Building passing linkage control method and system

Abstract

The invention discloses a building passing linkage control method and system, and the method comprises the steps: calling an edge processing module to obtain the multi-modal biological characteristic data of a person when at least one person appears in a target building region; performing identity recognition based on the multi-modal biological characteristic data through an edge processing module to generate a recognition result; if the recognition result is that the recognition is passed, calling an elevator control module to query a building permission database based on the recognition result, and determining a target floor; if all the elevators are not triggered by the target floor, a floor selection instruction corresponding to the target floor is generated through the elevator control module and issued to the corresponding elevator; and the door control module generates an access control equipment opening instruction and issues the instruction to the corresponding access control equipment. The elevator control and access control linkage in different scenes is effectively adapted through a mode of combining identity recognition of the multi-mode biological characteristic data with building permission query, and the flexibility and accuracy of linkage control are improved.

Description

Technical Field

[0001] This invention relates to the field of access control technology, and in particular to a building access linkage control method and system. Background Technology

[0002] With the rapid development of artificial intelligence and video recognition technologies and the in-depth advancement of smart city construction, the demand for intelligent management of personnel access in smart buildings, residential communities, office parks and other scenarios is becoming increasingly urgent. As core security components, access control and elevator control have become key directions for improving access efficiency and security through their linkage and collaboration.

[0003] Currently, access control systems mostly use card swiping, facial recognition, or password verification, while elevator control uses buttons or IC cards to authorize floor access. These two systems are often deployed separately. While some locations have achieved initial linkage, they rely on dedicated terminals, have limited identification methods, and are vulnerable to attacks using counterfeit cards or photos, making flexible and accurate linkage control difficult. Summary of the Invention

[0004] This invention provides a building access control method and system, which solves the technical problem that although some places have achieved initial linkage, they rely on dedicated terminals, have a single identification method, are vulnerable to attacks by counterfeit cards or photos, and are difficult to achieve flexible and accurate linkage control.

[0005] This invention provides a building access control method applied to a central control module, which is also communicatively connected to an edge processing module, a door control module, and an elevator control module. The method includes:

[0006] When at least one person is present in the target building area, the edge processing module is invoked to obtain the multimodal biometric data of the person.

[0007] The edge processing module performs identity recognition based on the multimodal biometric data and generates recognition results.

[0008] If the recognition result is successful, the elevator control module is invoked to query the building access database based on the recognition result to determine the target floor;

[0009] If none of the elevators are triggered at the target floor, the elevator control module generates a floor selection command corresponding to the target floor and sends it to the corresponding elevator.

[0010] The access control module generates an access control device opening command and sends it to the corresponding access control device.

[0011] Optionally, the method further includes a monitoring terminal communicatively connected to the central control module, and the method further includes:

[0012] If the identification result is that the identification fails, then the multimodal biometric data is used for behavioral analysis to determine the type of abnormal behavior corresponding to the person.

[0013] If the abnormal behavior type matches the target behavior type, an alert message is generated and sent to the monitoring terminal.

[0014] Optionally, it also includes a camera, EEG device, and / or iris recognition device communicatively connected to the edge processing module; the step of calling the edge processing module to obtain the multimodal biometric data of the person when at least one person appears in the target building area includes:

[0015] When at least one person appears in the target building area, the edge processing module calls the camera device to collect the person's facial data and calculates the occlusion rate corresponding to the facial data;

[0016] If the occlusion rate does not exceed the preset occlusion threshold, the face data is determined as the multimodal biometric data of the person.

[0017] If the occlusion rate exceeds a preset occlusion threshold, and the person is not carrying an EEG device, and / or there is no iris recognition device in the target building area, then the person's motion video sequence is collected by the camera device, and the motion video sequence is determined as the person's multimodal biometric data;

[0018] If the occlusion rate exceeds a preset occlusion threshold, and the person is wearing an EEG device, then the person's EEG data will be collected through the EEG device.

[0019] If the occlusion rate exceeds a preset occlusion threshold, and there is an iris recognition device in the target building area, then the iris data of the person is collected through the iris recognition device.

[0020] The edge processing module identifies the EEG data and / or the iris data as the person's multimodal biometric data.

[0021] Optionally, the step of performing identity recognition based on the multimodal biometric data through the edge processing module and generating recognition results includes:

[0022] If the multimodal biometric data is facial data, then the edge processing module matches the local personnel template of the person according to the facial data;

[0023] If the multimodal biometric data is an action video sequence, then the edge processing module compares the action video sequence with each local person template to calculate the first template similarity.

[0024] If the multimodal biometric data is EEG data and / or iris data, then the edge processing module extracts physiological features from the EEG data and / or extracts texture features from the iris data.

[0025] The edge processing module calculates a second template similarity with each of the local personnel templates based on the physiological characteristics and / or the texture characteristics.

[0026] If the local personnel template is successfully matched, or if there is a local personnel template whose similarity to the first template or the second template is greater than a preset similarity threshold, then a recognition result that has passed the recognition is generated.

[0027] If the local personnel template matching fails, and there is no local personnel template with a similarity greater than the preset similarity threshold of the first template or the second template, then a recognition result indicating failure to recognize is generated.

[0028] Optionally, the step of comparing the action video sequence with each local person template using the edge processing module to calculate the first template similarity includes:

[0029] The edge processing module extracts static contour features and dynamic trajectory features from the action video sequence and fuses them to obtain gait temporal features.

[0030] After aligning the gait temporal features using the edge processing module, classification and modeling are performed to determine the fusion feature template corresponding to the gait temporal features;

[0031] The edge processing module calculates the first template similarity between the fused feature template and each local person template.

[0032] Optionally, the step of extracting physiological features from the EEG data and / or extracting texture features from the iris data via the edge processing module includes:

[0033] After extracting frequency domain features and time domain features from the EEG data through the edge processing module, physiological features are fused to generate physiological features.

[0034] And / or, the edge processing module performs multi-scale, multi-directional encoding on the iris data to generate initial iris texture features;

[0035] Redundant features are removed from the initial iris texture features to obtain the texture features corresponding to the iris data.

