A method and system for interlocking escape doors based on fire signals

Abstract

This application relates to the field of security control system technology, and in particular to an escape door linkage method and system based on fire signals. The method includes the following steps: S1: Obtaining local control suggestions corresponding to each microcontroller; S2: Based on all obtained local control suggestions, generating a final global control command through linkage analysis; S3: Sending the final global control command to the door drive motor to control the door to perform corresponding opening and closing actions; wherein, the local control suggestions include the target opening angle, movement speed, and driving torque. This application can improve the reliability of fire security.

Description

Technical Field

[0001] This application relates to the field of security control system technology, and in particular to an escape door linkage method and system based on fire signals. Background Technology

[0002] In emergencies such as fires, the timely opening and closing of escape doors is crucial for the safe evacuation of personnel.

[0003] Currently, common escape door control systems typically rely on a single sensor or centralized control methods.

[0004] However, the deployment of a single sensor is insufficient to fully reflect the state of the environment surrounding the gate, while centralized control suffers from signal transmission delays and insufficient system reliability. Therefore, improvements are urgently needed. Summary of the Invention

[0005] Therefore, it is necessary to provide a fire signal-based escape door linkage method and system to improve the reliability of fire security in response to the above-mentioned technical problems.

[0006] In a first aspect, this application provides an escape door linkage method based on fire signals, executed by a central controller, which is communicatively connected to multiple microcontrollers deployed in different monitoring areas of the door; the method includes the following steps:

[0007] S1: Obtain the local control suggestions corresponding to each microcontroller;

[0008] S2: Based on all the acquired local control suggestions, the final global control command is generated through linkage analysis;

[0009] S3: Send the final global control command to the door drive motor to control the door to perform the corresponding opening and closing actions;

[0010] The local control recommendations for each microcontroller are generated by the microcontroller performing the following operations on the fire signals within its monitoring area:

[0011] Operation A: Map temperature data and smoke concentration data to environmental hazard assessment values, map contact force data to obstacle contact assessment values, and map door status data to door health assessment values;

[0012] Operation B: Based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, generate local control suggestions corresponding to the microcontroller; among which, the local control suggestions include target opening angle, movement speed, and driving torque.

[0013] In one embodiment, operation A maps temperature data and smoke concentration data to environmental hazard assessment values, including:

[0014] Temperature data and smoke concentration data are input into the coupled thermal diffusion sub-model and particle diffusion sub-model;

[0015] The predicted temperature distribution of the space surrounding the gate in the monitoring area was determined by the heat diffusion sub-model.

[0016] The thermal buoyancy effect generated by the predicted temperature distribution is used as the driving source and input into the particle diffusion sub-model for joint solution to obtain the predicted smoke concentration distribution.

[0017] The predicted temperature distribution and predicted smoke concentration distribution are converted into environmental hazard assessment values ​​through a nonlinear mapping function;

[0018] In the particle diffusion sub-model, the diffusion coefficient, which characterizes the diffusion rate, is dynamically adjusted based on the real-time rate of change of temperature data; when an increase in the real-time rate of change is detected, the diffusion coefficient is increased accordingly.

[0019] In one embodiment, operation A maps contact force data to obstacle contact evaluation values, including:

[0020] Based on the contact force data currently collected within the monitoring area, the obstacle contact evaluation value is determined through a preset mapping function.

[0021] The mapping function is configured to output a preset high safety rating value when the contact force in the contact force data is zero.

[0022] When the contact force is greater than zero, the obstacle contact evaluation value decreases as the contact force increases;

[0023] The decreasing pattern is as follows: the safety evaluation value decreases monotonically from high to low.

[0024] In one embodiment, operation B generates the target opening angle in the local control suggestion corresponding to the microcontroller based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, including:

[0025] The environmental hazard assessment value is input into the preset fire situation-opening degree mapping function to obtain the basic safe opening degree;

[0026] Based on the obstacle contact evaluation value and the door health evaluation value, the basic safety opening degree is corrected to obtain the target opening angle in the local control suggestion corresponding to the microcontroller.

[0027] Among them, the obstacle contact evaluation value is used to reduce the basic safety opening to avoid obstacles, and the door health evaluation value is used to limit the basic safety opening to ensure the safety of door movement.

[0028] In one embodiment, operation B generates the motion speed in the local control proposal corresponding to the microcontroller based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, including:

[0029] The comprehensive safety evaluation value is determined based on the environmental hazard evaluation value, obstacle contact evaluation value, and door health evaluation value.

[0030] When the overall safety evaluation value is lower than the first safety threshold, the motion speed in the local control suggestion corresponding to the microcontroller is set to zero.

[0031] When the comprehensive safety evaluation value is between the first safety threshold and the second safety threshold, the movement speed increases non-linearly from zero to the benchmark safety speed as the comprehensive safety evaluation value increases.

[0032] When the overall safety evaluation value is higher than the second safety threshold, the movement speed is set as the baseline safe speed.

[0033] In one embodiment, operation B generates the driving torque in the local control proposal corresponding to the microcontroller based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, including:

[0034] Based on the motion velocity and preset acceleration planning parameters, determine the expected angular acceleration;

[0035] The theoretical driving torque is determined based on the door dynamics model; whereby the theoretical driving torque is the sum of the product of the door's moment of inertia and the expected angular acceleration in the door dynamics model and the estimated frictional resistance torque in the door dynamics model.

[0036] The door health evaluation value is mapped to a torque limitation coefficient, where the lower the door health evaluation value, the smaller the mapped torque limitation coefficient.

[0037] The product of the theoretical driving torque and the torque limitation coefficient is used as the driving torque in the local control proposal corresponding to the microcontroller.

[0038] In one embodiment, S2 generates a final global control command based on all acquired local control suggestions through linkage analysis, including:

[0039] S21: Determine the decision confidence weights for each microcontroller;

[0040] S22: Based on the decision confidence weights of each microcontroller, the target opening angle, motion speed and driving torque in each local control proposal are weighted and fused to obtain the preliminary global control command;

[0041] S23: Perform security verification on the preliminary global control command; if the verification passes, the preliminary global control command is used as the final global control command; if the verification fails, a predefined conservative security strategy is adopted to generate the final global control command through linkage analysis.

[0042] In one embodiment, the decision confidence weight of each microcontroller in S21 is determined by the environmental perception confidence, contact perception confidence, and system health confidence of each microcontroller; wherein:

[0043] The confidence level of environmental perception is determined based on the spatial gradient entropy formed by the predicted temperature distribution and the predicted smoke concentration distribution corresponding to each microcontroller; the smaller the spatial gradient entropy, the higher the confidence level of environmental perception.

[0044] Contact perception confidence level is negatively correlated with obstacle contact evaluation value;

[0045] The system health confidence level is positively correlated with the gate health evaluation value.

[0046] In one embodiment, S23 performs a security check on the preliminary global control command, including:

[0047] The global safety score is calculated based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value of each microcontroller.

[0048] If the global safety score is lower than the preset safety threshold, or if the contact force data of any microcontroller exceeds the emergency threshold, the verification is deemed to have failed and a conservative safety strategy is triggered.

[0049] The conservative safety strategy stipulates that: the global motion speed in the final global control command is the minimum of the motion speeds in all local control suggestions; the global opening angle in the final global control command is the minimum of the target opening angles in all local control suggestions; and the global driving torque in the final global control command is the minimum of the driving torques in all local control suggestions.

[0050] Secondly, this application also provides an escape door linkage system based on fire signals, including a main controller and multiple microcontrollers; the multiple microcontrollers are deployed in different monitoring areas of the door and are connected to the main controller in communication.

[0051] The main controller is configured to: acquire local control suggestions corresponding to each microcontroller; generate a final global control command based on all acquired local control suggestions through linkage analysis; and send the final global control command to the door drive motor to control the door to perform corresponding opening and closing actions.

[0052] Each microcontroller is configured to perform the following operations on fire signals within its monitoring area: The fire signals include temperature data, smoke concentration data, contact force data, and door status data: Operation A: Mapping temperature data and smoke concentration data to environmental hazard assessment values, mapping contact force data to obstacle contact assessment values, and mapping door status data to door health assessment values; Operation B: Based on the environmental hazard assessment values, obstacle contact assessment values, and door health assessment values, generating local control suggestions corresponding to the microcontroller; wherein, the local control suggestions include target opening angle, movement speed, and driving torque.

[0053] The aforementioned escape door linkage method and system based on fire signals deploys multiple microcontrollers in different monitoring areas of the door and communicates with a central controller. Each microcontroller can map the temperature, smoke concentration, contact force, and door status data in the fire signal into environmental hazard level, obstacle contact, and door health evaluation values, respectively. This enables multi-dimensional and refined local perception of the door and its surrounding environment, and generates local control suggestions including target opening angle, movement speed, and driving torque. The central controller generates the final global control command by analyzing all local control suggestions in a coordinated manner. The multi-microcontroller architecture can reduce the impact of single-point failures, improve system reliability, and ultimately accurately control the door drive motor to perform opening and closing actions, ensuring timely and safe opening and closing of escape doors in emergencies such as fires. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is an application environment diagram of an escape door linkage method based on fire signals in one embodiment;

[0056] Figure 2 This is a flowchart illustrating an escape door linkage method based on a fire signal in one embodiment;

[0057] Figure 3 This is a flowchart illustrating operation A in one embodiment;

[0058] Figure 4 This is a flowchart illustrating operation B in one embodiment;

[0059] Figure 5 This is a flowchart illustrating operation B in another embodiment;

[0060] Figure 6 This is a flowchart illustrating S2 in one embodiment. Detailed Implementation

[0061] To make the technical solutions and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0062] In one exemplary embodiment, such as Figure 1 As shown, an escape door linkage method based on fire signals is provided, which is executed by a central controller. The central controller is communicatively connected to multiple microcontrollers deployed in different monitoring areas of the door.

[0063] Understandably, the central controller is the global decision-making core of the escape door intelligent control system. It is used to summarize the local control suggestions of each microcontroller and then generate the final global control command through linkage analysis. The microcontroller is a distributed sensing and processing unit deployed in different monitoring areas of the door (such as the two sides of the door frame, the top and bottom edges of the door leaf). It is used to collect temperature, smoke concentration, contact force and door status data of the corresponding areas and generate local control suggestions.

[0064] Optionally, the main controller can be a high-performance embedded processor with an integrated real-time operating system to meet the requirements of parallel processing of multiple data streams (data corresponding to each microcontroller); the microcontrollers can be low-power, highly integrated on-chip systems that can independently complete raw data processing and local control suggestion generation; the communication connection between the main controller and each microcontroller can be an industrial-grade bus.

