A smart hotel management method based on an RCU main field scenario

Abstract

The application discloses a kind of based on RCU main field scene intelligent hotel management method, the method includes: obtaining the real-time state parameter of each modular energy-using equipment in guest room calculates the weighted deviation between real-time state parameter and reference state vector, determines scene drift event;Real-time state parameter is decoupled and analyzed, and the main effective dimension of drift is identified, and the correction instruction set for corresponding modular energy-using equipment is generated;Retrieve the electrical characteristic parameter of to-be-acted equipment, based on electrical characteristic parameter, the transient power superposition prediction of correction instruction set is carried out, and the peak transient power of correction instruction set under concurrent execution condition is obtained;When the degree that peak transient power exceeds safe bearing baseline meets preset risk condition, the peak timing scheduling of correction instruction set is carried out.Therefore, while guaranteeing the smoothness of scene switching and the comfort of guest body, the power supply safety hidden danger caused by the concurrent start of multiple devices is reduced.

Description

Technical Field

[0001] This invention relates to the field of smart hotel management technology, and in particular to a smart hotel management method based on an RCU host scenario. Background Technology

[0002] In the field of smart hotel management, the RCU (Room Control Unit) serves as the core control unit for guest room equipment, widely used for centralized management and control of modular energy-consuming devices such as lighting, air conditioning, curtains, and audio-visual equipment in guest rooms. Currently, scene control modes based on the RCU are widely adopted. By pre-setting various scene modes (such as welcome mode, sleep mode, reading mode, and movie-watching mode), guests can trigger the RCU to issue corresponding status setting commands to various energy-consuming devices with a single click, thereby achieving rapid switching and unified adjustment of the guest room environment, enhancing the guest's stay experience and the hotel's intelligent service level.

[0003] However, existing RCU (Role Control Unit) hosts typically employ a "single-trigger, statically fixed" execution method for scene control. That is, after receiving a scene switching command, the RCU host adjusts each device to the preset state value corresponding to that scene. The system then assumes the current room environment is stable in the standard state of that scene until the next explicit scene switching command arrives. During this process, guests often manually adjust certain devices based on their immediate sensory preferences, such as adjusting the air conditioning temperature setting, changing the angle or brightness of reading lights, or adjusting the curtain opening. These localized adjustments cause a discrepancy between the actual operating state of the devices in the room and the standard scene state recorded by the RCU host's logic layer. When the guest triggers a scene switch again, the RCU host performs differential calculations based on the distorted logic state and generates control commands. This results in the actual environment after the scene switch not matching the guest's expectations, manifesting as a sluggish switching process, inaccurate target environment, and other degraded experience issues. It also affects the stability and reliability of the hotel's overall intelligent services. Summary of the Invention

[0004] This application provides a smart hotel management method based on RCU host scenarios. Through precise perception of scene drift events and coordinated control of peak suppression of transient power surges, it eliminates power safety hazards and cumulative electrical stress damage to equipment caused by concurrent startup of multiple devices while ensuring smooth scene switching and guest comfort. It enables dynamic perception and adaptive correction of guest room scene states, while ensuring power safety during the collaborative operation of multiple devices.

[0005] This application provides a smart hotel management method based on an RCU host scenario, including: S101: Obtain the real-time status parameters of each modular energy-consuming device in the guest room, construct the baseline state vector of the currently activated scene, calculate the weighted deviation between the real-time status parameters and the baseline state vector, and determine the scene drift event. S102, in response to the scene drift event, performs contribution decoupling analysis on the real-time state parameters, identifies at least one dimension whose deviation contribution rate meets the preset conditions as the main effect dimension of drift, and generates a set of correction instructions for the corresponding modular energy-consuming equipment. S103, retrieve the electrical characteristic parameters of the device to be operated, perform transient power superposition prediction on the correction instruction set based on the electrical characteristic parameters, and obtain the peak transient power of the correction instruction set under concurrent execution conditions; S104 When the peak transient power exceeds the safe bearing baseline to the extent that the preset risk conditions are met, the correction instruction set is staggered and then sent to each modular energy-consuming device for execution.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: By constructing a weighted deviation perception mechanism between the scene baseline vector and real-time state parameters, and combining the dual-condition judgment logic of time inertia delay window and accumulated drift energy, scene drift events can be accurately determined under the premise of excluding instantaneous disturbances. Furthermore, the deviation contribution rate of each energy consumption dimension is decoupled and analyzed, thereby achieving precise positioning of the main effect dimension of scene drift. This provides a clear directional basis for the generation of correction instructions, thus ensuring the effectiveness of the scene correction process.

