A remote door opening control method and system for unmanned convenience stores

Abstract

The application provides a remote door opening control method and system for unmanned convenience stores, and relates to the technical field of time registration. Whether the current period is in a preset business period is determined according to a time stamp of network time protocol time service in the unmanned convenience store, and a period determination result is obtained. A regulation curve of access control security level in the unmanned convenience store is fitted based on historical door opening and closing records and abnormal alarm events of the unmanned convenience store. The current opening and closing state of the door, the personnel retention state in the convenience store and the period determination result are input into the regulation curve of the access control security level, and a dynamic security level value is generated. The number of valid bits and the valid time length of a dynamic unlocking password are adjusted according to the dynamic security level value, and the adjusted dynamic unlocking password is sent to an access controller. The above scheme can realize adaptive dynamic adjustment of the unlocking password parameters according to the security situation of the unmanned convenience store, thereby improving the access control security protection capability.

Description

Technical Field

[0001] This application relates to the field of time registration technology, and more specifically, to a remote door opening control method and system for unmanned convenience stores. Background Technology

[0002] An unmanned convenience store is a retail establishment that does not have on-site staff and relies on automation technology to enable customers to shop and pay independently. The store manages customer entry and exit through access control devices, is equipped with sensors to detect personnel activities and changes in merchandise, and relies on remote communication links to enable back-end monitoring and management of front-end devices, thereby completing daily operations and security control without permanent staff.

[0003] Current remote door opening control methods for unmanned convenience stores typically employ preset dynamic unlocking passwords of fixed length and uniform validity periods. Once the password is generated, the number of valid digits and the validity period remain unchanged throughout the entire operating cycle. This fixed parameter mechanism fails to consider the varying security risks associated with different times of day, different personnel presence, and different door opening / closing states. For example, during off-peak hours at night or when abnormal personnel are detected inside the store, the access control system faces high-risk scenarios such as unauthorized tailgating and remote assistance from insiders. However, the fixed password validity period maintains the lenient time limit of normal daytime business hours, causing the password to remain valid for an extended period, increasing the probability of password interception or malicious use. Furthermore, the fixed password length cannot enhance password complexity in high-risk scenarios; low-length passwords are easily brute-forced. Due to the lack of integrated analysis of multi-dimensional environmental context information such as real-time door status, personnel presence, and business hours, existing methods cannot dynamically adjust password parameters to match different security postures. This results in a disconnect between access control security capabilities and current risk levels, hindering overall security. Therefore, how to achieve adaptive dynamic adjustment of unlocking password parameters according to the security situation of unmanned convenience stores, thereby improving access control security capabilities, is a challenge faced by the industry. Summary of the Invention

[0004] This application provides a remote door opening control method and system for unmanned convenience stores, which can adaptively and dynamically adjust the unlocking password parameters according to the security status of the unmanned convenience store, thereby improving the access control security protection capability.

[0005] In a first aspect, this application provides a remote door opening control method for unmanned convenience stores, the control method comprising the following steps:

[0006] Acquire the door status signal uploaded by the access control controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol;

[0007] The door status signal and the human body sensing signal are analyzed to determine the current opening and closing status of the door and the status of people staying in the convenience store. Based on the timestamp, it is determined whether the current time period is within the preset business period, and the time period determination result is obtained.

[0008] Based on the historical door opening and closing records and abnormal alarm events of the unmanned convenience store, an adjustment curve for the access control security level of the unmanned convenience store is obtained. The current opening and closing status, the personnel stay status, and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value.

[0009] The dynamic unlocking password is adjusted based on the dynamic security level value, including the number of valid digits and the validity period. The adjusted dynamic unlocking password is then sent to the access control controller.

[0010] In this embodiment, analyzing the door status signal and the human body sensing signal to determine the current opening / closing status of the door and the status of people remaining in the convenience store specifically includes:

[0011] The door magnetic switch level value is extracted from the door status signal, and the door magnetic switch level value is compared with the door closure reference level threshold to obtain the current opening and closing state of the door.

[0012] The infrared pyroelectric induction voltage pulse sequence is extracted from the human body induction signal, and the cumulative number of pulse triggers within a unit time window is counted.

[0013] The cumulative number of pulse triggers is compared with the threshold for determining the presence of people to generate the status of people staying in the convenience store.

[0014] In this embodiment, determining whether the current time period falls within a preset business period based on the timestamp, and obtaining the time period determination result specifically includes:

[0015] Parse the current hour and minute from the timestamp and concatenate them to obtain the current time value;

[0016] Retrieve the preset business hours configuration table, which includes business start time and business end time values, to obtain the business hours interval;

[0017] The current time value is compared with the business hours interval to determine the interval inclusion, and the time period determination result is obtained.