[0065] For example, in an office building's escape door system, monitoring area 1 is the upper edge of the door leaf, monitoring area 2 is the lower edge of the door leaf, monitoring area 3 is the edge of the door leaf on the non-hinged side (lock side), monitoring area 4 is the upper frame of the door frame, monitoring area 5 is the hinged side of the door frame, and monitoring area 6 is the lock side of the door frame.

[0066] The first microcontroller installed in monitoring area 1 is located inside the reinforcing profile (which has a U-shaped / rectangular groove structure) on the upper edge of the door leaf, and does not interfere with the sealing assembly on the upper edge of the door frame. The second microcontroller installed in monitoring area 2 is located inside the anti-slip profile (which has a U-shaped / rectangular groove structure) on the lower edge of the door leaf, and does not interfere with the sealing assembly on the lower edge of the door frame or the ground. The third microcontroller installed in monitoring area 3 is located inside the profile (which has a U-shaped / rectangular groove structure) on the non-hinged side edge (lock side) of the door leaf, and does not interfere with the sealing assembly on the lock side of the door frame or the mechanical operation of the lock. The fourth microcontroller installed in monitoring area 4 is located in the non-sealed area outside the upper frame of the door frame (specifically, the outer surface where the upper frame of the door frame meets the wall; it does not contact the sealing strip on the upper edge of the door leaf when the door is closed). The fifth microcontroller installed in monitoring area 5 is located in the non-sealed area outside the hinge side of the door frame (specifically, the outer surface where the hinge side of the door frame meets the wall; it does not contact the sealing strip on the hinge side of the door leaf when the door is closed, and it does not affect the hinge rotation). The sixth microcontroller installed in monitoring area 6 is located in the non-sealed area outside the lock side of the door frame (specifically, the outer surface where the lock side of the door frame meets the wall; it does not contact the sealing strip on the lock side of the door leaf when the door is closed, and it does not affect the latch operation).

[0067] Based on the above distributed architecture, the failure of the entire system due to a single point of failure can be avoided, significantly improving the overall reliability of the system; the regional perception of the microcontroller can cover the entire range of the gate, avoiding the omission of local risks (such as obstacles at the bottom of the gate); at the same time, the main controller only receives pre-processed local control suggestions, which can reduce the computing load and speed up the decision response.

[0068] like Figure 2 As shown, the method includes the following steps:

[0069] S1: Obtain the local control suggestions corresponding to each microcontroller.

[0070] Among them, the local control suggestion is a set of door control parameters generated by the microcontroller based on the monitoring data (temperature, smoke concentration, contact force, door status) of the area. It includes the target opening angle (the final opening and closing angle that the door needs to reach), movement speed (the rotation rate of the door during the opening and closing process) and driving torque (the amount of power that the drive motor needs to output).

[0071] Optionally, when the microcontroller collects data, temperature data is obtained through temperature sensors deployed in the monitoring area, smoke concentration data is collected through smoke sensors, contact force data is detected through force sensors, and door status data (such as the operating status of the drive motor) is collected through corresponding monitoring elements. When the microcontroller generates local control suggestions, it can convert the above-collected data into target opening angle, movement speed, and driving torque through preset mapping rules.

[0072] For example, the microcontroller at the top of an emergency exit door in a shopping mall collects data showing that there is no high temperature, no smoke, no obstacles in the door's movement path, and the door itself is in normal condition. Based on this, it generates local control suggestions: target opening angle 90° (fully open), movement speed 0.3 rad / s, and driving torque 10 Nm. After receiving these suggestions through a communication connection, the main controller continues to collect suggestions from the microcontrollers on the left and right sides of the door frame and the bottom of the door. Once all suggestions from the microcontrollers have been collected, step S1 is completed.

[0073] In this step, this decision-making mechanism based on distributed information fusion overcomes the blind spots of centralized perception and significantly improves the accuracy and safety of gate control in dealing with complex local conditions (such as bottom obstacles). The local data preprocessing of the microcontroller can reduce the amount of raw data transmitted, distribute the computing load to multiple microcontrollers, avoid response delays caused by data overload of a single device, and ensure processing efficiency in emergency scenarios.

[0074] S2: Based on all the acquired local control suggestions, the final global control command is generated through linkage analysis.

[0075] The final global control command is a unified command generated by the main controller after integrating the local control suggestions of all microcontrollers. It can directly drive the door's movement and includes global opening angle, global movement speed, and global driving torque parameters. Based on all local control suggestions, the main controller needs to comprehensively consider the differences in working conditions of each area of ​​the door, without relying on the suggestions of a single microcontroller, to ensure that the command can take into account the safety and practical needs of the entire range of the door.

[0076] Optionally, when the main controller integrates local control suggestions, a weighted fusion strategy based on data reliability can be adopted: microcontrollers with small fluctuations in continuously acquired data (such as stable deviations in temperature and smoke concentration data) are assigned higher weights, while microcontrollers with large fluctuations in data (such as frequent parameter jumps) are assigned lower weights, and global control parameters are obtained by weighted averaging. If the suggestions of each microcontroller have too large deviations (such as the difference in target opening angles exceeding 40°), a simplified strategy of taking the median value or majority consensus can be adopted to ensure that the instructions are reasonable and easy to execute. The purpose is to make the entire integration process based on linear operations, without the need for complex models, and to ensure decision-making efficiency.

[0077] For example, in an office building's emergency exit system, the local control suggestions for the four microcontrollers are as follows: target opening angles of 90°, 85°, 90°, and 88°, movement speeds of 0.3 rad / s, and driving torques of 10 Nm, 9.5 Nm, 10 Nm, and 10.5 Nm, respectively. The main controller determines that the data from each microcontroller is stable and reliable, assigns them the same weight, and calculates the final global control command: global opening angle 88°, global movement speed 0.3 rad / s, and global driving torque 10 Nm.

[0078] In this step, by integrating suggestions from multiple regions, the operational requirements of different locations on the gate can be balanced, avoiding the impact of local risks (such as obstacles in a certain area) on overall safety. The weighted fusion strategy can highlight the decision-making role of reliable data, reduce the interference of abnormal data (such as temporary false alarms from sensors) on instructions, and meet the real-time requirements in emergency evacuation scenarios such as fires.

[0079] S3: Send the final global control command to the door drive motor to control the door to perform the corresponding opening and closing actions.

[0080] Understandably, the door drive motor is the power unit that performs the opening and closing actions of the door. It is usually a motor with position feedback function (such as a DC brushless motor with an encoder), which can achieve precise control of the door position, movement speed and driving torque. Performing the corresponding opening and closing actions means that the drive motor drives the door mechanical structure (such as a reduction gear set) to complete the opening or closing process according to the parameters in the command.

[0081] Optionally, the door drive motor is a brushless DC motor with a position feedback element, which is connected to the door mechanical structure through a reduction gear set to adapt to the torque and speed required for the door to open and close. The main controller and the drive motor transmit commands through a dedicated drive interface. By adjusting the command parameters, the motor speed (corresponding to the door movement speed) and output torque (corresponding to the drive torque) are controlled. At the same time, the position feedback function of the motor is used to correct the door angle in real time to ensure that the door can accurately reach the target opening angle.

[0082] For example, the final global control command generated by the main controller is a global opening angle of 88°, a global movement speed of 0.3 rad / s, and a global driving torque of 10 Nm, which is sent to the door drive motor through the drive interface. After receiving the command, the drive motor adjusts its operating state according to the parameters, driving the door to rotate slowly, while monitoring the door angle in real time through the position feedback function. When the door angle reaches 88°, the motor stops running and the door completes the opening action. If the door needs to be closed, the main controller adjusts the global opening angle to 0° and the motor reverses to perform the closing action.

[0083] Precise parameter control of the drive motor in this step can avoid the risk of personnel collision caused by sudden opening and closing of the door, while reducing wear on the door's mechanical structure and extending the service life of the equipment; the position feedback function can correct the deviation of the door's movement in real time, and even in the presence of slight external interference (such as airflow), it can still ensure that the door operates stably according to instructions, ensuring reliability in emergency evacuation scenarios.

[0084] Each microcontroller's local control suggestion is generated by performing the following operations on the fire signal within its monitoring area; the door status data is mapped to a door health evaluation value. The fire signal includes temperature data, smoke concentration data, contact force data, and door status data.

[0085] Operation A: Map temperature data and smoke concentration data to environmental hazard assessment values, map contact force data to obstacle contact assessment values, and map door status data to door health assessment values.

[0086] Understandably, the environmental hazard assessment value is an indicator that quantifies the degree of environmental hazard by converting temperature data and smoke concentration data of the area where the microcontroller is located through preset rules (value range 0-1, 0 represents no hazard, 1 represents extreme hazard); the obstacle contact assessment value is an indicator that quantifies the obstacle risk of the door's movement path by converting contact force data (value range 0-1, 0 represents high obstacle risk, 1 represents no obstacle); and the door health assessment value is an indicator that quantifies the door's operational reliability by converting door status data (such as the operating status of the drive motor) (value range 0-1, 0 represents serious failure, 1 represents healthy status).

[0087] Optionally, the environmental hazard assessment value can adopt a segmented mapping rule: the assessment value is 0 when there is no high temperature or smoke; as the temperature or smoke concentration gradually increases, the assessment value increases linearly; when the temperature or smoke concentration is too high, the assessment value is 1; the obstacle contact assessment value can adopt a linear mapping rule: the assessment value is 1 when there is no contact force, and the assessment value decreases as the contact force increases; the door health assessment value can be mapped based on the door's key status data: the assessment value is 1 when the status is normal, and the assessment value decreases as the degree of abnormality increases when the status is abnormal (such as motor operating parameters deviating from the normal range); for example, the door status data includes: motor winding temperature deviation. Gearbox vibration amplitude Evaluation value , where 80℃ and 10mm / s are preset fault thresholds.

[0088] For example, the microcontroller on the left side of the door frame of a hotel's escape door collects data on the area's temperature (35℃), smoke concentration (100ppm), contact force (0N), and door drive motor operating parameters slightly deviating from the normal range. Through the mapping rules of operation A, it calculates the following values: environmental hazard assessment value (0.33), obstacle contact assessment value (1.0), and door health assessment value (0.4). These three assessment values ​​provide core inputs for generating subsequent local control suggestions.

[0089] In operation A, data mapping standardizes raw monitoring data (temperature, contact force, motor parameters, etc.) of different types and units into evaluation values ​​in the 0-1 range, eliminating data dimensional differences and providing a unified quantitative basis for subsequent multi-factor integrated generation of control recommendations. The mapping rules reduce the computational load of the microcontroller, avoid delays in the generation of local control recommendations due to complex calculations, and ensure the real-time nature of local decisions.

[0090] Operation B: Based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, generate local control suggestions corresponding to the microcontroller.