[0007] By introducing a set of electrical characteristic parameters including rated power, load inertia type, power ramp-up time constant, and starting inrush current multiple, transient power superposition prediction is performed on the correction command set. This allows for a quantitative assessment of the transient power surge risk caused by concurrent execution of multiple devices before the actual command is issued. Based on this, when the peak transient power exceeds the safe bearing baseline, a multi-objective constrained staggered timing optimizer allocates a transmission delay time to each command, enabling the correction commands to be executed sequentially on the time axis. This eliminates the transient power spike impact caused by concurrent startup of multiple devices at its source, avoids power line overload and non-faulty operation of protection devices, and reduces the electrical stress accumulation rate of power switching devices, thus extending the service life of the equipment.

[0008] During peak-shifting scheduling, by introducing a closed-loop power envelope monitoring thread to compare the measured power with the predicted power in real time, an emergency insertion suppression mechanism can be dynamically activated when unexpected disturbances such as power supply voltage fluctuations occur, ensuring the robustness of scheduling. At the same time, by reserving a soft-start overlap area at the end of the scheduling sequence and staggering the arrival time of steady-state power in a pulse-width modulation manner, the smoothness of the power curve is further optimized, avoiding steep rises in the power plateau period.

[0009] By reading back the end physical feedback values ​​of each device after the correction is performed as calibration status parameters, and replacing the original reference status vector with these calibration status parameters, dynamic alignment between the scene reference and the actual physical environment of the guest room is achieved. This ensures that the differential calculations for subsequent scene switching are all based on real physical anchor points, avoiding continuous cumulative deviations caused by reference distortion. At the same time, by recording key scheduling execution data into the long-term health profile library of the RCU host, data support is provided for equipment aging trend analysis and preventive maintenance.

[0010] In summary, this solution addresses the issues of scene state mismatch caused by local manual adjustments and transient power surges during concurrent execution of multiple devices in existing RCU host scene management, achieving end-to-end closed-loop management of scene state awareness, safe execution of correction instructions, and dynamic self-sustaining of the reference vector. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the smart hotel management method based on an RCU host scenario according to an embodiment of the present invention. Detailed Implementation

[0012] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0013] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0015] In existing smart hotel management systems based on RCU hosts, scene control generally adopts a "single-trigger, statically fixed" command issuance mode. That is, after receiving scene signals such as "sleep mode" or "reading mode", the RCU host assumes that the guest room energy environment has been stably anchored to the standard parameter range corresponding to that scene, until the next explicit scene switching command arrives.

[0016] However, a pseudo-steady-state phenomenon has long been overlooked in actual check-in scenarios: during the active phase of a scene, guests often make minor adjustments to local devices based on instantaneous sensory preferences (such as lowering the air conditioner by 2°C, turning the reading light, or manually turning off the corridor spotlights). At this time, the logic layer of the RCU host stubbornly believes that it is currently in the initial scene, while the actual energy consumption state of the physical layer has deviated significantly (i.e., scene drift). This cognitive disconnect may lead to: on the one hand, when the guest triggers a scene switch again, the RCU will perform differential calculations based on the distorted initial state, inevitably outputting abnormal control commands, resulting in abrupt or even chaotic scene switching; on the other hand, if the energy-saving comparison strategy based on the total budgeted energy consumption fails completely due to inaccurate sampling base points.