[0018] In this embodiment, the adjustment curve for the access control security level of the unmanned convenience store, obtained by fitting historical door opening and closing records and abnormal alarm events, specifically includes:

[0019] Obtain historical door opening and closing records and abnormal alarm events of unmanned convenience stores;

[0020] Extract the timestamp and duration of each door opening and closing event from the historical door opening and closing records, calculate the door opening and closing frequency within a unit time period, and obtain the door opening and closing frequency sequence.

[0021] Extract the alarm type label and alarm occurrence timestamp for each alarm from the abnormal alarm events, assign alarm weight values ​​according to the severity of the alarm type label, and statistically analyze the weighted alarm frequency sequence within a unit time period;

[0022] Piecewise linear fitting is performed on the door opening / closing frequency sequence and the weighted alarm frequency sequence to obtain the adjustment curve of the access control security level in the unmanned convenience store.

[0023] In this embodiment, inputting the current opening / closing status, the personnel's lingering status, and the time period judgment result into the adjustment curve of the access control security level to generate a dynamic security level value specifically includes:

[0024] Based on the door opening / closing identifier in the current opening / closing state and the personnel staying identifier in the personnel staying state, the current scene status code is generated by combining them.

[0025] Map the business hours identifier or non-business hours identifier in the time period determination result to a time period weighting coefficient;

[0026] On the adjustment curve of the access control security level, the baseline security level value is obtained by querying the value at the current moment as the horizontal axis;

[0027] The baseline security level value is weighted and superimposed with the scene offset corresponding to the current scene status code and the time period weight coefficient to output a dynamic security level value.

[0028] In this embodiment, adjusting the valid number of digits and validity period of the dynamic unlocking password based on the dynamic security level value specifically includes:

[0029] Retrieve a preset security level and password parameter mapping table, which contains the correspondence between security level value ranges and password valid length ranges and password valid duration ranges;

[0030] Using the dynamic security level value as the query key, locate the corresponding security level value range in the mapping table, and extract the corresponding password valid length range value and password valid duration range value;

[0031] The effective number of digits and the effective duration of the password are used as adjustment parameters and input into the dynamic password generation algorithm to generate the adjusted dynamic unlocking password and the corresponding effective duration marker.

[0032] In this embodiment, the infrared pyroelectric sensor is an infrared sensing sensor based on the pyroelectric effect.

[0033] Secondly, this application provides a remote door opening control system for unmanned convenience stores, used to execute a remote door opening control method for unmanned convenience stores, the control system comprising:

[0034] The acquisition module is used to acquire the door status signal uploaded by the access controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol.

[0035] The parsing module is used to parse the door status signal and the human body sensing signal to determine the current opening and closing status of the door and the status of people staying in the convenience store, and to determine whether the current time period is within the preset business period based on the timestamp, so as to obtain the time period judgment result.

[0036] The security level generation module is used to fit the historical door opening and closing records and abnormal alarm events of the unmanned convenience store to obtain the adjustment curve of the access control security level. The current opening and closing status, the personnel stay status and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value.

[0037] The password adjustment module is used to adjust the effective number of digits and the effective duration of the dynamic unlocking password according to the dynamic security level value, and send the adjusted dynamic unlocking password to the access control controller.

[0038] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described remote door opening control method for unmanned convenience stores.

[0039] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned remote door opening control method for unmanned convenience stores.

[0040] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0041] The system acquires the door status signal uploaded by the access control controller, the human body induction signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol synchronization; it analyzes the door status signal and the human body induction signal to determine the current opening / closing status of the door and the personnel lingering status in the convenience store, and determines whether the current time period is within the preset business hours based on the timestamp, thus obtaining the time period judgment result; it obtains the adjustment curve of the access control security level in the unmanned convenience store by fitting the historical door opening / closing records and abnormal alarm events, and inputs the current opening / closing status, the personnel lingering status, and the time period judgment result into the access control security level adjustment curve to generate a dynamic security level value; it adjusts the effective number of digits and the effective duration of the dynamic unlocking password according to the dynamic security level value, and sends the adjusted dynamic unlocking password to the access control controller.

