A multi-protocol adaptive access internet of things intelligent control gateway and an intelligent scene linkage method thereof

Abstract

The application discloses a kind of multi-protocol adaptive access's internet of things intelligent control gateway and its intelligent scene linkage method.The method is by establishing multi-protocol sharing device access map, in combination with the actual service boundary of the field space state determination equipment;Through multi-dimensional weighted calculation boundary drift intensity, after identifying space merging, partition adjustment or temporary borrowing and other drift causes, automatically execute rebinding operation to the original scene linkage chain;Rebinding strategy is dynamically adjusted according to the cause type, supports trigger source merging, temporary strategy hanging and expiration automatic fallback, and introduces public safety conflict detection mechanism;After execution, through closed loop verification detection action out of bounds, realize the self-healing and reconstruction of linkage chain.The application solves the problem of cross-regional mislinkage caused by frequent changes of physical boundary in multi-tenant shared space, realizes the automatic adaptation and safety control of heterogeneous protocol device linkage logic.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things and intelligent building control technology, and more specifically, to an IoT intelligent control gateway with multi-protocol adaptive access and its intelligent scene linkage method. Background Technology

[0002] With the widespread application of IoT technology in smart buildings, multi-tenant shared spaces such as co-working spaces, multi-store floors in shopping malls, and shared areas in industrial parks have commonly deployed IoT devices such as lighting circuits, air conditioning terminals, fresh air branch lines, and access control channels. These devices are connected to a scene linkage system through an IoT intelligent control gateway, supporting unified access for devices using various heterogeneous protocols such as BACnet, Modbus, KNX, and MQTT. This enables scene linkage functions such as "lights turn on when someone enters," "lights turn off when someone leaves," and "air conditioning automatically turns on when a meeting room is booked." The scene linkage chain records the spatial affiliation of each IoT device—which space unit the device is registered to serve and which tenant it belongs to.

[0003] A key difference between multi-tenant shared spaces and traditional fixed office buildings is the frequent change of spatial boundaries: partition adjustments, temporary meeting room mergers, shop wall demolitions and reconstructions, and temporary borrowing of public corridors are common on-site modifications. These changes in physical spatial boundaries cause a deviation between the actual spatial service range of IoT devices and their registered spatial affiliation in the scenario linkage chain—resulting in "spatial service boundary drift." As tenants move in, relocate, and their space usage needs change, the traditional method of manually modifying device affiliation one by one can no longer meet the operational efficiency requirements of high-frequency changing scenarios such as co-working spaces.

[0004] The drift of spatial service boundaries directly leads to cross-regional mis-linking issues. For example, after the partition between two adjacent meeting rooms is removed and merged into one large meeting room, the lighting circuits and air conditioning terminals originally belonging to the two meeting rooms now jointly serve the merged space. However, the scene linkage chain still operates according to the spatial ownership before the merger: when people enter from one side, only the lights in the original meeting room turn on, while the lights on the other side do not respond. Similarly, after the wall between two adjacent shops in a shopping mall is removed and new tenants move in, the lighting circuits of the tenants who have moved out may still be triggered by the linkage rules left behind by the old tenants, incorrectly controlling the equipment now belonging to the new tenants. Furthermore, when the temperature in a co-working space exceeds a threshold due to increased personnel density, if the partitions have been removed but the linkage ownership of the air conditioning terminals has not been updated in time, the cooling capacity of a single air conditioner cannot meet the cooling needs of the merged space, impacting the user experience.

[0005] To address the above issues, there are currently two main types of existing solutions:

[0006] The first type of solution involves deploying a rule engine on the edge gateway to achieve localized parsing and execution of scene-linking rules. For example, CN119135783A discloses a solution that independently parses scene-linking rules at the edge gateway and supports autonomous linkage even when the network is offline. The rule engine can issue control commands to designated devices based on trigger conditions. However, in this solution, the device space ownership relationship bound to each linkage rule is static, failing to consider the dynamic factor that space boundaries may change. When space changes occur, such as partition adjustments or meeting room mergers, maintenance personnel need to manually check the affected linkage rules one by one and manually modify the device ownership relationships. The response time is usually several hours to several days, during which cross-regional erroneous linkages continue to occur. For co-working spaces managing hundreds of devices, a single partition change may affect dozens of linkage rules, and the workload and error risk of manual modification are not negligible.

[0007] The second type of solution dynamically adjusts the service area of ​​IoT devices through optimization algorithms. For example, patent application CN202411521703 discloses a method for dynamically adjusting the service area of ​​IoT devices based on buffer merging, hotspot analysis, and path analysis. This method uses optimization algorithms to dynamically adjust the device's service area to eliminate blind spots and duplicate coverage. However, this type of solution addresses the coverage optimization problem of the communication service area—its "service area" refers to the geographical range within which the device can communicate. The adjustment is based on signal coverage quality parameters, and the goal is to optimize the utilization of communication resources. This type of solution does not involve changes in device spatial service ownership due to changes in physical spatial boundaries (partitions, wall modifications, etc.), nor does it involve spatial adaptation and reconstruction of scene linkage chains, nor does it involve the detection and prevention of cross-regional mis-linkage. The technical problems it addresses, the technical means it employs, and the technical effects it achieves belong to different technical fields and solutions than the reconstruction of linkage chains in scenarios of spatial service boundary drift.

[0008] In summary, there is no publicly available technical solution that can automatically identify the drift of space service boundaries when the physical boundaries of space change, and automatically reconstruct the scene linkage chain accordingly to prevent cross-regional erroneous linkage. Summary of the Invention

[0009] This invention provides an intelligent scene linkage method, applied to an IoT intelligent control gateway deployed at the edge of a multi-tenant shared space and supporting multi-protocol adaptive access, comprising the following steps:

[0010] S1: Perform multi-protocol access and shared device marking on IoT devices in multi-tenant shared spaces, and generate a multi-protocol shared device access map;

[0011] S2: Based on the multi-protocol shared device access map, combined with the on-site spatial status information, determine the actual service space corresponding to each IoT device and generate a spatial service boundary status table;

[0012] S3: Compare the actual service space of each IoT device in the spatial service boundary status table with the old space ownership relationship of the IoT device in the original scene linkage chain, calculate the boundary drift intensity through multi-dimensional weighting, and correct the boundary drift intensity through boundary confidence. When the corrected boundary drift intensity reaches the preset threshold, identify the boundary drift cause type and output the boundary drift confirmation result.

[0013] S4: Based on the boundary drift confirmation result and the boundary drift cause type, generate a rebinding scenario linkage chain consistent with the current spatial service boundary;

[0014] S5: Receive a scene trigger event, perform spatial access verification on the rebinding scene linkage chain, obtain the actual status feedback of each IoT device and calculate the closed-loop boundary crossing strength. The closed-loop boundary crossing strength needs to exceed the current actual service space boundary. When the closed-loop boundary crossing strength does not exceed the closed-loop boundary crossing threshold and the solidification credibility reaches the solidification threshold, perform progressive solidification on the rebinding scene linkage chain. When the closed-loop boundary crossing strength exceeds the closed-loop boundary crossing threshold, add the out-of-bounds device to the set of objects to be rebinded, return to S3 to re-perform boundary drift identification and rebinding, and remove the devices that have not crossed the boundary from the set of objects to be rebinded. Repeat until the set of objects to be rebinded is empty.

[0015] The solidification reliability is determined by the ratio of the out-of-bounds occurrence frequency and the boundary drift stability coefficient; the ratio of the out-of-bounds occurrence frequency is the ratio of the number of times the closed-loop out-of-bounds threshold is exceeded in the most recent executions to the total number of executions; the boundary drift stability coefficient is determined by the absolute value of the difference between the boundary drift intensity of the current evaluation period and the previous evaluation period.

[0016] Furthermore, in S3, the dimensions of the multi-dimensional weighting include: the degree of crossing the original spatial affiliation range (referring to whether the number of the actual service space of the device has changed from the original registered spatial unit), the degree of crossing the original tenant affiliation range (referring to whether the tenant to which the actual service space of the device belongs has changed from the original tenant), the degree of change in public area attributes, and the degree of inconsistency in service loop affiliation.

[0017] The boundary credibility includes the lighting service boundary strength and access credibility. The lower the boundary credibility of a device, the higher the weighted corrected boundary drift strength when it deviates in multiple dimensions.

[0018] The intensity of the lighting service boundary is determined by a weighted average of the standardized distance, the intersection ratio of light coverage, and the barrier obstruction coefficient.

[0019] The access credibility is determined by weighting the proportion of successful responses in the gateway's most recent heartbeat detections of IoT devices to the total number of detections and the average response delay timeout.

[0020] Furthermore, in S4, the types of boundary drift causes include:

[0021] The removal of physical boundaries leading to the merging of multiple spaces refers to the situation where movable partitions are removed or retracted, resulting in the physical connection of multiple independent spatial units.

