Personnel management method and system based on space grid
By dividing a closed space into spatial grid cells and establishing topological relationships, and combining multi-source sensor data for positioning, the problems of extensive management and delayed alarms in existing technologies are solved, achieving precise management and highly reliable personnel monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- DACE INFORMATION TECH CO LTD
- Filing Date
- 2025-12-17
- Publication Date
- 2026-04-21
AI Technical Summary
Existing technologies for managing specific personnel such as visitors, patients, and researchers in enclosed spaces suffer from insufficient positioning accuracy, crude management, simple and lagging alarm mechanisms, and data silos, making it difficult to achieve refined, intelligent, and highly reliable personnel management.
By dividing the area to be managed into independent spatial grid units, and establishing spatial topological relationships by combining special grid bodies with connectivity status and directional attributes, multi-source sensor data is acquired for positioning, and movement trajectories are generated, enabling precise management and trajectory reproduction of personnel.
It achieves refined spatial-level management, improves the accuracy of positioning and status judgment, reduces false alarms and missed alarms, has a forward-looking and diversified alarm mechanism, and enhances the system's automation level and scalability.
Smart Images
Figure CN121908213A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of personnel management, intelligent security and digital twin technology, and in particular to a personnel management method and system. Background Technology
[0002] In enclosed spaces with high security requirements, such as office buildings, hospitals, nursing homes, laboratories, and prisons, effectively managing the activity range and behavior of specific personnel, including visitors, patients, researchers, and administrators, is often key to ensuring space security and improving management efficiency. Existing management methods have the following main shortcomings: 1) Insufficient positioning accuracy: Traditional access control systems can only record the entry and exit points of people, but cannot obtain their specific location and activity trajectory inside the room. Area positioning technologies such as Wi-Fi and Bluetooth have low accuracy and are difficult to accurately correspond to the physical spatial structure of rooms, corridors and other spaces.
[0003] 2) Inefficient management and lack of spatial intelligence: Existing systems typically treat an entire floor or a large area as a management unit, failing to provide refined management for each individual room or functional area, and unable to intelligently determine the rationality of personnel movement paths.
[0004] 3) The alarm mechanism is simple and lagging: alarms are mostly based on simple rules such as crossing boundaries or not moving for a long time, which are prone to false alarms or missed alarms.
[0005] 4) Data silos and lack of collaborative verification: Data from video surveillance, access control systems, wearable devices, etc., are independent of each other, resulting in low confidence in location and status information.
[0006] It can be seen that a personnel management method that can deeply integrate spatial structure information and achieve refined, intelligent and highly reliable management has become a demand. Summary of the Invention
[0007] To address the aforementioned problems, this invention provides a personnel management method and system based on spatial grids. By constructing spatial topological relationships and integrating multi-source sensing data, it achieves precise personnel management and trajectory reproduction.
[0008] The technical solution provided by this invention is as follows: On the one hand, the present invention provides a personnel management method based on spatial grids, including: The area to be managed is divided into independent spatial grid units; Establish spatial topological relationships by combining special grids with connectivity status and connectivity direction attributes in the area to be managed; The sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the personnel to be managed are acquired to locate the personnel to be managed, and a movement trajectory is generated by combining the spatial topology relationship to realize the management of the personnel to be managed.
[0009] In some preferred embodiments, dividing the area to be managed into independent spatial grid cells includes: Obtain the BIM model or CAD drawings of the area to be managed; The resulting physically continuous and functionally independent closed regions are divided, and each closed region is treated as a spatial grid unit. Determine whether there is a semi-enclosed area; If so, the area is divided according to the function of the corresponding semi-enclosed area, or the surrounding sensing devices are used to divide the area, and each area is treated as a spatial grid unit.
[0010] In some preferred embodiments, the process of establishing spatial topological relationships by combining special grid cells with connectivity state and connectivity direction attributes in the region to be managed includes: Identify doors and windows with connectivity status and connectivity direction attributes in the area to be managed, and designate them as special grid bodies; Configure the connection direction and connection status of each special grid body based on the status of surrounding sensing devices; Configure entry rules for each spatial grid cell; Identify physically adjacent spatial grid cells and establish connection paths between spatial grid cells on the same floor through the special grid body; Establish the topological relationships between spatial grid units on different floors to form the final spatial topological relationships.
[0011] In some preferred embodiments, the step of acquiring sensor data from sensors configured within the spatial grid cell, around a special grid body, and on the person being managed to locate the person being managed, and generating a movement trajectory by combining the spatial topology, includes: Acquire sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the personnel being managed; The acquired data is compared and fused to locate the personnel to be managed and determine their spatial grid cell. Based on continuous positioning data, the movement trajectories of the personnel to be managed are generated and recorded on the spatial topology network.
[0012] In some preferred embodiments, the process of comparing and fusing the acquired data, locating the personnel to be managed, and determining their spatial grid cell is as follows: when there are data conflicts between the acquired sensor data, the accuracy of the sensor devices at the time of acquisition of each sensor data is compared, and the personnel to be managed is located based on the sensor data of the sensor device with higher accuracy, thus determining their spatial grid cell.
