normalized dataset for data fusion. The target fusion data refers to the data obtained by weighted summation of the datasets to be fused. For example, the normalized dataset for vibration type is [0.05, 0.12, 0.85, 0.33, 0.01, 0.98], the normalized dataset for the number of abnormal people outside the door is [0.00, 0.3, 0.6, 0.4, 0.2, 0.6], and the normalized dataset for the number of lock cylinder rotations is [0.2, 0.4, 0.8, 1.00, 0.00, 0.6]. After extracting normalized data sequentially from the normalized datasets, the dataset to be fused is (0.05, 0.00, 0.2), with a weight of 0.6 for vibration data, 0.2 for the number of abnormal people outside the door, and 0.2 for the number of lock cylinder rotations in the security door lock. Therefore, the target fusion data is 0.07.

[0047] S5. Receive a risk assessment instruction from the risk assessment unit, and obtain a risk level set based on the risk assessment instruction and target fusion data. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively.

[0048] It should be explained that the risk assessment unit is a functional unit in the security monitoring system used to perform risk assessment on the target fusion dataset.

[0049] Furthermore, obtaining the risk level set based on the risk assessment instruction and the target fusion dataset includes: A risk threshold set is obtained based on the risk assessment instruction, wherein the risk threshold set includes multiple risk thresholds, and the risk thresholds are low risk thresholds and high risk thresholds, respectively. The target fusion mean is obtained based on the target fusion dataset, wherein the target fusion mean is the mean of the target fusion data in the target fusion dataset; The target fusion extreme value is obtained based on the target fusion dataset, wherein the target fusion extreme value is the largest target fusion data in the target fusion dataset; The target fusion variance is obtained based on the target fusion dataset, wherein the target fusion variance is the variance of the target fusion data in the target fusion dataset; The risk assessment value is obtained based on the target fusion mean, target fusion extreme value, and target fusion variance. By comparing the risk assessment value with the risk threshold set, if the risk assessment value is less than the low risk threshold, the risk level is determined to be low risk level. If the risk assessment value is greater than or equal to the low risk threshold and less than the high risk threshold, the risk level is determined to be medium risk. If the risk assessment value is greater than or equal to the high-risk threshold, the risk level is determined to be high-risk. By summarizing the low-risk, medium-risk, and high-risk levels, a risk level set is obtained.

[0050] It is clear that a risk assessment instruction refers to an operational instruction issued by the risk assessment unit to begin a risk assessment. Obtaining a risk threshold set based on the risk assessment instruction means setting a set of risk thresholds to measure different risk levels at the start of the risk assessment. This risk threshold set includes multiple risk thresholds, which are categorized as low-risk and high-risk thresholds. Obtaining the target fusion mean based on the target fusion dataset means calculating the mean of the target fusion data in the target fusion dataset. The target fusion mean is the average of the target fusion data in the target fusion dataset. Obtaining the target fusion extreme value based on the target fusion dataset means extracting the largest target fusion data point from the target fusion dataset. The target fusion extreme value is the largest target fusion data point in the target fusion dataset. Obtaining the target fusion variance based on the target fusion dataset means calculating the variance of the target fusion data in the target fusion dataset. The target fusion variance is the variance of the target fusion data in the target fusion dataset. Obtaining a risk assessment value based on the target fusion mean, target fusion extreme value, and target fusion variance involves assigning different weights to these three values, and then using these weights to perform a weighted summation to obtain the risk assessment value. The target fusion mean reflects the average threat level within the monitoring period, demonstrating a stable and continuous risk base; the target fusion extreme value captures the most severe instantaneous peak threats, preventing sudden high-risk events; and the target fusion variance characterizes data volatility and environmental uncertainty, identifying probing attacks or abnormal states. The risk assessment value obtained through the weighted summation of these three values ​​allows for a more comprehensive and accurate judgment of the security status of security door locks. The risk assessment value is the value used to assess the risk level.

[0051] In essence, if the risk assessment value is less than the low-risk threshold, it indicates that the environment in which the security lock is located poses no security risks; therefore, the risk level is determined to be low-risk. If the risk assessment value is greater than or equal to the low-risk threshold but less than the high-risk threshold, it indicates that the environment in which the security lock is located contains events that threaten user safety, such as lock picking; therefore, the risk level is determined to be medium-risk. If the risk assessment value is greater than or equal to the high-risk threshold, it indicates that the environment in which the security lock is located contains relatively serious security events; therefore, the risk level is determined to be high-risk.