[0017] Furthermore, even if the RCU host identifies the aforementioned drift through intelligent algorithms and initiates proactive correction (such as step-back), in pursuit of rapid response, the system often simultaneously issues state change commands to multiple high-inertia loads such as air conditioning compressors, multi-channel dimming circuits, and electric curtain motors within a very short time window. This concurrent command storm triggers severe transient power spikes at the electrical physical layer, which not only easily triggers non-faulty thermal tripping of miniature circuit breakers in guest room power distribution branches, but also accelerates the aging of electrical contacts in the power relay array inside the RCU host, significantly shortening the equipment's service life.

[0018] Example 1: Figure 1 This is a flowchart illustrating the smart hotel management method based on an RCU host scenario according to an embodiment of the present invention.

[0019] like Figure 1 As shown, a smart hotel management method based on an RCU host scenario includes the following steps: S101, obtain the real-time status parameters of each modular energy-consuming device in the guest room, construct the reference state vector of the currently activated scene, calculate the weighted deviation between the real-time status parameters and the reference state vector, and determine the scene drift event.

[0020] In some embodiments, obtaining the real-time status parameters of each modular energy-consuming device in the guest room specifically includes: The RCU host communicates via a data bus (such as RS-485 or CAN bus) at a preset sampling period. (Preferably, Δt = 500ms) A status readback command is sent to the end actuators of each modular energy-consuming device in the guest room. The status readback command is used to obtain the physical feedback value or mechanical limit feedback value directly measured by the end sensor of the actuator, rather than relying on the expected value under open-loop control; Let there be n dimensions to the modular energy-consuming devices in the guest room that participate in scene management. The real-time state parameters collected by the system at the current time t are represented in vector form as follows: , in, This represents the actual physical quantification value of the i-th dimension at time t (e.g., the actual dimming duty cycle of a lighting circuit, the normalized value of the actual return air temperature and the set temperature difference of an air conditioning system, the actual travel percentage of an electric curtain, etc.). The value range of each dimension is pre-standardized to the interval [0,1] to eliminate the order-of-magnitude differences between different physical dimensions. It should be noted that... The value must be a converted physical quantity directly measured at the sensor end or a mechanical limit feedback value from the actuator, not a set value in the RCU's internal register. For example, for motorized curtains, The percentage of travel is derived from the pulse count accumulated by the motor Hall sensor. This value can effectively reflect the displacement deviation caused by mechanical jamming or manual dragging.

[0021] Specifically, constructing the baseline state vector of the currently active scene includes: Retrieve the currently active scene from the standardized scene feature vector library pre-stored in the non-volatile storage partition. Corresponding reference state vector: , in, Indicates in the scene The standard expected value of the i-th dimension. The scene feature vector library contains at least the baseline vectors corresponding to various preset scenes such as welcoming mode, sleep mode, reading mode, and movie viewing mode.

[0022] The n dimensions can be flexibly selected according to the actual equipment configuration of different hotel rooms, and are not limited to the specific number in the following example. The following uses 5 typical dimensions as an example for illustration, but does not impose any limitation on the value of n.

[0023] As an example, the scene feature vector library is pre-stored, including: the system pre-abstracts and quantizes all modular energy-consuming devices in hotel rooms controlled by the RCU host, defining each preset scene (such as welcome mode, sleep mode, reading mode, and movie-watching mode) as a reference centroid point in a high-dimensional space. Specifically, n key energy-consuming dimensions are selected to construct the device feature space, and the quantization mapping rules for each dimension are as follows: Lighting environment dimension : Characterizes the overall illuminance level and color temperature tendency of the guest room, with a value range standardized to [0,1], and mapped from the average feedback duty cycle of each loop dimming driver; Thermal comfort dimension : Characterizes the operating status of the air conditioning system, and is composed of the normalized difference between the target temperature setpoint and the current return air temperature, coupled with the fan coil unit speed range coefficient; Shading Dimensions : Characterizes the opening degree of motorized curtains or blinds, using the percentage of curtain displacement travel as the quantitative basis; Audio and video dimensions : Represents the playback status and loudness level of background music or television system; Airflow interruption dimension : Indicates the opening and closing status and power ratio of the fresh air valve or exhaust fan; For the j-th preset scenario Its standard reference vector is represented as The baseline vectors of all preset scenes constitute a standardized scene feature vector library. It is stored in the non-volatile storage partition of the RCU host.