[0013] In some preferred embodiments, the step of comparing and fusing the acquired data to locate the personnel to be managed and determine their location within the spatial grid cell further includes the step of marking the confidence level of the location results. When there are no data conflicts between the acquired sensor data, the localization result is marked as high confidence. When there are data conflicts among the acquired sensor data, the localization result is marked as low confidence.
[0014] In some preferred embodiments, after acquiring sensor data from sensors configured within the spatial grid cell, around a special grid body, and on the person to be managed to locate the person to be managed, and generating a movement trajectory based on the spatial topology, the method further includes an alarm step based on the movement trajectory of the person to be managed. When it is determined that the person to be managed has moved from an authorized spatial grid cell into an unauthorized adjacent spatial grid cell, an out-of-bounds alarm is triggered; An intrusion alarm is triggered when it is determined that the person to be managed is in an unauthorized spatial grid cell. When it is determined that the person to be managed has not detected the presence or movement trajectory within a preset time period in a pre-specified spatial grid cell, an alarm for loss of contact or abnormal stillness is triggered.
[0015] In some preferred embodiments, after acquiring the sensor data of the sensors configured inside the spatial grid cell, around the special grid body, and on the person to be managed to locate the person to be managed, and generating a movement trajectory in combination with the spatial topology, the method further includes a step of analyzing the behavior of the person to be managed by combining historical trajectory data, the current movement trajectory, and the attribute characteristics of the spatial grid cell to determine whether abnormal behavior has occurred.
[0016] In some preferred embodiments, after acquiring the sensor data of the sensors configured inside the spatial grid unit, around the special grid body, and on the person to be managed to locate the person to be managed, and generating a movement trajectory in combination with the spatial topology, the method further includes: synchronizing the status of each spatial grid unit and the status of the sensors in the area to be managed to the digital spatial model in real time and displaying it.
[0017] On the other hand, the present invention provides a personnel management system based on a spatial grid, applied to the above-mentioned personnel management method, wherein the personnel management system includes: The spatial grid cell division module is used to divide the area to be managed into independent spatial grid cells; The spatial topology relationship establishment module is connected to the spatial grid unit division module and is used to establish spatial topology relationships by combining special grid bodies with connectivity state attributes and connectivity direction attributes in the area to be managed. The movement trajectory generation module, connected to the spatial topology relationship establishment module, is used to acquire sensor data from sensors configured inside the spatial grid unit, around special grid bodies, and on the personnel to be managed, to locate the personnel to be managed, and to generate a movement trajectory in combination with the spatial topology relationship, thereby realizing the management of the personnel to be managed.
[0018] The personnel management method and system based on spatial grids provided by this invention can bring at least the following beneficial effects: Refined management: The management granularity is refined from floors or areas to individual rooms / functional areas (spatial grid units), achieving true spatial-level refined management.
[0019] Spatial intelligence: By introducing spatial topological relationships, the system acquires the ability of "spatial cognition," enabling it to understand the internal structure of the area (building) to be managed and the reasonable movement path of the personnel to be managed, thus laying a solid foundation for intelligent early warning.
[0020] High reliability: By cross-validating the sensor data (multi-source data such as video, wearable devices, and access control) of sensors configured inside the spatial grid cell, around special grid bodies, and on the personnel to be managed, the accuracy and confidence of personnel positioning and status judgment are greatly improved, and false alarms and missed alarms are reduced.
[0021] The alarm mechanism is forward-looking and diversified: it not only focuses on "crossing the boundary", but also on "intrusion" and "abnormal disappearance", which can cover more potential risk scenarios and make the early warning more timely and comprehensive.
[0022] Deep integration with the physical world: By treating doors and windows as special grids and binding them to sensing devices (IoT devices), a digital twin-level virtual-physical interaction is achieved, improving the system's automation level and response speed.
[0023] Strong scalability: This digital space model can be easily integrated with advanced applications such as behavioral analysis, energy management, and emergency evacuation, and has good business scalability. Attached Figure Description
[0024] The preferred embodiments will now be described in a clear and easy-to-understand manner, with reference to the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods.
[0025] Figure 1This is a schematic flowchart of one embodiment of the personnel management method of the present invention; Figure 2 This is a schematic diagram of the spatial grid units and spatial topology relationships divided according to the BIM model of a laboratory in an example. Figure 3 This is a block diagram illustrating the principle of cross-validation localization using multi-source data in a single example. Figure 4 This is a schematic diagram of alarm triggering in an example; Figure 5 This is a schematic diagram of one embodiment of the personnel management system of the present invention; Figure 6 This is a diagram showing the overall system architecture and flowchart of one embodiment of the personnel management system of the present invention.