[0091] The local control recommendations include target opening angle, movement speed, and driving torque.

[0092] Optionally, the target opening angle refers to the base opening determined by the environmental hazard assessment value (the higher the environmental hazard, the larger the base opening). If the obstacle contact assessment value is low (there is an obstacle risk) or the door health assessment value is low (the door is in poor condition), the opening value is reduced from the base opening value, down to a minimum of 0°. The movement speed refers to the base speed determined by the environmental hazard assessment value (the higher the environmental hazard, the faster the base speed). If the obstacle contact assessment value is low or the door health assessment value is low, the base speed is halved. The driving torque refers to the theoretical driving torque calculated based on the movement speed. If the door health assessment value is low, the theoretical driving torque is reduced proportionally to ensure that the driving torque is compatible with the door's health status.

[0093] For example, following the example of Operation A, the three evaluation values ​​of the microcontroller on the left side of the door frame are: environmental hazard evaluation value 0.33, obstacle contact evaluation value 1.0, and door health evaluation value 0.4. Based on the rules of Operation B, the target opening angle (determined by the environmental hazard as a base opening of 40°, reduced by 20° due to low door health) is 20°, the movement speed (determined by the environmental hazard as a base speed of 0.2 rad / s, halved due to low door health) is 0.1 rad / s, and the driving torque (calculated from the movement speed as a theoretical torque of 1.6 Nm, halved due to low door health) is 0.8 Nm. Finally, the local control recommendations for this microcontroller are: target opening angle 20°, movement speed 0.1 rad / s, and driving torque 0.8 Nm.

[0094] In Operation B, control parameters are generated by integrating three evaluation values, so that the local control recommendations can take into account the three operating conditions of environmental hazard, obstacle risk and door health, and avoid control deviations caused by a single factor (such as only considering the degree of environmental hazard). For example, when the door health is low, reducing the driving torque and opening angle can prevent the door from being damaged due to overload; when the obstacle risk is high, reducing the movement speed and opening angle can prevent the door from colliding with the obstacle.

[0095] In one exemplary embodiment, such as Figure 3 As shown, operation A maps temperature data and smoke concentration data to environmental hazard assessment values, including:

[0096] Sa1 inputs temperature data and smoke concentration data into the coupled thermal diffusion sub-model and particle diffusion sub-model.

[0097] The coupled thermal diffusion sub-model and particle diffusion sub-model refer to two interconnected physical models with data exchange: the thermal diffusion sub-model is used to analyze the heat transfer (conduction, convection, radiation) laws within the monitoring area, and the particle diffusion sub-model is used to analyze the diffusion path and concentration changes of smoke particles in space; through coupling, the two sub-models are not calculated independently, but are linked through the thermal buoyancy effect (output of the thermal diffusion sub-model), ensuring that the movement laws of heat and smoke conform to the actual physical scenario.

[0098] For example, the thermal diffusivity sub-model includes:

[0099] The solid domain is: ;

[0100] in, This is the density of the solid (kg / m³). Specific heat capacity of solid (J / (kg·K)) is the thermal conductivity of solid (W / (m·K)). Let t be the temperature (K) and t be the time (s).

[0101] The fluid domain is: ;

[0102] in, air density (kg / m³). Specific heat capacity of air (J / (kg·K)) The thermal conductivity of air (W / (m·K)). This is the air velocity vector (m / s).

[0103] For example, the particle diffusion sub-model includes:

[0104] The continuity equation is ;

[0105] The momentum equation (Boussinesq approximation) is: ;

[0106] Pressure (Pa) The aerodynamic viscosity is expressed in Pa·s. The acceleration due to gravity (m / s²) is the air volume expansion coefficient (1 / K). Reference temperature (K);

[0107] The smoke diffusion equation is ;

[0108] The smoke concentration is expressed in ppm. The smoke diffusion coefficient (m² / s) is used.

[0109] Optionally, the microcontroller can input the temperature data and smoke concentration data in the area into the coupled model through the built-in program interface. The thermal diffusion sub-model receives the temperature data as the initial boundary condition, and the particle diffusion sub-model receives the smoke concentration data as the initial particle distribution condition. At the same time, a data interface is reserved to receive the thermal buoyancy information output by the thermal diffusion sub-model, thereby achieving coupling between the two.

[0110] For example, a microcontroller on the top of an escape door in a shopping mall collects real-time data of 28°C (no localized high temperatures) and 0ppm smoke concentration (no initial smoke). After preprocessing these two sets of data, they are input into a coupled thermal diffusion sub-model and a particle diffusion sub-model: the thermal diffusion sub-model uses 28°C as the initial ambient temperature, and the particle diffusion sub-model uses 0ppm as the initial smoke concentration, waiting for subsequent thermal buoyancy effect data input to complete the joint calculation.

[0111] This step provides the raw data foundation for subsequent temperature and smoke prediction analysis. By processing the data through a coupled model rather than an independent model, the physical relationship between heat and smoke movement (such as heat driving smoke diffusion) can be avoided, ensuring that the subsequent prediction results conform to the actual emergency scenario (such as the law of smoke rising with hot air flow in a fire), and providing a more accurate input basis for environmental hazard assessment.

[0112] Sa2, through the thermal diffusion sub-model, determines the predicted temperature distribution of the space surrounding the corresponding gate in the monitoring area.

[0113] It is understandable that the predicted temperature distribution is the result of the temperature field distribution of the space around the door in the future (e.g., 5 to 30 seconds) calculated by the heat diffusion sub-model based on real-time temperature data. It is not the temperature value of a single monitoring point, but a two-dimensional / three-dimensional distribution of temperature in different spatial locations (e.g., the top, bottom, left, and right sides of the door). The core function of the heat diffusion sub-model is to simulate the heat transfer process between the air and the solid (door) and output the dynamic temperature change trend.

[0114] For example, the space around the door corresponding to monitoring area 1 is: the public space of the corridor directly above the door, specifically including the air circulation area within 10 to 30 cm below the corridor ceiling (hot air tends to gather upwards in the early stage of a fire, and smoke will also spread in this area first, which is the key monitoring range for fire early warning), as well as the space above the door when the upper edge sweeps across it when the door is opened / closed.

[0115] The space surrounding the door corresponding to monitoring area 2 is the corridor floor and surrounding space directly below the door, specifically including the 10-20cm wide area of ​​the floor where the lower edge of the door contacts the floor when the door is closed (this area is prone to accumulating paper, debris, or being accidentally blocked by people's feet, which may cause the door to jam and fail to close, affecting the fireproof and smoke-proof effect), as well as the floor space swept by the lower edge of the door when the door is open.

[0116] The space around the door corresponding to monitoring area 3 is the lateral corridor space in the opening direction of the door leaf on the non-hinged side (lock side), specifically including the 15-30cm wide lateral passage swept by the lock side when the door is opened (this area may contain items such as fire extinguisher cabinets, green plants, and trash cans placed against the wall, or people passing through may stand sideways and easily collide with the door), as well as the local space within 10cm in front of the lock.

[0117] Monitoring area 4 corresponds to the space around the door, which includes the space above the top frame of the door and the space around the ceiling. Specifically, it includes the 5-15cm gap between the top frame of the door and the ceiling (this gap is prone to dust accumulation, and smoke can seep in from the gap between the ceiling and the door frame during a fire, so it is necessary to monitor the changes in smoke concentration to assist in fire assessment), as well as the upper space of the walls on both sides of the top frame of the door (to prevent the door frame from loosening due to wall cracks, or foreign objects from falling from the wall gaps and getting stuck in the gap between the door frame and the door leaf).

[0118] The space surrounding the door corresponding to monitoring area 5 is the space around the door frame hinge side wall and the space around the motor. Specifically, it includes the wall area 5-10cm outside the hinge (when the hinge rotates, it is easy to interfere with foreign objects such as protruding nails and decorative strips on the wall. Long-term friction may cause the hinge to wear. It is necessary to monitor whether there are obstacles in this area), and the area around the door drive motor (usually installed below the door frame hinge side) 10-20cm (the motor needs to dissipate heat when running. If this area is blocked by debris, it will cause the motor to overheat and fail. It is necessary to monitor the heat dissipation environment).

[0119] The space surrounding the door corresponding to monitoring area 6 is the space around the door frame lock side wall and the space around the lock mating groove. Specifically, it includes the wall area 5-15cm outside the door frame lock side (people may lean against this wall when passing through, and long-term stress will cause the door frame to deform, affecting the mating accuracy of the lock tongue and lock groove), and the area 3-5cm outside the lock mating groove (the door frame groove into which the lock tongue is inserted) (dust, hair and other small debris are easy to fall into, and after accumulation, they will hinder the normal insertion of the lock tongue. It is necessary to monitor whether there are foreign objects blocking this area).

[0120] Optionally, the heat diffusion sub-model can be constructed using a simplified finite element method or finite volume method, with built-in calculation logic related to heat conduction, heat convection, and heat radiation (no need for complex industrial-grade simulation accuracy, just enough to meet the control requirements of the escape door); the model uses real-time temperature data input from the microcontroller as initial conditions (such as the surface temperature of the door and the ambient air temperature), combined with preset parameters such as the thermal conductivity of the door material and the convection coefficient of the ambient air, to calculate the temperature changes of each spatial node in the next few seconds, and finally outputs the predicted temperature distribution in the form of a grid (such as each 0.1 meter is a grid node, and the predicted temperature of each node is marked).

[0121] For example, following the example in S1, if the monitoring area on the left side of the door suddenly experiences a local temperature rise (temperature rises to 55℃), the left-side microcontroller will input the real-time temperature data of 55℃ into the heat diffusion sub-model. The model, combined with the thermal conductivity of the door's metal frame (preset parameter), calculates that in the next 10 seconds, the temperature around the left side of the door will spread from 55℃ to the right, the temperature at the top will rise to 42℃ due to the heat convection effect, and the temperature at the bottom will remain at around 30℃. Finally, it outputs a predicted temperature distribution table containing a 2-meter radius around the door and 0.1-meter grid nodes.

[0122] This step can overcome the limitations of traditional single-point instantaneous temperature monitoring. By predicting the temperature distribution, it can grasp the overall heat change trend around the door (such as whether the high-temperature area is spreading to the evacuation channel), rather than relying solely on the current temperature value of a single sensor. This provides a spatial dimension basis for subsequent judgment of the environmental hazard range and avoids ignoring the overall hazard due to local sensors not triggering thresholds.

[0123] Sa3 uses the thermal buoyancy effect generated by the predicted temperature distribution as the driving source, inputs it into the particle diffusion sub-model, and performs joint solution to obtain the predicted smoke concentration distribution.