[0042] Therefore, in this application, the effective number of digits and the effective duration of the dynamic unlocking password are adjusted according to the dynamic security level value, and the adjusted dynamic unlocking password is sent to the access control controller. First, by determining the adjustment curve of the access control security level, a continuous mapping relationship between the security level and the time and event density can be obtained, thereby converting the discrete historical door opening and closing frequency and the weighted alarm frequency into a queryable security status change trend. This adjustment curve reflects the distribution of security risks in unmanned convenience stores under different operating cycles and abnormal event densities. This allows the determination of security levels to move beyond static thresholds and instead dynamically correlate with the store's actual operating rhythm and alarm history. This provides a quantitative benchmark for subsequent password parameter adjustments that matches the store's actual risk situation. Then, by determining the dynamic security level value, the query values ​​on the adjustment curve for the current door opening / closing status, personnel presence status, and operating hours are integrated with the scene offset and time period weighting coefficient. This transforms real-time multi-dimensional environmental context information into a single security level value. This dynamic security level value comprehensively reflects the store's composite security risks in the time, spatial, and personnel dimensions at the current moment. This enables adjustments to the effective password length and duration to directly respond to real-time security situation changes, achieving precise matching between password parameters and risk levels. This avoids insufficient password strength in high-risk scenarios or excessive password strength in low-risk scenarios.

[0043] In summary, the above scheme can achieve adaptive dynamic adjustment of the unlocking password parameters according to the security situation of the unmanned convenience store, thereby improving the access control security protection capability. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the remote door opening control method for unmanned convenience stores provided in this application;

[0046] Figure 2 This is an exemplary flowchart for determining dynamic security level values ​​provided in this application;

[0047] Figure 3 This is an exemplary flowchart based on the execution password adjustment provided in this application;

[0048] Figure 4 This is a modular structure diagram of a remote door opening control system for an unmanned convenience store provided in this application;

[0049] Figure 5 This is a schematic diagram of the structure of a computer device for implementing a remote door opening control method for unmanned convenience stores, according to some embodiments of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1 As shown in the figure, this is an exemplary flowchart of a remote door opening control method for an unmanned convenience store according to this embodiment of the present application. The control method includes the following steps:

[0052] In step S1, the door status signal uploaded by the access controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol are obtained.

[0053] It should be noted that in this application, the infrared pyroelectric sensor is an infrared sensing sensor based on the pyroelectric effect; the door status signal is a switching electrical signal that characterizes the open or closed state of the unmanned convenience store door; the human body sensing signal is an infrared pyroelectric sensing voltage signal that reflects whether there is human activity inside the unmanned convenience store; and the timestamp is a time value that marks the current moment in the network time protocol synchronization.

[0054] In practice, firstly, a magnetic door sensor is installed on the door of the access control system. The switching electrical signal output by the magnetic door sensor is used as the door status signal, which is then uploaded by the access control system. Secondly, an infrared pyroelectric sensor based on the pyroelectric effect is installed inside the unmanned convenience store. The induced voltage pulse sequence generated by the sensor detecting changes in human infrared radiation is collected as the human body sensing signal. Finally, a time synchronization request is sent to the network time protocol server, and the standard time data packet returned by the server is received. The year, month, day, hour, minute, and second fields are parsed from the standard time data packet and concatenated into a timestamp.

[0055] In step S2, the door status signal and the human body sensing signal are parsed to determine the current opening and closing status of the door and the status of people staying in the convenience store. Based on the timestamp, it is determined whether the current time period is within the preset business period to obtain the time period determination result.

[0056] In this embodiment, the determination of the current opening / closing status of the door and the presence / absence status of people inside the convenience store by parsing the door status signal and the human body sensing signal can be achieved in the following manner:

[0057] The door magnetic switch level value is extracted from the door status signal, and the door magnetic switch level value is compared with the door closure reference level threshold to obtain the current opening and closing state of the door.

[0058] The infrared pyroelectric induction voltage pulse sequence is extracted from the human body induction signal, and the cumulative number of pulse triggers within a unit time window is counted.

[0059] The cumulative number of pulse triggers is compared with the threshold for determining the presence of people to generate the status of people staying in the convenience store.

[0060] It should be noted that in this application, the current open / closed state is the result of judging whether the door is in an open or closed state; the cumulative number of pulse triggers is the cumulative number of times the sensor response is triggered by changes in human infrared radiation within a unit time window; and the personnel lingering state is the result of judging whether there are personnel inside the unmanned convenience store.