[0022] Changes in tenant occupancy leading to changes in ownership refer to situations where changes in the leased area of ​​a specific tenant result in changes in the ownership of equipment space.

[0023] Temporary occupation of public areas refers to situations where a tenant temporarily occupies a public area, resulting in the inclusion of equipment within that area into the tenant's service space;

[0024] Generate a rebinding scenario linkage chain that is consistent with the current spatial service boundary, including:

[0025] When the cause of boundary drift is physical boundary cancellation leading to multi-space merging, devices originally belonging to different spaces will be incorporated into the same scene linkage chain.

[0026] When the boundary drift cause type is a change in ownership due to a change in the tenant's occupied area, a temporary holding binding with a time validity parameter is generated for cross-space devices;

[0027] When the boundary drift cause type is public area temporary occupation type, the cross-space device will be connected to the current space linkage chain in a lightweight mounting manner and configured with over-boundary fallback conditions; the lightweight mounting means that the device only responds to the control commands in the linkage chain during the access period without changing its original device configuration.

[0028] Furthermore, before generating the rebinding scenario linkage chain in S4, the method further includes: performing public safety conflict detection on the devices involved in the rebinding; when cross-space rebinding involving safety-critical devices is detected, performing graded demotion processing on the safety-critical devices or preventing automatic rebinding and generating a manual confirmation prompt; devices with higher safety levels correspond to smaller demotion ranges; the safety-critical devices include emergency lighting equipment and fire-fighting related equipment.

[0029] Furthermore, in S2, the on-site spatial state information is obtained through multi-source fusion, and the fused signal sources include isolation sensor signals, control panel input signals, reservation system data, and infrared thermal image data.

[0030] Furthermore, in S5, the scenario triggering events include environmental condition exceeding limits events, which are generated based on environmental monitoring data of temperature, humidity, CO2 concentration, and PM2.5 concentration of each spatial unit within the shared space;

[0031] The closed-loop boundary crossing strength is determined by the ratio of the number of devices exceeding the current actual service space boundary to the total number of devices involved in the linkage chain;

[0032] The progressive solidification includes gradually reducing the spatial access verification frequency of the rebinding scenario linkage chain according to the increase of solidification credibility; when the solidification credibility is lower than the degradation threshold, the solidified linkage chain is unsolidified and reverted to the previous effective rebinding state.

[0033] This invention also provides a multi-protocol adaptive access IoT intelligent control gateway, deployed at the edge of a multi-tenant shared space, comprising:

[0034] The multi-protocol access and map generation module is used to perform multi-protocol access and shared device marking for IoT devices in a multi-tenant shared space, and generate a multi-protocol shared device access map.

[0035] The on-site status fusion and boundary table generation module is used to determine the actual service space corresponding to each IoT device based on the multi-protocol shared device access map and the on-site spatial status information, and generate a spatial service boundary status table.

[0036] The boundary drift detection and cause identification module is used to compare the actual service space of each IoT device in the spatial service boundary status table with the old space ownership relationship of the IoT device in the original scene linkage chain, calculate the boundary drift intensity through a multi-dimensional weighted method, and correct the boundary drift intensity through the boundary confidence. When the corrected boundary drift intensity reaches a preset threshold, the boundary drift cause type is identified and the boundary drift confirmation result is output.

[0037] The differentiated rebinding execution module is used to generate a rebinding scenario linkage chain that is consistent with the current spatial service boundary based on the boundary drift confirmation result and the boundary drift cause type.

[0038] The scene linkage verification and solidification module is used to receive scene trigger events, perform spatial access verification on the rebinding scene linkage chain, obtain the actual status feedback of each IoT device and calculate the closed-loop boundary crossing strength. The closed-loop boundary crossing strength needs to exceed the current actual service space boundary. When the closed-loop boundary crossing strength does not exceed the closed-loop boundary crossing threshold and the solidification credibility reaches the solidification threshold, the rebinding scene linkage chain is progressively solidified. When the closed-loop boundary crossing strength exceeds the closed-loop boundary crossing threshold, the out-of-bounds device is re-marked as a rebinding object and returned to the boundary drift detection and cause identification module.

[0039] Furthermore, in the boundary drift detection and cause identification module, the multi-dimensional weighted dimensions include the degree of crossing the original spatial ownership range, the degree of crossing the original tenant ownership range, the degree of change in public area attributes, and the degree of inconsistency in service loop ownership; the boundary credibility includes the lighting service boundary strength and access credibility; in the scene linkage verification and solidification module, the solidification credibility is determined by the ratio of the number of consecutive successful executions to the total number of effective executions, the ratio of the frequency of boundary crossings, and the boundary drift stability coefficient.

[0040] The present invention also provides a terminal, including a processor and a storage medium;

[0041] The storage medium is used to store instructions;

[0042] The processor is configured to operate according to the instructions to execute the steps of the method.

[0043] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0044] The technical effects of this invention are as follows:

[0045] The intelligent scene linkage method provided by this invention solves the technical problem of cross-regional mis-linking caused by changes in physical space boundaries in multi-tenant shared spaces by combining the multi-dimensional weighted recognition of boundary drift in S3 with the linkage chain rebinding operation in S4. Specific beneficial effects are as follows:

[0046] (1) Avoiding response delays and false linkage windows caused by manual modification of linkage rules. In S3, this invention uses a multi-dimensional weighted method to calculate the boundary drift intensity, so that after the spatial physical boundary changes, the gateway can automatically identify whether the spatial service boundary of each IoT device has drifted, eliminating the need for maintenance personnel to manually check each affected device and manually modify linkage rules. Thus, within the time window between the occurrence of spatial change and the confirmation of drift, the original drift identification step is changed from manual judgment to automatic execution, reducing cross-regional false linkage caused by the continued execution of linkage based on the old affiliation due to manual response delay.

[0047] (2) Ensure that the linkage command matches the current actual service space of the device. In S4, based on the drift confirmation result, the present invention performs a rebinding operation on cross-space devices, stripping them of their original affiliation and attaching them to the current actual service space, so that the rebinding scene linkage chain is consistent with the actual space service boundary of each device. Compared with the existing technology where the linkage chain binding relationship remains static, the scope of the linkage command can adaptively adjust with the change of physical space boundary, avoiding the triggering event from crossing the actual space boundary and incorrectly controlling the cross-regional linkage of devices in non-target spaces.

[0048] (3) Balancing the automation efficiency and judgment accuracy of drift recognition. In the graded threshold judgment of S3, this invention divides the boundary drift intensity into a confirmation interval and a verification interval. It automatically judges the situation that is clearly confirmed to ensure the response speed, and marks the situation with ambiguous boundaries for manual verification to ensure the judgment accuracy. It achieves a balance between automated recognition and manual intervention, and reduces the risk of misjudgment or missed judgment caused by boundary conditions under a single threshold strategy.

[0049] (4) Avoiding manual secondary modifications after temporary space changes are restored. In S4, this invention sets a rebinding strategy with a time validity parameter for space changes with a validity period. After the validity period expires, it automatically triggers a rollback to the original scene linkage chain, eliminating the need for manual modification of the linkage rules after space restoration and reducing the workload of operation and maintenance. At the same time, by setting a conflict detection mechanism between the temporary rebinding strategy and the public basic security strategy, it prevents temporary linkage from affecting the normal operation of public safety functions such as fire evacuation and public passage due to exclusive control during the validity period. Attached Figure Description

[0050] Figure 1 A flowchart illustrating the implementation of the present invention is shown. Detailed Implementation

[0051] Example 1: See Figure 1 This embodiment provides a method for intelligent scene linkage using an IoT intelligent control gateway with multi-protocol adaptive access, executed by an IoT intelligent control gateway deployed at the edge of a multi-tenant shared space. This embodiment uses a typical co-working space as an example, comprising 30 spatial units (including private offices, open workstations, meeting rooms, public corridors, and tea rooms), with a total of 150 IoT devices connected to four types: lighting circuits, air conditioning terminals, fresh air branch lines, and access control channels, and 10 preset scene linkage chains. This embodiment describes each step in detail:

[0052] S1: Perform multi-protocol access and shared device marking on IoT devices in multi-tenant shared spaces, and generate a multi-protocol shared device access map;

[0053] S11: Establish a multi-protocol device access record;

[0054] The gateway connects to the lighting circuit controller, air conditioning terminal controller, fresh air branch controller, and access control controller via four protocols: BACnet, Modbus, KNX, and MQTT, respectively, establishing a unified access record for each device. Each access record contains at least the following fields: Device ID, Access Channel (recording protocol type and physical port), Device Address, Device Type (values ​​are lighting / air conditioning / fresh air / access control), Installation Location Coordinates (based on the Building Information Model coordinate system), Service Circuit Number, and Access Status.