[0024] Specifically, calculating the weighted deviation between the real-time state parameters and the reference state vector includes: The weighted scene deviation D(t) at time t is defined as: , in, Indicates the current active scenario The pre-set scene sensitivity weight coefficient for the i-th dimension follows the principle of "high sensitivity for core elements and low sensitivity for auxiliary elements," and is configured differently in different scenarios. For example, in sleep mode, the light environment dimension... Harmony and environmental dimensions The weighting coefficient was set to an extremely high value (0.35), significantly higher than that of the thermal comfort dimension. , Because the human body is less sensitive to temperature changes, its weighting coefficient is set to a low value (0.10); conversely, in reading mode, the weighting coefficients for the light environment dimension and the shading dimension are set to a higher value. The weights are increased synchronously; the weight coefficients of all dimensions satisfy... ; This represents the normalization scaling factor for the current i-th dimension, used to eliminate the interference of differences in physical dimensions and value ranges on the deviation calculation. It is a constant positive number obtained in advance through statistical calibration. Characterizes the actual offset of this dimension at time t; as an example, the normalized scaling factor The calibration method is as follows: Under the condition that each modular energy-consuming device in the guest room is in a standard installation state and there is no abnormal disturbance, the RCU host continuously collects the state value sequence of the i-th dimension within a preset calibration time period (preferably 24 hours) at the sampling period described in step S101, calculates the standard deviation of the sequence, and uses the standard deviation as... The value of ; under different guest rooms or different equipment configurations Each device is independently calibrated and stored in the device management database.

[0025] In some embodiments, to avoid frequent misjudgments by the system due to the instantaneous, unconscious actions of guests (such as touching a switch while passing by), the conditions for determining scene drift events are set as a dual-condition judgment logic of time inertia delay window and accumulated drift energy, specifically including: Set the time inertia delay window (Preferred) =30s), defined as the cumulative drift energy within the window. for: , A real scene drift event is determined to have occurred if and only if both of the following conditions are met simultaneously: Condition 1 (Instantaneous Exceedance): The instantaneous deviation D(t) at the current moment is greater than the first preset threshold. (Preferred value: 0.25); Condition 2 (Cumulative Confirmation): Within a continuous time window Accumulated drift energy within Greater than the second preset threshold (Preferred value: 4.5); For example, the aforementioned threshold parameters are obtained in advance by statistically fitting multiple sets of guest behavior operation data under different scenario types, and stored in the configuration partition of the RCU host; wherein, the first preset threshold The second preset threshold is determined based on the 95% confidence upper limit of the instantaneous deviation in a drift-free state. The value is based on the 90% confidence limit of the accumulated drift energy in the drift-free state; at the same time, the various threshold parameters allow engineers to make differentiated adjustments based on the actual equipment layout and scene configuration of the guest room through the host computer software.

[0026] The aforementioned dual-condition mechanism is used to filter out instantaneous spike disturbances (such as momentary high deviations caused by accidental touches that then recover), ensuring that only continuous and cumulative state changes are recognized as scene drift events.

[0027] S102, in response to the scene drift event, performs contribution decoupling analysis on the real-time state parameters, identifies at least one dimension whose deviation contribution rate meets the preset conditions as the main effect dimension of drift, and generates a correction instruction set for the corresponding modular energy-consuming equipment.