[0026] Figure label: 100 - Personnel Management System, 110 - Spatial Grid Unit Division Module, 120 - Spatial Topology Relationship Establishment Module, 130 - Movement Trajectory Generation Module. Detailed Implementation
[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.
[0028] A first embodiment of the present invention provides a personnel management method based on spatial grids, such as... Figure 1 As shown, it includes: S10 divides the area to be managed into independent spatial grid units; S20 establishes spatial topological relationships by combining special grids with connectivity status and connectivity direction attributes in the area to be managed; S30 acquires sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the personnel to be managed, locates the personnel, and generates a movement trajectory by combining spatial topology relationships, thereby realizing the management of the personnel to be managed.
[0029] In this embodiment, the area to be managed can be a section of a building, a floor, or even the entire building. The management scope is determined according to the application requirements. When dividing the managed area into spatial grid units, the shape of the divided spatial grid units is not required to be regular (e.g., cubes, cuboids, etc.) for enclosed areas. Instead, the division is based on the functionality and physical continuity of the enclosed areas. This minimizes the cost of constructing spatial grid units, reduces the number of grid units, and improves the final calculation speed. For non-enclosed areas, such as semi-enclosed outdoor spaces separated by walls or railings, grid division can also be based on functional areas or the coverage area of the configured sensing devices.
[0030] Specifically, step S10 divides the area to be managed into independent spatial grid cells, including: S11 Obtain the BIM model or CAD drawings of the area to be managed.
[0031] BIM models and CAD drawings can reflect the geometric structure, topological relationships, semantic information and other three-dimensional building information of a building, and can quickly create spatial grid cells.
[0032] S12 divides the space into physically continuous and functionally independent closed regions, and treats each closed region as a spatial grid unit.
[0033] For enclosed areas, such as individual rooms, a corridor, a stairwell, or a hall, division is based directly on functionality and physical continuity. Physical continuity refers to a completely enclosed area without physical separation by doors or other barriers. Different enclosed areas with the same function are divided into different spatial grid units. For example, two laboratories physically separated by a door are divided into two spatial grid units. If two laboratories are physically continuous despite having walls, but with passageways (without doors), they are divided into one spatial grid unit. If two enclosed areas have different functions, even if physically continuous, they are still divided into two spatial grid units.
[0034] S13 Determine whether there is a semi-closed region.
[0035] Check the BIM model or CAD drawings of the area to be managed to see if there are any semi-enclosed areas that need to be gridded.
[0036] S14 If so, divide the area according to the function of the corresponding semi-enclosed area, or according to the surrounding sensor devices, and treat each area as a spatial grid unit. The sensor devices mentioned can be cameras, etc., and the division is based on the coverage range of the cameras.
[0037] After dividing the area to be managed into independent spatial grid units, the grid attributes of the divided spatial grid units are further defined, including configuring a unique number, floor, room number, and function type for each spatial grid unit, which facilitates the subsequent management of each spatial grid unit.
[0038] Step S20, establishing spatial topology relationships by combining special meshes with connectivity state and connectivity direction attributes in the region to be managed, includes: S21 identifies doors and windows with connectivity status and connectivity direction attributes in the area to be managed, and treats them as special grid bodies.
[0039] In the area to be managed, the opening and closing status of doors and windows has connection status attributes, such as open, closed, locked, etc.; and connection direction attributes, such as one-way passage, two-way passage, no passage, etc. Therefore, in this example, it is defined as a special grid body.
[0040] S22 configures the connection direction and connection status of each special grid body in combination with the status of surrounding sensing devices.
[0041] Because different spatial grid units have different functional attributes, the connectivity status and direction of different special grid bodies will vary. For example, if a corridor is closed to traffic during a certain period, the connectivity direction attribute of the corresponding door in the corridor during that period will be configured to be closed. This can be achieved by configuring sensors on the side of the door. In this case, personnel inside the managed area cannot pass through by swiping access cards, and the connectivity status is closed. During this process, the special grid body is associated with the configured sensor, allowing the connectivity status of the special grid body to be changed based on the sensor's status.
[0042] S23 configures entry rules for each spatial grid cell.
[0043] The access rules mentioned refer to the accessibility rules of spatial grid cells, such as whether specific personnel are allowed to enter at certain times.
[0044] S24 identifies physically adjacent spatial grid cells and establishes connection paths between spatial grid cells on the same floor through special grid bodies.
[0045] For spatial grid cells located on the same floor, adjacency relationships are identified: in addition to physically adjacent spatial grid cells, connectivity relationships are also established between spatial grid cells through special grid elements such as doors and windows. This connectivity is based on the connection direction attribute of the special grid elements, forming unidirectional or bidirectional topological relationships. It should be understood that adjacent spatial grid cells are not necessarily connected, such as two adjacent rooms without doors or windows in between. Furthermore, the connectivity relationships mentioned can be dynamic, for example, connected when a door is open and closed otherwise; or static, such as a staircase without doors.
[0046] S25 establishes the topological relationships between spatial grid units on different floors, forming the final spatial topological relationships.