[0036] Optionally, the step of calling the elevator control module to query the building access database based on the recognition result to determine the target floor includes:

[0037] The elevator control module is invoked to query the building access database according to the local personnel template corresponding to the recognition result, and to determine the corresponding building access.

[0038] If the building access permissions only include single floor permissions, then the floor to which the floor permission belongs is determined as the target floor through the elevator control module;

[0039] If the building permissions include multiple floor permissions, the elevator control module matches historical floor selection data according to the current time period, and determines the historical floor corresponding to the current time period as the target floor.

[0040] Optionally, the access control device is further equipped with a video screen; the method further includes:

[0041] If the number of people in the target building area is greater than the preset flow restriction threshold, the people are classified according to the target floor corresponding to each person, and the required floor corresponding to each elevator is determined.

[0042] According to the floor requirements, the elevator icon corresponding to each person is displayed on the visual screen; wherein, the elevator icon corresponds one-to-one with the elevator.

[0043] Optionally, the method further includes a gateway middleware that connects the original access control device and the original elevator control device to the edge processing module; the method further includes:

[0044] When the edge processing module outputs the floor selection command and the access control device activation command, the gateway middleware converts the floor selection command and the access control device activation command into identifiable signals of the original access control device and the original elevator control device.

[0045] The present invention also provides a building access control system, including a central control module, an edge processing module, a door control module and an elevator control module, wherein the central control module is also communicatively connected to the edge processing module, the door control module and the elevator control module respectively;

[0046] The edge processing module includes:

[0047] The feature data acquisition submodule is used to acquire the multimodal biometric data of at least one person when at least one person appears in the target building area.

[0048] The identity recognition submodule is used to perform identity recognition based on the multimodal biometric data and generate recognition results;

[0049] The elevator control module includes:

[0050] The floor determination submodule is used to query the building access database based on the recognition result if the recognition result is successful, and determine the target floor.

[0051] The elevator instruction sending submodule is used to generate a floor selection instruction corresponding to the target floor and send it to the corresponding elevator if none of the elevators have been triggered at the target floor.

[0052] The access control module is used to generate access control device opening commands and send them to the corresponding access control devices.

[0053] As can be seen from the above technical solutions, the present invention has the following advantages:

[0054] When at least one person is present in the target building area, the edge processing module is invoked to acquire the person's multimodal biometric data. The edge processing module then performs identity verification based on this data, generating a verification result. If the verification is successful, the elevator control module queries the building access control database to determine the target floor. If none of the elevators are triggered at the target floor, the elevator control module generates a floor selection command corresponding to the target floor and sends it to the corresponding elevator. The access control module generates an access control device activation command and sends it to the corresponding access control device. By combining multimodal biometric data-based identity verification with building access control queries, this approach effectively adapts to elevator control and access control linkage in different scenarios, improving the flexibility and accuracy of the linkage control. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating the steps of a building access control method according to an embodiment of the present invention;

[0057] Figure 2 This is a structural block diagram of a building access control system provided in an embodiment of the present invention. Detailed Implementation

[0058] This invention provides a building access control method and system to address the technical problem that although some locations have achieved initial linkage, they rely on dedicated terminals, have a single identification method, are vulnerable to attacks from counterfeit cards or photos, and are difficult to achieve flexible and accurate linkage control.

[0059] In this embodiment, prominent labels are affixed to building access control structures and elevators where user facial and gait data need to be collected. These labels inform users that data collection is in progress and that access control and elevator management functions rely on this data. Furthermore, for temporary users, the acquired data is deleted immediately after access control and elevator management are completed. For regular users, the relevant data is encrypted and stored in a local offline database to prevent privacy breaches.

[0060] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0061] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a building access control method according to an embodiment of the present invention.

[0062] This invention provides a building access control method, applied to a central control module, which is also communicatively connected to an edge processing module, a door control module, and an elevator control module. The method includes:

[0063] Step 101: When at least one person appears in the target building area, call the edge processing module to obtain the person's multimodal biometric data;

[0064] The target building area refers to the pre-defined personnel access recognition area in application scenarios such as smart buildings, office parks, and residential communities, which is the designated range that triggers biometric data collection.

[0065] The edge processing module refers to a functional module integrated into the AI ​​camera that has edge computing capabilities and can locally complete biometric feature acquisition, preprocessing, and preliminary identification, supporting synchronous processing of multi-source data.

[0066] Multimodal biometric data refers to a collection of various biometric information acquired through various data acquisition devices for identity recognition, including but not limited to face data (visible light face images and / or infrared face images), iris data, gait data, electroencephalogram (EEG) data, and voiceprint data.

[0067] In this embodiment, the central control module can be located in the cloud, providing backend computing power support to the edge processing modules located in each building via wireless communication. It is also responsible for full data storage, remote operation and maintenance, and unified management of multiple buildings. The edge processing modules can be located in the building's server room or integrated into AI cameras for local processing of acquired multimodal biometric data. The access control module and elevator control module, as the actual execution modules, are deployed nearby in access control equipment, elevator machine rooms, or control boxes on the top of the elevator car. For example, they can be integrated inside access control gates (smart gates), inside control boxes on the inside of unit doors (traditional access control upgrades), or inside explosion-proof control boxes next to explosion-proof doors in factory areas (factory scenarios). When older elevators lack independent control boxes, they are deployed externally on the top of the elevator car via wireless relay modules, avoiding modifications to the existing elevator wiring.

[0068] In this embodiment, when a person enters the target building area, the central control module detects the person's presence through a preset sensing mechanism and then sends a data acquisition command to the edge processing module. After responding to the command, the edge processing module starts the associated multi-source acquisition device to simultaneously acquire the person's visual feature data (face image, gait video sequence) and / or non-visual feature data (iris texture, EEG signal, voiceprint information). During the acquisition process, environmental interference factors are automatically filtered to ensure data integrity and validity. At the same time, the acquired raw data is initially packaged and transmitted to the local computing unit of the edge processing module.