[0028] In some embodiments, after confirming that a scene drift event has been triggered, the decoupling analysis of the contribution of real-time state parameters specifically includes: Define the i-th dimension with respect to the current total deviation. Deviation contribution rate for: , in, Let be the actual offset of this dimension at time t, with the numerator being the weighted squared deviation value of the i-th dimension and the denominator being the sum of the weighted squared deviation values ​​of all dimensions. Output as a percentage, reflecting the proportion of each dimension's contribution to the current scene distortion; Contribution rate of each dimension Sort the data in descending order from high to low, and accumulate them sequentially. Select the top few dimensions whose cumulative contribution rate reaches the preset percentage P (preferably 85%) and mark them as the main effect dimensions of drift. Simultaneously, drift types are categorized and labeled based on the physical attributes of the main effects of drift: if the main effects are concentrated in the thermal comfort and airflow dimensions, it is classified as comfort preference drift; if concentrated in the lighting environment and shading dimensions, it is classified as activity scenario drift. These classifications are used for the differentiated selection of subsequent correction strategies.

[0029] Optionally, to improve the sensitivity of the direction of the body's perceived comfort, a direction sensitivity correction coefficient is introduced. This is used to differentiate the different degrees of disruption to scene comfort caused by positive and negative deviations. For example, in heating mode, a negative deviation where the actual temperature is lower than the set value significantly impairs sleep quality than a positive deviation; therefore, a higher penalty coefficient is assigned to negative deviations. Therefore, the actual offset... To reflect the different impacts of different deviation directions on comfort, a direction sensitivity correction is applied to the actual offset, resulting in a corrected offset. The corrected offset is defined as follows: , in, This is the actual offset before correction. It is a dimensionless positive coefficient. When the deviation direction is unfavorable to maintaining comfort (such as when the actual temperature is lower than the set temperature under heating conditions). >1 amplifies the penalty; when the deviation direction does not significantly impair comfort, ≤1. The corrected offset As the new actual offset Perform the relevant calculations.

[0030] In some embodiments, generating a set of correction instructions for the corresponding modular energy-consuming device specifically includes: Based on the main dimensions of the drift-causing effect and their corresponding actual deviation values Generate a set of correction instructions to be executed. Where q is the number of devices to be acted upon, each The target state correction value (e.g., target brightness percentage, target travel position, or target temperature setpoint) corresponds to the i-th modular energy-consuming device to be activated.

[0031] It should be noted that this step generates differentiated scene correction schemes based on the positioning and classification results of step S101. However, instructions are not immediately issued to the executor at this time; instead, the correction intent is encapsulated into an intermediate instruction set. The system has two built-in parallel correction paths: passive synchronization and active guidance. Passive synchronization path: When the drift amplitude is in the moderate range (i.e. Preferably, When the value is 0.65, the system determines that the guest's manual intervention has a clear personalized intention; at this time, the RCU host does not forcibly revert to the physical state of the device, but generates a temporary reference vector. This vector is compared with the original active scene. Associate and bind the data, and cache it as a personalized sub-scene, serving as the actual starting point for subsequent switching; the temporary reference vector generated in the passive synchronization path It is used as a benchmark for subsequent differential calculations during the current scene activation period; Active guidance path: When the drift amplitude exceeds the limit (i.e. When the system determines that the actual state has deviated significantly from the safe and comfortable range, active intervention is required. At this point, the system calculates the required backtracking step for each major effect dimension causing the drift. Where N is the preset number of steps (preferably, N=5), and these steps, along with the target value, are packaged into a set of correction instructions to be executed. .

[0032] While generating the correction instruction set, the identification and action range of each device to be acted are recorded to provide an input basis for subsequent electrical impact prediction.

[0033] S103, retrieve the electrical characteristic parameters of the device to be operated, perform transient power superposition prediction on the correction instruction set based on the electrical characteristic parameters, and obtain the peak transient power of the correction instruction set under concurrent execution conditions.