[0047] For spatial grid units located on different floors, the topological relationship between different floors is established through hierarchical relationships such as stairs and elevators. Among them, the spatial grid units that are adjacent to each other on different floors can be in an adjacent relationship, which can be configured according to the actual situation.
[0048] After establishing the spatial topology, the personnel to be managed can be located and their corresponding movement trajectories generated based on the sensor data obtained from the interior of the spatial grid cells, the area around special grid bodies, and the sensors on the personnel being managed. Specifically, this includes: S31 acquires sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the person being managed.
[0049] All the sensing devices mentioned are networked devices. Sensing devices configured inside the spatial grid unit can be cameras (video recognition), etc. Sensing devices configured around special grid bodies can be access control card readers, etc. Sensing devices configured on the personnel to be managed can be UWB (ultra-wideband) devices, BLE (Bluetooth) devices, RFID (Radio Frequency Identification) devices, etc. The configuration can be based on the specific situation in the application.
[0050] S32 compares and merges the acquired data, locates the personnel to be managed, and determines their spatial grid cell.
[0051] The process involves comparing and fusing data from different sensing devices. When there are data conflicts between the acquired sensing data, i.e., different data indicate that the person to be managed is in a different spatial grid cell, the accuracy of the sensing devices at the time of acquisition of each sensing data is obtained and compared. The sensing data of the sensing device with higher accuracy is used as the standard to locate the person to be managed and determine the spatial grid cell in which they are located.
[0052] Because different types of sensing devices have varying degrees of accuracy, accuracy levels can be pre-configured for different types of sensors. For example, UWB positioning accuracy is centimeter-level, BLE positioning accuracy is decimeter-level, and RFID positioning accuracy is meter-level. This way, when data conflicts arise, the data with higher confidence is selected based on the accuracy level of the sensing device at the time of data acquisition. If the accuracy of different sensing devices is at the same level, data can be acquired again and fused as needed. It should be understood that the accuracy of different sensing devices is related to their relative distance and angle to the personnel being managed, and can be dynamically adjusted; therefore, the final positioning data will differ under different circumstances.
[0053] Furthermore, this step also includes marking the confidence level of the positioning results: when there is no data conflict between the acquired sensor data, the positioning result is marked as high confidence; when there is data conflict between the acquired sensor data, the positioning result is marked as low confidence, providing a data basis for subsequent analysis of the behavior of management personnel.
[0054] Based on continuous positioning data, S33 generates and records the movement trajectory of the personnel to be managed on the spatial topology network.
[0055] In one example, such as Figure 2 As shown, the BIM model of the laboratory was imported, and spatial grid units such as "R1 Laboratory", "R2 Office", "H1 Corridor", "Main Entrance" (not shown in the figure) and "R3 Storage Room" were divided. Simultaneously, the doors between "R1 Laboratory" and "H1 Corridor", and between "R3 Storage Room" and "H1 Corridor" were defined as "two-way connected" special grids, and the door between "R2 Office" and "H1 Corridor" was defined as a "one-way connected" special grid, and these were bound to door magnetic sensors. The windows of "R3 Storage Room" were defined as "normally closed, no passage" special grids. Based on this, the established spatial topology is as follows: "Main Entrance" connects to "H1 Corridor"; "H1 Corridor" connects to "R1 Laboratory" via door D1, to "R2 Office" via door D3, and to "R3 Storage Room" via door D2, where door D3 is a "one-way connected" door.
[0056] When visitor A attempts to enter "Office R2," their activity is restricted to "Office R2," and they are fitted with a positioning wearable device. A camera is then deployed within "Office R2." The wearable device's signal locates visitor A in "Office R2." If the camera also detects visitor A indoors, the confidence level is marked as "high." If the wearable device locates visitor A in "Office R2," but the camera detects visitor A in "H1 corridor," their location is confirmed based on the accuracy of the wearable device and camera data at the time of acquisition, and the confidence level is marked as "low."
[0057] In another example, such as Figure 3 As shown, when visitor B wants to enter "R1 Lab", if the camera's video data identifies visitor B as being in "R1 Lab", and visitor B's wristband data is also displayed in "R1 Lab", the confidence level is marked as "high", and the system outputs that visitor B is in "R1 Lab". When the access control data shows visitor B is in "R2 Office", a data conflict occurs. The system confirms the location by comparing the accuracy of each device and marks the confidence level as "low".
[0058] This embodiment is an improvement on the above embodiment. In this embodiment, after step S30 obtains the sensor data of the sensing devices configured inside the spatial grid unit, around the special grid body, and on the person to be managed to locate the person to be managed, and generates a movement trajectory by combining the spatial topology relationship, it also includes the step of issuing an alarm based on the movement trajectory of the person to be managed. S41 When it is determined that the person to be managed has moved from an authorized spatial grid cell into an unauthorized adjacent spatial grid cell, an out-of-bounds alarm is triggered; S42 When it is determined that the person to be managed is in an unauthorized spatial grid cell, an intrusion alarm is triggered. S43 When it is determined that the person to be managed has not detected the presence or movement trajectory within a preset time period in a pre-specified spatial grid cell, an alarm for loss of contact or abnormal stillness is triggered.