[0069] Furthermore, when a single data acquisition device fails, it automatically switches to a backup device (e.g., a visible light camera is used to supplement data acquisition when an infrared camera fails), ensuring continuous data collection. Simultaneously, it dynamically adjusts the data acquisition trigger threshold based on time of day and population density to avoid invalid data acquisition during off-peak hours, reducing device energy consumption and data redundancy. Additionally, it automatically adjusts data acquisition device parameters for different scenarios such as factories and older residential areas; for example, it activates explosion-proof acquisition mode in factories and low-power acquisition mode in older residential areas, adapting to specific scenario requirements.

[0070] In one example of the present invention, a camera, an EEG device, and / or an iris recognition device communicatively connected to the edge processing module are also included; step 101 may include the following sub-steps:

[0071] When at least one person appears in the target building area, the edge processing module calls the camera device to collect the person's facial data and calculates the occlusion rate corresponding to the facial data;

[0072] If the occlusion rate does not exceed the preset occlusion threshold, the facial data will be identified as the person's multimodal biometric data.

[0073] If the occlusion rate exceeds the preset occlusion threshold, and the person is not carrying an EEG device, and / or there is no iris recognition device in the target building area, then the person's action video sequence is collected by the camera device, and the action video sequence is determined as the person's multimodal biometric data;

[0074] If the occlusion rate exceeds the preset occlusion threshold and the person is wearing an EEG device, then the person's EEG data will be collected through the EEG device.

[0075] If the occlusion rate exceeds the preset occlusion threshold and there is an iris recognition device in the target building area, then the iris data of the personnel will be collected through the iris recognition device.

[0076] The edge processing module identifies EEG data and / or iris data as multimodal biometric data of individuals.

[0077] Electroencephalography (EEG) devices refer to biosensor devices that non-invasively acquire electrophysiological signals (i.e., brain waves) generated by the activity of neurons in the brain, and then condition, convert, analyze, and output these signals. In this embodiment, it can be implemented using a safety helmet or an EEG (Electroencephalography) head-mounted device that integrates a signal acquisition module, a signal conditioning module, a data conversion module, and a data processing and output module.

[0078] Iris recognition devices refer to intelligent devices that collect the texture features of the iris region of a person's eye (the unique ring, spot, stripe, and other structures of the iris), process them digitally, and match them with preset feature templates to achieve identity verification or recognition. Examples of such devices include near-infrared cameras and iris scanners.

[0079] In this embodiment, when at least one person is detected within the target building area, the central controller sends a data acquisition command to the edge processing module. Upon response, the edge processing module calls the associated camera device to acquire the person's facial data. It analyzes the visibility of key facial feature points using an image semantic segmentation algorithm and calculates the occlusion rate. Combined with a preset occlusion threshold, the device configuration within the target building area, and the person's device status, multimodal biometric data is determined. If the occlusion rate does not exceed the preset occlusion threshold, the acquired facial data is directly used as the person's multimodal biometric data. If the occlusion rate exceeds the preset occlusion threshold, and the person is not carrying an EEG device, or the target building... If no iris recognition equipment is installed in the area, the camera equipment is controlled to collect video sequences of the person's movements and use them as multimodal biometric data. If the occlusion rate exceeds the preset occlusion threshold and the person is wearing an EEG device, the EEG data of the person is collected through the EEG device. If the target building area is equipped with iris recognition equipment, the iris data of the person is collected through the iris recognition device. Finally, the edge processing module determines the collected EEG data and / or iris data as the person's multimodal biometric data. During the collection process, the camera equipment parameters are automatically adjusted according to the scene lighting conditions. At the same time, it supports switching to the backup collection mode in case of equipment failure to ensure the continuity of data collection.

[0080] Furthermore, the occlusion threshold can be customized for different scenario types, such as safety helmet wearing in factories or mask wearing in hospitals. Additionally, for different buildings, if there are EEG or iris recognition devices, the communication module can verify the operating status and connection stability of these devices before data collection. If a device malfunctions, it will automatically switch to motion video sequence collection to avoid data interruption.

[0081] Step 102: The edge processing module performs identity recognition based on multimodal biometric data and generates recognition results;

[0082] The identification result refers to the identity determination conclusion and related data output after the identity recognition is completed. The conclusion includes identification passed, identification suspicious, identification failed, etc.

[0083] In this embodiment, after receiving multimodal biometric data, the edge processing module first performs preprocessing operations such as normalization and redundancy removal on various types of data. Then, it uses a CNN+LSTM (Convolutional Neural Network + Long Short-Term Memory Network) fusion model to perform single-modal modeling on static features (face, iris) and dynamic features (gait, EEG) respectively, generating feature sub-templates for each modality. Subsequently, the weights of each sub-template are dynamically adjusted according to the occlusion situation and scene type, and deep fusion is performed to form a unified identity feature template. Finally, this template is compared with the local personnel template stored locally. If there is no match, a fallback comparison is performed with the cloud-based full-access template library through an encrypted channel. Finally, the corresponding recognition result is generated and fed back to the central control module.

[0084] It should be noted that when some biometric data is missing, the specific content of identity recognition can be dynamically adjusted. For example, the occlusion status of biometric features can be detected in real time during the recognition process. When the feature is covered by a mask, the system will automatically switch to the iris + gait recognition channel, and when the feature is covered by a helmet, it will switch to the EEG + gait recognition channel, thereby improving the recognition accuracy in occluded scenarios.

[0085] In one example of the present invention, step 102 may include the following sub-steps S11-S16:

[0086] S11. If the multimodal biometric data is facial data, then the local personnel template of the personnel is matched according to the facial data by the edge processing module.

[0087] Facial data refers to facial image information of people collected through camera equipment, including a set of visual features such as facial contours and key feature points that can be used for identity matching.

[0088] In this embodiment, when the multimodal biometric data is facial data, the edge processing module calls the built-in facial feature extraction algorithm to extract core visual features from the facial data. Then, the feature is compared one by one with the facial baseline features in all local personnel templates stored locally. Template matching is completed through feature point alignment and similarity quantification calculation. The matching process prioritizes the use of local computing power and does not rely on cloud data interaction, ensuring the real-time matching response.