[0061] In practice, firstly, the stable level value output by the door magnetic sensor when the door is closed is measured in advance, and the range of ±5% of this stable level value is set as the door closure reference level threshold. The real-time level value is read from the door status signal as the door magnetic switch level value. The door magnetic switch level value is compared with the door closure reference level threshold. If the door magnetic switch level value falls within the door closure reference level threshold range, a door closure indicator is generated as the current opening and closing state; otherwise, a door opening indicator is generated as the current opening and closing state. Then, the unit time window is set to 2 seconds by default. The infrared pyroelectric sensing voltage pulse sequence is extracted from the human body sensing signal at 2-second intervals. The rising edges of the voltage pulses occurring within this 2-second window are accumulated and counted, and the accumulated count is used as the pulse trigger count. Finally, the pulse trigger count of the infrared pyroelectric sensor is continuously collected for 10 minutes in an empty store state, and the maximum value of the pulse trigger count within 10 minutes is taken as the personnel presence determination threshold. The pulse trigger count is compared with the personnel presence determination threshold. If the pulse trigger count is greater than or equal to the personnel presence determination threshold, a lingering status indicator is generated as a personnel lingering status. If the pulse trigger count is less than the personnel presence determination threshold, an empty store status indicator is generated as a personnel lingering status.

[0062] It should be noted that in this application, the duration of the unit time window is set according to the human motion response characteristics of the infrared pyroelectric sensor. Specifically, the average duration of a human walking through the sensor detection area is 1.5 to 2.5 seconds, and the signal holding time after a single trigger of the sensor is 1 second. To balance detection sensitivity and false alarm resistance, the unit time window is set to 2 seconds. This duration can fully capture the pulse sequence generated by a single human passage and avoid false triggering caused by environmental thermal noise interference due to an excessively long window. The method for determining the threshold for determining the presence of personnel is as follows: In an unmanned convenience store that is empty and in a stable environment, the cumulative number of infrared pyroelectric pulse triggers every 2 seconds is continuously collected over 24 hours. The maximum value of all sampled data is taken as the baseline environmental noise value N0. The baseline environmental noise value N0 is multiplied by the redundancy coefficient K to obtain the threshold for determining the presence of personnel T = N0 × K, where the value of K ranges from 1.5 to 2.0, and the specific value is determined based on the sensor installation height and the detection area: when the installation height is ≤3m and the detection area is ≤10m², K is 1.5; when the installation height is >3m or the detection area is >10m², K is 2.0. Those skilled in the art can calibrate the specific threshold value using the above method according to the actual deployment environment.

[0063] In this embodiment, the determination of whether the current time period falls within a preset business period based on the timestamp can be achieved in the following manner:

[0064] Parse the current hour and minute from the timestamp and concatenate them to obtain the current time value;

[0065] Retrieve the preset business hours configuration table, which includes business start time and business end time values, to obtain the business hours interval;

[0066] The current time value is compared with the business hours interval to determine the interval inclusion, and the time period determination result is obtained.

[0067] It should be noted that in this application, the current time value represents the time value formed by concatenating the current hour and the current minute extracted from the timestamp; the preset business hours configuration table is a preset data table that stores the business start time value and the business end time value; the time period judgment result is the judgment result that identifies whether the current time is in a business period or a non-business period.

[0068] In practice, firstly, the hour field value is extracted from the timestamp as the current hour, and the minute field value is extracted as the current minute. The current hour and minute values ​​are concatenated in the order of hour first, minute last to form a four-digit number, which is then used as the current time value. Secondly, the daily start and end times are determined in advance according to the operating rules of the unmanned convenience store. The hour and minute values ​​of the start time are concatenated to form the start time value, and the hour and minute values ​​of the end time are concatenated to form the end time value. The start and end time values ​​are stored in a preset operating period configuration table. The preset operating period configuration table is retrieved, and the start and end time values ​​are read. The closed interval formed by the start and end time values ​​is used as the operating period interval. Finally, the current time value is compared with the operating period interval for inclusion. If the current time value is greater than or equal to the start time value and less than or equal to the end time value, an operating period identifier is generated as the period determination result; otherwise, a non-operating period identifier is generated as the period determination result.

[0069] In step S3, an adjustment curve for the access control security level of the unmanned convenience store is obtained by fitting the historical door opening and closing records and abnormal alarm events. The current opening and closing status, the personnel stay status and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value.

[0070] In this embodiment, the adjustment curve for the access control security level of the unmanned convenience store, which is obtained by fitting historical door opening and closing records and abnormal alarm events, can be obtained in the following way:

[0071] Obtain historical door opening and closing records and abnormal alarm events of unmanned convenience stores;

[0072] Extract the timestamp and duration of each door opening and closing event from the historical door opening and closing records, calculate the door opening and closing frequency within a unit time period, and obtain the door opening and closing frequency sequence.

[0073] Extract the alarm type label and alarm occurrence timestamp for each alarm from the abnormal alarm events, assign alarm weight values ​​according to the severity of the alarm type label, and statistically analyze the weighted alarm frequency sequence within a unit time period;

[0074] Piecewise linear fitting is performed on the door opening / closing frequency sequence and the weighted alarm frequency sequence to obtain the adjustment curve of the access control security level in the unmanned convenience store.