[0052] S6. Based on the emergency management unit, confirm the door lock controller, video recorder, audible and visual alarm and communication unit.

[0053] It should be explained that the emergency management unit is a functional module in the safety monitoring system used to customize emergency measures based on risk levels.

[0054] It's clear that a door lock controller is a controller used to control the strength of opening and closing security door locks. Enhancing security door locks using a door lock controller means using it to lock the door, making it difficult to open. An audible and visual alarm is an alarm that emits warning signals, including preset warning voice messages and red lights. A video recorder is a device used to record video information in front of a security door lock, such as a surveillance camera. A communication unit is a device used to communicate with security personnel.

[0055] S7. Extract the risk level from the risk level set. If the risk level is low, start the video recorder to record the entire process and use the door lock controller to enhance the security door lock, thus obtaining the first emergency measure. If the risk level is medium, use the sound and light alarm to issue a warning signal and send the warning information through the communication unit, thus obtaining the second emergency measure. If the risk level is high, start the sound and light alarm to issue an emergency evacuation alarm and use the communication unit to send a help signal to the pre-built emergency center, thus obtaining the third emergency measure.

[0056] Understandably, if the risk level is low, a video recorder will be activated to record the entire process, and enhanced locking will be implemented using the door lock controller. This means that when the risk level is low, a video recorder will record video information in front of the security door lock, and the door lock controller will be used to reinforce the security door lock. The first emergency measure refers to the emergency measures tailored for low-risk levels. A warning signal is a distress signal sent to security personnel. The second emergency measure refers to the emergency measures tailored for medium-risk levels. An emergency evacuation alarm refers to a pre-set alarm message, such as "Please leave the monitoring area immediately." The third emergency measure refers to the emergency measures tailored for high-risk levels.

[0057] S8. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0058] Furthermore, the intelligent security door lock that achieves security monitoring and emergency response based on the first emergency measure, the second emergency measure, and the third emergency measure includes: Obtain multiple reference monitoring dataset groups; Multiple reference fusion datasets are obtained based on the aforementioned multiple reference monitoring dataset groups; Multiple reference risk levels are obtained based on the aforementioned multiple reference fusion datasets; By summarizing the first, second, and third emergency measures, multiple emergency measures are obtained. Based on the multiple reference risk levels and multiple emergency measures, a set of emergency measures to be trained is obtained; A model is built by training a reference fusion dataset and a set of emergency measures to be trained using a pre-built machine learning method, resulting in an intelligent emergency decision-making model. An intelligent security door lock that achieves security monitoring and emergency response based on the aforementioned intelligent emergency decision-making model and security door lock.

[0059] It needs to be explained that the purpose of establishing an intelligent emergency decision-making model through emergency strategies and reference datasets is to overcome the limitations of rigid rules by introducing an intelligent emergency decision-making model based on the established hierarchical emergency strategies, thereby achieving intelligent and adaptive emergency decision-making. This model, through deep learning of the complex nonlinear relationship between massive historical monitoring data and emergency measures, can dynamically perceive subtle evolutions in the risk situation and identify more complex threat scenarios. Consequently, the security monitoring system can generate emergency measures that accurately match the current scenario, achieving a leap from static response based on a single threshold to dynamic intelligent decision-making based on multi-factor collaboration, thus comprehensively improving the proactive defense and intelligent handling capabilities of security door locks. For example, the security monitoring system detects multiple abnormal rotations of the lock cylinder of a security door lock within a short period, but the vibration outside the door is very small, and the personnel loitering detection signal is weak. Based on the aforementioned risk assessment method, the risk level is determined to be low, and the first emergency measure is directly formulated. However, if analyzed through the intelligent emergency decision-making model, the decision-making process and results may be completely different. During the training process, the intelligent emergency decision-making model has learned massive amounts of historical data and its final emergency measures. This model can identify the current sensor data combination (abnormal rotation of the lock cylinder at a specific frequency + ...). The extremely low environmental vibration and the absence of personnel loitering closely match the characteristics of the "technical unlocking attempt" high-threat pattern in historical records, while significantly differing from the ordinary "accidental touch" or "low-risk interference" patterns. Therefore, the risk level of this incident is determined to be medium risk. Training an intelligent emergency decision-making model on multiple historical monitoring data points and their corresponding emergency measures can effectively avoid erroneous risk assessments.