[0089] During the matching process, local personnel templates can be grouped using feature clustering algorithms. When matching, the cluster group to which the target template belongs is located first, and then a precise comparison is performed, reducing invalid calculations and improving batch matching efficiency.

[0090] S12. If the multimodal biometric data is an action video sequence, then the edge processing module compares the action video sequence with each local person template to calculate the first template similarity.

[0091] Furthermore, S12 may include the following sub-steps:

[0092] The static contour features and dynamic trajectory features are extracted from the action video sequence by the edge processing module and then fused to obtain gait temporal features;

[0093] After aligning the gait temporal features using the edge processing module, classification modeling is performed to determine the fusion feature template corresponding to the gait temporal features;

[0094] The edge processing module calculates the first template similarity between the fused feature template and each local person template.

[0095] Static contour features refer to the inherent static structural features of the human body extracted from motion video sequences, including height, limb proportions, shoulder width to hip width ratio, etc., which are not affected by the state of motion.

[0096] Dynamic trajectory features refer to the dynamic features of human movement extracted from action video sequences, including changes in stride length, arm swing trajectory, and temporal changes in joint movement angles.

[0097] In this embodiment, the edge processing module analyzes the motion video sequence frame by frame, extracting static contour features of the person from keyframes, and extracting dynamic trajectory features using a temporal trajectory tracking algorithm. A weighted fusion algorithm is then used to fuse the two types of features, dynamically adjusting the weights according to the scene (e.g., increasing the weight of static contour features for non-standard gait) to generate gait temporal features with both stability and discriminative power, representing the person's gait attributes. Then, a Dynamic Time Warping (DTW) algorithm is used to temporally align the gait temporal features, resolving the feature temporal misalignment problem caused by different walking speeds. Subsequently, a Support Vector Machine (SVM) is used to classify and model the aligned gait temporal features, enhancing the uniqueness of individual gait features and generating standardized fused feature templates. The generated fused feature templates are compared one by one with the gait baseline templates in each locally stored person template. A cosine similarity algorithm is used to quantify the feature consistency between the two types of templates, calculating the first template similarity for each group, and recording the identifier and corresponding value of the local person template with the highest similarity.

[0098] If the same person has multiple historical gait reference templates, the similarity between the fused feature template and each historical template is calculated separately and then the average value is taken to improve the reliability of the similarity results.

[0099] S13. If the multimodal biometric data is EEG data and / or iris data, then physiological features are extracted from the EEG data and / or texture features are extracted from the iris data through the edge processing module.

[0100] Furthermore, S13 may include the following sub-steps:

[0101] After extracting frequency domain features and time domain features from EEG data through the edge processing module, physiological features are fused to generate physiological features.

[0102] And / or, the iris data is encoded in multiple scales and directions through the edge processing module to generate initial iris texture features;

[0103] Redundant features are removed from the initial iris texture features to obtain the texture features corresponding to the iris data.

[0104] In this embodiment, after the edge processing module preprocesses the EEG data (denoising and filtering), it extracts frequency domain features (such as the energy ratio of each band) through fast Fourier transform and extracts time domain features (such as the timing distribution of signal peaks) through wavelet transform. Then, it uses a feature concatenation fusion method to integrate the frequency domain features and time domain features into physiological features of a unified dimension. During the fusion process, normalization is used to eliminate the difference in magnitude between the two types of features, ensuring the consistency and comparability of the features.

[0105] Meanwhile, the iris data is precisely segmented (separating the iris from the pupil and sclera). Then, a multi-scale Gabor filtering algorithm is used to encode the iris texture in multiple scales and directions to generate initial iris texture features containing rich texture details. The initial iris texture features are then dimensionality reduced by principal component analysis algorithm to remove redundant features and noise interference, retain the core discriminative texture features, and form standardized iris texture features for subsequent comparison.

[0106] S14. Calculate the second template similarity with each local person template based on physiological characteristics and / or texture characteristics through the edge processing module;

[0107] After extracting physiological features and / or texture features, they are compared with the EEG baseline physiological features and iris baseline texture features in each local personnel template. The physiological features focus on the consistency verification of frequency domain and temporal patterns, while the texture features focus on the matching degree of encoded information. The similarity of the second template corresponding to each group is obtained through a unified similarity calculation model to ensure that the similarity results of different types of features are comparable.

[0108] Furthermore, if both physiological and textural features exist simultaneously, the similarity of the comprehensive second template can be calculated by dynamically allocating weights according to feature stability, thereby improving the accuracy of multi-feature matching.

[0109] S15. If the local personnel template is successfully matched, or if there is a local personnel template with a first template similarity or a second template similarity greater than the preset similarity threshold, then a recognition result that has passed the recognition is generated.

[0110] The edge processing module summarizes the matching results of S11, the first template similarity of S12, and the second template similarity of S14, and makes a comprehensive judgment. If the face data in S11 is accurately matched with a local person template, or if the first template similarity of any local person template is greater than the preset similarity threshold, or if the second template similarity of any local person template is greater than the preset similarity threshold, then the recognition result that has passed the recognition is directly generated, and the template identifier and similarity data of the successfully matched template are recorded simultaneously.

[0111] S16. If the local personnel template matching fails, and there is no local personnel template with a first template similarity or second template similarity greater than the preset similarity threshold, then a recognition result indicating failure to recognize is generated.

[0112] If the face data in S11 fails to match all local personnel templates, and the first template similarity of all local personnel templates does not exceed the preset similarity threshold, and the second template similarity of all local personnel templates also does not exceed the preset similarity threshold, then it is determined that the legitimate identity cannot be confirmed, a recognition result of recognition failure is generated, and all key data of the comparison (such as the highest similarity value and the reason for the matching failure) are recorded for future reference.

[0113] Step 103: If the recognition result is successful, the elevator control module is called to query the building access database based on the recognition result to determine the target floor;

[0114] The building access database refers to the collection of access permission information stored in the central control module's associated database and bound to the personnel identity of the local personnel template. This includes the range of accessible floors, the effective time period of the permission, and the priority of the identity.