[0059] In this embodiment, for ease of management, different types of alarms are triggered during the monitoring process based on different situations: Boundary crossing alarm: When an administrator leaves their authorized or designated spatial grid cell and enters an unauthorized adjacent spatial grid cell.
[0060] Intrusion alarm: An administrator is present in a space grid cell that has been entered without authorization.
[0061] Loss of contact / abnormal inactivity alarm: Within the set time window, the administrator should not detect the presence or activity of the target within the designated grid cell through any sensing means.
[0062] Combination such as Figure 2 The example shown is as follows: Figure 4 As shown, Visitor C's authorized area is "R1 Laboratory". When Visitor C leaves "R1 Laboratory" and enters "H1 Corridor", an "out-of-bounds alarm" is immediately triggered. When Visitor C attempts to enter the unauthorized area "H1 Corridor", an "intrusion alarm" is immediately triggered. When Visitor C is in "R1 Laboratory", the wearable device signal disappears and the camera does not capture Visitor C's activity within 5 minutes, a "disconnection alarm" is immediately triggered, reminding management personnel to check on-site.
[0063] This embodiment is an improvement on the above embodiment. In this embodiment, after step S30 obtains the sensing data of the sensing devices configured inside the spatial grid unit, around the special grid body and on the person to be managed to locate the person to be managed and generates a movement trajectory in combination with the spatial topology relationship, it also includes a step of analyzing the behavior of the person to be managed by combining historical trajectory data, current movement trajectory and attribute characteristics of the spatial grid unit to determine whether abnormal behavior has occurred.
[0064] In this embodiment, after generating the movement trajectory of the personnel to be managed, the behavioral patterns, such as activity patterns, frequently visited areas, and preferred movement paths, are further analyzed based on a combination of historical trajectory data, current movement trajectory, attribute features of spatial grid units, and semantic features. This analysis is used to analyze any abnormal behavior. The resulting historical text can also be used for subsequent retrieval and analysis using the Big Data Prediction Model. The attribute features mentioned refer to the functional attributes of the corresponding spatial grid unit. For example, if the spatial grid unit is a canteen, the time that the personnel to be managed are active in it is usually between 12:00-12:30 noon and 17:30-18:00.
[0065] In one example, the divided spatial grid units include the cafeteria, library, restrooms, and activity room. Zhang San's historical activity trajectory is generally as follows: arriving at the activity room at 8:30, leaving at 10:05, staying for 95 minutes; arriving at the library at 10:30, leaving at 11:50, staying for 80 minutes. Therefore, if on a certain day Zhang San's activity trajectory is: arriving at the activity room at 8:30, leaving at 9:00, staying for 30 minutes; arriving at the cafeteria at 9:10, leaving at 9:30, this indicates abnormal behavior that day.
[0066] Combination such as Figure 2 In another example, the system analyzes the behavior of researcher D and finds that he usually works long hours in the "R1 laboratory". If his trajectory shows frequent and brief visits to the "R3 storage room" on a certain day, the system can mark this behavior as "abnormal" for the administrator's reference.
[0067] This embodiment is an improvement on the above embodiment. In this embodiment, after step S30 obtains the sensing data of the sensing devices configured inside the spatial grid unit, around the special grid body and on the person to be managed, locates the person to be managed, and generates a movement trajectory in combination with the spatial topology, it also includes: synchronizing the status of each spatial grid unit and the status of the sensing devices in the area to be managed to the digital spatial model in real time and displaying it.
[0068] In this embodiment, the states of spatial grid cells in the physical world (such as the opening and closing of doors, and the presence of personnel to be managed) and the states of sensing devices are synchronized in real time to the digital spatial model. Furthermore, within the digital spatial model, the entry rules (accessibility) and alarm rules of each spatial grid cell can be modified, and even the sensing devices can be controlled in reverse, such as remotely locking doors, thus achieving bidirectional mapping between digital twins.
[0069] Combination such as Figure 2 Another example shown is that the location of all personnel to be managed can be viewed in real time in the digital space model. After an abnormal alarm is detected, the personnel can also remotely lock the door of the "R3 storage room" on the digital space model to prevent accidents.
[0070] Another example of the present invention is a personnel management system based on spatial grids, applied to the above-mentioned personnel management method, such as... Figure 5 As shown, the personnel management system 100 includes: The spatial grid cell division module 110 is used to divide the area to be managed into independent spatial grid cells; The spatial topology relationship establishment module 120 is connected to the spatial grid cell division module and is used to establish spatial topology relationships by combining special grid bodies with connectivity state attributes and connectivity direction attributes in the area to be managed. The movement trajectory generation module 130 is connected to the spatial topology relationship establishment module. It is used to acquire the sensor data of the sensor devices configured inside the spatial grid unit, around the special grid body, and on the person to be managed, to locate the person to be managed, and to generate a movement trajectory in combination with the spatial topology relationship to realize the management of the person to be managed.