[0034] The electrical characteristic parameters include at least rated power, load inertia type, power ramp-up time constant, and starting inrush current multiple.

[0035] In some embodiments, retrieving the electrical characteristic parameters of the device to be operated specifically includes: Before issuing the correction command, retrieve the electrical characteristic parameter sets of all modular energy-consuming devices to be activated (a total of p valid physical activation devices, p≤q) from the device management database. For the i-th device to be activated, its electrical characteristic parameter set... Defined as: , in, This represents the rated steady-state power of the i-th device, in watts (W), and is used to characterize the base power scale of the load. The label representing the load inertia type of the i-th device is a discrete enumeration variable. ∈{resistive inertia, inductive inertia, capacitive inertia, mechanical inertia}, different types correspond to different starting impact characteristics and power ramp-up patterns; The power ramp-up time constant of the i-th device is expressed in milliseconds (ms) and is defined as the time window required from the moment the execution command is issued until the actual power reaches 90% of the steady-state value. This represents the starting inrush current multiple of the i-th device. It is a dimensionless positive number and is defined as the ratio of the peak current at startup to the rated steady-state current.

[0036] As an example, the above set of electrical characteristic parameters is pre-acquired and stored in the equipment management database in the following manner: for rated power and load inertia type It can be directly read and entered from the technical specifications provided by the equipment manufacturer; for the power ramp-up time constant and starting inrush current multiple The power ramp-up time constant is obtained by the RCU host executing an adaptive identification program during the initial installation of the device. Specifically, the adaptive identification program involves the RCU host issuing a standard startup command to the device under test (DUT) and collecting voltage and current waveform data on the power supply bus during startup at a preset sampling rate (sampling interval of 100μs). The time interval from the moment the command is issued to when the actual power reaches 90% of the rated steady-state power is extracted from the current waveform data and used as the power ramp-up time constant for the device. Extract the maximum instantaneous current value that occurs during startup, and calculate the ratio of this maximum instantaneous current value to the rated steady-state current, which will be used as the startup inrush current multiple of the equipment. It should be noted that the standard start command is defined as a step start command from when the device is completely powered off (zero power state) to when the device starts directly at full voltage with rated voltage, so as to ensure the consistency of the measurement conditions of the start-up inrush current. Before the adaptive identification program is executed, the RCU host first cuts off the power supply circuit of the device under test to make it completely powered off, and maintains the power off state for no less than 5 seconds to ensure that the internal energy storage components of the device are fully discharged, and then issues the standard start command and starts waveform acquisition simultaneously.

[0037] The above parameter set is obtained in advance through offline calibration or adaptive learning, and is dynamically refreshed during equipment maintenance and replacement.

[0038] It should be noted that before determining whether each instruction is a valid entity instruction, the correction instruction set is first decomposed into actions: Let p be the number of entity drive instructions in the set of correction instructions to be executed. Calculate the state change amount for each device. ,like Less than the preset dead zone threshold For example, if the brightness change is less than 2% or the temperature change is less than 0.3℃, the instruction is marked as a virtual instruction and removed from subsequent power scheduling; otherwise, it is marked as a valid entity instruction and retained for superposition prediction. This involves calculating the state change amount. Before, it is necessary to and Unify to the same dimensional system; target state correction values ​​for all equipment Similarly, it is pre-standardized to the [0,1] interval, and the standardization rule is the same as... Consistency makes It becomes a dimensionless normalized change, which can be compared with a preset dead zone threshold. Direct comparison.