[0115] The target floor refers to the elevator floor that a person can access, determined based on the building access database corresponding to the person's identity, combined with factors such as the current task scenario and time period.

[0116] In this embodiment, after receiving the recognition result, the central control module forwards the person's identity information and recognition result to the elevator control module. Upon response, the elevator control module calls the building permission database to query data such as the range of accessible floors and the effective time period of the permission corresponding to the identity. At the same time, it makes a comprehensive judgment based on the identity priority rules, the current time period (such as commuting peak hours, night), and the task scenario associated with the person (such as office, meeting, work). If there is a conflict of permissions for multiple floors (such as having permissions for multiple office floors at the same time), the current task scenario and scheduling information shall be used to determine the priority access floor. Finally, the target floor is determined and fed back to the central control module.

[0117] In one example of the present invention, step 103 may include the following sub-steps:

[0118] The elevator control module is invoked to query the building access database according to the local personnel template corresponding to the recognition result, and the corresponding building access is determined.

[0119] If the building access permissions only include single floor permissions, then the floor to which the floor permission belongs is determined as the target floor through the elevator control module;

[0120] If the building access permissions include multiple floor permissions, the elevator control module will match the historical floor selection data according to the current time period and determine the historical floor corresponding to the current time period as the target floor.

[0121] Local personnel templates refer to a set of biometric templates and permission-related information that are stored in the edge processing module or local database and are bound to the personnel's identity, supporting fast local query and retrieval.

[0122] Building permissions refer to the specific building and elevator floor permissions that individuals are authorized to access, as defined in the building permissions database. Building permissions include specific floor permissions and building permissions.

[0123] Historical floor selection data refers to the historical data of people accessing each floor at different times, including key information such as access frequency and access duration.

[0124] In this embodiment, after receiving the identity verification result, the central controller sends a permission query command to the elevator control module. Upon responding to the command, the elevator control module accurately queries the corresponding building permissions in the associated building permission database based on the local personnel template corresponding to the identification result, determining the building accessible to the personnel and the range of floor permissions within that building. If the retrieved building permissions only include a single floor permission, the elevator control module directly determines the specific floor to which that floor permission belongs as the target floor. If the retrieved building permissions cover multiple floor permissions, the elevator control module automatically obtains the current time period information, matches it with the personnel's historical floor selection data stored in the system, and counts the floor with the highest access frequency or most frequently used by the personnel during the current time period, determining it as the target floor. Throughout the process, the elevator control module provides real-time feedback to the central controller on the permission query results and target floor determination, ensuring the continuity and accuracy of command execution.

[0125] In addition to the current time period, factors such as personnel priority, currently associated tasks (such as meeting appointments and job scheduling), and floor load status can be incorporated into the weighted calculation. The weights can be equal or set according to the actual situation. If a high-priority task is associated with a floor, that floor will be prioritized as the target floor.

[0126] Furthermore, if multi-floor permissions include temporarily unavailable floors (such as equipment maintenance or area control), the elevator control module automatically removes the permissions for that floor and re-matches historically frequently accessed floors from the remaining floor permissions to avoid invalidating target floors. In the event of conflicts between multiple identities and multiple floor permissions, the current task and scheduling information take precedence, and multi-person collaborative confirmation is supported when necessary. In abnormal situations, usage is automatically restricted.

[0127] Step 104: If none of the elevators have been triggered to the target floor, the elevator control module generates a floor selection command corresponding to the target floor and sends it to the corresponding elevator.

[0128] In this embodiment, after receiving the information of the target floor, the elevator control module first queries the current trigger status of all elevators to confirm whether there is an elevator that has been triggered on the target floor. If none of the elevators have been triggered, the module selects suitable elevators based on factors such as elevator distribution location and current load status. Then, it generates a floor selection command compatible with the elevator control board protocol. For traditional elevators in older communities, the command is converted into traditional protocol formats such as Wiegand and RS485 through the protocol adaptation layer, and then sent to the corresponding elevator through the communication module. After the command is sent, the elevator response status is monitored in real time and fed back to the central control module.

[0129] In addition, when issuing floor selection instructions, priority is given to elevators with lower current load and shorter dispatch distance to avoid excessive load on a single elevator and improve overall traffic efficiency. At the same time, during peak hours such as commuting, multiple selection instructions for the same target floor are merged and elevator group control and zonal dispatch mode is activated to reduce the phenomenon of elevators stopping at every floor.

[0130] Step 105: Generate an access control device opening command through the access control module and send it to the corresponding access control device.

[0131] In this embodiment of the invention, while the central controller sends a target floor query command to the elevator control module, it simultaneously sends an access authorization signal to the door control module. After receiving the signal, the door control module generates a corresponding opening command based on the type of access control device. For smart access control devices, it directly issues a power lock unlocking command; for traditional access control devices in older communities, the opening command is converted into a control signal recognizable by the device through a wireless relay module, and then the command is sent to the corresponding access control device. After the device unlocks, it sends feedback of the opening status to the door control module, which then synchronizes the status to the central controller to complete the opening of the access control device.

[0132] In addition, the activation duration of access control devices can be dynamically adjusted based on the type of person and the time of day. For example, the activation duration can be shortened at night to improve safety, while the activation duration can be extended for the elderly to ensure convenience. If a person needs to pass through multiple access control devices consecutively (such as a community gate and an apartment building entrance), the activation command can be automatically synchronized to the subsequent access control devices to achieve seamless passage.

[0133] In one example of the present invention, a monitoring terminal communicatively connected to the central control module is also included, and the method further includes the following steps:

[0134] If the identification result is that the identification fails, multimodal biometric data will be used for behavioral analysis to determine the type of abnormal behavior of the person.

[0135] If the abnormal behavior type matches the target behavior type, an alert message is generated and sent to the monitoring terminal.

[0136] Abnormal behavior types refer to categories of behavior that deviate from normal traffic rules, as determined through behavioral analysis. These include tailing, forced entry, multiple identification attempts, prolonged stay, and unauthorized loitering.

[0137] The target behavior type refers to a set of high-risk abnormal behaviors that are preset to trigger an alert mechanism, which can be set according to the security requirements of the scenario.