[0124] It is understandable that the thermal buoyancy effect refers to the phenomenon where the air density decreases in high-temperature regions and generates airflow that flows upward or towards low-temperature regions (such as the hot air column formed by the rising of hot air in a fire) in the temperature distribution predicted by the thermal diffusion sub-model. Here, it serves as the power source for the particle diffusion sub-model and determines the direction of smoke particle movement. The joint solution means that the particle diffusion sub-model takes the thermal buoyancy effect (airflow direction and velocity) as input, combines it with the initial smoke concentration data, and simultaneously calculates the diffusion path and concentration change of smoke particles driven by the airflow, and finally outputs the predicted smoke concentration distribution (the predicted smoke concentration values ​​at various locations in the space around the door).

[0125] Optionally, the thermal diffusion sub-model first converts the predicted temperature distribution into airflow velocity and direction (e.g., for every 10°C increase in temperature, the corresponding airflow upward velocity increases by 0.5 m / s), and outputs it as thermal buoyancy effect parameters to the particle diffusion sub-model. The particle diffusion sub-model, based on simplified fluid dynamics logic, uses thermal buoyancy effect as the airflow driving force, and combines it with initial smoke concentration data (e.g., initial smoke concentration of 200 ppm in a certain area) to calculate the diffusion distance and concentration decay of smoke particles with airflow at each time node (e.g., one node every 2 seconds), and finally outputs a smoke concentration distribution consistent with the spatial range of the predicted temperature distribution (e.g., the predicted smoke concentration value of each grid node).

[0126] For example, following the example in S2, the thermal diffusion sub-model outputs the thermal buoyancy effect parameter of an upward hot airflow at a speed of 0.8 m / s at the top left side of the door. After receiving this parameter, the particle diffusion sub-model combines it with the initial smoke concentration of 150 ppm collected by the microcontroller on the left side (assuming that smoke has been generated locally) to solve for the following: In the next 10 seconds, smoke particles will diffuse from the top left side to the top right side under the influence of the 0.8 m / s hot airflow. The predicted smoke concentration at the top right side will rise to 80 ppm, while the smoke concentration at the bottom will remain below 20 ppm due to the absence of hot airflow. Finally, the model outputs a predicted smoke concentration distribution table within a 2-meter radius around the door.

[0127] This step addresses the shortcomings of traditional methods that predict smoke diffusion based solely on a fixed velocity. By using the thermal buoyancy effect to drive the smoke diffusion calculation, the prediction results are more realistic (e.g., smoke in a fire does not diffuse uniformly but moves directionally with the hot airflow). At the same time, the joint solution ensures that the temperature and smoke predictions match each other, avoiding the contradiction of predicting high-temperature areas but not covering them in the smoke prediction. This provides a spatial distribution basis for environmental hazard assessment based on both temperature and smoke dimensions.

[0128] Sa4 converts the predicted temperature distribution and predicted smoke concentration distribution into environmental hazard assessment values ​​through a nonlinear mapping function.

[0129] It is understandable that a nonlinear mapping function refers to a pre-calibrated function that can transform multi-dimensional prediction data (characteristic values ​​of predicted temperature distribution, characteristic values ​​of predicted smoke concentration distribution) into a single quantitative index. The nonlinearity is manifested in the fact that small changes in hazard parameters (such as high temperature and high smoke concentration) may lead to a significant increase in the evaluation value (which conforms to the law of rapid escalation of danger in emergency scenarios). The environmental hazard evaluation value is the output of this function, usually taking a value of 0-1 (0 for no danger and 1 for extreme danger), which quantitatively represents the overall hazard level of the monitoring area.

[0130] For example, ;

[0131] Where T is the highest predicted temperature, T_max = 100℃, and C is the highest predicted smoke concentration. ;when or Time evaluation value = 1.0.

[0132] Optionally, key feature values ​​(such as the highest predicted temperature and the percentage of high-temperature areas) are extracted from the predicted temperature distribution, and key feature values ​​(such as the highest predicted smoke concentration and the rate of increase in smoke concentration) are extracted from the predicted smoke concentration distribution. The nonlinear mapping function, pre-calibrated under simulated fire scenarios (e.g., a maximum temperature of 80℃ corresponds to a hazard level of 0.8, and a smoke concentration of 300ppm corresponds to a hazard level of 0.9), is then used to weight and calculate the above feature values, outputting an evaluation value in the 0-1 range. For example, the function formula can be set as: ;

[0133] Among them, 100℃ and 500ppm are preset extremely dangerous thresholds, and when any feature value exceeds the threshold, the evaluation value is directly taken as 1.0.

[0134] For example, following the example in S3, the highest predicted temperature of 55℃ is extracted from the predicted temperature distribution, and the highest predicted smoke concentration of 150ppm is extracted from the predicted smoke concentration distribution. Substituting into the nonlinear mapping function: the risk assessment value = 0.4×(55 / 100)+0.6×(150 / 500)=0.4×0.55+0.6×0.3=0.22+0.18=0.4. Finally, the environmental risk assessment value of the monitoring area where the microcontroller is located is 0.4 (medium risk).

[0135] This step transforms spatially distributed temperature and smoke data into a single quantitative evaluation value, solving the problem that multi-dimensional data is difficult to directly and comprehensively judge. The nonlinear mapping function can highlight the sensitivity of escalation of danger (e.g., if the temperature rises from 70℃ to 80℃, the evaluation value may jump from 0.7 to 0.9), which meets the needs of rapid response in emergency scenarios and provides a comparable basis for the generation of control recommendations.

[0136] Furthermore, the diffusion coefficient used to characterize the diffusion rate in the particle diffusion sub-model is dynamically adjusted based on the real-time rate of change of temperature data; when an increase in the real-time rate of change is detected, the diffusion coefficient is increased accordingly.

[0137] Understandably, the diffusion coefficient is a parameter in the particle diffusion sub-model that characterizes the diffusion rate of smoke particles (the larger the coefficient, the faster the smoke diffuses); the real-time change rate of temperature data refers to the magnitude of temperature change per unit time in the continuous temperature data collected by the microcontroller (e.g., temperature increases by 2℃ or decreases by 1℃ per second); dynamic adjustment means that the diffusion coefficient is not a fixed value, but increases or decreases synchronously with the increase or decrease of the temperature change rate, ensuring that the prediction of smoke diffusion rate is consistent with the actual heat change trend.

[0138] Optionally, the microcontroller performs first-order difference calculations on 3-5 consecutively collected temperature data (e.g., current temperature T1, previous temperature T2, rate of change = |T1-T2| / sampling interval) to obtain the real-time temperature change rate; preset diffusion coefficient adjustment rules: when the rate of change is ≤1℃ / s, the diffusion coefficient takes a baseline value (e.g., 0.5m² / s); when the rate of change is >1℃ / s, the diffusion coefficient increases by 20% of the baseline value for every 1℃ / s increase (e.g., when the rate of change is 2℃ / s, the diffusion coefficient = 0.5×1.2=0.6m² / s); the particle diffusion sub-model reads the adjusted diffusion coefficient in real time for smoke concentration distribution calculation.

[0139] For example, a microcontroller continuously collects temperature data as follows: 28℃ (time t0), 30℃ (time t1, 1 second interval), and 33℃ (time t2, 1 second interval). The calculated temperature change rate from t0 to t1 is 2℃ / s, and the change rate from t1 to t2 is 3℃ / s. The particle diffusion sub-model first adjusts the diffusion coefficient from the baseline value of 0.5m² / s to 0.6m² / s (corresponding to 2℃ / s), and then further adjusts it to 0.7m² / s (corresponding to 3℃ / s) to ensure that the prediction of smoke diffusion speed accelerates synchronously with the increase in temperature.

[0140] In this embodiment, the adjustment mechanism avoids the prediction lag caused by a fixed diffusion coefficient. When the fire develops rapidly (the rate of temperature change increases), the diffusion coefficient increases synchronously, making the prediction of the smoke diffusion speed more realistic and preventing the risk of rapid smoke spread from being underestimated due to a fixed diffusion coefficient. Conversely, when the temperature changes slowly, the diffusion coefficient maintains a baseline value to avoid over-predicting the smoke diffusion range and ensure the accuracy of the environmental hazard assessment value.

[0141] In an exemplary embodiment, operation A maps contact force data to obstacle contact evaluation values, including: determining obstacle contact evaluation values ​​based on the contact force data currently collected within the monitoring area using a preset mapping function.

[0142] Among them, the mapping function is configured as follows: when the contact force in the contact force data is zero, a preset high safety evaluation value is output; when the contact force is greater than zero, the obstacle contact evaluation value decreases as the contact force increases; the decreasing manner is: monotonically decreasing from the high safety evaluation value to the low safety evaluation value.

[0143] It can be understood that the contact force data refers to the numerical value of the force generated when the door body contacts an object (such as a person or an obstacle) on the movement path of the door body collected by sensors within the monitoring area where the microcontroller is located, which directly reflects whether there is a collision risk for the door body; the preset mapping function is a mathematical relationship that is set in advance in the microcontroller and is used to convert the contact force data into a quantitative risk index, and its output result is the obstacle contact evaluation value. That is, the value range of this evaluation value is usually 0 - 1, where 1 represents high safety (no collision risk) and 0 represents low safety (high collision risk).

[0144] Optionally, the contact force data can be collected by a piezoresistive force sensor array or a thin-film pressure sensor evenly arranged along the edge of the door leaf and the inner side of the door frame, and the sensors need to cover the key paths of the door body movement (such as the edges where the door leaf is easy to contact objects when opening and closing).

[0145] Optionally, the preset mapping function can adopt a piecewise linear attenuation function, and the specific form can be set as follows:

[0146] Let the high safety evaluation value be 1.0, the contact force thresholds F1 (slight contact threshold, such as 5N), F2 (severe contact threshold, such as 20N);

[0147] When the contact force F = 0, the evaluation value P = 1.0; when 0 < F ≤ F1, P = 1.0 - (F / F1) × 0.2 (slow attenuation in the initial stage);

[0148] When F1 < F ≤ F2, P = 0.8 - [(F - F1) / (F2 - F1)] × 0.6 (rapid attenuation in the middle stage);

[0149] When F > F2, P = 0.2 - [(F - F2) / (F_max - F2)] × 0.2 (approaching 0 in the later stage, F_max is the maximum measurement value of the sensor, such as 100N).

[0150] This piecewise linear attenuation function can meet the requirement of monotonic decrease and conform to the actual needs that slight contact has little impact and severe contact has a great impact.