[0060] It is clear that a reference monitoring dataset group refers to a collection of historical datasets monitored by multiple target sensors. Obtaining multiple reference fusion datasets based on these multiple reference monitoring dataset groups means fusing the data from these multiple reference monitoring dataset groups to obtain multiple reference fusion datasets. The method for obtaining multiple reference fusion datasets is the same as the method for obtaining the target fusion dataset, and will not be repeated here. Obtaining multiple reference risk levels based on these multiple reference fusion datasets means performing risk assessments on these multiple reference fusion datasets. The method for performing risk assessments on these multiple reference fusion datasets is the same as the method for obtaining the risk level set, and will not be repeated here. Obtaining a set of emergency measures to be trained based on the multiple reference risk levels and multiple emergency measures means customizing emergency measures for multiple reference risk levels based on the multiple emergency measures. The method for obtaining the set of emergency measures to be trained is the same as the method for obtaining multiple emergency measures, and will not be repeated here.

[0061] Understandably, the reference fusion dataset refers to the collection of fused data obtained by fusing multiple reference monitoring datasets. The reference risk level refers to the risk level corresponding to the reference fusion data. The emergency measures to be trained refer to the emergency measures corresponding to the reference risk level. Training and building a model using a pre-built machine learning method on the reference fusion dataset and the set of emergency measures to be trained refers to using a convolutional neural network model from machine learning to train the reference fusion dataset and the set of emergency measures to be trained. The method of using machine learning to train and build the model is existing technology and will not be elaborated further here. The intelligent emergency decision-making model refers to a convolutional neural network model used to make emergency measure decisions regarding risks in security door locks.

[0062] For example, when a user issues the security monitoring command, an intelligent emergency decision-making model is set up to combine security monitoring with emergency response, and the security door lock will automatically combine security monitoring with emergency response based on the intelligent emergency decision-making model.

[0063] To address the problems described in the background art, this invention receives a security monitoring command and, based on the command, identifies the security monitoring environment. This environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system comprises a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. Therefore, before intelligently combining security monitoring and emergency response in a security door lock, this invention considers the operating conditions of the security door lock under different environments or circumstances. Thus, it identifies the security monitoring system and the security door lock to be monitored. Based on the security door lock, it identifies multiple target sensors, receives a multi-source data acquisition command from the multi-source data acquisition unit, and obtains an initial dataset based on the command and the multiple target sensors. This invention also considers the different sampling performance and sampling periods of different sensors when monitoring the security door lock. Therefore, by evaluating sensor performance, the subsequently collected target dataset is more accurate, laying the foundation for subsequent risk assessment. The initial dataset is preprocessed to obtain the target dataset. The invention also considers preprocessing the initial dataset group and fusing it with the target dataset group to obtain the target fused dataset. By performing risk assessment on the target fused dataset, the problem of inaccurate risk assessment due to data from a single sensor can be avoided, thus improving the accuracy of security monitoring. The invention receives risk assessment instructions from the risk assessment unit and obtains a risk level set based on these instructions and the target fused dataset. This risk level set includes multiple risk levels, namely low, medium, and high risk. Therefore, the invention also considers the actual situation of the area where the security door lock is located, dividing the risk level into three levels, and considers different emergency measures for different risk levels. Furthermore, the invention receives emergency management instructions from the emergency management unit and obtains multiple emergency measures based on these instructions and the risk level set. Based on these multiple emergency measures, the invention achieves intelligent security door lock integration of security monitoring and emergency response. Therefore, the invention can realize the intelligent integration of security monitoring and emergency response in security door locks.

[0064] like Figure 2 The diagram shown is a functional block diagram of an intelligent security door lock system that combines security monitoring and emergency response, provided in an embodiment of the present invention.