[0138] Warning information refers to the prompt data generated for abnormal situations that match the target behavior type, including core information such as details of the abnormal behavior, time of occurrence, location, and on-site footage.

[0139] In this embodiment, when the identification result is that the identification fails, the central control module calls the collected multimodal biometric data (including face data, action video sequences, EEG data or iris data, etc.) and the dynamic behavior data of the person in the target building area. Through the preset behavior analysis algorithm (such as dynamic trajectory tracking, behavior pattern comparison, abnormal action feature extraction, etc.), multi-dimensional analysis is performed to accurately determine the abnormal behavior type of the person.

[0140] Subsequently, the central control module compares the identified abnormal behavior type with the system's preset target behavior type (i.e., risky behaviors that require key prevention and control and trigger warnings). If the two match (i.e., the abnormal behavior belongs to the preset high-risk type), it quickly generates structured warning information containing the abnormal behavior type, personnel characteristic summary, real-time on-site data (image / video clips), occurrence time, and specific location. This information is transmitted to designated monitoring terminals such as the central control room monitoring screen, administrator-specific App, and computer client via encrypted communication protocols. At the same time, the central control platform automatically records abnormal behavior logs for source tracing. The entire process relies on edge computing to achieve real-time analysis and push, ensuring that administrators respond to risks in a timely manner.

[0141] It should be noted that in multi-dimensional behavioral analysis, biometric data and environmental context data (such as personnel density, passage time, and area control level) can be integrated for cross-validation. For example, if unfamiliar personnel linger at night outside of passage time, the anomaly detection can be strengthened by combining the surrounding access control lock status, thus improving the accuracy of identification. Simultaneously, alerts can be pushed according to the risk level of abnormal behavior. For example, multiple failed identifications indicate general risk, while forced entry indicates high risk. High-risk information triggers simultaneous audio and visual alerts on the monitoring terminal and pushes to multiple administrator terminals, while general risk information is only logged and pushed to specific administrators, avoiding redundancy in alerts.

[0142] In one example of the present invention, the access control device is further provided with a visual screen; the method further includes the following steps:

[0143] If the number of people in the target building area exceeds the preset flow restriction threshold, the required floors for each elevator are determined according to the target floors corresponding to each person.

[0144] Each person's elevator icon is displayed on a visual screen according to their floor requirements; each elevator icon corresponds to a specific elevator.

[0145] The flow restriction threshold refers to the maximum number of people allowed to pass through the target building area at the same time, which is used to trigger the elevator diversion and allocation mechanism. It is configured according to the elevator's carrying capacity and the size of the area.

[0146] Demand floors refer to the set of floors that each elevator needs to respond to after classifying people's target floors, that is, the range of floors that the elevator needs to serve.

[0147] In this embodiment, the central control module counts the number of people in the target building area in real time and compares it with a preset flow restriction threshold. If the number of people exceeds the flow restriction threshold, it sends an elevator diversion and allocation command to the edge processing module. The edge processing module collects the target floor information corresponding to all people according to the process described in steps 101-103 above, categorizes and groups them according to the target floors, and matches the optimal elevator for each group of target floors based on the current operating status of each elevator (load, current floor, dispatch direction), thus clarifying the required floor corresponding to each elevator.

[0148] The edge processing module sends information to the corresponding access control devices, synchronizing the matching relationship between personnel and elevators to the visual screens of each access control device in the target building area. When personnel pass through the access control device, they can see the elevator icon they need to go to, along with the elevator's current location and estimated arrival time, making it easy for personnel to quickly find the corresponding elevator. Throughout the process, the elevator control module synchronizes elevator status changes in real time and updates the displayed content on the visual screens to ensure accurate information allocation.

[0149] In one example of the present invention, a gateway middleware is also included to connect the original access control device and the original elevator control device to the edge processing module; the method further includes the following steps:

[0150] When the edge processing module outputs floor selection instructions and access control device activation instructions, the gateway middleware converts these instructions into recognizable signals from the original access control device and the original elevator control device.

[0151] Gateway middleware refers to middleware components deployed between edge processing modules and original equipment, which have the ability to parse and convert multiple protocols.

[0152] Original access control equipment refers to access control-related equipment that does not have intelligent linkage functions and only supports traditional control signals, including old-fashioned electromagnetic locks, mechanical access controllers, etc., which rely on specific traditional protocols to receive control commands.

[0153] Original elevator control equipment refers to elevator-related equipment that lacks intelligent access control functions and only supports physical buttons or traditional signal control, including old-fashioned elevator control boards and traditional floor trigger modules.

[0154] In this embodiment, a communication connection is established between the edge processing module and the original access control device and the original elevator control device through the gateway middleware. The gateway middleware pre-integrates bidirectional conversion logic between traditional communication protocols such as Wiegand, RS485, Modbus, and CAN and TCP / IP and edge computing module adaptation protocols. For example, edge devices such as ESP32 and Raspberry Pi can be used to realize protocol conversion and data forwarding.

[0155] After the edge processing module outputs floor selection instructions and access control device activation instructions based on the identity recognition results, the instructions are first transmitted to the gateway middleware. The gateway middleware parses the protocol format and data structure of the instructions and converts them into recognizable signals corresponding to the original access control devices and elevator control devices according to the pre-configured device protocol mapping relationship. During the conversion process, the integrity and adaptability of the signals are automatically verified to ensure that the signals meet the device execution requirements. Then, the converted signals are sent to the original access control devices and elevator control devices respectively through the corresponding communication interfaces. At the same time, the signal conversion and sending status are fed back to the edge processing module, realizing seamless compatibility and linkage between intelligent instructions and traditional devices without modifying the hardware structure and control logic of the original devices.