[0055] After successful access, the gateway sends heartbeat probe messages to each device at a fixed period (30 seconds in this embodiment), and calculates the device access credibility based on the response of the most recent number of probes (10 in this embodiment):

[0056] Access credibility is determined by multiplying the proportion of successful responses in the most recent probes to the total number of probes by a weighting coefficient, adding one and subtracting the ratio of average response latency to timeout threshold multiplied by one and subtracting the weighting coefficient, and then summing the two weighted sums.

[0057] In this embodiment, the average response delay timeout level T is specifically:

[0058] T = 1 - t / t0; where t is the average response latency of the gateway to the IoT device in the most recent heartbeat probes (10 probes in this embodiment), i.e., the arithmetic mean of the response time of each probe; t0 is the preset response timeout threshold (5000ms in this embodiment), when the response time of a single probe exceeds t0, that probe is counted as a timeout; the value range of T is (-∞, 1]. When the average response latency is close to 0, T approaches 1, indicating timely response and low latency timeout; when the average response latency is close to or exceeds the timeout threshold, T approaches 0 or a negative value, indicating slow response and high latency timeout. In this embodiment, when T < 0, it is counted as 0 to avoid negative values ​​for access credibility.

[0059] Among them, the number of successful responses is the number of successful probes in the most recent few attempts, the average response latency is the average response latency, the timeout threshold is the timeout threshold (5000ms in this embodiment), and the weighting coefficient is 0.6 in this embodiment. The access credibility belongs to [0,1], and the larger the value, the more stable and reliable the device access. Devices with access credibility ≥ access credibility threshold (0.75 in this embodiment) are included in subsequent processing, while abnormal devices with access credibility lower than this value are excluded.

[0060] Access trustworthiness is assessed based on two factors: response success rate and response latency. Devices with high success rates and low latency receive higher trustworthiness scores, while devices with low success rates or high latency receive lower trustworthiness scores. A weighting coefficient of 0.6 is used to balance the contribution of success rate and latency to trustworthiness, taking into account the impact of latency differences between different protocols in a multi-protocol heterogeneous access environment. Devices with access trustworthiness ≥ the access trustworthiness threshold (0.75) are selected for further processing.

[0061] S12: Identify service loops and associate devices with loops;

[0062] Based on the device's loop number and installation location, each device is associated with its corresponding service loop. In this embodiment, devices within the same service loop share the same controller and the same circuit / pipe / duct branch, thus physically serving the same spatial area. The association results are stored as a device-loop mapping table.

[0063] S13: Calculate the spatial correlation strength;

[0064] For each device, all candidate spatial units are traversed, and the association strength between the device and each space is calculated. Spatial association strength is determined by three factors: the standardized distance from the device installation location to the geometric center of the spatial unit, the intersection ratio of the device service loop coverage area and the spatial unit area, and the current tenant occupancy mark.

[0065] The standardized distance is the standardized distance from the equipment installation location to the geometric center of the spatial unit (calculated by ray projection using the walls in the building information model as boundaries), the coverage intersection ratio is the intersection ratio between the equipment service loop coverage area and the spatial unit area, and the tenant occupancy mark is the current tenant occupancy mark (1 for occupied, 0.5 for unoccupied, and 0.3 for public areas). The spatial association strength belongs to [0,1].

[0066] Spatial association strength incorporates three factors: distance, coverage, and occupancy. Distance reflects the physical proximity of the equipment installation location to the geometric center of the spatial unit (the closer the distance, the higher the association strength). Coverage reflects the physical connection between the equipment service loop and the spatial unit (the higher the coverage ratio, the higher the association strength). Occupancy reflects the constraint of tenant contract ownership on equipment association (the association weight of occupied space is higher than that of unoccupied space and higher than that of public areas).

[0067] S14: Mark cross-space shared devices and space-exclusive devices;

[0068] Based on spatial association strength, calculate the sharing determination strength of each device:

[0069] The shared determination strength is the difference between the maximum and the second largest value of the spatial association strength of the device with all candidate spatial units;

[0070] The difference between the maximum and second-largest association strength. The smaller the shared determination strength, the more likely the device has similar association strengths with at least two spatial units, and the more likely it is to experience service affiliation drift when spatial boundaries change.

[0071] Set a sharing threshold (0.25 in this embodiment) and an exclusive threshold (0.60 in this embodiment):

[0072] When the sharing determination strength is less than the sharing threshold, it is marked as a "cross-space sharing device" - the device may serve multiple spaces at the same time, and its space service boundary needs to be closely monitored in subsequent steps;

[0073] When the sharing determination strength is greater than the exclusive threshold, it is marked as a "space exclusive device" - the service space of the device is clearly defined and changes in space have little impact on it;

[0074] When the shared determination strength is between the two, it is marked as "device to be observed".

[0075] The difference between the maximum and second-largest association strengths is used as the sharing criterion: the smaller the difference, the more likely the device has similar association strengths with at least two spatial units, and the more likely it is to serve across multiple spaces when spatial boundaries change; the larger the difference, the more likely the device has a significantly stronger association with a certain spatial unit than with other spaces, and the service affiliation is clear. The three-level classification (shared, exclusive, and pending observation) retains flexibility for intermediate cases and avoids sudden classification changes due to minor fluctuations.

[0076] S15: Generate a multi-protocol shared device access map and check the map's completeness;

[0077] The system aggregates all device nodes (attributes: device ID, type, access trustworthiness, sharing determination strength, and tag type), service loop edges (attributes: loop number and device list), and spatial unit ownership objects (attributes: spatial number and tenant occupancy tag) to generate a multi-protocol shared device access map.

[0078] The map completeness is calculated as the ratio of the number of devices that have successfully registered and been included in the access map to the total number of devices expected to be accessed. If the ratio is lower than the map completeness threshold (0.90 in this embodiment), the gateway will issue a supplementary access prompt.

[0079] S16: Establish tenant requirement configuration files and integrate linkage chain parameters;

[0080] After generating the multi-protocol shared device access map, a tenant requirement configuration file is created for each tenant. Tenant requirement configuration files are configured independently by the tenant through the management interface or mobile terminal. Each configuration file must contain at least the following configurable items:

[0081] (1) Environmental comfort preference: upper / lower temperature threshold (e.g., tenant A sets the lower temperature threshold to 20℃ and the upper temperature threshold to 26℃), target humidity range (e.g., 40%-60%), target CO2 concentration upper limit (e.g., 800ppm). The above thresholds cover the system default values ​​in S23;

[0082] (2) Time period strategy: Switching between scene linkage modes on weekdays and non-weekdays. For example, on non-weekdays, only basic lighting and security linkage are maintained, while automatic linkage of air conditioning and fresh air is turned off to reduce energy consumption.

[0083] (3) Energy saving priority: Tenants can choose "comfort priority", "balanced mode" or "energy saving priority". This choice is used as a weighting coefficient in S4 conflict detection - when environmental conditions conflict with energy saving strategy, the priority set by the tenant will be used for adjudication.

[0084] (4) Custom linkage rules: Tenants can add space-specific scene linkage rules, such as "turn on the air conditioner 10 minutes before the meeting room is booked" and "turn off the automatic lighting linkage in the tenant's exclusive area during non-working hours".

[0085] The parameters of the tenant requirement configuration file are integrated into the system operation in the following ways: when the environmental comfort preference in the tenant requirement configuration file is different from the system default threshold in S23, the tenant's set value shall prevail; the energy saving priority is substituted into the public safety conflict detection step in S4 in the form of weight, and is used as the sorting basis when adjudicating the regular strategy in non-security conflict; the custom linkage rule is inserted into the S5 scenario linkage chain as an independent rule entry, and has the same execution priority as the linkage rule automatically generated by the system.

[0086] The introduction of the aforementioned tenant requirement configuration file enables the intelligent scene linkage method of this invention to further empower tenants with the ability to personalize linkage strategies, based on automatically identifying changes in physical boundaries and automatically reconstructing linkage chains—boundary adaptation is automatically completed by the system, while comfort preferences are defined by the tenants themselves—achieving an organic combination of automation and personalization.

[0087] S2: Based on the multi-protocol shared device access map, combined with the on-site spatial status information, determine the actual service space corresponding to each IoT device and generate a spatial service boundary status table;

[0088] S21: Extract the boundary basic data of the shared device;

[0089] From the map, filter all "cross-space shared devices" and "devices to be observed" (collectively referred to as "boundary-sensitive devices") and extract their installation location, service loop number, and current spatial affiliation information.

[0090] Boundary analysis is performed on only boundary-sensitive devices instead of all devices, reducing computational load while ensuring accuracy and avoiding interference from tiny floating-point fluctuations of exclusive devices on drift intensity distribution.

[0091] S22: Multi-source fusion to obtain on-site spatial status;

[0092] Select at least two of the following four signal sources for multi-source fusion:

[0093] Signal source A (partition status detector): A magnetic opening and closing sensor is installed on the movable partition to report the opening and closing status of the partition in real time;

[0094] Signal Source B (Wall-mounted Control Panel): The walls of the conference room and multifunction room are equipped with a "Space Merging Mode" button. When pressed manually, a merging signal is reported.