[0075] It should be noted that, in this application, the historical door opening and closing records are historical datasets that record the occurrence time and duration of each door opening and closing event in the unmanned convenience store; the abnormal alarm events are historical datasets that record the types and occurrence times of various security alarms that have occurred in the unmanned convenience store; the door opening and closing frequency sequence is an ordered numerical sequence reflecting the number of door opening and closing events within each unit of time; the alarm type label is a marker that distinguishes different alarm event categories; the alarm weight value represents the numerical value of the severity of the alarm event corresponding to different alarm type labels; the weighted alarm frequency sequence is an ordered numerical sequence reflecting the frequency of alarm events after weighting by the alarm weight value within each unit of time; and the access control security level adjustment curve is a mapping curve that characterizes the dynamic adjustment of the access control security level with changes in time and event density.

[0076] In practical implementation, firstly, an event logging function is pre-configured in the access control system of the unmanned convenience store. Each time the access control system drives the door to open and close, it automatically records the timestamp of the opening / closing event and the duration from opening to closing, and writes the recorded results into the historical door opening / closing record. Simultaneously, an alarm logging function is pre-configured in the security monitoring device of the unmanned convenience store. When the security monitoring device detects an abnormal event, it automatically records the alarm type label and the timestamp of the alarm occurrence, and writes the recorded results into the abnormal alarm event. The historical door opening / closing record is read from the access control system, and the abnormal alarm event is read from the security monitoring device. Secondly, the timestamp and duration of each door opening / closing event are read from the historical door opening / closing record. The day is divided into multiple time periods of fixed duration, and the number of door opening / closing events within each time period is counted. The number of door opening / closing events in each time period is arranged in chronological order to obtain the door opening / closing frequency sequence. The fixed duration of each time period is preset to 1 hour.

[0077] Then, in the specific implementation, the alarm type label and alarm occurrence timestamp of each alarm are read one by one from the abnormal alarm events. An alarm weight value is assigned to each alarm type label. The alarm weight value of unauthorized intrusion alarm is preset to 3, the alarm weight value of multiple password attempt alarm is preset to 2, and the alarm weight value of door not closed properly alarm is preset to 1. The alarm weight values ​​of each alarm in each unit time period are summed. The weighted alarm occurrence frequency of each unit time period is arranged in chronological order to obtain the weighted alarm frequency sequence. Finally, the door opening and closing frequency sequence and the weighted alarm frequency sequence are aligned according to the same unit time period. With time as the horizontal axis and security level as the vertical axis, the door opening and closing frequency and the weighted alarm frequency in each unit time period are compared. The weighted summation is performed, and the result is mapped to a security level value. The security level values ​​of adjacent time periods are then segmented and linearly connected. Specifically, the door opening and closing frequency sequence and the weighted alarm frequency sequence are aligned in hourly time periods. After Min-Max normalization, the comprehensive security event density is synthesized by weighting the results with α=0.4 and β=0.6. This density is then mapped to a preset security level range (1-9) and rounded to obtain the security level value for each time period. Discrete points are plotted with the time period index on the horizontal axis and the security level value on the vertical axis, and then connected by straight line segments to obtain a complete segmented linear continuous curve. When the current time is within the time period, the baseline security level value is obtained by linear interpolation of the values ​​at both endpoints. This segmented linear continuous curve is the adjustment curve for the access control security level.

[0078] It should be noted that in this application, the alarm weight values ​​are based on the relative quantification of the degree of threat to access control security by each alarm type. The alarm of a door not being closed properly is used as the benchmark (weight 1), the harm of multiple password trial and error alarms is about twice that of the alarm (weight 2), and the harm of unauthorized intrusion alarms is about three times that of the alarm (weight 3). Before deployment, the alarm weight values ​​can also be verified and linearly scaled and corrected based on the correlation probability between historical alarm data and actual intrusion events. Those skilled in the art can determine the actual weight values ​​to be used in this way.

[0079] In this embodiment, reference Figure 2 As shown, this diagram is an exemplary flowchart for determining the dynamic security level value in an embodiment of this application. In this embodiment, the current opening / closing status, the personnel's stay status, and the time period judgment result are input into the adjustment curve of the access control security level to generate the dynamic security level value. This can be achieved through the following steps:

[0080] First, in step S31, the current scene status code is generated by combining the door opening / closing identifier in the current opening / closing state and the personnel staying identifier in the personnel staying state.

[0081] Secondly, in step S32, the business hours identifier or non-business hours identifier in the time period determination result is mapped to a time period weight coefficient;

[0082] Then, in step S33, the baseline security level value is obtained by querying the current value on the adjustment curve of the access control security level.