[0151] For example, a microcontroller on the right edge of an office building's escape door has five thin-film pressure sensors in its monitoring area. When the door is about to open, if the contact force data collected by the sensors are 0N, 0N, 2N, 0N, and 0N respectively, and the maximum contact force F=2N (≤F1=5N), substituting into the mapping function, we get: P=1.0-(2 / 5)×0.2=1.0-0.08=0.92, that is, the obstacle contact evaluation value is 0.92 (high safety, only a slight contact risk exists); if a person accidentally gets close to the door, and the sensor collects the maximum contact force F=15N (F1<15N≤F2=20N), then P=0.8-[(15-5) / (20-5)]×0.6=0.8-(10 / 15)×0.6=0.8-0.4=0.4 (medium safety, there is a significant contact risk).

[0152] In this embodiment, the gradient differentiation of obstacle severity provides a more refined control basis compared to the traditional binary (present / absent) detection method. This evaluation value has a negative correlation with the contact force, ensuring that the door's response intensity matches the risk level and avoiding malfunctions caused by a single threshold judgment (e.g., complete locking upon slight contact, or maintaining high power output despite severe obstruction). Furthermore, the rapid computational capability based on real-time data can promptly capture instantaneous contact events during movement, effectively reducing the risk of personal injury or equipment damage caused by collisions.

[0153] In one exemplary embodiment, such as Figure 4 As shown, in operation B, based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, the target opening angle in the local control suggestion corresponding to the microcontroller is generated, including:

[0154] Sb1 inputs the environmental hazard assessment value into the preset fire-opening degree mapping function to obtain the basic safe opening degree.

[0155] Understandably, the fire-opening mapping function is a mathematical model pre-set in the microcontroller. Its core function is to convert the quantified environmental hazard assessment value (ranging from 0 to 1, with higher values ​​representing higher environmental hazard levels, such as fire spread intensity and smoke concentration levels) into the initial opening angle of the door. The basic safe opening angle is the output of this function, specifically referring to the door opening angle determined solely based on the environmental hazard level under ideal conditions where there are no obstacles in the door's movement path and the door itself is in good condition. Its value increases with the increase of environmental hazard level, providing an initial reference benchmark for subsequent opening correction.

[0156] Optionally, the fire-opening degree mapping function needs to satisfy the monotonically increasing characteristic to ensure that the environmental hazard level is positively correlated with the basic safety opening degree, and be compatible with the real-time computing capability of the microcontroller. It can be implemented using an exponential function or a piecewise linear function.

[0157] If an exponential function is selected, in this embodiment, the exponential function can be: ;

[0158] in, The basic safe opening (degrees) represents the door opening angle determined solely by environmental hazard level under unobstructed and undamaged conditions, ranging from 0 to 90 degrees. k is an exponential coefficient that controls the rate at which the opening increases with environmental hazard level, with a typical value of 2.0. This is an environmental hazard assessment value, derived by combining temperature and smoke levels, ranging from 0 to 1, where 0 indicates no hazard and 1 indicates extreme hazard. e is a natural constant (approximately 2.71828).

[0159] Specifically, let the environmental hazard assessment value be... The basic safety opening is (Unit: degrees, maximum value 90 degrees, corresponding to a fully open door), the function expression is: This function can achieve the effect of slowly increasing the opening when the risk level is low and rapidly approaching the maximum opening when the risk level is high;

[0160] If a piecewise linear function is selected, in this embodiment, the piecewise linear function is: ;

[0161] Where 100 represents the slope of the first segment, in degrees per unit of hazard, indicating the rate of increase in opening angle when there is slight hazard. 150 represents the slope of the second segment, indicating that the rate of increase in opening angle accelerates when there is moderate hazard. -15 is the intercept of the second segment, balancing the two segments. The transition is smooth. 90 represents the fixed opening (fully open) of the third segment.

[0162] Specifically, it can be divided into three intervals: hour, ( hour, Spend); hour, (like hour, Spend); hour, Take 90 degrees directly. The parameters of this function need to be calibrated by simulating scenarios such as fire and smoke diffusion to ensure the accuracy of matching between the degree of danger and the opening.

[0163] For example, a microcontroller on the left side of an office building's emergency exit detected a small amount of smoke in the corridor, and after data processing, obtained an environmental hazard assessment value. If a piecewise linear function is used to calculate the basic safety opening: because Substituting into the formula, we get The results indicate that, under ideal conditions of no obstacles and a healthy door, the door should initially open at a 45-degree angle, which can meet the basic evacuation needs of personnel while avoiding response delays caused by over-opening.

[0164] This step quantifies the environmental hazard level into an opening benchmark, overcoming the limitations of traditional escape doors with fixed opening degrees or threshold-triggered full-open / full-close mechanisms. Traditional solutions can only determine whether to open based on a single threshold, failing to dynamically adjust the initial opening degree according to the level of hazard. This step, through a mapping function, adapts the initial opening degree to the magnitude of the hazard. In cases of minor hazard, large-angle opening is unnecessary, reducing ineffective actions; in cases of severe hazard, the opening degree quickly approaches the maximum, improving evacuation efficiency.

[0165] Sb2, based on the obstacle contact evaluation value and the door health evaluation value, corrects the basic safety opening degree to obtain the target opening angle in the local control suggestion corresponding to the microcontroller.

[0166] Among them, the obstacle contact evaluation value is used to reduce the basic safety opening to avoid obstacles, and the door health evaluation value is used to limit the basic safety opening to ensure the safety of door movement.

[0167] The basic safe opening angle is corrected based on obstacle contact evaluation value and door health evaluation value, including: using a safety constraint algorithm to correct the basic safe opening angle; using obstacle contact evaluation value to generate obstacle avoidance constraints: when the obstacle contact evaluation value is lower than the contact safety threshold, the door opening angle is limited to avoid collision; using door health evaluation value to generate health constraints: when the door health evaluation value is lower than the health safety threshold, the maximum door opening angle is limited to ensure movement safety; and comparing the basic safe opening angle with the obstacle avoidance constraint and the health constraint respectively, taking the minimum value of the three as the final target opening angle.

[0168] Understandably, the obstacle contact evaluation value is an indicator that quantifies the risk of obstacles along the door's movement path (ranging from 0 to 1, with higher values ​​indicating lower obstacle risk). Its function is to mitigate collision risks by reducing the basic safety opening; that is, when the evaluation value is below the safety threshold, the opening needs to be reduced to prevent the door from contacting the obstacle. The door health evaluation value is an indicator that quantifies the reliability of the door's operation (ranging from 0 to 1, with higher values ​​indicating a healthier door condition). Its function is to protect the device by limiting the basic safety opening; that is, when the evaluation value is below the safety threshold, the opening needs to be reduced to prevent the door malfunction from worsening. The target opening angle is the minimum value among the basic safety opening, obstacle avoidance constraint opening, and health constraint opening, after the basic safety opening has been corrected by the above two values, ensuring that environmental evacuation requirements, obstacle avoidance safety, and door health protection are met simultaneously.

[0169] The safety constraint algorithm is the core logic used to correct the basic safe opening degree. It generates specific restrictions for obstacle risk and door health risk respectively, ensuring that the corrected opening degree simultaneously meets obstacle avoidance and equipment protection requirements. The obstacle avoidance constraint is the upper limit of the door opening angle generated based on the obstacle contact evaluation value. That is, the obstacle contact evaluation value quantifies the obstacle risk of the door's movement path (a value of 0-1, with higher values ​​indicating lower risk). The contact safety threshold is the critical value for determining whether obstacle avoidance is necessary (e.g., 0.8). When the evaluation value is lower than this threshold, the obstacle avoidance constraint will be less than the basic safe opening degree, preventing... The door is designed to prevent collisions with obstacles. The health constraint is the upper limit of the door opening angle generated based on the door's health evaluation value. The door health evaluation value quantifies the door's operational reliability (ranging from 0 to 1, with higher values ​​indicating better performance). The health safety threshold is a critical value (e.g., 0.7) for determining whether the door is suitable for large-angle operation. When the evaluation value is lower than this threshold, the health constraint will be less than the basic safe opening angle to prevent the door malfunction from worsening. The final target opening angle is the minimum value among the basic safe opening angle, obstacle avoidance constraint, and health constraint, ensuring that environmental evacuation needs, obstacle avoidance safety, and door health are all taken into account simultaneously.

[0170] Optionally, the safety constraint algorithm can adopt a simplified implementation logic of constraint generation + minimum value comparison, specifically as follows:

[0171] First, preset the contact safety threshold (e.g., 0.8) and the health safety threshold (e.g., 0.7), and set the maximum mechanical opening angle of the door to 90 degrees; obstacle avoidance constraints are generated using a linear mapping rule:

[0172] When the obstacle is contacted, the evaluation value is... Obstacle avoidance constraints when encountering a safety threshold Take the basic safety opening degree (No collision risk, no restrictions required) When the safety threshold is reached, The opening is reduced by scaling the scale proportionally;

[0173] Similarly, health constraints are generated: when the health evaluation value of the gate... Health constraints at health and safety thresholds Pick (The body is healthy and no restrictions are required) When When the health and safety threshold is reached, ;

[0174] Finally, the comparison is performed using the microcontroller's built-in program. , , Take the minimum value as the target opening angle. All calculations require only basic arithmetic operations and no complex models are needed.

[0175] In this example, the obstacle avoidance constraint in the safety constraint algorithm can be: ;

[0176] The opening (degrees) is the result of obstacle avoidance correction. : Obstacle contact evaluation value, ranging from 0 to 1, where 0 indicates severe obstacle and 1 indicates no obstacle. Contact safety threshold, typically 0.8. If the value is below this, the opening needs to be reduced to avoid obstacles.

[0177] In this example, the health constraint in the safety constraint algorithm can be: ;

[0178] The opening (degrees) after health correction. : Door health evaluation value, ranging from 0 to 1, where 0 indicates a serious fault and 1 indicates a good condition. Health and safety threshold, typically 0.7. If the value is below this, the opening needs to be limited to protect the door.

[0179] In this embodiment, the final target opening is: ;

[0180] To ensure safety, the minimum value among the three constraints is taken as the final door opening angle (in degrees).

[0181] For example, the microcontroller on the right side of an emergency exit in a shopping mall calculates the basic safe opening degree based on an environmental hazard assessment value of 0.5. The initial opening degree (under moderately dangerous scenarios) is set with a preset contact safety threshold of 0.8 and a health safety threshold of 0.7. If the sensor at the bottom of the door detects a suitcase parked there (increased obstacle risk), the obstacle contact evaluation value is calculated. If the obstacle avoidance threshold is below the contact safety threshold, then obstacle avoidance constraints apply. Degree; simultaneously, the motor current monitoring shows an abnormality (decreased door health), door health evaluation value. (Below the health and safety threshold), then health constraints apply. Degree. Compare the three values: The minimum value of 35.7 degrees was chosen as the target opening angle, which not only meets the basic requirements of environmental evacuation, but also avoids the risks of collision and equipment overload.