[0095] Signal source C (reservation system / management ledger): Obtain space usage registration information from the meeting room reservation system or property management ledger;

[0096] Signal source D (sensor heat map distribution): Collects heat maps of human infrared sensors or millimeter-wave radar occupancy in the covered space, and infers the partition displacement by the deviation between the heat map cluster boundary and the actual wall boundary.

[0097] For each spatial element pair (j,k), calculate the field state consistency coefficient:

[0098] The field status consistency coefficient is obtained by summing the judgment values ​​of each signal source on spatial connectivity according to their respective weights, and then dividing by the sum of the weights.

[0099] The signal source judgment value is {0,1} (signal source m's judgment on whether spaces j and k are connected, with each signal source weighted (A:0.35, B:0.30, C:0.20, D:0.15). A field status consistency coefficient of 0.5 or higher indicates physical connectivity; a coefficient below 0.3 indicates physical isolation; and values ​​between these are marked as uncertain and require further confirmation.

[0100] Multi-source fusion of complementary information from various signal sources avoids systemic failure modes caused by a single signal source. Weights are assigned in reliability order: direct physical sensing > human presence verification > system plan registration > indirect statistical inference. A three-level decision-making process (connectivity / uncertainty / isolation) reserves space for drift verification in uncertain cases.

[0101] S23: Access environmental sensor data and establish an environmental condition monitoring layer;

[0102] Based on the aforementioned on-site spatial status signal sources, environmental sensors deployed within each spatial unit are further integrated to establish an environmental condition monitoring layer. The types of integrated environmental sensors include at least: temperature sensors (collecting spatial temperature), humidity sensors (collecting spatial humidity), CO2 concentration sensors (collecting spatial CO2 concentration), and PM2.5 / PM10 particulate matter sensors (collecting spatial air quality index). Each sensor is uniformly managed through the IoT intelligent control gateway using its corresponding access protocol (BACnet, Modbus, KNX, or MQTT).

[0103] The gateway polls and collects environmental sensor data from each spatial unit at a fixed period (60 seconds in this embodiment), and aggregates the collected results into an environmental condition vector by spatial unit. When the value of any dimension exceeds the preset condition trigger threshold—for example, if the measured value of the temperature sensor is greater than the upper limit temperature threshold (28℃ in this embodiment, configurable) to trigger cooling linkage, if the measured value of the humidity sensor is greater than the upper limit humidity value (70% in this embodiment) to trigger dehumidification linkage, if the measured value of the CO2 concentration exceeds the upper limit CO2 concentration threshold (1000ppm in this embodiment) to trigger fresh air linkage—the gateway marks this environmental limit violation event as a scene trigger source, and incorporates it into the scene linkage execution process of S5 along with the space occupancy sensor trigger source.

[0104] The introduction of the environmental condition monitoring layer means that the scene linkage chain of this invention is not limited to explicit trigger sources such as human body sensing, panel buttons, and reservation systems, but also covers implicit environmental condition trigger sources such as temperature, humidity, and air quality. Taking temperature exceeding the limit as an example: when the temperature of a certain space unit exceeds the threshold, even if there is no personnel entry event, the gateway can still trigger linkage based on environmental conditions—turning on the air conditioning terminal cooling of that space and starting the fresh air branch ventilation of that space—ensuring that the space environmental parameters are always maintained within the comfortable range.

[0105] S23a: Calculate the intensity of the lighting service boundary;

[0106] For each lighting fixture, all candidate spatial units are traversed, and the lighting service boundary intensity of the fixture for each space is calculated. The lighting service boundary intensity is determined by three factors: the standardized distance from the fixture installation location to the geometric center of the spatial unit, the intersection ratio of the fixture's illumination coverage area and the area of ​​the spatial unit, and the partition blocking coefficient.

[0107] In this embodiment, the formula for calculating the lighting service boundary intensity L is:

[0108] L = α × (1 - d) + β × r + γ × b;

[0109] Wherein, d is the standardized distance, which is the ratio of the straight-line distance from the installation location of the lighting equipment to the geometric center of the spatial unit to the nominal effective lighting radius of the lighting equipment, with a value range of [0,1]. The smaller the value of d, the closer the equipment is to the center of the space, and the greater its contribution to lighting; r is the light coverage intersection ratio, which is the intersection area of ​​the equipment's light coverage range and the area of ​​the spatial unit divided by the area of ​​the spatial unit, with a value range of [0,1]. The larger the value of r, the higher the proportion of the space actually illuminated by the equipment; b is the partition blocking coefficient, which is 0 or 1 (0 when there is a solid partition that completely blocks the light, and 1 when there is no blockage or only a glass partition), reflecting the blocking effect of the wall on the lighting service; α, β, and γ are the weight coefficients of the corresponding factors. In this embodiment, α=0.3, β=0.5, and γ=0.2, respectively. The weight allocation is based on the following: the light coverage intersection ratio directly reflects the actual illuminated area and has the largest contribution; the standardized distance reflects the spatial proximity of the equipment location and has the second largest contribution; the partition blocking coefficient, as a boundary condition factor, has the lowest contribution. The value of L ranges from [0,1]. A larger value indicates a more significant contribution of the device to the lighting service of the space unit. The standardized distance is the ratio of the straight-line distance between the lighting device and the farthest illuminated area in the current actual service space to the nominal effective lighting radius of the lighting device. The greater the distance, the larger the ratio, and the lower the corresponding intensity contribution. When this ratio is greater than or equal to 1, it indicates that the farthest illuminated area of ​​the device has exceeded its nominal effective lighting radius, and the distance contribution in the lighting service boundary intensity approaches zero.

[0110] Of the three factors mentioned above, standardized distance and the intersection ratio of light coverage jointly reflect the degree of physical light contribution of the equipment to the space, while the partition blocking coefficient, as a multiplicative factor, reflects the blocking effect of solid walls on lighting services. The lighting service boundary intensity is in the range of zero to one; the larger the value, the more significant the contribution of the equipment to the lighting service of the space unit.

[0111] After the traversal calculation is completed, for each lighting device, the maximum value of the lighting service boundary intensity in all candidate spaces is taken as the basis for determining its current actual lighting service space affiliation. This maximum value is used for the subsequent calculation of the S25 boundary confidence.

[0112] S24: Confirm the service boundaries of air conditioning and access control equipment;

[0113] Similarly, the service boundary strength of air supply equipment (air outlet direction, duct branch design coverage, distance, and airflow obstruction by partitions) is calculated for air conditioning / fresh air equipment, and the service boundary strength of access control equipment (access direction, card reader authorization range, and existence of physical access paths) is calculated for access control equipment. These two boundaries differ physically from lighting boundaries—air supply is more significantly obstructed by partitions (airflow cannot bypass walls), while access control involves both authorization and access constraints (physical access is possible but without authorization, so the service boundary strength of access is 0). Separate processing ensures that the spatial service boundaries of all three types of equipment can be accurately characterized.

[0114] S25: Calculate boundary confidence:

[0115] The boundary confidence is obtained by linearly summing the maximum intensity of the lighting service boundary in all candidate spaces multiplied by the lighting weight coefficient, the maximum intensity of the air supply service boundary in all candidate spaces multiplied by the air supply weight coefficient, and the maximum intensity of the passage service boundary in all candidate spaces multiplied by the passage weight coefficient.

[0116] The lighting weight coefficient, air supply weight coefficient, and access weight coefficient are weights related to equipment type (lighting equipment: lighting weight coefficient is 1, air supply weight coefficient is 0, access weight coefficient is 0; air conditioning / fresh air equipment: lighting weight coefficient is 0, air supply weight coefficient is 1, access weight coefficient is 0; access control equipment: lighting weight coefficient is 0, air supply weight coefficient is 0, access weight coefficient is 1). Equipment with a boundary confidence level <0.6 is marked as "boundary unstable equipment".

[0117] Generate a spatial service boundary status table, which includes: device ID, device type, tag type, actual service space list (the top three candidate spaces sorted by boundary strength), boundary confidence, and boundary instability tag.

[0118] S3: Compare the actual service space of each IoT device in the spatial service boundary status table with the old space ownership relationship of the IoT device in the original scene linkage chain, calculate the boundary drift intensity through a multi-dimensional weighted method, and output the boundary drift confirmation result when the boundary drift intensity is reached.

[0119] S31: Establish a record comparing the old and new attribution;

[0120] For each device i, the spatial unit with the highest boundary strength is extracted from the spatial service boundary state table as its current actual service space. The old spatial affiliation registered by the device is extracted from the original scene linkage chain, and a comparison record is established. The reason for choosing "highest strength" instead of "all candidates" as the actual service space is that the response of the device in the linkage chain is "one-to-one" - a linkage command is either sent to a certain spatial affiliation of the device or not sent at all, and there is no semantic ambiguity of sending the same command to multiple spaces at the same time. The complete list of candidate spaces is used in the S4 rebinding stage, but it does not affect the first-level determination of whether drift "occurred".