[0065] The intelligent security door lock system 100 combining security monitoring and emergency response described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent security door lock system 100 may include a monitoring environment verification module 101, a data acquisition and processing module 102, a risk assessment module 103, and an emergency management module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0066] The monitoring environment confirmation module 101 is used to receive a security monitoring instruction and confirm the security monitoring environment based on the security monitoring instruction. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. The data acquisition and processing module 102 is used to identify multiple target sensors based on the security door lock; Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. The risk assessment module 103 is used to receive risk assessment instructions from the risk assessment unit, and obtain a risk level set based on the risk assessment instructions and the target fusion dataset. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively. The emergency management module 104 is used to confirm the door lock controller, video recorder, audible and visual alarm and communication unit based on the emergency management unit; The risk level is extracted from the risk level set. If the risk level is low, the video recorder is activated to record the entire process, and the security door lock is enhanced using the door lock controller, which is the first emergency measure. If the risk level is medium, the sound and light alarm is used to issue a warning signal, and the warning information is sent through the communication unit, which is the second emergency measure. If the risk level is high, the sound and light alarm is activated to issue an emergency evacuation alarm, and the communication unit is used to send a distress signal to the pre-built emergency center, which is the third emergency measure. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0067] In detail, the modules in the intelligent security door lock system 100 combining security monitoring and emergency response described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method used here is the same as the intelligent security door lock method that combines security monitoring and emergency response, and it can produce the same technical effect, so it will not be elaborated here.

[0068] like Figure 3 The diagram shown is a structural schematic of an electronic device that implements a smart security door lock method combining security monitoring and emergency response, according to an embodiment of the present invention.

[0069] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a smart security door lock method program that combines security monitoring and emergency linkage.

[0070] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a smart security door lock method program combining security monitoring and emergency linkage, but also to temporarily store data that has been output or will be output.

[0071] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a smart security door lock method program combining security monitoring and emergency linkage), and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0072] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0073] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0074] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0075] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0076] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0077] The intelligent security door lock method program combining security monitoring and emergency linkage, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following: The system receives a security monitoring command and identifies the security monitoring environment based on the command. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. Based on the security door lock, multiple target sensors were identified; Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. Receive risk assessment instructions from the risk assessment unit, and obtain a risk level set based on the risk assessment instructions and the target fusion dataset. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively. Based on the emergency management unit, the door lock controller, video recorder, audible and visual alarm, and communication unit were identified; The risk level is extracted from the risk level set. If the risk level is low, the video recorder is activated to record the entire process, and the security door lock is enhanced using the door lock controller, which is the first emergency measure. If the risk level is medium, the sound and light alarm is used to issue a warning signal, and the warning information is sent through the communication unit, which is the second emergency measure. If the risk level is high, the sound and light alarm is activated to issue an emergency evacuation alarm, and the communication unit is used to send a distress signal to the pre-built emergency center, which is the third emergency measure. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0078] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0079] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0080] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: The system receives a security monitoring command and identifies the security monitoring environment based on the command. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The security monitoring system includes a multi-source data acquisition unit, a risk assessment unit, and an emergency management unit. Based on the security door lock, multiple target sensors were identified; Receive multi-source data acquisition instructions from the multi-source data acquisition unit, and acquire an initial dataset group based on the multi-source data acquisition instructions and multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. Receive risk assessment instructions from the risk assessment unit, and obtain a risk level set based on the risk assessment instructions and the target fusion dataset. The risk level set includes multiple risk levels, and the multiple risk levels are low risk level, medium risk level and high risk level, respectively. Based on the emergency management unit, the door lock controller, video recorder, audible and visual alarm, and communication unit were identified; The risk level is extracted from the risk level set. If the risk level is low, the video recorder is activated to record the entire process, and the security door lock is enhanced using the door lock controller, which is the first emergency measure. If the risk level is medium, the sound and light alarm is used to issue a warning signal, and the warning information is sent through the communication unit, which is the second emergency measure. If the risk level is high, the sound and light alarm is activated to issue an emergency evacuation alarm, and the communication unit is used to send a distress signal to the pre-built emergency center, which is the third emergency measure. A smart security door lock that achieves safety monitoring and emergency response based on the first, second, and third emergency measures.

[0081] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0082] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0084] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent security door locks that combines security monitoring and emergency response, characterized in that, The method includes: Receive a security monitoring command, and confirm the security monitoring environment based on the security monitoring command, wherein the security monitoring environment includes a security monitoring system and a security door lock to be monitored; Based on the security door lock, multiple target sensors were identified; Initial dataset groups are acquired based on multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. Risk levels are obtained based on the target fusion dataset, where the risk level is low risk, medium risk, or high risk. The security monitoring system identifies the door lock controller, video recorder, audible and visual alarm, and communication unit. If the risk level is low, the video recorder will be activated to record the entire process, and the security door lock will be enhanced using the door lock controller. If the risk level is medium, the sound and light alarm will be activated to issue a warning signal, and the warning information will be sent through the communication unit. If the risk level is high, the sound and light alarm will be activated to issue an emergency evacuation alarm, and the communication unit will be used to send a distress signal to the pre-built emergency center, thus realizing a smart security door lock that integrates safety monitoring and emergency response.