[0182] This revised method addresses the shortcomings of traditional single-dimensional decision-making in door opening control: traditional solutions determine opening based solely on environmental hazard levels, which can easily lead to head-on collisions with obstacles (without considering obstacles) or forced opening despite door malfunctions (without considering door health), increasing the risk of personnel injury and equipment damage. This new method, however, uses obstacle avoidance and health constraints, combined with a minimum value selection strategy, to ensure the opening finds the most conservative balance between environmental evacuation needs, obstacle avoidance safety, and equipment protection. Specifically, it reduces the opening to avoid collisions when obstacle risk is high, and limits the opening to protect equipment when door condition is poor, while avoiding excessive restrictions that could negatively impact evacuation efficiency. Furthermore, the algorithm is simple, and the microcontroller can respond in real time. In emergency scenarios, it can quickly adjust the opening without delaying evacuation due to computational latency, balancing safety and real-time performance.

[0183] This step addresses the shortcomings of relying solely on a single dimension for basic safety opening decisions, enabling collaborative management of multiple risk factors. Traditional solutions determine opening based solely on environmental hazard levels, which can lead to issues such as forcibly opening doors despite obstacles or malfunctions, increasing the risk of personnel injury and equipment damage. This step, through dual constraints of obstacles and door health, combined with a minimum selection strategy, ensures that the opening simultaneously adapts to environmental requirements, obstacle avoidance safety, and equipment status. While maintaining efficient personnel evacuation, it significantly reduces the probability of collisions and worsening door malfunctions, enhancing the safety and reliability of the control process.

[0184] In an exemplary embodiment, operation B generates the motion speed in the local control proposal corresponding to the microcontroller based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, including: determining the comprehensive safety assessment value based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value.

[0185] Understandably, the comprehensive safety evaluation value is a single quantitative indicator that integrates the environmental hazard evaluation value (reflecting environmental risk), the obstacle contact evaluation value (reflecting collision risk), and the door health evaluation value (reflecting equipment risk) through preset rules. The value ranges from 0 to 1, and the higher the value, the higher the overall safety level.

[0186] Based on this, the motion speed in the local control proposal corresponding to the microcontroller is generated, including the following three cases:

[0187] Case 1: When the overall safety evaluation value is lower than the first safety threshold, the motion speed in the local control suggestion corresponding to the microcontroller is set to zero.

[0188] Case 2: When the comprehensive safety evaluation value is between the first safety threshold and the second safety threshold, the movement speed increases non-linearly from zero to the benchmark safety speed as the comprehensive safety evaluation value increases.

[0189] Scenario 3: When the comprehensive safety evaluation value is higher than the second safety threshold, the movement speed is set as the baseline safe speed.

[0190] Understandably, the first and second safety thresholds are pre-set safety level dividing points used to delineate speed control ranges. That is, the risk is extremely high when the speed is below the first threshold, moderate when it is between the two thresholds, and low when it is above the second threshold. The movement speed is the rotation rate of the door when it is opened / closed, and its value is determined by the range of the comprehensive safety evaluation value to ensure that the speed matches the current safety level.

[0191] Optionally, the comprehensive safety evaluation value can be calculated using a weighted average method. The weights are set according to the principle of prioritizing the environment, followed by obstacles, and then supplementing with health. For example, the environmental hazard evaluation value has a weight of 0.5, the obstacle contact evaluation value has a weight of 0.3, and the door health evaluation value has a weight of 0.2. The formula is: Comprehensive Safety Evaluation Value for: ;

[0192] in, This is the environmental hazard assessment value. This is the obstacle contact evaluation value. (This refers to the health evaluation value of the portal vein).

[0193] The nonlinear increasing function of the motion velocity is: ;

[0194] in, The velocity of the door movement is rad / s. The baseline safe speed is typically 0.5 rad / s. This is a comprehensive safety evaluation value, ranging from 0 to 1. This is the first safety threshold, typically 0.3; below this value, the speed is 0. This is the second safety threshold, typically 0.7; at this value, the speed is... n is a power exponent, controlling the degree of non-linearity in speed as the safety factor increases; a typical value is 2.0.

[0195] Based on this, the speed control rule is as follows: when P < 0.3, the speed = 0; when 0.3 ≤ P ≤ 0.7, the speed adopts an exponentially increasing function. ( As a baseline safe speed (e.g., 0.5 rad / s), achieve speeds from 0 to... The nonlinear increase; when P>0.7, the speed = Vbase, no complex calculation is required, and it is adapted to the real-time processing capability of microcontrollers.

[0196] For example, the microcontroller on the left side of an office building's emergency exit collects data showing Penv=0.6 (medium environmental risk), Pobs=0.8 (low collision risk), and Phealth=0.9 (high device health). Using a weighted formula, the following calculations are performed: If the value exceeds the second threshold of 0.7, the movement speed is set to the baseline safe speed of 0.5 rad / s;

[0197] If an obstacle is subsequently detected at the bottom of the door, the Pobs value drops to 0.4, and the calculation is repeated. The value is between 0.3 and 0.7. Substituting this into the velocity formula, we get... ;

[0198] If the fire suddenly intensifies and Penv drops to 0.2, the comprehensive safety assessment value is recalculated: P = 0.5 × 0.2 + 0.3 × 0.4 + 0.2 × 0.9 = 0.1 + 0.12 + 0.18 = 0.4, which is between 0.3 and 0.7.

[0199] If Penv is further reduced to 0.1, P = 0.5 × 0.1 + 0.3 × 0.4 + 0.2 × 0.9 = 0.05 + 0.12 + 0.18 = 0.35, which is still within the range;

[0200] When Penv = 0.05, P = 0.5 × 0.05 + 0.3 × 0.4 + 0.2 × 0.9 = 0.025 + 0.12 + 0.18 = 0.325, which is still within the range;

[0201] If Pobs drops to 0.1, P = 0.5 × 0.05 + 0.3 × 0.1 + 0.2 × 0.9 = 0.025 + 0.03 + 0.18 = 0.235, which is lower than the first threshold of 0.3, so the speed is set to 0.

[0202] This step solves the rigidity problem of fixed speed in traditional escape doors. Traditional solutions either operate at a fixed speed or stop only in extreme situations. This step, however, dynamically adjusts the speed based on a comprehensive safety assessment value: slowing down or stopping when the risk is high to avoid collisions or equipment overload; speeding up when the risk is low to improve evacuation efficiency. The non-linear, incremental speed adjustment method can balance safety and efficiency when the risk is moderate. For example, the speed increases slowly when approaching the first threshold to prioritize safety, and increases rapidly when approaching the second threshold to quickly transition to a high-efficiency state. This prevents the door from shaking due to sudden speed changes and avoids missing the best evacuation opportunity due to delayed response.

[0203] Furthermore, such as Figure 5 As shown, in operation B, based on the environmental hazard assessment value, obstacle contact assessment value, and door health assessment value, the driving torque in the local control proposal corresponding to the microcontroller is generated, including:

[0204] Sb01 determines the expected angular acceleration based on the motion velocity and preset acceleration planning parameters.

[0205] Understandably, the motion speed is one of the local control suggestion parameters generated in operation B, referring to the target rotation rate (unit: rad / s) when the door opens or closes, directly determining the speed of the door's movement; the acceleration planning parameters are a set of motion control parameters preset in the microcontroller, typically including maximum angular acceleration, acceleration time, deceleration time, etc., used to avoid shocks caused by sudden acceleration or deceleration during door movement; the expected angular acceleration is the door's rotational acceleration (unit: rad / s²) calculated based on the target motion speed and acceleration planning parameters, reflecting the rate at which the door transitions from its current speed to the target speed, and is a key input for subsequent calculation of the driving torque.

[0206] Optionally, acceleration programming can employ either trapezoidal acceleration programming or S-curve acceleration programming strategies:

[0207] If trapezoidal acceleration programming is selected, the trapezoidal programming parameters in this embodiment are: ;

[0208] Maximum angular acceleration (rad / s²), typical value 2 rad / s². Acceleration time (s), typical value 1s. Deceleration time (s), typical value 1s. Specifically, preset maximum angular acceleration. (e.g., 3 rad / s²), when the target's velocity With current speed The difference When smaller, the expected angular acceleration ( (For preset acceleration time, such as 1 second); when hour, Pick ;

[0209] If S-curve acceleration programming is selected, the additional parameters for S-curve programming are: ; Acceleration time (s), typical value 0.2s. Acceleration / deceleration time (s), typical value 0.2s. Within the first 0.2s... linearly increasing from 0 to Maintain during the intermediate stage The speed drops to 0 within 0.2 seconds, and finally the speed transitions to zero through integration. All calculations are performed through microcontroller embedded programs, requiring no complex hardware support.

[0210] For example, in the local control proposal generated by a microcontroller, the motion speed... Current gate speed stationary state, preset acceleration time Maximum angular acceleration .because , Therefore, the expected angular acceleration That is, the door will accelerate from rest to 0.5 rad / s².

[0211] Traditional escape door drives often use fixed acceleration, which can easily cause the door to shake when starting or jerk when stopping, potentially causing discomfort to personnel or mechanical wear. This step calculates the expected angular acceleration through preset planning parameters to ensure a smooth transition of door speed, which not only improves the comfort of personnel evacuation but also reduces the impact loss of the door's mechanical structure and extends the service life of the equipment.

[0212] Sb02, the theoretical driving torque is determined based on the door dynamics model.

[0213] The theoretical driving torque is the sum of the product of the door's moment of inertia and the expected angular acceleration in the door dynamics model and the estimated frictional resistance torque in the door dynamics model.

[0214] Understandably, the door dynamics model is a physical model describing the torque-acceleration-resistance relationship during door rotation, used to quantify the power required to drive the door; the door's moment of inertia is the core parameter of the door dynamics model (unit: kg・m²), reflecting the magnitude of the door's inertia during rotation, and is related to the door's mass and size distribution; the estimated frictional resistance torque is the torque value (unit: Nm) used in the model to characterize the mechanical friction (such as friction between hinges and gears) during door rotation, which needs to be preset or estimated online in conjunction with the door's mechanical characteristics; the theoretical driving torque is the torque required to drive the door to the expected angular acceleration, equal to the product of the moment of inertia and the expected angular acceleration, plus the estimated frictional resistance torque, which is the theoretical value of the door's power demand.