[0071] In this embodiment, the area to be managed can be a section of a building, a floor, or even the entire building. The management scope is determined according to the application requirements. When dividing the managed area into spatial grid units, the shape of the divided spatial grid units is not required to be regular (e.g., cubes, cuboids, etc.) for enclosed areas. Instead, the division is based on the functionality and physical continuity of the enclosed areas. This minimizes the cost of constructing spatial grid units, reduces the number of grid units, and improves the final calculation speed. For non-enclosed areas, such as semi-enclosed outdoor spaces separated by walls or railings, grid division can also be based on functional areas or the coverage area of the configured sensing devices.
[0072] Specifically, the processing steps of the spatial grid unit division module include: acquiring the BIM model or CAD drawings of the area to be managed; dividing the area into physically continuous and functionally independent closed areas, and treating each closed area as a spatial grid unit; determining whether there are semi-closed areas, and if so, dividing the area according to the function of the corresponding semi-closed area, or dividing the area according to the surrounding sensor devices, and treating each area as a spatial grid unit.
[0073] BIM models and CAD drawings can reflect the geometric structure, topological relationships, semantic information, and other three-dimensional building information of a building, enabling the rapid creation of spatial grid units. For enclosed areas, such as independent rooms, a corridor, a stairwell, or a lobby, division is directly based on functionality and physical continuity. Physical continuity refers to a complete enclosed area without physical barriers such as doors. Different enclosed areas with the same functionality are divided into different spatial grid units. Further, grid attributes are defined for each spatial grid unit, including a unique number, floor, room number, and function type, facilitating subsequent management of each spatial grid unit.
[0074] The processing steps of the spatial topology relationship establishment module include: identifying doors and windows with connectivity status and connectivity direction attributes in the area to be managed, as special grid bodies; configuring the connectivity direction and connectivity status of each special grid body in combination with the status of surrounding sensing devices; configuring entry rules for each spatial grid unit; identifying physically adjacent spatial grid units, and establishing connection paths between spatial grid units on the same floor through special grid bodies; establishing topological relationships between spatial grid units on different floors, forming the final spatial topology relationship.
[0075] In the area to be managed, the opening and closing status of doors and windows has connection status attributes, such as open, closed, locked, etc.; and connection direction attributes, such as one-way passage, two-way passage, no passage, etc. Therefore, in this example, it is defined as a special grid body.
[0076] Because different spatial grid units have different functional attributes, the connectivity status and direction of different special grid bodies will vary. For example, if a corridor is closed to traffic during a certain time period, the connectivity direction attribute of the corresponding door in that corridor will be configured to be closed during that time period. This can be achieved by configuring sensors on the side of the door. In this case, personnel inside the managed area cannot pass through by swiping their access cards, and the connectivity status is closed. During this process, the special grid body is associated with the configured sensor, allowing the connectivity status of the special grid body to be changed based on the sensor's status. The entry rules mentioned refer to the accessibility rules of the spatial grid unit, such as whether specific personnel are allowed to enter during certain times.
[0077] For spatial grid cells located on the same floor, adjacency relationships are identified: in addition to physically adjacent spatial grid cells, connectivity relationships are also established between spatial grid cells through special grid elements such as doors and windows. This connectivity is based on the connection direction attribute of the special grid elements, forming unidirectional or bidirectional topological relationships. It should be understood that adjacent spatial grid cells are not necessarily connected, such as two adjacent rooms without doors or windows in between. Furthermore, the connectivity relationships mentioned can be dynamic, for example, connected when a door is open and closed otherwise; or static, such as a staircase without doors.
[0078] For spatial grid units located on different floors, the topological relationship between different floors is established through hierarchical relationships such as stairs and elevators. Among them, the spatial grid units that are adjacent to each other on different floors can be in an adjacent relationship, which can be configured according to the actual situation.
[0079] The processing steps of the movement trajectory generation module include: acquiring sensor data from sensors configured inside the spatial grid cell, around special grid bodies, and on the personnel to be managed; comparing and fusing the acquired data to locate the personnel to be managed and determine their spatial grid cell; and generating and recording the movement trajectory of the personnel to be managed on the spatial topology network based on continuous positioning data.
[0080] Sensing devices configured inside spatial grid units can be cameras (video recognition), etc. Sensing devices configured around special grid bodies can be access control card readers, etc. Sensing devices configured on personnel can be UWB (ultra-wideband) devices, BLE (Bluetooth) devices, RFID (Radio Frequency Identification) devices, etc. The configuration can be based on the specific situation in the application.