[0121] S32: Multi-dimensional weighted calculation of boundary drift intensity;

[0122] This embodiment incorporates four weighted dimensions:

[0123] The first dimension is the room span dimension, used to determine whether the actual service space of the device spans the original registered room number. If it spans, the room span dimension value is 1; otherwise, it is 0. The second dimension is the tenant span dimension, used to determine whether the actual service space spans the original tenant area. If it spans, the tenant span dimension value is 1; otherwise, it is 0. The third dimension is the public attribute change dimension, used to determine whether the public / private attributes of the actual service space have changed. If they have changed, the public attribute change dimension value is 1; otherwise, it is 0. The fourth dimension is the loop affiliation inconsistency dimension, used to determine whether the device service loop is inconsistent with the space affiliation. If they are inconsistent, the fourth dimension loop affiliation is 1; otherwise, it is 0.

[0124] For example, in this embodiment, the formula for calculating the boundary drift intensity D is:

[0125] ;

[0126] Among them, V1 represents the degree of crossing the original space ownership scope, indicating whether the number of the actual service space of the device has changed from the original registered space unit (the value is 0 or 1, where 1 indicates that the original space number of the current actual service space of the device is different from the old ownership space number, and 0 indicates that they are the same); V2 represents the degree of crossing the original tenant ownership scope, indicating whether the tenant to which the actual service space of the device belongs has changed from the original ownership tenant (the value is 0 or 1, where 1 indicates that the tenant to which the current actual service space of the device belongs is different from the original ownership tenant, and 0 indicates that they are the same); V3 represents the degree of change in public area attributes. (Values ​​are 0 or 1, where 1 indicates that the public / private attributes of the device's current actual service space are different from its original affiliation, and 0 indicates that they are the same); V4 is the degree of inconsistency in service loop affiliation (values ​​are 0 or 1, where 1 indicates that the service loop number to which the device belongs does not match the loop number of its current actual service space, and 0 indicates that they match); W1~W4 are the weight coefficients of the corresponding dimensions, which are 0.30, 0.35, 0.15, and 0.20 respectively in this embodiment; C is the boundary confidence level, which is [0,1]; (1-C+0.05) is the boundary confidence level correction factor. When D reaches the preset threshold (0.72 in this embodiment), it is determined to be a confirmed drift.

[0127] The four-dimensional combination covers a panoramic view of spatial boundary drift—room boundaries, ownership boundaries, property boundaries, and physical loops. At least two dimensions must be included in the weighted calculation to retain deployment flexibility.

[0128] The method for calculating the boundary drift intensity is as follows: multiply each of the four dimensions (room crossing dimension value, tenant crossing dimension value, public attribute change dimension value, and loop affiliation inconsistency dimension value) by the corresponding weight coefficient (0.3, 0.35, 0.15, and 0.2 respectively) and sum them with the corresponding available dimension markers, then divide by the sum of the weights of each available dimension, and finally multiply by the boundary confidence correction factor (one minus the boundary confidence plus the zero constant).

[0129] The weights are as follows: first dimension 0.30, second dimension 0.35, third dimension 0.15, and fourth dimension 0.20 (weights are ranked as follows: tenant ownership boundary > room physical boundary > loop physical ownership > changes in public attributes, because the legal and economic constraints on tenant boundaries are the strongest, and changes have the most serious consequences). Dimension availability is indicated by the dimension availability marker, and the zero-prevention constant is set to 0.05. The boundary confidence correction factor (one minus boundary confidence plus zero-prevention constant) works as follows: when boundary confidence is high, this factor has a smaller value, suppressing drift intensity and preventing misjudgment of drift due to small fluctuations in boundary data; when boundary confidence is low, this factor has a larger value, amplifying drift intensity and ensuring that devices with high boundary uncertainty can be identified promptly when multi-dimensional deviations occur.

[0130] The boundary confidence correction factor (one minus boundary confidence plus zero constant) inversely weights the input uncertainty of boundary state judgment in the drift intensity calculation: the higher the boundary confidence of the device, the more reliable its spatial service boundary judgment, and the less susceptible its drift intensity is to small data fluctuations; the lower the boundary confidence of the device, the greater its judgment uncertainty, and the drift intensity is amplified accordingly to trigger further confirmation when there is a multi-dimensional deviation.

[0131] S33: Threshold determination;

[0132] Boundary drift intensity belongs to [0,1] (after adjustment by a correction factor, if it exceeds 1, it is counted as 1), and a three-level judgment interval is set:

[0133] The first level is the confirmation interval. When the boundary drift intensity is greater than or equal to 0.72, it is determined as a confirmed drift and automatically enters S4 to perform rebinding. The second level is the pending review interval. When the boundary drift intensity is greater than or equal to 0.60 but less than 0.72, it is marked as pending manual review and the system pushes a review notification. The third level is the exclusion interval. When the boundary drift intensity is less than 0.60, it is determined as a excluded drift and the original linkage chain remains unchanged.

[0134] The three-level judgment (confirmation interval / review interval / exclusion interval) strikes a balance between automatic judgment and manual review. Thresholds of 0.72 and 0.60 automatically confirm drift when at least three dimensions are abnormal simultaneously, and fall into the review interval when two dimensions are abnormal.

[0135] S34: Identify the type of drift cause;

[0136] Identify the cause type based on the combination pattern of four dimension values:

[0137] The first category is partition adjustment / meeting room merging type, with the corresponding characteristic pattern being a room span dimension value of 1 (room span) and a circuit ownership inconsistency dimension value of 1 (circuit ownership inconsistency), while the tenant span dimension value and public attribute change dimension value are 0. The typical scenario is the removal or restoration of movable partitions. The second category is shop renovation type, with the corresponding characteristic pattern being a tenant span dimension value of 1 (tenant span) and a circuit ownership inconsistency dimension value of 1 (circuit ownership inconsistency), while the room span dimension value and public attribute change dimension value are 0. The typical scenario is the removal or construction of shop walls. The third category is public corridor borrowing type, with the corresponding characteristic pattern being a public attribute change dimension value of 1 (public attribute change) and a circuit ownership inconsistency dimension value of 1 (circuit ownership inconsistency), while the room span dimension value and tenant span dimension value are 0. The typical scenario is the temporary borrowing of public corridors.

[0138] In S3, the cause types are distinguished so that the rebinding operation in S4 can be implemented according to the type of cause—space merging executes trigger source merging, shop renovation executes stripping and attachment, and corridor borrowing executes temporary strategies with time parameters.

[0139] S35: Output drift confirmation result;

[0140] Boundary drift strength and boundary confidence are used to calculate drift confirmation confidence: The drift confirmation confidence is obtained by multiplying the boundary drift strength by the confidence coefficient (which is composed of the base item of 0.5 and the device item of 0.5 multiplied by the boundary confidence). The output results include: device ID, boundary drift strength value, judgment interval, cause type, drift confirmation confidence and rebinding flag.

[0141] S4: Based on the boundary drift confirmation result, perform a rebinding operation on the original scene linkage chain. The rebinding operation includes at least removing the cross-space device from the old home chain and attaching the cross-space device to the linkage chain corresponding to the current actual service space, generating a rebinding scene linkage chain consistent with the current space service boundary.

[0142] S41: Establish a rebinding task queue;

[0143] Extract all devices marked as 1 for rebinding. Rebinding priority is calculated by multiplying the drift confirmation confidence level by the device importance coefficient. The device importance coefficient is set as follows: 1.0 for emergency lighting and fire-fighting related equipment, 0.8 for public area equipment, and 0.5 for equipment in isolated spaces, ensuring that safety-critical equipment is rebinded first. The device importance coefficient is tiered by device type: 1.0 for emergency lighting and fire-fighting related equipment; 0.8 for public area equipment (including air conditioning, fresh air systems, access control, and environmental sensors); and 0.5 for non-safety-critical equipment in isolated spaces (including general lighting and non-emergency sockets), ensuring that safety-critical equipment is rebinded first.

[0144] Rebinding priority drift confirmation confidence and equipment importance coefficient: The higher the drift confirmation confidence and the more critical the equipment (emergency lighting and fire protection related equipment take the highest importance coefficient value), the higher the priority to enter the rebinding queue, ensuring that safety-critical equipment completes rebinding first.

[0145] S42: Temporary merging linkage unit for spatial merging scenarios;

[0146] When the cause type is spatial merging, a temporary merging linkage unit is constructed:

[0147] Trigger source merging: Trigger source devices that were originally in different spaces are marked as a common trigger source, and any trigger is regarded as a trigger on the entire merged space;

[0148] Unified execution devices: Execution devices that were originally distributed across different spaces are added to a unified execution object list, and all devices respond when any one is triggered.