[0083] Finally, in step S34, the baseline security level value is weighted and superimposed with the scene offset corresponding to the current scene status code and the time period weight coefficient to output the dynamic security level value.

[0084] It should be noted that, in this application, the current scenario status code represents the encoding value of the combined scenario of door opening / closing status and personnel lingering status; the time period weight coefficient is a numerical factor that adjusts the degree of influence of different business hours on the security level; the baseline security level value represents the initial security level value obtained from the adjustment curve based on the current time value; and the dynamic security level value represents the final security level value after correction by the scenario offset and the time period weight coefficient.

[0085] In specific implementation, firstly, a scene status code mapping table is pre-built, assigning a unique scene status code to each combination of door opening / closing identifiers and personnel lingering identifiers. Specifically, the scene status code corresponding to the combination of door closing identifier and empty store status identifier is preset to 01, the scene status code corresponding to the combination of door closing identifier and lingering identifier is preset to 02, the scene status code corresponding to the combination of door opening identifier and empty store status identifier is preset to 03, and the scene status code corresponding to the combination of door opening identifier and lingering identifier is preset to 04. Based on the door opening / closing identifier in the current opening / closing state and the personnel lingering identifier in the personnel lingering state, the corresponding scene status code is retrieved from the scene status code mapping table, and the query result is used as the current scene status code. Secondly, a time period weight mapping table is pre-built, with the time period weight coefficient corresponding to the business hours identifier preset to 1.0 and the time period weight coefficient corresponding to the non-business hours identifier preset to 1.5. The business hours identifier or non-business hours identifier is extracted from the time period judgment result, and the corresponding time period weight coefficient is retrieved from the time period weight mapping table, and the query result is used as the time period weight coefficient.

[0086] It should be noted that, in this application, based on the safety incident statistics of 12 stores of the chain unmanned convenience store brand for 6 consecutive months, the average severity of safety incidents occurring outside of business hours is 1.43 times that of those occurring during business hours. Based on this, the weight of business hours is rounded down to 1.0 (baseline value), and the weight of non-business hours is set to 1.5 (adjusted upwards to cover safety margin). Those skilled in the art can calculate the localized weighting coefficient in the same way based on the actual operating data of the target store and the historical safety incident statistics.

[0087] Then, in the specific implementation, the current time value is used as the horizontal axis input value. The horizontal coordinate position corresponding to the current time value is located on the access control security level adjustment curve. The vertical coordinate value corresponding to the horizontal coordinate position is read and used as the baseline security level value. Finally, a scene offset mapping table is pre-built, and a scene offset is assigned to each scene status code. The scene offset corresponding to scene status code 01 is preset to 0, the scene offset corresponding to scene status code 02 is preset to 1, the scene offset corresponding to scene status code 03 is preset to 2, and the scene offset corresponding to scene status code 04 is preset to 3. The scene offset corresponding to the current scene status code is queried from the scene offset mapping table. The baseline security level value, scene offset, and time period weight coefficient are weighted and superimposed by adding the baseline security level value and the scene offset and then multiplying by the time period weight coefficient. The value obtained after weighted superposition is used as the dynamic security level value.

[0088] It should be noted that in this application, the scene offset represents the increase in security risk of different door-person combination scenarios relative to the baseline scenario (closed door and empty store): the baseline scenario offset is 0; when the door is closed but people are staying inside, there is a risk that internal personnel will actively open the door to receive external personnel, the offset is 1; when the door is open and the store is empty, there are convenient conditions for external personnel to directly break in, the offset is 2; when the door is open and people are staying inside, there are both physical passage and internal reception conditions, the offset is 3. Each value increases arithmetically to reflect the relative proportion of risk levels.

[0089] In step S4, the effective number of digits and the effective duration of the dynamic unlocking password are adjusted according to the dynamic security level value, and the adjusted dynamic unlocking password is sent to the access control controller.

[0090] Preferably, in this embodiment, reference Figure 3 As shown, this diagram is an exemplary flowchart of password adjustment in an embodiment of this application. In this embodiment, adjusting the effective number of digits and the validity period of the dynamic unlocking password based on the dynamic security level value can be achieved through the following steps:

[0091] First, in step S41, a preset security level and password parameter mapping table is retrieved. The mapping table contains the correspondence between security level value ranges and password valid length ranges and password valid duration ranges.

[0092] Then, in step S42, the dynamic security level value is used as the query key to locate the security level value range in the mapping table and extract the corresponding password valid length range value and password valid duration range value.

[0093] Finally, in step S43, the effective password length range value and the effective password duration range value are used as adjustment parameters and input into the dynamic password generation algorithm to generate the adjusted dynamic unlocking password and the corresponding effective duration marker.