[0081] The process involves comparing and fusing data from different sensing devices. When there are data conflicts between the acquired sensing data, i.e., different data indicate that the person to be managed is in a different spatial grid cell, the accuracy of the sensing devices at the time of acquisition of each sensing data is obtained and compared. The sensing data of the sensing device with higher accuracy is used as the standard to locate the person to be managed and determine the spatial grid cell in which they are located.
[0082] Because different types of sensing devices have varying degrees of accuracy, accuracy levels can be pre-configured for different types of sensors. For example, UWB positioning accuracy is centimeter-level, BLE positioning accuracy is decimeter-level, and RFID positioning accuracy is meter-level. This way, when data conflicts arise, the data with higher confidence is selected based on the accuracy level of the sensing device at the time of data acquisition. If the accuracy of different sensing devices is at the same level, data can be acquired again and fused as needed. It should be understood that the accuracy of different sensing devices is related to their relative distance and angle to the personnel being managed, and can be dynamically adjusted; therefore, the final positioning data will differ under different circumstances.
[0083] Furthermore, the movement trajectory generation module is also used to mark the confidence level of the positioning results: when there is no data conflict between the acquired sensor data, the positioning result is marked as high confidence; when there is data conflict between the acquired sensor data, the positioning result is marked as low confidence, providing a data basis for subsequent analysis of the behavior of management personnel.
[0084] This embodiment is an improvement on the above embodiment. In this embodiment, the personnel management system also includes an alarm module for triggering an alarm based on the movement trajectory of the personnel to be managed. The processing steps include: when it is determined that the personnel to be managed moves from an authorized spatial grid cell into an unauthorized adjacent spatial grid cell, an out-of-bounds alarm is triggered; when it is determined that the personnel to be managed appears in an unauthorized spatial grid cell, an intrusion alarm is triggered; when it is determined that the personnel to be managed has not been detected to exist or have a movement trajectory within a preset time period in a pre-specified spatial grid cell, a disconnection or abnormal stillness alarm is triggered.
[0085] In this embodiment, for ease of management, different types of alarms are triggered during the monitoring process based on different situations: Boundary crossing alarm: When an administrator leaves their authorized or designated spatial grid cell and enters an unauthorized adjacent spatial grid cell.
[0086] Intrusion alarm: An administrator is present in a space grid cell that has been entered without authorization.
[0087] Loss of contact / abnormal inactivity alarm: Within the set time window, the administrator should not detect the presence or activity of the target within the designated grid cell through any sensing means.
[0088] This embodiment is an improvement on the above embodiment. In this embodiment, the personnel management system also includes a behavior analysis module for analyzing the behavior of the personnel to be managed by combining historical trajectory data, current movement trajectory and attribute characteristics of spatial grid cells, and determining whether abnormal behavior has occurred.
[0089] In this embodiment, after generating the movement trajectory of the personnel to be managed, the behavioral patterns, such as activity patterns, frequently visited areas, and preferred movement paths, are further analyzed based on a combination of historical trajectory data, current movement trajectory, attribute features of spatial grid units, and semantic features. This analysis is used to analyze any abnormal behavior. The resulting historical text can also be used for subsequent retrieval and analysis using the Big Data Prediction Model. The attribute features mentioned refer to the functional attributes of the corresponding spatial grid unit. For example, if the spatial grid unit is a canteen, the time that the personnel to be managed are active in it is usually between 12:00-12:30 noon and 17:30-18:00.
[0090] This embodiment is obtained by improving the above embodiment. In this embodiment, the personnel management system also includes a digital mapping module for synchronizing and displaying the status of each spatial grid unit and the status of sensing devices in the area to be managed in real time to the digital spatial model.
[0091] In this embodiment, the states of spatial grid cells in the physical world (such as the opening and closing of doors, and the presence of personnel to be managed) and the states of sensing devices are synchronized in real time to the digital spatial model. Furthermore, within the digital spatial model, the entry rules (accessibility) and alarm rules of each spatial grid cell can be modified, and even the sensing devices can be controlled in reverse, such as remotely locking doors, thus achieving bidirectional mapping between digital twins.
[0092] The overall system architecture diagram and flowchart of this embodiment are as follows: Figure 6 As shown in the diagram, the system is mainly divided into three parts: the physical world layer, the data processing and model layer, and the application service layer. The physical world layer includes spatial grid units such as rooms, doors, and corridors in the managed space, as well as cameras configured within these units and wristbands worn by the personnel. The data processing and model layer includes the established topological relationship network (corresponding to the aforementioned spatial topology) and the spatial network model (corresponding to the aforementioned digital spatial model). The application service layer includes entity positioning, trajectory tracking, intelligent alarms, and behavior analysis. Data collected in the physical world layer is used to create the topological relationship network and synchronize it to the spatial network model, while also mapping data from the spatial network model to the physical world layer. Data output from the data processing and model layer is sent to the application service layer for corresponding services.