[0149] The combined linkage strength is calculated by multiplying the lowest value of equipment integrity in each combined spatial unit by one minus the spatial area difference rate (the area difference rate is the ratio of the standard deviation to the average value of the area of ​​each combined space). When the combined linkage strength is <0.5, it is marked as "low confidence" and only a suggestion is pushed during execution, without automatic execution.

[0150] Trigger source merging and unified execution strategies ensure the consistency of linkage logic in the merged space. The merged linkage strength acts as a quality gate, preventing forced linkage when device integrity is insufficient.

[0151] S43: Stripping and attaching shop renovation scenes;

[0152] Perform stripping (removing devices from the execution device list of the old chain) and attaching (inserting linkage rules of the corresponding type in the new home space).

[0153] Reliability is calculated by subtracting the number of affected linked rules from the total number of rules associated with the device, and multiplying this by the device's boundary reliability. A reliability less than 0.5 indicates that manual confirmation is required.

[0154] The proportion of rules affected by reliability and the boundary credibility: the lower the proportion of rules affected and the higher the boundary credibility, the higher the reliability, indicating that the rebinding operation is more reliable; when the reliability is low (less than 0.5 in this embodiment), it is marked that manual confirmation is required to avoid silent failure when the rule association is complex or the boundary credibility is insufficient.

[0155] S44: Temporary rebinding and security conflict detection in public corridor borrowing scenarios;

[0156] Before conducting public safety conflict detection, security levels are configured for each safety-critical device. These security levels are pre-configured based on the criticality of the device in public safety. This embodiment employs a three-level security system:

[0157] Safety Level 1 (High): Emergency lighting equipment, fire exhaust fans, fireproof roller shutter controllers - These types of equipment are directly related to personnel evacuation and fire fighting, and have the highest safety level;

[0158] Security Level 2 (Medium): Public passageway lighting circuits, public area access control systems – these devices affect public traffic order;

[0159] Safety Level 3 (Low): Public fresh air equipment and public area air conditioning terminals - These devices affect the comfort of the public environment but do not directly involve life safety.

[0160] The higher the security level of the equipment, the smaller the reduction in weight in conflict detection: the reduction in weight of security level 1 equipment is 0~20% of the original control strength, the reduction in weight of security level 2 equipment is 20~40%, and the reduction in weight of security level 3 equipment is 40~60%.

[0161] In this embodiment, when the temporary borrowing strategy of the public corridor lighting circuit (safety level 2) conflicts with the fire evacuation strategy, the reduction coefficient of 0.50 exceeds the allowable reduction range (20~40%) of safety level 2. The system will cut off the reduction coefficient to the upper limit of 0.40 - that is, reduce the weight to 60% of the original control intensity, so as to ensure that even if it is temporarily borrowed, the public corridor lighting still retains the basic fault safety level.

[0162] When the cause type is public corridor borrowing, a temporary rebinding policy with time validity parameters (including effective start time and effective end time) is established. The gateway checks periodically (every 5 minutes), and automatically reverts the device to its original ownership upon expiration.

[0163] The time validity parameter encapsulates the borrowing period linkage and automatic recovery upon expiration into a fully automated lifecycle management, realizing time-segmented reuse of the linkage chain and reducing manual rollback operations.

[0164] Public safety conflict detection: The temporary rebinding policy is compared one by one with the preset public basic safety policy. If the temporary control objective (such as "corridor lighting is only triggered by the borrower during the borrowing period") conflicts with the fire protection policy "corridor lights must be fully on during a fire"), the shared control strength of the temporary policy is reduced.

[0165] The control strength after security reduction is the base control strength minus the sum of the reduction coefficients corresponding to all conflicting security policies. The control strength after reduction determines the enforcement strength of the temporary policy.

[0166] Fire evacuation strategy reduced by 0.50, public access strategy reduced by 0.20, and basic fresh air strategy reduced by 0.10.

[0167] The restricted conflict detection is applied only to temporary rebinding of public attribute changes (public attribute change dimension value is 1), and the hierarchical demotion mechanism maintains the availability of temporary linkage while ensuring security.

[0168] S45: Generate a re-binding scene linkage chain;

[0169] The rebinding operations are summarized to generate a linkage chain containing nine fields (trigger area, trigger device, execution device, control scope, feedback confirmation scope, applicable time, applicable space, tenant affiliation, and rollback conditions).

[0170] The consistency of the linkage chain is calculated as the ratio of the number of re-bound devices that have been verified and determined to be consistent to the total number of all checked devices. When the consistency of the linkage chain is greater than or equal to 0.85, the data is written to the execution engine; when the consistency of the linkage chain is less than 0.85, it falls back to S3 for re-identification.

[0171] The consistency gate W ensures that only complete and consistent linkage chains enter the execution engine, preventing incomplete linkage chains caused by device failures in multi-protocol heterogeneous environments.

[0172] S5: Receive a scene trigger event, perform spatial access verification on the re-bound scene linkage chain, send control commands to devices in the actual service space that pass the verification, and solidify the execution result into a shared space intelligent scene linkage result after confirming the execution result.

[0173] S51: Spatial access verification;

[0174] When an event is triggered, verify whether the triggering space is in the "Applicable Spaces" list of the linkage chain. If not, refuse to trigger.

[0175] Spatial access verification may be triggered by ghosting after the event source is truncated and rebounded—a situation where the physical location of the sensor has not changed, but the applicable space of its new attribution chain is different.

[0176] S52: Issue control commands;

[0177] The gateway sends control commands to devices within the actual service space that have passed the spatial access verification.

[0178] S53: Collect feedback signals and calculate the closed-loop boundary crossing strength;

[0179] After execution, the actual status feedback signals of each device are collected. The closed-loop boundary violation strength is the ratio of the number of devices exceeding the current actual service space boundary in the execution result to the total number of devices involved in the linkage chain, representing the degree to which the execution result exceeds the current actual service space boundary. The closed-loop boundary violation strength belongs to [0,1]. The higher the value, the greater the proportion of out-of-bounds devices in this linkage execution, and the higher the boundary violation risk.

[0180] In this embodiment, a certain linkage involves the execution of 8 devices in the lighting circuit. The feedback signals of 2 of these devices indicate that their actual control range exceeds the boundary of the "applicable space" of the linkage. Therefore, the closed-loop boundary crossing strength = 2 / 8 = 0.25.

[0181] S54: Retreat after exceeding the boundary;

[0182] When the closed-loop out-of-bounds strength exceeds the closed-loop out-of-bounds threshold (0.25 in this embodiment), an out-of-bounds rollback operation is triggered: subsequent linkage command issuance is paused, the out-of-bounds device is added to the set of objects to be rebounded, and the process returns to S3.

[0183] After rollback, S3 only re-identifies the boundary drift of devices in the set of objects to be rebounded (not a full rescan of all devices), S4 re-binds the drifting devices re-identified this time, and S5 performs closed-loop boundary detection on the devices after this rebinding. After detection, devices that did not cross the boundary in this time are removed from the set of objects to be rebounded—the number of out-of-bounds devices gradually converges in each iteration due to the incremental update of spatial boundary information.

[0184] Repeat the above iterative process until one of the following termination conditions is met:

[0185] (1) The set of objects to be rebounded is empty, that is, all devices have passed the closed-loop boundary detection, exited the rollback operation, and entered the S55 progressive solidification judgment;

[0186] (2) If the number of devices in the set of objects to be rebounded does not decrease after several consecutive iterations, it is determined that the convergence has stalled, the rollback operation is exited and an alarm notification is output (in this embodiment, it is taken as 3 consecutive iterations).

[0187] (3) When the number of iterations reaches the preset upper limit (5 times in this embodiment), the rollback operation is forcibly terminated, the non-converged device is marked as a manually intervened object and an alarm notification is output.