[0039] In some embodiments, transient power superposition prediction is performed on the modified instruction set based on electrical characteristic parameters to obtain the peak transient power of the modified instruction set under concurrent execution conditions, specifically including: For the remaining p valid entity instructions after filtering, a natural superposition prediction curve of concurrent execution power without scheduling intervention is constructed on the time axis t. Its expression is: , Where p is the number of valid entity instructions remaining after filtering. The unit step function takes a value of 1 when t≥0 and a value of 0 otherwise, and is used to characterize the i-th device receiving the start command at time zero and starting power ramp-up; the exponential term... This describes the physical process of the starting inrush current decaying from its peak value to its steady-state value, specifically the process of the starting inrush current decaying from its peak value to its steady-state value according to a first-order inertial hysteresis law. This exponential model is an engineering approximation for the energy load characteristics of a hotel. The global factor within parentheses describes the instantaneous power-time function of the i-th device during the startup process, starting from... (i.e., peak impact power), asymptotically decaying to (i.e., steady-state power); By algebraically superimposing the above p individual power-time curves on the same time axis, we obtain the total instantaneous power demand curve under ideal concurrent execution conditions. Two key feature values ​​were extracted from the superimposed curve: peak transient power. Peak arrival time : , This serves as a time reference point for subsequent scheduling optimization.

[0040] S104 When the peak transient power exceeds the safe bearing baseline to the extent that the preset risk conditions are met, the correction instruction set is staggered and then sent to each modular energy-consuming device for execution.

[0041] The method for determining the safe bearing baseline is as follows: Obtain the safety bearing baseline of the RCU host power supply branch The power distribution capacity of the guest rooms is multiplied by the safety margin factor, and it is defined as follows: , in, The electrical design capacity (in watts) for guest rooms is specified in the electrical design drawings for that guest room. The safety margin factor (preferably 0.8, dimensionless) is used to reserve dynamic redundancy space for power supply lines and protection devices.

[0042] The method for determining the preset risk conditions is as follows: Define a transient impact failure factor Ψ to quantify the extent to which the current peak transient power exceeds the safe load-bearing baseline: , When Ψ>0, the preset risk condition is met, indicating that the peak power has exceeded the safety baseline.

[0043] Based on the numerical range of Ψ, the current set of instructions to be executed is divided into three scheduling urgency levels: Level 1 (Safe Pass-through): When Ψ≤0, it indicates that even if all concurrent executions are performed, the peak power is still within the safe carrying zone. The subsequent complex scheduling algorithm is skipped, and the concurrent command transmission is executed directly. Level 2 (Controllable Risk): When 0 < Ψ ≤ 20%, it indicates a slight risk of exceeding the limit, and peak-shifting scheduling needs to be initiated. The 20% threshold can be set according to the thermal tripping characteristic curve of the miniature circuit breaker (MCB) in the guest room power distribution branch. For example, when the corresponding power exceeds the safety baseline by 20%, the thermal protection element of the MCB may trigger non-fault tripping if it continues for more than 100ms. Therefore, 20% is set as the dividing threshold between controllable risk and high-risk warning.

[0044] Level 3 (High-Risk Warning): When Ψ>20%, it indicates a serious risk of tripping or equipment damage. Based on off-peak scheduling, the execution of non-critical equipment will be forcibly postponed to the next control cycle, and an anomaly warning will be reported to the hotel's engineering and maintenance platform simultaneously. The non-critical equipment is defined as having a scene sensitivity weight coefficient in the current active scenario. Less than the preset criticality threshold Modular energy-consuming devices with a value of (preferably 0.15) or those whose energy consumption dimension does not participate in the core semantics of the scenario in the current scenario are determined to be non-critical devices.

[0045] It should be noted that when the transient impact damage coefficient Ψ satisfies 0 < Ψ ≤ 20% (secondary risk controllable) or Ψ > 20% (third-level high-risk warning), the peak-shifting timing optimizer is activated; when Ψ ≤ 0 (first-level safety pass-through), the optimizer is not activated, and concurrent command transmission is executed directly.