2. The intelligent security door lock method combining security monitoring and emergency response as described in claim 1, characterized in that, The security door lock identifies multiple target sensors, including: Based on the security door lock, obtain the sensor type set; For each sensor type in the sensor type set, perform the following operation: A candidate sensor set is obtained based on the sensor type; For each candidate sensor in the candidate sensor set, perform the following operation: Based on the candidate sensors, a set of performance indicators was identified, which includes multiple performance indicators, namely, accuracy level score, response time score, environmental adaptability score, and power consumption level score. The performance evaluation value is obtained based on the set of performance indicators; The performance evaluation values ​​are summarized to obtain a performance evaluation value set; Obtain the optimal performance evaluation value based on the aforementioned performance evaluation value set; The candidate sensor corresponding to the optimal performance evaluation value is taken as the target sensor; By combining the aforementioned target sensors, multiple target sensors are obtained.

3. The intelligent security door lock method combining security monitoring and emergency response as described in claim 2, characterized in that, The sensor type set obtained based on the security door lock includes: The security area is obtained based on the security door lock; The security area is divided using a preset sensing area to obtain multiple effective monitoring areas; For each of the multiple valid monitoring areas, perform the following operations: The target sensor is extracted sequentially from multiple target sensors, and the extracted target sensor is placed in the effective monitoring area to obtain the registered sensor; By combining the registered sensors, a registration sensor set is obtained; For each registration sensor in the registration sensor set, the following operation is performed: The data sampling period is determined based on the registration sensor. By summing the data sampling periods, a sampling period set is obtained; A reference sampling period is obtained based on the sampling period set, wherein the reference sampling period is the largest data sampling period in the sampling period set; Based on the aforementioned reference sampling period and the pre-constructed time-scale synchronizer, the registered sensor set is time-domain aligned to obtain a synchronously acquired sensor set; An initial dataset group is obtained based on the synchronously acquired sensor set, wherein the initial dataset group includes multiple initial datasets, and each initial dataset corresponds one-to-one with a synchronously acquired sensor.

4. The intelligent security door lock method combining security monitoring and emergency response as described in claim 3, characterized in that, The preprocessing of the initial dataset group to obtain the target dataset group includes: Perform the following operation on each initial dataset in the initial dataset group: A set of statistical features is obtained based on the initial dataset, wherein the set of statistical features includes multiple statistical features, and the statistical features are kurtosis coefficient, skewness coefficient and coefficient of variation. The data quality assessment value is obtained based on the statistical feature set, wherein the calculation formula for the data quality assessment value is as follows: Among them, it means Data quality assessment value, Represents the kurtosis factor. Indicates the skewness coefficient. Indicates the coefficient of variation; Compare the data quality assessment value with a preset quality threshold. If the data quality assessment value is greater than or equal to the quality threshold, then the initial dataset is used as the target dataset. If the data quality assessment value is less than the quality threshold, then initial data is extracted from the initial dataset in sequence, and the extracted initial data is compared with the preset abnormal threshold. If the extracted data is greater than or equal to the abnormal threshold, then the extracted initial data is taken as abnormal data. The abnormal data is summarized to obtain an abnormal dataset. The abnormal dataset is removed from the initial dataset to obtain the target dataset.

5. The intelligent security door lock method combining security monitoring and emergency response as described in claim 4, characterized in that, The process of obtaining a set of statistical features based on the initial dataset includes: Count the number of initial data points in the initial dataset to obtain the number of data points; The initial data mean is obtained based on the initial dataset, wherein the initial data mean is the arithmetic mean of the initial data in the initial dataset; The initial data standard deviation is obtained based on the initial dataset, wherein the initial data standard deviation is the standard deviation of the initial data in the initial dataset; The kurtosis coefficient is obtained based on the initial dataset, the number of data points, and the initial data mean. The formula for calculating the kurtosis coefficient is as follows: in, Represents the kurtosis factor. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points; The skewness coefficient is obtained based on the initial dataset; The coefficient of variation is obtained based on the initial data mean and initial data standard deviation, wherein the coefficient of variation is the ratio of the initial data standard deviation to the initial data mean; By summing up the kurtosis coefficient, skewness coefficient, and coefficient of variation, a set of statistical characteristics is obtained.