[0093] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A personnel management method based on spatial grids, characterized in that, include: The area to be managed is divided into independent spatial grid units; Establish spatial topological relationships by combining special grids with connectivity status and connectivity direction attributes in the area to be managed; The sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the personnel to be managed are acquired to locate the personnel to be managed, and a movement trajectory is generated by combining the spatial topology relationship to realize the management of the personnel to be managed.
2. The personnel management method as described in claim 1, characterized in that, The process of dividing the area to be managed into independent spatial grid units includes: Obtain the BIM model or CAD drawings of the area to be managed; The resulting physically continuous and functionally independent closed regions are divided, and each closed region is treated as a spatial grid unit. Determine whether there is a semi-enclosed area; If so, the area is divided according to the function of the corresponding semi-enclosed area, or the surrounding sensing devices are used to divide the area, and each area is treated as a spatial grid unit.
3. The personnel management method as described in claim 1 or 2, characterized in that, The process of establishing spatial topological relationships by combining special grid cells with connectivity state and connectivity direction attributes in the region to be managed includes: Identify doors and windows with connectivity status and connectivity direction attributes in the area to be managed, and designate them as special grid bodies; Configure the connection direction and connection status of each special grid body based on the status of surrounding sensing devices; Configure entry rules for each spatial grid cell; Identify physically adjacent spatial grid cells and establish connection paths between spatial grid cells on the same floor through the special grid body; Establish the topological relationships between spatial grid units on different floors to form the final spatial topological relationships.
4. The personnel management method as described in claim 1 or 2, characterized in that, The process of acquiring sensor data from sensors configured within spatial grid cells, around special grid bodies, and on the personnel being managed, to locate the personnel, and generating a movement trajectory by combining this data with the spatial topology, includes: Acquire sensor data from sensors configured inside spatial grid cells, around special grid bodies, and on the personnel being managed; The acquired data is compared and fused to locate the personnel to be managed and determine their spatial grid cell. Based on continuous positioning data, the movement trajectories of the personnel to be managed are generated and recorded on the spatial topology network.
5. The personnel management method as described in claim 4, characterized in that, The process involves comparing and fusing the acquired data to locate the personnel to be managed and determine their spatial grid cell. When there are data conflicts among the acquired sensor data, the accuracy of the sensor devices at the time of acquisition of each sensor data is compared. The personnel to be managed is located based on the sensor data of the device with higher accuracy, and their spatial grid cell is determined.
6. The personnel management method as described in claim 4, characterized in that, The process of comparing and fusing the acquired data to locate the personnel to be managed and determine their spatial grid cell also includes the step of marking the confidence level of the location results. When there are no data conflicts between the acquired sensor data, the localization result is marked as high confidence. When there are data conflicts among the acquired sensor data, the localization result is marked as low confidence.
7. The personnel management method as described in claim 1, 2, 5, or 6, characterized in that, After acquiring sensor data from sensors configured within the spatial grid unit, around special grid bodies, and on the personnel being managed to locate them, and generating a movement trajectory based on the spatial topology, the process further includes an alarm step based on the movement trajectory of the personnel being managed. When it is determined that the person to be managed has moved from an authorized spatial grid cell into an unauthorized adjacent spatial grid cell, an out-of-bounds alarm is triggered; An intrusion alarm is triggered when it is determined that the person to be managed is in an unauthorized spatial grid cell. When it is determined that the person to be managed has not detected the presence or movement trajectory within a preset time period in a pre-specified spatial grid cell, an alarm for loss of contact or abnormal stillness is triggered.
8. The personnel management method as described in claim 1, 2, 5, or 6, characterized in that, After acquiring sensor data from sensors configured inside the spatial grid unit, around the special grid body, and on the person to be managed to locate the person to be managed, and generating a movement trajectory in combination with the spatial topology, the process further includes analyzing the behavior of the person to be managed by combining historical trajectory data, the current movement trajectory, and the attribute characteristics of the spatial grid unit to determine whether abnormal behavior has occurred.
9. The personnel management method as described in claim 1, 2, 5, or 6, characterized in that, After acquiring the sensor data of the sensors configured inside the spatial grid unit, around the special grid body, and on the person to be managed to locate the person to be managed, and generating a movement trajectory in combination with the spatial topology, the method further includes: synchronizing the status of each spatial grid unit and the status of the sensors in the area to be managed to the digital spatial model in real time and displaying it.
10. A personnel management system based on spatial grids, characterized in that, The personnel management system, applied to the personnel management method as described in any one of claims 1-9, comprises: The spatial grid cell division module is used to divide the area to be managed into independent spatial grid cells; The spatial topology relationship establishment module is connected to the spatial grid unit division module and is used to establish spatial topology relationships by combining special grid bodies with connectivity state attributes and connectivity direction attributes in the area to be managed. The movement trajectory generation module, connected to the spatial topology relationship establishment module, is used to acquire sensor data from sensors configured inside the spatial grid unit, around special grid bodies, and on the personnel to be managed, to locate the personnel to be managed, and to generate a movement trajectory in combination with the spatial topology relationship, thereby realizing the management of the personnel to be managed.