[0094] It should be noted that, in this application, the security level and password parameter mapping table is a preset data table that stores the correspondence between security level value ranges and password valid length and password valid duration levels; the password valid length level value represents the number of digits required for the dynamic unlocking password; the password valid duration level value represents the number of seconds the dynamic unlocking password is valid; the adjusted dynamic unlocking password is the password string generated after the current unlocking operation and the password valid length and password valid duration have been dynamically adjusted; the valid duration marker is the expiration time value that indicates the dynamic unlocking password remains valid from the time of generation.

[0095] In practice, firstly, a pre-defined correspondence between security level value ranges and password parameter levels is established. Security level value ranges 1 to 3 correspond to a password validity length of 4 digits and a password validity duration of 300 seconds; security level value ranges 4 to 6 correspond to a password validity length of 6 digits and a password validity duration of 120 seconds; and security level value ranges 7 to 9 correspond to a password validity length of 8 digits and a password validity duration of 60 seconds. This correspondence is stored as a security level-password parameter mapping table, which is then retrieved. Next, using the dynamic security level value as the query key, the dynamic security level value is mapped to the password parameter level. The upper and lower limits of each security level value range in the password parameter mapping table are compared one by one to locate the security level value range into which the dynamic security level value falls. The password valid length range value and password valid duration range value are extracted from the record corresponding to the range. Finally, the password valid length range value is used as the length input parameter of the dynamic password generation algorithm, and the password valid duration range value is used as the valid duration input parameter of the dynamic password generation algorithm. The dynamic password generation algorithm randomly generates a number sequence of a specified length based on the length input parameter as the adjusted dynamic unlocking password. At the same time, the current timestamp is read and added to the valid duration input parameter, and the result is used as the valid duration marker.

[0096] It should be noted that in this application, the password parameter mapping follows a tiered strategy of "increasing password complexity by one level and shortening validity period by one level for every 3 units increase in security level". Specifically, security level 1-3 (low risk) corresponds to 4-digit password and 300-second validity period, which meets the convenience of normal operation; level 4-6 (medium risk) corresponds to 6-digit password and 120-second validity period, which is the industry standard configuration; level 7-9 (high risk) corresponds to 8-digit password and 60-second validity period, which improves the ability to resist brute-force attacks; the interval width 3 is obtained by dividing the total security level range (1-9) into three equal parts, and the levels are evenly progressive.

[0097] Example 2: This application provides a remote door opening control system for unmanned convenience stores, referencing... Figure 4 As shown in the figure, this is a schematic diagram of a control system according to this embodiment of the present application. The control system includes:

[0098] The acquisition module 100 is used to acquire the door status signal uploaded by the access control controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol.

[0099] The parsing module 200 is used to parse the door status signal and the human body sensing signal to determine the current opening and closing status of the door and the status of people staying in the convenience store, and to determine whether the current time period is within the preset business period based on the timestamp, so as to obtain the time period judgment result.

[0100] The level generation module 300 is used to fit the historical door opening and closing records and abnormal alarm events of the unmanned convenience store to obtain the adjustment curve of the access control security level of the unmanned convenience store. The current opening and closing status, the personnel stay status and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value.

[0101] The password adjustment module 400 is used to adjust the effective number of digits and the effective duration of the dynamic unlocking password according to the dynamic security level value, and send the adjusted dynamic unlocking password to the access control controller.

[0102] In embodiment three, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for calling and running the computer programs from the memory, so that the computer device executes the above-described remote door opening control method for unmanned convenience stores.

[0103] Example 4, Reference Figure 5 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for implementing a remote door opening control method for unmanned convenience stores according to an embodiment of this application. The remote door opening control method for unmanned convenience stores described in the above embodiments can... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device may be a terminal device, a server or a chip.

[0104] Processor 501 can be a general-purpose processor or a special-purpose processor. For example, processor 501 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 505 for inputting (receiving) and outputting (transmitting) signals.

[0105] For example, the computer device may be a chip, and the communication unit 505 may be the input and / or output circuit of the chip, or the communication unit 505 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0106] For example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0107] The computer device may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0108] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0109] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gate, transistor logic devices, or discrete hardware components.

[0110] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compactdisc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0112] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

Claims

1. A method for remotely controlling the opening of an unmanned convenience store, characterized in that, The control method includes the following steps: Acquire the door status signal uploaded by the access control controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol; The door status signal and the human body sensing signal are analyzed to determine the current opening and closing status of the door and the status of people staying in the convenience store. Based on the timestamp, it is determined whether the current time period is within the preset business period, and the time period determination result is obtained. Based on the historical door opening and closing records and abnormal alarm events of the unmanned convenience store, an adjustment curve for the access control security level of the unmanned convenience store is obtained. The current opening and closing status, the personnel stay status, and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value. The dynamic unlocking password is adjusted based on the dynamic security level value, including the number of valid digits and the validity period. The adjusted dynamic unlocking password is then sent to the access control controller.

2. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, Analyzing the door status signal and the human body sensor signal to determine the current opening / closing status of the door and the status of people remaining in the convenience store specifically includes: The door magnetic switch level value is extracted from the door status signal, and the door magnetic switch level value is compared with the door closure reference level threshold to obtain the current opening and closing state of the door. The infrared pyroelectric induction voltage pulse sequence is extracted from the human body induction signal, and the cumulative number of pulse triggers within a unit time window is counted. The cumulative number of pulse triggers is compared with the threshold for determining the presence of people to generate the status of people staying in the convenience store.

3. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, Based on the timestamp, it is determined whether the current time period falls within the preset business hours. The specific time period determination result includes: Parse the current hour and minute from the timestamp and concatenate them to obtain the current time value; Retrieve the preset business hours configuration table, which includes business start time and business end time values, to obtain the business hours interval; The current time value is compared with the business hours interval to determine the interval inclusion, and the time period determination result is obtained.

4. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, The adjustment curve for the access control security level of unmanned convenience stores, obtained by fitting historical door opening and closing records and abnormal alarm events, specifically includes: Obtain historical door opening and closing records and abnormal alarm events of unmanned convenience stores; Extract the timestamp and duration of each door opening and closing event from the historical door opening and closing records, calculate the door opening and closing frequency within a unit time period, and obtain the door opening and closing frequency sequence. Extract the alarm type label and alarm occurrence timestamp for each alarm from the abnormal alarm events, assign alarm weight values ​​according to the severity of the alarm type label, and statistically analyze the weighted alarm frequency sequence within a unit time period; Piecewise linear fitting is performed on the door opening / closing frequency sequence and the weighted alarm frequency sequence to obtain the adjustment curve of the access control security level in the unmanned convenience store.

5. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, The process of inputting the current open / closed status, the personnel's loitering status, and the time period determination result into the access control security level adjustment curve to generate a dynamic security level value specifically includes: Based on the door opening / closing identifier in the current opening / closing state and the personnel staying identifier in the personnel staying state, the current scene status code is generated by combining them. Map the business hours identifier or non-business hours identifier in the time period determination result to a time period weighting coefficient; On the adjustment curve of the access control security level, the baseline security level value is obtained by querying the value at the current moment as the horizontal axis; The baseline security level value is weighted and superimposed with the scene offset corresponding to the current scene status code and the time period weight coefficient to output a dynamic security level value.

6. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, Adjusting the valid number of digits and validity period of the dynamic unlocking password based on the dynamic security level value specifically includes: Retrieve a preset security level and password parameter mapping table, which contains the correspondence between security level value ranges and password valid length ranges and password valid duration ranges; Using the dynamic security level value as the query key, locate the corresponding security level value range in the mapping table, and extract the corresponding password valid length range value and password valid duration range value; The effective number of digits and the effective duration of the password are used as adjustment parameters and input into the dynamic password generation algorithm to generate the adjusted dynamic unlocking password and the corresponding effective duration marker.

7. The remote door opening control method for an unmanned convenience store as described in claim 1, characterized in that, The infrared pyroelectric sensor is an infrared sensing sensor based on the pyroelectric effect.

8. A remote door opening control system for an unmanned convenience store, used to execute the remote door opening control method for an unmanned convenience store as described in any one of claims 1 to 7, characterized in that, The control system includes: The acquisition module is used to acquire the door status signal uploaded by the access controller, the human body sensing signal collected by the infrared pyroelectric sensor in the unmanned convenience store, and the timestamp of the network time protocol. The parsing module is used to parse the door status signal and the human body sensing signal to determine the current opening and closing status of the door and the status of people staying in the convenience store, and to determine whether the current time period is within the preset business period based on the timestamp, so as to obtain the time period judgment result. The security level generation module is used to fit the historical door opening and closing records and abnormal alarm events of the unmanned convenience store to obtain the adjustment curve of the access control security level. The current opening and closing status, the personnel stay status and the time period judgment result are input into the adjustment curve of the access control security level to generate a dynamic security level value. The password adjustment module is used to adjust the effective number of digits and the effective duration of the dynamic unlocking password according to the dynamic security level value, and send the adjusted dynamic unlocking password to the access control controller.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the remote door opening control method for unmanned convenience stores as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the remote door opening control method for unmanned convenience stores as described in any one of claims 1 to 7.

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