[0156] In addition, differentiated deployments can be adopted for factory scenarios and some older residential buildings. In factory scenarios, the system can be integrated with the factory's MES (Manufacturing Execution System) to link personnel status with tasks, and use EEG / fatigue recognition to restrict access to high-risk operations. Explosion-proof, dustproof, and waterproof cameras are also supported. In older residential areas, due to the lack of network connectivity and elevator control interfaces, wireless relay modules can be used to control access control / elevator access. Low-power AI cameras powered by solar energy and batteries can be used, supporting multiple recognition methods including face recognition, QR codes, and temporary visitor codes. For visitors without pre-registration information, their information (name, photo, and visit time) can be registered in advance via an app or website. The system generates temporary facial features or QR codes / dynamic codes. Upon arrival, temporary authorization is granted via ID card / facial photo, which can be combined with identity verification devices for dual verification. Visitor access is also time-limited (e.g., valid for 2 hours), limiting access to specific floors / accessible areas.

[0157] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0158] The building access control system provided in the embodiments of the present invention is described below. The building access control system described below can be referred to in correspondence with the building access control method described above.

[0159] Please see Figure 2 The present invention provides a building access linkage control system, including a central control module 201, an edge processing module 202, a door control module 203 and an elevator control module 204. The central control module 201 is also communicatively connected to the edge processing module 202, the door control module 203 and the elevator control module 204 respectively.

[0160] Edge processing module 202 includes:

[0161] The feature data acquisition submodule is used to acquire multimodal biometric data of people when at least one person is present in the target building area;

[0162] The identity recognition submodule is used to perform identity recognition based on multimodal biometric data and generate recognition results;

[0163] Elevator control module 204 includes:

[0164] The floor determination submodule is used to query the building access database based on the recognition result if the recognition result is successful, and then determine the target floor.

[0165] The elevator instruction sending submodule is used to generate a floor selection instruction corresponding to the target floor and send it to the corresponding elevator if none of the elevators have been triggered to the target floor.

[0166] The access control module 203 is used to generate access control device opening commands and send them to the corresponding access control devices.

[0167] Optionally, it also includes a monitoring terminal that communicates with the central control module, the central control module 201 being further used for:

[0168] If the identification result is that the identification fails, multimodal biometric data will be used for behavioral analysis to determine the type of abnormal behavior of the person.

[0169] If the abnormal behavior type matches the target behavior type, an alert message is generated and sent to the monitoring terminal.

[0170] Optionally, it also includes a camera, EEG device, and / or iris recognition device that are communicatively connected to the edge processing module; the feature data acquisition submodule is specifically used for:

[0171] When at least one person appears in the target building area, the camera equipment is activated to collect the person's facial data and the occlusion rate corresponding to the facial data is calculated.

[0172] If the occlusion rate does not exceed the preset occlusion threshold, the facial data will be identified as the person's multimodal biometric data.

[0173] If the occlusion rate exceeds the preset occlusion threshold, and the person is not carrying an EEG device, and / or there is no iris recognition device in the target building area, then the person's action video sequence is collected by the camera device, and the action video sequence is determined as the person's multimodal biometric data;

[0174] If the occlusion rate exceeds the preset occlusion threshold and the person is wearing an EEG device, then the person's EEG data will be collected through the EEG device.

[0175] If the occlusion rate exceeds the preset occlusion threshold and there is an iris recognition device in the target building area, then the iris data of the personnel will be collected through the iris recognition device.

[0176] EEG data and / or iris data are identified as multimodal biometric data of individuals.

[0177] Optionally, the identity recognition submodule includes:

[0178] The first matching unit is used to match the local personnel template of the person according to the face data if the multimodal biometric data is face data.

[0179] The first template similarity calculation unit is used to calculate the first template similarity by comparing the action video sequence with each local person template if the multimodal biometric data is an action video sequence.

[0180] The feature extraction unit is used to extract physiological features from the electroencephalogram (EEG) data and / or texture features from the iris data if the multimodal biometric data is EEG data and / or iris data.

[0181] The second template similarity calculation unit is used to calculate the second template similarity with each local person template based on physiological characteristics and / or texture characteristics;

[0182] The first result generation unit is used to generate a recognition result that has passed if the local personnel template is successfully matched, or if there is a local personnel template with a first template similarity or a second template similarity greater than a preset similarity threshold.

[0183] The second result generation unit is used to generate a recognition result indicating that the recognition failed if the local personnel template matching fails and there is no local personnel template with a first template similarity or a second template similarity greater than a preset similarity threshold.

[0184] Optionally, the first template similarity calculation unit is specifically used for:

[0185] Static contour features and dynamic trajectory features are extracted from the action video sequence and then fused to obtain gait temporal features;

[0186] After aligning the gait temporal features, classification modeling is performed to determine the fusion feature templates corresponding to the gait temporal features;

[0187] Calculate the first template similarity between the fused feature template and each local person template.

[0188] Optionally, the feature extraction unit is specifically used for:

[0189] After extracting frequency domain features and time domain features from EEG data, they are fused to generate physiological features.

[0190] And / or, encode the iris data in multiple scales and directions to generate initial iris texture features;

[0191] Redundant features are removed from the initial iris texture features to obtain the texture features corresponding to the iris data.

[0192] Optionally, the floor determination submodule is specifically used for:

[0193] Query the building access database according to the local personnel template corresponding to the recognition results to determine the corresponding building access;

[0194] If the building permissions only include permissions for a single floor, then the floor to which the floor permission belongs is determined as the target floor;

[0195] If the building permissions include multiple floor permissions, then the historical floor selection data is matched according to the current time period, and the historical floor corresponding to the current time period is determined as the target floor.

[0196] Optionally, the access control device is also equipped with a video screen; the central control module 201 is also used for:

[0197] If the number of people in the target building area exceeds the preset flow restriction threshold, the required floors for each elevator are determined according to the target floors corresponding to each person.

[0198] Each person's elevator icon is displayed on a visual screen according to their floor requirements; each elevator icon corresponds to a specific elevator.

[0199] Optionally, it also includes gateway middleware that connects the original access control devices and original elevator control devices to the edge processing module; the gateway middleware is specifically used for:

[0200] When the edge processing module outputs floor selection instructions and access control device activation instructions, it converts these instructions into recognizable signals from the original access control device and the original elevator control device.

[0201] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0202] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or modules may be electrical, mechanical, or other forms.