[0215] Alternatively, the door dynamics model may employ a simplified second-order rotational dynamics equation: ;

[0216] in, J is the theoretical driving torque, and J is the moment of inertia of the door (which can be calculated from the door design parameters, such as a door leaf with a mass of m=50kg and a width of L=1m). , For the expected angular acceleration, To estimate the frictional resistance torque (which can be calibrated experimentally, for example, when the door rotates under no-load, it is measured that a torque of 2 Nm is required to maintain a speed of 0.3 rad / s), To improve accuracy, the gate's historical operating data can be used for online correction. For example, when the torque corresponding to the actual current deviates from the theoretical value by more than 10%, fine-tuning is required. .

[0217] For example, following example Sb1, the moment of inertia of the door. Expected angular acceleration Predicting frictional resistance torque Substituting into the dynamic model formula, we get: In other words, to drive the door to achieve the expected acceleration effect, theoretically, a torque of 10.35 Nm is required.

[0218] This step quantifies the door's power requirements through a dynamic model, addressing the problem of traditional drives relying on experience to set torque: traditional escape door drives often use fixed torque output, which may result in insufficient torque leading to slow door start-up, or excessive torque causing motor overload; this step calculates the theoretical drive torque through a model, ensuring that the output torque can meet the door's acceleration requirements without blindly increasing the torque, leading to energy waste or equipment overload, thus providing a reasonable benchmark for subsequent torque limits.

[0219] Sb03 maps the door health evaluation value to the torque limitation coefficient.

[0220] The lower the door health rating, the smaller the corresponding torque limiting coefficient. The door health rating is a quantitative indicator (range 0-1) generated in operation A, reflecting the operating status of components such as the door drive motor and mechanical structure. A higher value indicates better door health, while a lower value indicates a risk of door malfunction (such as motor overheating or mechanical jamming). The torque limiting coefficient (range 0-1) is a coefficient converted from the door health rating and used to limit the output of the theoretical driving torque. The two are positively correlated; that is, the lower the door health rating, the smaller the torque limiting coefficient, preventing the door from bearing excessive load when its health is poor.

[0221] Optionally, the mapping relationship can be a linear mapping or a piecewise linear mapping to ensure that the lower the health level, the smaller the coefficient.

[0222] If linear mapping is selected, the torque limit coefficient can be set directly. ( (For the health evaluation value of the portal vein), such as hour, ;

[0223] If piecewise linear mapping is selected, a preset health and safety threshold (e.g., 0.7) is used. hour, (The body is healthy and no restrictions are required); when hour, (Linear transition); when hour, (Strictly limited).

[0224] Traditional drives do not consider the health status of the door, and will continue to output rated torque even if the motor is overheated, which can easily lead to the aggravation of the fault. This step dynamically adjusts the limiting coefficient according to the health status. When the health status is low, the output torque is reduced to prevent motor overload and accelerated wear of mechanical structure. While ensuring the basic operation of the door, it extends the service life of the faulty door and reduces the risk of door jamming in emergency scenarios.

[0225] Sb04 uses the product of the theoretical driving torque and the torque limitation coefficient as the driving torque in the local control proposal corresponding to the microcontroller.

[0226] Understandably, the theoretical driving torque is the theoretical value of the door's power demand calculated in Sb2, reflecting the ideal torque required for the door to reach the expected motion state; the torque limit coefficient is the door's health state constraint coefficient generated in Sb3, reflecting the proportion of the maximum torque that the door can currently withstand; the driving torque in the local control proposal is the final output torque parameter (unit: Nm) that can be directly used to drive the motor, obtained by multiplying the theoretical torque by the limit coefficient, which satisfies both the basic power demand of the door's motion and the constraints of the door's health state.

[0227] Optionally, the calculation process uses direct multiplication: ;

[0228] in, This refers to the driving torque in the local control proposal.

[0229] If the calculated result is less than the torque required to maintain the minimum movement of the door (e.g., 1 Nm), then... Set to 1 Nm to avoid insufficient torque causing the door to stop;

[0230] If the health evaluation value of the gate is extremely low (e.g.) When the torque limiting factor is less than 0.2, then... If the torque is less than 1 Nm, a door malfunction alarm can be triggered, and the torque will be set to 0 to prevent damage caused by forced door movement.

[0231] This step achieves a balance between power demand and equipment protection, solving the problem of disconnect between traditional drive torque output and gate status: traditional drives either output a fixed torque or only passively protect against overload, failing to actively adapt to the gate's health status; this step combines theoretical torque with limiting coefficients to ensure that the gate outputs sufficient torque to improve efficiency when healthy, and reduces torque to protect the equipment when unhealthy. Especially in emergency evacuation scenarios, it can both prevent gate jamming due to insufficient torque, thus avoiding equipment failure due to excessive torque, and improve the safety and reliability of control.

[0232] In one exemplary embodiment, such as Figure 6 As shown, in S2, based on all acquired local control suggestions, a final global control command is generated through linkage analysis, including:

[0233] S21: Determine the decision confidence weights for each microcontroller.

[0234] In S21, the decision confidence weight of each microcontroller is determined by the environmental perception confidence, contact perception confidence, and system health confidence of each microcontroller.

[0235] Among them: the environmental perception confidence is determined based on the spatial gradient entropy formed by the predicted temperature distribution and the predicted smoke concentration distribution corresponding to each microcontroller; the smaller the spatial gradient entropy, the higher the environmental perception confidence; the contact perception confidence is negatively correlated with the obstacle contact evaluation value; the system health confidence is positively correlated with the door health evaluation value.

[0236] Optional, environmental perception confidence calculation: first, calculate the predicted temperature distribution. and predict smoke concentration distribution Find the spatial gradient, such as the two-dimensional gradient. ;

[0237] Next, calculate the probability distribution of the gradient magnitude. According to the information entropy formula Calculate the spatial gradient entropy (take the average of temperature and smoke entropy).

[0238] Finally, the gradient entropy is converted into environmental perception confidence through a linear mapping. (e.g., when the gradient entropy H ≤ 0.5) , hour ).

[0239] Among them, contact perception confidence Linear negative correlation mapping is used: ( (This refers to the obstacle contact evaluation value).

[0240] Among them, system health confidence Directly equal to the health evaluation value of the portal vein. .

[0241] Ultimately, the decision confidence weights Multiplication fusion is employed: This ensures that when any sub-confidence is low, the overall weight is significantly reduced, thus adapting to the real-time computing capabilities of the microcontroller.

[0242] In this embodiment, the gradient operator (Prewitt) used to calculate the spatial gradient entropy can be: ;

[0243] The gradient operator in the x-direction is used to detect changes in the horizontal direction. This is the gradient operator in the y-direction, used to detect changes in the vertical direction.

[0244] The gradient magnitude used to calculate the spatial gradient entropy is: ;

[0245] Gradient magnitude represents the intensity of spatial variation.

[0246] The Shannon entropy used to calculate the spatial gradient entropy is: ;

[0247] Information entropy represents the degree of disorder in the gradient distribution. is the probability that the gradient magnitude falls into the k-th interval, and m is the number of intervals, typically 5.

[0248] The Shannon entropy used to calculate the spatial gradient entropy is: ;

[0249] For temperature gradient entropy, The smoke gradient entropy, This is the comprehensive spatial gradient entropy.

[0250] This step addresses the shortcoming of treating all local suggestions equally in global decision-making: traditional solutions do not distinguish the reliability of microcontroller perception and are easily swayed by abnormal data (such as false alarms from faulty sensors); this step determines the weights through multi-dimensional confidence fusion, allowing microcontroller suggestions that are accurate in environmental prediction, reliable in obstacle detection, and healthy in the door to take the lead, filtering low-reliability data from the source, improving the basic reliability of global decision-making, and preventing a single abnormal microcontroller from affecting the overall instructions.

[0251] S22: Based on the decision confidence weights of each microcontroller, the target opening angle, motion speed and driving torque in each local control proposal are weighted and fused to obtain the preliminary global control command.

[0252] The weighted fusion process involves calculating the target opening angle, motion speed, and driving torque in the local control proposal by weighting the decision confidence weights of each microcontroller. The higher the weight of the microcontroller, the greater its contribution to the global result. The preliminary global control command is a temporary control command generated after fusion, which includes the preliminary global opening angle, preliminary global motion speed, and preliminary global driving torque. It has not yet undergone safety verification and serves as the input basis for subsequent verification.

[0253] Optionally, a weighted average algorithm can be used to fuse the three control parameters separately, with the formula being uniformly as follows: ;

[0254] in, Number of microcontrollers, local parameters For the first Local suggested parameters for each microcontroller (target opening angle) Speed ​​of movement driving torque If the sum of the decision confidence weights of all microcontrollers is 0 (an extreme case where all microcontrollers are unreliable), the arithmetic mean of each local parameter is taken as the initial instruction to avoid no instruction output; if the weight of a certain microcontroller is 0, its local parameters are not included in the calculation. All calculations are implemented through the built-in program of the main controller, requiring only basic arithmetic operations to ensure real-time performance.

[0255] For example, following example S21, the local control suggestions and weights for the three microcontrollers are as follows: Microcontroller microcontroller It is not included in the calculation.

[0256] Calculate the initial global opening angle: (Take 72°); Preliminary global motion velocity: (Take 0.32 rad / s); Preliminary global driving torque: (Take 8 Nm).

[0257] Traditional solutions may be affected by the microcontroller's recommendations in a high-risk area (such as false alarms of full opening). This step, through weight allocation, allows the conservative recommendations of reliable microcontrollers (such as M2's 70° opening) to dominate the overall instructions. This takes into account the differences in operating conditions in each area and avoids being swayed by low-reliability recommendations. It ensures that the initial instructions conform to the majority of reliable sensing results and also reserves adjustment space for subsequent safety verification.

[0258] S23: Perform security verification on the preliminary global control command; if the verification passes, the preliminary global control command is used as the final global control command; if the verification fails, a predefined conservative security strategy is adopted to generate the final global control command through linkage analysis.

[0259] S23 includes a safety verification of the initial global control command, which includes: calculating a global safety score based on the environmental hazard evaluation value, obstacle contact evaluation value, and door health evaluation value of each microcontroller; if the global safety score is lower than the preset safety threshold, or if the contact force data of any microcontroller exceeds the emergency threshold, the verification is deemed to have failed and a conservative safety strategy is triggered.

[0260] The conservative safety strategy stipulates that: the global motion speed in the final global control command is the minimum of the motion speeds in all local control suggestions; the global opening angle in the final global control command is the minimum of the target opening angles in all local control suggestions; and the global driving torque in the final global control command is the minimum of the driving torques in all local control suggestions.