6. The intelligent security door lock method combining security monitoring and emergency response as described in claim 5, characterized in that, The skewness coefficient is obtained based on the initial dataset, and its calculation formula is as follows: in, Indicates the skewness coefficient. Represents the first in the initial dataset Initial data, This represents the initial data mean. Indicates the number of data points.

7. The intelligent security door lock method combining security monitoring and emergency response as described in claim 6, characterized in that, The process of fusing the target dataset group to obtain the target fused dataset includes: Perform the following operation on each target dataset in the target dataset group: Obtain the maximum and minimum measured values ​​of the synchronous acquisition sensors corresponding to the target dataset; The extracted target dataset is normalized using the maximum and minimum measured values ​​to obtain a normalized dataset. The target data type is obtained based on the normalized dataset and its corresponding synchronous acquisition sensor; The normalized datasets are then aggregated to obtain a normalized dataset group; By summarizing the target data types, a target data type set is obtained; The initial weight set is obtained based on the target data type set and the pre-built expert database; Perform the following operation on each normalized dataset in the normalized dataset group: Normalized data is extracted sequentially from the normalized dataset to obtain the data to be fused; By summarizing the data to be merged, a dataset to be merged is obtained; The target fused data is obtained by weighted summation of the dataset to be fused using the initial weight set. The target fusion data are then aggregated to obtain the target fusion dataset.

8. The intelligent security door lock method combining security monitoring and emergency response as described in claim 7, characterized in that, The method of obtaining risk levels based on target fusion datasets includes: Obtain low-risk and high-risk thresholds; Based on the target fusion dataset, obtain the target fusion mean, target fusion extreme value, and target fusion variance; The risk assessment value is obtained based on the target fusion mean, target fusion extreme value, and target fusion variance. By comparing the risk assessment value, the low-risk threshold, and the high-risk threshold, if the risk assessment value is less than the low-risk threshold, the risk level is determined to be low-risk. If the risk assessment value is greater than or equal to the low risk threshold and less than the high risk threshold, the risk level is determined to be medium risk. If the risk assessment value is greater than or equal to the high-risk threshold, the risk level is determined to be high-risk.

9. The intelligent security door lock method combining security monitoring and emergency response as described in claim 8, characterized in that, The intelligent security door lock that enables security monitoring and emergency response includes: Obtain multiple reference monitoring dataset groups; Multiple reference fusion datasets are obtained based on the aforementioned multiple reference monitoring dataset groups; Multiple reference risk levels are obtained based on the aforementioned multiple reference fusion datasets; A set of emergency response measures to be trained is obtained based on the multiple reference risk levels; A model is built by training a reference fusion dataset and a set of emergency measures to be trained using a pre-built machine learning method, resulting in an intelligent emergency decision-making model. An intelligent security door lock that achieves security monitoring and emergency response based on the aforementioned intelligent emergency decision-making model and security door lock.

10. An intelligent security door lock system combining security monitoring and emergency response, characterized in that, The system includes: The monitoring environment confirmation module is used to receive security monitoring instructions and confirm the security monitoring environment based on the security monitoring instructions. The security monitoring environment includes a security monitoring system and a security door lock to be monitored. The data acquisition and processing module is used to identify multiple target sensors based on the security door lock; Initial dataset groups are acquired based on multiple target sensors; The initial dataset group is preprocessed to obtain the target dataset group, and the target dataset group is fused to obtain the target fused dataset. The risk assessment module is used to obtain the risk level based on the target fusion dataset, where the risk level is low risk, medium risk, or high risk. The emergency management module is used to identify the door lock controller, video recorder, audible and visual alarm, and communication unit based on the security monitoring system. If the risk level is low, the video recorder will be activated to record the entire process, and the security door lock will be enhanced using the door lock controller. If the risk level is medium, the sound and light alarm will be activated to issue a warning signal, and the warning information will be sent through the communication unit. If the risk level is high, the sound and light alarm will be activated to issue an emergency evacuation alarm, and the communication unit will be used to send a distress signal to the pre-built emergency center, thus realizing a smart security door lock that integrates safety monitoring and emergency response.

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