[0188] (4) S55: Progressive curing;

[0189] The reliability of the solidified data is calculated based on three factors: the ratio of consecutive successful executions to the total number of valid executions in the current rebinding scenario linkage chain, the ratio of out-of-bounds occurrence frequency, and the boundary drift stability coefficient. The ratio of consecutive successful executions to the total number of valid executions reflects the current execution reliability of the linkage chain. The out-of-bounds occurrence frequency ratio is the ratio of the number of out-of-bounds occurrences in the most recent executions to the total number of executions; the higher this frequency, the lower the reliability of the solidified data. The boundary drift stability coefficient is determined by the absolute value of the difference between the current drift intensity and the drift intensity of the previous evaluation period. The evaluation period is a fixed time window (5 minutes in this embodiment) during which the gateway periodically performs boundary drift detection from S2 to S3. The boundary drift intensity value for the current evaluation period is the intensity value calculated in S32, i.e.: Boundary drift intensity = [Σ(values ​​of each dimension)]. i ×Dimensional Weights i ) / Σ(Dimensional weights) i]×(1-Boundary Credibility+0.05), where the values ​​of each dimension (four dimensions) are the room crossing dimension value, tenant crossing dimension value, public attribute change dimension value, and loop affiliation inconsistency dimension value, respectively; the boundary drift intensity of the previous evaluation period is the corresponding value calculated by the same formula in the previous time window. The smaller the value of this coefficient, the smaller the change amplitude of the spatial boundary in the adjacent period, and the more stable the spatial state tends to be. Among the above three factors, execution reliability is the basic item, and the boundary crossing frequency ratio and boundary drift stability coefficient are used to adjust the solidification credibility in the form of penalty or reward. The solidification credibility belongs to [0,1], and the higher the value, the more suitable it is to enter the solidification state. After 5 consecutive successful executions and the solidification credibility is greater than or equal to 0.90, solidification is achieved - subsequent triggers directly call the solidification result, eliminating the need for repeated verification. 3 consecutive boundary crossings will result in permanent rollback and an alarm.

[0190] Progressive hardening strikes a balance between adaptability and stability: hardening occurs after five consecutive successful executions with no out-of-bounds errors, eliminating the delay caused by repeated verification.

[0191] Example 2: Adjustment of partitions and temporary corridor use in co-working spaces

[0192] A co-working space includes Meeting Room A (30m) 2 Meeting Room B (30m) 2 The space is separated from A by movable partitions, and includes a public corridor (C, 2m wide and 15m long, connecting the meeting room and the office lobby). Lighting circuits, air conditioning terminals, and human presence sensors have been installed in each space, all connected to a scene-linking system via an IoT smart control gateway.

[0193] Initial linkage chain (T0):

[0194] Chain #1 (Conference Room A): Sensor A → Lighting Circuit A 1-3 + Air Conditioning Terminal A

[0195] Chain #2 (B Meeting Room): B Sensor → B Lighting Circuit 4-6 + B Air Conditioning Terminal

[0196] Chain #3 (C Corridor): C Sensor → C Lighting Loop 7-8 (Constant On Mode, Maintaining Basic Illumination)

[0197] Event 1 (T1): Partition Removal → Merging of Large Conference Room

[0198] At 8:00 AM, the administrator pressed the "Space Merge" button on the control panel, and the partition detector reported "open".

[0199] S2 Execution: Gateway multi-source fusion (A+B dual signal sources), calculate the consistency coefficient of the example site state. Multiply the weight of the partition detector signal source (0.35) and the weight of the control panel signal source (0.30) by their respective judgment values ​​(both are 1, indicating confirmed connectivity), sum them, and then divide by the sum of the weights. The result is an example site state consistency coefficient of 1.0, which is greater than 0.50, confirming connectivity between A and B. Recalculate the lighting boundary—the intensity of the central pendant downlight in conference room A on the original B side lighting service boundary is approximately 0.15 (vertically downward, the optical axis direction remains unchanged after the partition is removed), and the intensity of the wall-mounted, angled light fixture on side B on side A lighting service boundary is approximately 0.65 (after the partition is removed, the illumination direction covers part of the A side area). Note: Even though connectivity is confirmed, the actual light contribution difference of the lighting equipment is still precisely quantified by S23. S23a (Lighting service boundary intensity calculation) precisely quantifies—although the central downlight is "geometrically located in the merged space," it actually hardly illuminates side B and will not be connected as an execution device in the B side linkage chain in subsequent S4.

[0200] S3 Execution: Compare the old assigned location with the actual service space for each device. Taking the B-side angled light fixture as an example—the old assigned location is "B Meeting Room," and the candidate for the highest boundary intensity in the actual service space is "Merged Meeting Room (A+B) Area" (because its illumination direction covers part of A side). Judgment: The room spanning dimension value is 1 (spanning the original room number).

[0201] Tenant crossover dimension value is 0 (same tenant), public attribute change dimension value is 0 (both before and after the merger are private), loop affiliation inconsistency dimension value is 1 (loop affiliation is inconsistent with the boundary of the extended space). Cause type: partition adjustment / meeting room merger.

[0202] S4 Execution (Key Differentiated Operation): Trigger type is "Spatial Merge", system constructs temporary merge linkage unit:

[0203] Trigger source merging: Sensor A and Sensor B are marked as a common trigger source;

[0204] Unified execution equipment: All equipment that contributes to actual lighting or air supply in the merged space (the angled lights on side B, and the air conditioning terminals on sides A and B—because the air supply coverage overlaps after the merge) are included in the unified execution list. The center pendant downlight on side A is not included in the B-side linkage because its intensity to the lighting service boundary on side B is approximately 0.15, which does not reach the connection threshold (0.3 in this embodiment).

[0205] The precise boundary assessment of the present invention, based on device type and irradiation direction, ensures that only devices that actually contribute to the target space unit are included in the rebinding linkage chain.

[0206] Expected benefits: After being transformed into a unified large conference room, entering from any entrance triggers the sensor, and all effective lighting and air conditioning terminals respond in unison; personnel do not need to manually operate the panel to switch linkage modes; the linkage chain is automatically reconstructed within seconds after the space changes.

[0207] Event 2 (T2): Temporary use of public corridor

[0208] On the same day, from 2:00 PM to 4:00 PM, Company D borrowed the C public corridor as a temporary exhibition area. The borrowing application has been registered in the management ledger.

[0209] S3 Execution: The original ownership of lighting circuits 7-8 in corridor C was "Corridor C (Public)," but after being borrowed, the actual service space has been expanded to the exhibition area of ​​Company D.

[0210] The room span dimension is 0 (corridor room number unchanged), the tenant span dimension is 0, the public attribute change dimension is 1 (public → private attribute change), and the loop affiliation inconsistency dimension is 1 (loop affiliation inconsistent with the borrowed display area). Cause type: Public corridor borrowing.

[0211] S4 Execution: Establish a temporary rebinding strategy with time validity parameters {14:00, 16:00} - switch the lighting circuit 7-8 of corridor C from "always on mode" to "D company exhibition area human body sensor triggered → turn on" borrowed linkage mode.

[0212] Safety Conflict Detection: A conflict exists between the temporary policy's "corridor lighting is only triggered by the borrower's sensor" and the public infrastructure safety policy's fire evacuation policy's "corridor lights must be fully on during a fire." If a fire occurs during the borrowing period, the corridor lights may not be on according to the temporary policy (if the borrower's sensor is not triggered or the borrower has evacuated but the fire occurs afterward). Upon detecting this conflict, the system reduces the control intensity: the fire policy reduction factor is 0.50, and the control intensity is 1.0-0.50, which is 0.50. The specific effect of this reduced intensity is: during the borrowing period, lighting is normally triggered by the borrower; once a fire alarm signal is triggered, the fire policy's "forced full illumination" instruction overrides the temporary policy, and the corridor lighting switches back to full illumination mode.

[0213] Without a conflict detection mechanism, in the event of a fire during the lease period, the corridor lights may not turn on—a serious breach of public safety. The conflict detection and tiered priority mechanism of this invention ensures that temporary coordination strategies proactively give way to safety priorities.

[0214] 16:00 Rollback: When the time validity parameter expires, the gateway automatically removes the C corridor device from the borrowed chain and restores it to the always-on mode of chain #3. No manual operation is required.

[0215] Automatic reconstruction of linkage chain driven by spatial boundary change: after the partition is removed, the scene linkage automatically switches from the linkage mode of "two independent meeting rooms" to "one merged meeting room" within only 5 seconds, without manually reconfiguring the linkage rules. Compared with the process in existing solutions that requires IT personnel to log in to the management background to modify the linkage chains one by one, which takes from minutes to hours, the efficiency is improved by more than two orders of magnitude.

[0216] Full-lifecycle security management for temporary borrowing: the entire process from the start of borrowing → temporary policy taking effect → security conflict detection → automatic rollback upon expiration of borrowing → return to normal state is automated and equipped with public security protection. Compared with existing solutions that rely on manual switching of linkage modes before and after borrowing (with a high probability of forgetting to roll back), the present invention eliminates safety hazards and operational burdens caused by manual procedures.

[0217] Event 3 (T3): Ambient temperature out of limit → link air conditioner and fresh air system

[0218] At 14:30 on the same day, in an open workstation area (Area D, with an area of 80 m 2 , accommodating 30 workstations) of the co-working space, due to the increase in personnel density, the temperature sensor reports that the measured temperature value is 29.2℃, which exceeds the preset upper temperature threshold of 28℃.

[0219] S2 execution: The real-time environmental data vector of Area D obtained by the gateway from the environmental condition monitoring layer is (29.2℃, 55%, 780ppm, good). It is determined that the measured temperature value is greater than the upper temperature threshold, and the cooling linkage condition is triggered.