[0203] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0204] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0205] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A building access control method, characterized in that, The method, applied to a central control module which is also communicatively connected to an edge processing module, a door control module, and an elevator control module, includes: When at least one person is present in the target building area, the edge processing module is invoked to obtain the multimodal biometric data of the person. The edge processing module performs identity recognition based on the multimodal biometric data and generates recognition results. If the recognition result is successful, the elevator control module is invoked to query the building access database based on the recognition result to determine the target floor; If none of the elevators are triggered at the target floor, the elevator control module generates a floor selection command corresponding to the target floor and sends it to the corresponding elevator. The access control module generates an access control device opening command and sends it to the corresponding access control device.

2. The building access control method according to claim 1, characterized in that, The method also includes a monitoring terminal that is communicatively connected to the central control module, and further includes: If the identification result is that the identification fails, then the multimodal biometric data is used for behavioral analysis to determine the type of abnormal behavior corresponding to the person. If the abnormal behavior type matches the target behavior type, an alert message is generated and sent to the monitoring terminal.

3. The building access control method according to claim 1, characterized in that, It also includes a camera, EEG device, and / or iris recognition device communicatively connected to the edge processing module; the step of calling the edge processing module to obtain the multimodal biometric data of the person when at least one person appears in the target building area includes: When at least one person appears in the target building area, the edge processing module calls the camera device to collect the person's facial data and calculates the occlusion rate corresponding to the facial data; If the occlusion rate does not exceed the preset occlusion threshold, the face data is determined as the multimodal biometric data of the person. If the occlusion rate exceeds a preset occlusion threshold, and the person is not carrying an EEG device, and / or there is no iris recognition device in the target building area, then the person's motion video sequence is collected by the camera device, and the motion video sequence is determined as the person's multimodal biometric data; If the occlusion rate exceeds a preset occlusion threshold, and the person is wearing an EEG device, then the person's EEG data will be collected through the EEG device. If the occlusion rate exceeds a preset occlusion threshold, and there is an iris recognition device in the target building area, then the iris data of the person is collected through the iris recognition device. The edge processing module identifies the EEG data and / or the iris data as the person's multimodal biometric data.

4. The building access control method according to claim 3, characterized in that, The step of performing identity recognition based on the multimodal biometric data through the edge processing module and generating recognition results includes: If the multimodal biometric data is facial data, then the edge processing module matches the local personnel template of the person according to the facial data; If the multimodal biometric data is an action video sequence, then the edge processing module compares the action video sequence with each local person template to calculate the first template similarity. If the multimodal biometric data is EEG data and / or iris data, then the edge processing module extracts physiological features from the EEG data and / or extracts texture features from the iris data. The edge processing module calculates a second template similarity with each of the local personnel templates based on the physiological characteristics and / or the texture characteristics. If the local personnel template is successfully matched, or if there is a local personnel template whose similarity to the first template or the second template is greater than a preset similarity threshold, then a recognition result that has passed the recognition is generated. If the local personnel template matching fails, and there is no local personnel template with a similarity greater than the preset similarity threshold of the first template or the second template, then a recognition result indicating failure to recognize is generated.

5. The building access control method according to claim 4, characterized in that, The step of comparing the action video sequence with each local person template using the edge processing module to calculate the first template similarity includes: The edge processing module extracts static contour features and dynamic trajectory features from the action video sequence and fuses them to obtain gait temporal features. After aligning the gait temporal features using the edge processing module, classification and modeling are performed to determine the fusion feature template corresponding to the gait temporal features; The edge processing module calculates the first template similarity between the fused feature template and each local person template.

6. The building access control method according to claim 4, characterized in that, The steps of extracting physiological features from the EEG data and / or extracting texture features from the iris data through the edge processing module include: After extracting frequency domain features and time domain features from the EEG data through the edge processing module, physiological features are fused to generate physiological features. And / or, the edge processing module performs multi-scale, multi-directional encoding on the iris data to generate initial iris texture features; Redundant features are removed from the initial iris texture features to obtain the texture features corresponding to the iris data.

7. The building access control method according to claim 1, characterized in that, The step of calling the elevator control module to query the building access database based on the recognition result and determine the target floor includes: The elevator control module is invoked to query the building access database according to the local personnel template corresponding to the recognition result, and to determine the corresponding building access. If the building access permissions only include single floor permissions, then the floor to which the floor permission belongs is determined as the target floor through the elevator control module; If the building permissions include multiple floor permissions, the elevator control module matches historical floor selection data according to the current time period, and determines the historical floor corresponding to the current time period as the target floor.

8. The building access control method according to claim 1, characterized in that, The access control device is also equipped with a video screen; the method further includes: If the number of people in the target building area is greater than the preset flow restriction threshold, the people are classified according to the target floor corresponding to each person, and the required floor corresponding to each elevator is determined. According to the floor requirements, the elevator icon corresponding to each person is displayed on the visual screen; wherein, the elevator icon corresponds one-to-one with the elevator.

9. The building access control method according to claim 1, characterized in that, It also includes gateway middleware that connects the original access control devices and original elevator control devices to the edge processing module; the method further includes: When the edge processing module outputs the floor selection command and the access control device activation command, the gateway middleware converts the floor selection command and the access control device activation command into identifiable signals of the original access control device and the original elevator control device.

10. A building access control system, characterized in that, It includes a central control module, an edge processing module, a door control module, and an elevator control module. The central control module is also communicatively connected to the edge processing module, the door control module, and the elevator control module, respectively. The edge processing module includes: The feature data acquisition submodule is used to acquire the multimodal biometric data of at least one person when at least one person appears in the target building area. The identity recognition submodule is used to perform identity recognition based on the multimodal biometric data and generate recognition results; The elevator control module includes: The floor determination submodule is used to query the building access database based on the recognition result if the recognition result is successful, and determine the target floor. The elevator instruction sending submodule is used to generate a floor selection instruction corresponding to the target floor and send it to the corresponding elevator if none of the elevators have been triggered at the target floor. The access control module is used to generate access control device opening commands and send them to the corresponding access control devices.

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