[0261] Understandably, safety verification is a check to determine whether there are safety risks in the initial global control commands. It is judged from two dimensions: global safety score and emergency contact force threshold. The global safety score is the lowest level of safety status of all microcontrollers, calculated from the environmental hazard evaluation value, obstacle contact evaluation value, and door health evaluation value of each microcontroller (usually the minimum value of the comprehensive safety evaluation value of each microcontroller). A score lower than the preset safety threshold indicates a serious safety hazard. The emergency contact force threshold is the critical value of the contact force (far higher than the normal contact force). If any microcontroller detects a contact force exceeding this threshold, it means that there is an insurmountable obstacle in the door path (such as a collapsed object or severe crushing of people). The conservative safety strategy is a fallback plan when verification fails. It stipulates that the movement speed, target opening angle, and driving torque of the final global control command are all taken as the minimum value among all local control suggestions to ensure absolute safety under extreme risks.

[0262] Optional, global security score calculation: First, calculate the comprehensive security evaluation value of each microcontroller using a weighted average. For the first The environmental hazard assessment value of each microcontroller is weighted according to the following order: environment first, obstacle second, health supplementation. Then all... The minimum value is used as the global security score. The preset safety threshold is set to 0.35 (calibrated by simulating emergency scenarios; values ​​below this indicate a fatal risk); the emergency contact force threshold is set to 50N (far exceeding the normal contact force of 10N; exceeding this will trigger emergency obstacle avoidance).

[0263] The verification rule is: if Or contact force of any microcontroller If the verification fails, a conservative security policy will be implemented, namely:

[0264] Final global opening angle ,

[0265] Final global motion speed ,

[0266] Final global driving torque ;

[0267] If the verification passes, the preliminary instruction is directly used as the final instruction.

[0268] This step serves as a safety fallback mechanism for global decision-making, addressing the issue of traditional solutions directly executing after integration and ignoring hidden risks. Traditional solutions may lead to accidents due to initial instructions failing to consider local fatal risks (such as M1's low safety score). However, this step, through dual verification of global score and emergency threshold, can promptly identify extreme risks and trigger conservative strategies, namely, minimizing opening to avoid collisions, minimizing speed to reduce damage, and minimizing torque to protect equipment. Even in complex scenarios with inconsistent risks in multiple areas, it can ensure the absolute safety of door operation, preventing personnel injury or equipment damage due to decision-making errors, and providing a final safety guarantee for emergency evacuation.

[0269] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0270] Based on the same inventive concept, this application also provides an escape door linkage system based on fire signals, including a main controller and multiple microcontrollers; the multiple microcontrollers are deployed in different monitoring areas of the door and are communicatively connected to the main controller;

[0271] The main controller is configured to: acquire local control suggestions corresponding to each microcontroller; generate a final global control command based on all acquired local control suggestions through linkage analysis; and send the final global control command to the door drive motor to control the door to perform corresponding opening and closing actions.

[0272] Each microcontroller is configured to perform the following operations on fire signals within its monitoring area: fire signals include temperature data, smoke concentration data, contact force data, and door status data: Operation A: Mapping temperature data and smoke concentration data to environmental hazard assessment values, mapping contact force data to obstacle contact assessment values, and mapping door status data to door health assessment values; Operation B: Based on the environmental hazard assessment values, obstacle contact assessment values, and door health assessment values, generating local control suggestions corresponding to the microcontroller; wherein, the local control suggestions include target opening angle, movement speed, and driving torque.

[0273] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0274] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0275] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A fire signal-based escape door linkage method, characterized by, The method is executed by a general controller which is communicatively connected to a plurality of microcontrollers deployed in different monitoring areas of a door body; the method comprises the following steps: S1: obtaining local control suggestions corresponding to each microcontroller; S2: based on all the obtained local control suggestions, generating a final global control instruction through linkage analysis; S3: sending the final global control instruction to a door body driving motor to control the door body to perform corresponding opening and closing actions; Wherein, the local control suggestion of each microcontroller is generated by the microcontroller through the following operations on the fire signals in the monitoring area where the microcontroller is located, including temperature data, smoke concentration data, contact force data and door body state data: Operation A: mapping the temperature data and the smoke concentration data into an environmental danger degree evaluation value, mapping the contact force data into an obstacle contact evaluation value, and mapping the door body state data into a door body health degree evaluation value; Operation B: based on the environmental danger degree evaluation value, the obstacle contact evaluation value and the door body health degree evaluation value, generating the local control suggestion corresponding to the microcontroller; wherein, the local control suggestion includes target opening angle, movement speed and driving torque.

2. The method of claim 1, wherein, In the operation A, the temperature data and the smoke concentration data are mapped into an environmental danger degree evaluation value, including: Input the temperature data and the smoke concentration data into the coupled heat diffusion sub-model and particle diffusion sub-model; Determine the predicted temperature distribution of the door body surrounding space corresponding to the monitoring area through the heat diffusion sub-model; Input the thermal buoyancy effect generated by the predicted temperature distribution into the particle diffusion sub-model as a driving source to jointly solve and obtain the predicted smoke concentration distribution; Convert the predicted temperature distribution and the predicted smoke concentration distribution into the environmental danger degree evaluation value through a nonlinear mapping function; Wherein, the diffusion coefficient in the particle diffusion sub-model for characterizing the diffusion speed is dynamically adjusted according to the real-time change rate of the temperature data; when the real-time change rate is detected to increase, the diffusion coefficient is correspondingly increased.

3. The method of claim 1, wherein, In the operation A, the contact force data is mapped into an obstacle contact evaluation value, including: Based on the current contact force data collected in the monitoring area, determine the obstacle contact evaluation value through a preset mapping function; Wherein, the mapping function is configured to output a preset high safety evaluation value when the contact force in the contact force data is zero; When the contact force is greater than zero, the obstacle contact evaluation value decreases with the increase of the contact force; The decreasing mode is a monotonic decrease from the high safety evaluation value to a low safety evaluation value.

4. The method of claim 1, wherein, In the operation B, based on the environmental danger degree evaluation value, the obstacle contact evaluation value and the door body health degree evaluation value, the target opening angle in the local control suggestion corresponding to the microcontroller is generated, including: Input the environmental danger degree evaluation value into a preset fire condition-opening degree mapping function to obtain a basic safety opening degree; The base safety opening degree is corrected based on the obstacle contact evaluation value and the door body health degree evaluation value, to obtain a target opening angle in the local control suggestion corresponding to the microcontroller; The obstacle contact evaluation value is used to reduce the base safety opening degree to avoid obstacles, and the door body health degree evaluation value is used to limit the base safety opening degree to ensure the safety of door body movement.

5. The method of claim 1, wherein, In the operation B, the movement speed in the local control suggestion corresponding to the microcontroller is generated based on the environmental risk degree evaluation value, the obstacle contact evaluation value, and the door body health degree evaluation value, including: A comprehensive safety evaluation value is determined based on the environmental risk degree evaluation value, the obstacle contact evaluation value, and the door body health degree evaluation value; When the comprehensive safety evaluation value is lower than a first safety threshold, the movement speed in the local control suggestion corresponding to the microcontroller is set to zero; When the comprehensive safety evaluation value is between the first safety threshold and a second safety threshold, the movement speed is non-linearly increased from zero to a reference safety speed as the comprehensive safety evaluation value increases; When the comprehensive safety evaluation value is higher than the second safety threshold, the movement speed is set to the reference safety speed.

6. The method of claim 5, wherein, In the operation B, the driving torque in the local control suggestion corresponding to the microcontroller is generated based on the environmental risk degree evaluation value, the obstacle contact evaluation value, and the door body health degree evaluation value, including: An expected angular acceleration is determined based on the movement speed and preset acceleration planning parameters; A theoretical driving torque is determined according to a door body dynamics model; wherein the theoretical driving torque is the sum of the product of the door body moment of inertia in the door body dynamics model and the expected angular acceleration and the estimated friction torque in the door body dynamics model; The door body health degree evaluation value is mapped to a torque limitation coefficient, wherein the lower the door body health degree evaluation value, the smaller the mapped torque limitation coefficient; The product of the theoretical driving torque and the torque limitation coefficient is taken as the driving torque in the local control suggestion corresponding to the microcontroller.

7. The method of claim 2, wherein, In the S2, based on all the local control suggestions obtained, a final global control instruction is generated through linkage analysis, including: S21: determining a decision confidence weight corresponding to each microcontroller; S22: based on the decision confidence weight corresponding to each microcontroller, weighting and fusing the target opening angle, the movement speed, and the driving torque in each local control suggestion to obtain a preliminary global control instruction; S23: performing safety verification on the preliminary global control instruction; if the verification passes, the preliminary global control instruction is taken as the final global control instruction; if the verification fails, a predefined conservative safety strategy is used to generate the final global control instruction.

8. The method of claim 7, wherein, The decision confidence weight of each microcontroller in the S21 is determined by the environmental perception confidence, the contact perception confidence, and the system health confidence of each microcontroller; wherein: The environment-aware confidence is determined based on a spatial gradient entropy formed by a predicted temperature distribution and a predicted smoke concentration distribution corresponding to each microcontroller; the smaller the spatial gradient entropy, the higher the environment-aware confidence; The contact-aware confidence is negatively correlated with the obstacle contact evaluation value; The system health confidence is positively correlated with the door body health degree evaluation value.

9. The method of claim 7, wherein, The safety check on the preliminary global control instruction in S23 includes: calculating a global safety score based on the environment danger degree evaluation value, the obstacle contact evaluation value, and the door body health degree evaluation value of each microcontroller; If the global safety score is lower than a preset safety threshold, or if the contact force data of any microcontroller exceeds an emergency threshold, it is determined that the check fails, triggering the conservative safety strategy; The conservative safety strategy specifies that the global movement speed in the final global control instruction takes the minimum value of the movement speeds in all the local control suggestions, the global opening angle in the final global control instruction takes the minimum value of the target opening angles in all the local control suggestions, and the global driving torque in the final global control instruction takes the minimum value of the driving torques in all the local control suggestions.

10. A fire signal based escape door linkage system, characterized by, The system includes a total controller and multiple microcontrollers; the multiple microcontrollers are deployed in different monitoring areas of a door body and are communicatively connected to the total controller; The total controller is configured to: obtain local control suggestions corresponding to each microcontroller; based on all the obtained local control suggestions, generate a final global control instruction through linkage analysis; and send the final global control instruction to a door body driving motor to control the door body to perform corresponding opening and closing actions; Each microcontroller is configured to: perform the following operations on fire signals in the monitoring area where the microcontroller is located, the fire signals including temperature data, smoke concentration data, contact force data, and door body state data: Operation A: map the temperature data and the smoke concentration data into an environment danger degree evaluation value, map the contact force data into an obstacle contact evaluation value, and map the door body state data into a door body health degree evaluation value; Operation B: based on the environment danger degree evaluation value, the obstacle contact evaluation value, and the door body health degree evaluation value, generate the local control suggestion corresponding to the microcontroller; wherein the local control suggestion includes a target opening angle, a movement speed, and a driving torque.

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