[0220] At this time, the spatial service boundary of Area D does not drift because there is no partition change on the day, and the boundary drift intensity in S3 is lower than 0.60 (exclusion interval), so the original scene linkage chain remains unchanged.

[0221] S5 execution: After the space access verification is passed, the gateway issues a cooling instruction (set temperature to 24℃) to the 3 air conditioning terminals corresponding to Area D, and issues an instruction to increase the air exchange volume to the fresh air branch of Area D at the same time. After execution, the temperature is continuously monitored, and when the measured temperature value drops below 26℃, the cooling intensity is automatically reduced to restore the normal mode.

[0222] The environmental conditions automatically trigger linkage, and the air conditioner and fresh air system can be automatically adjusted without manual intervention when the temperature rises due to the change of personnel density, so as to ensure the comfort of the space environment. The introduction of environmental sensors as the scene trigger source expands the linkage system from a single "space occupancy driven" mode to a dual-drive mode of "space occupancy + environmental conditions", which covers more extensive intelligent scene linkage requirements.

[0223] The present application has been described by the above-mentioned related embodiments, however, the above-mentioned embodiments are only examples for implementing the present application. It must be pointed out that the disclosed embodiments do not limit the scope of the present application. On the contrary, changes and modifications made without departing from the spirit and scope of the present application are within the scope of the patent protection of the present application.

Claims

1. A multi-protocol adaptive access intelligent scene linkage method, characterized in that, Includes the following steps: S1: Perform multi-protocol access and shared device marking on IoT devices in multi-tenant shared spaces, and generate a multi-protocol shared device access map; S2: Based on the multi-protocol shared device access map, combined with the on-site spatial status information, determine the actual service space corresponding to each IoT device and generate a spatial service boundary status table; S3: Compare the actual service space of each IoT device in the spatial service boundary status table with the old space ownership relationship of the IoT device in the original scene linkage chain, calculate the boundary drift intensity through multi-dimensional weighting, and correct the boundary drift intensity through boundary confidence. When the corrected boundary drift intensity reaches the preset threshold, identify the boundary drift cause type and output the boundary drift confirmation result. S4: Based on the boundary drift confirmation result and the boundary drift cause type, generate a rebinding scenario linkage chain consistent with the current spatial service boundary; S5: Receive a scene trigger event, perform spatial access verification on the rebinding scene linkage chain, obtain the actual status feedback of each IoT device and calculate the closed-loop boundary crossing strength. The closed-loop boundary crossing strength needs to exceed the current actual service space boundary. When the closed-loop boundary crossing strength does not exceed the closed-loop boundary crossing threshold and the solidification credibility reaches the solidification threshold, perform progressive solidification on the rebinding scene linkage chain. When the closed-loop boundary crossing strength exceeds the closed-loop boundary crossing threshold, add the out-of-bounds device to the set of objects to be rebinded, return to S3 to re-perform boundary drift identification and rebinding, and remove the devices that have not crossed the boundary from the set of objects to be rebinded. Repeat until the set of objects to be rebinded is empty. The solidification reliability is determined by the ratio of the out-of-bounds occurrence frequency and the boundary drift stability coefficient; the ratio of the out-of-bounds occurrence frequency is the ratio of the number of times the closed-loop out-of-bounds threshold is exceeded in the most recent executions to the total number of executions; the boundary drift stability coefficient is determined by the absolute value of the difference between the boundary drift intensity of the current evaluation period and the previous evaluation period.

2. The method according to claim 1, characterized in that, In S3, the dimensions of the multi-dimensional weighting include: the degree of crossing the original spatial ownership scope, the degree of crossing the original tenant ownership scope, the degree of change in public area attributes, and the degree of inconsistency in service loop ownership. The boundary credibility includes the lighting service boundary strength and access credibility. The lower the boundary credibility of a device, the higher the weighted corrected boundary drift strength when it deviates in multiple dimensions. The intensity of the lighting service boundary is determined by a weighted average of the standardized distance, the intersection ratio of light coverage, and the barrier obstruction coefficient. The access credibility is determined by weighting the proportion of successful responses in the gateway's most recent heartbeat detections of IoT devices to the total number of detections and the average response delay timeout.

3. The method according to claim 1, characterized in that, In S4, the types of boundary drift causes include: The removal of physical boundaries leading to the merging of multiple spaces refers to the situation where movable partitions are removed or retracted, resulting in the physical connection of multiple independent spatial units. Changes in tenant occupancy leading to changes in ownership refer to situations where changes in the leased area of ​​a specific tenant result in changes in the ownership of equipment space. Temporary occupation of public areas refers to situations where a tenant temporarily occupies a public area, resulting in the inclusion of equipment within that area into the tenant's service space; Generate a rebinding scenario linkage chain that is consistent with the current spatial service boundary, including: When the cause of boundary drift is physical boundary cancellation leading to multi-space merging, devices originally belonging to different spaces will be incorporated into the same scene linkage chain. When the boundary drift cause type is a change in ownership due to a change in the tenant's occupied area, a temporary holding binding with a time validity parameter is generated for cross-space devices; When the boundary drift cause type is public area temporary occupation type, the cross-space device will be connected to the current space linkage chain in a lightweight mounting manner and configured with over-boundary fallback conditions; the lightweight mounting means that the device only responds to the control commands in the linkage chain during the access period without changing its original device configuration.

4. The method according to claim 1, characterized in that, Before generating the rebinding scenario linkage chain in S4, the process also includes: performing public safety conflict detection on the devices involved in the rebinding; when cross-space rebinding is detected to involve safety-critical devices, performing graded downgrading processing on the safety-critical devices or preventing automatic rebinding and generating a manual confirmation prompt; devices with higher safety levels correspond to smaller downgrading levels; the safety-critical devices include emergency lighting equipment and fire-fighting related equipment.

5. The method according to claim 1, characterized in that, In S2, the on-site spatial state information is obtained through multi-source fusion, and the fused signal sources include isolation sensor signals, control panel input signals, reservation system data, and infrared thermal image data.

6. The method according to claim 1, characterized in that, In S5, the scenario triggering events include environmental condition exceeding limits events, which are generated based on environmental monitoring data of temperature, humidity, CO2 concentration and PM2.5 concentration of each spatial unit in the shared space; The closed-loop boundary crossing strength is determined by the ratio of the number of devices exceeding the current actual service space boundary to the total number of devices involved in the linkage chain; The progressive solidification includes gradually reducing the spatial access verification frequency of the rebinding scenario linkage chain according to the increase of solidification credibility; when the solidification credibility is lower than the degradation threshold, the solidified linkage chain is unsolidified and reverted to the previous effective rebinding state.

7. A multi-protocol adaptive access IoT intelligent control gateway, deployed at the edge of a multi-tenant shared space, characterized in that, include: The multi-protocol access and map generation module is used to perform multi-protocol access and shared device marking for IoT devices in a multi-tenant shared space, and generate a multi-protocol shared device access map. The on-site status fusion and boundary table generation module is used to determine the actual service space corresponding to each IoT device based on the multi-protocol shared device access map and the on-site spatial status information, and generate a spatial service boundary status table. The boundary drift detection and cause identification module is used to compare the actual service space of each IoT device in the spatial service boundary status table with the old space ownership relationship of the IoT device in the original scene linkage chain, calculate the boundary drift intensity through a multi-dimensional weighted method, and correct the boundary drift intensity through the boundary confidence. When the corrected boundary drift intensity reaches a preset threshold, the boundary drift cause type is identified and the boundary drift confirmation result is output. The differentiated rebinding execution module is used to generate a rebinding scenario linkage chain that is consistent with the current spatial service boundary based on the boundary drift confirmation result and the boundary drift cause type. The scene linkage verification and solidification module is used to receive scene trigger events, perform spatial access verification on the rebinding scene linkage chain, obtain the actual status feedback of each IoT device and calculate the closed-loop boundary crossing strength. The closed-loop boundary crossing strength needs to exceed the current actual service space boundary. When the closed-loop boundary crossing strength does not exceed the closed-loop boundary crossing threshold and the solidification credibility reaches the solidification threshold, the rebinding scene linkage chain is progressively solidified. When the closed-loop boundary crossing strength exceeds the closed-loop boundary crossing threshold, the out-of-bounds device is re-marked as a rebinding object and returned to the boundary drift detection and cause identification module.

8. The gateway according to claim 7, characterized in that, In the boundary drift detection and cause identification module, the multi-dimensional weighted dimensions include the degree of crossing the original spatial ownership range, the degree of crossing the original tenant ownership range, the degree of change in public area attributes, and the degree of inconsistency in service loop ownership; the boundary credibility includes the lighting service boundary strength and access credibility; in the scene linkage verification and solidification module, the solidification credibility is determined by the ratio of the number of consecutive successful executions to the total number of effective executions, the ratio of the frequency of boundary crossings, and the boundary drift stability coefficient.

9. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-6.

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