[0046] In some embodiments, staggered timing scheduling of the modified instruction set is performed, specifically including: introducing a staggered timing optimizer with multi-objective constraints to calculate the optimal instruction issue delay matrix. Specifically: This is achieved through a multi-objective constraint optimizer that assigns an optimal transmit delay time to each valid entity command without altering the final target state values ​​of each device. This results in the superimposed actual power curve after scheduling. The peak value was suppressed to the safe bearing baseline. The objective function of the optimizer is as follows: , in, This represents the instantaneous power contribution of the i-th device at time t after the applied delay, and its expression is obtained by shifting the individual power time function in step S103. The above optimization must simultaneously satisfy the following constraints: A1. Execute window constraints: ,in The maximum scheduling delay window allowed by the system (preferably 5 seconds) is used to ensure that the perceived delay during human-computer interaction is not noticeably perceived by guests. A2. Equipment Status Constraints: If the starting inrush current multiples of both the i-th and j-th devices are greater than the preset inrush threshold... (Preferably 2, dimensionless), then the absolute value of the difference between the two emission delay times must satisfy... To avoid overlap in the startup periods of any two devices with significant impact characteristics; A3. Scene semantic constraints: If the correction instruction comes from the light environment dimension and the current scene is the process of switching from sleep mode to morning mode, then the transmission delay time of this dimension must be less than the preset light sensing delay limit (preferably 1s) to ensure the light sensing comfort of gradual wake-up. This limit value is set according to the physiological perception experimental data of ergonomics on sudden changes in lighting environment, in order to ensure that the rate of change of light environment does not exceed the tolerance threshold of human eye adaptation during the transition from sleep to morning, so as to avoid light sensing discomfort.

[0047] The optimizer uses a greedy sequential decision algorithm to solve the problem: first, each device to be acted on is determined according to its power ramp-up time constant. The devices are sorted in descending order of latency (i.e., those with longer impact times are prioritized for transmission); then, under the constraints described above, the earliest available idle time slot on the current time axis is matched for each device in turn; finally, the optimal delay matrix is ​​output, which is the optimal transmission delay time allocated to each valid entity command. composition: ,

[0048] When the above constraints cannot be satisfied simultaneously, i.e., when there is no feasible delay matrix that makes all constraints valid, the optimizer relaxes the constraint boundaries sequentially according to preset constraint priorities: the first priority (highest) is scene semantic constraints, the second priority is device state constraints, and the third priority is execution window constraints; the penalty coefficient for violating constraints is reduced step by step and the solution is resolved until a feasible solution is obtained; if no feasible solution is found after relaxing all constraints, all instructions are forcibly evenly distributed across the set of constraints. Within a time window and at fixed intervals Sequential firing.

[0049] In addition, when allocating the launch delay time, the system also performs differential processing based on the drift type classification label output in step S102: If the current drift type is labeled as "comfort preference drift", it indicates that the drift dimension is mainly caused by thermal comfort dimension and airflow dimension. In this case, during off-peak scheduling, priority should be given to ensuring the transmission order of the correction command corresponding to this dimension (i.e., giving it a relatively low transmission delay) in order to shorten the duration of discomfort for guests. If the current drift type is labeled as "activity scene type drift", it indicates that the drift dimension is mainly caused by the light environment dimension and the shading dimension. In this case, priority should be given to ensuring the rapid response of commands of this type of dimension, while the transmission order of thermal comfort dimension commands can be appropriately relaxed.

[0050] In some embodiments, S104 further includes: performing flexible scheduling with soft-start overlap region, monitoring the actual power envelope and dynamically fine-tuning it.

[0051] Specifically, the RCU host expands the valid entity instructions in the correction instructions on the time axis according to the optimal delay matrix (or the delay is all zero in the first-level direct mode), and sends them one by one to each modular power-consuming device for execution in a serialized delay manner; During the launch process, the system synchronously starts a closed-loop power envelope monitoring thread: The bus current transformer located at the RCU host input terminal reads the measured instantaneous power value of the bus in real time at a preset high sampling rate (preferably 100μs hardware interrupt sampling). ; measured values The unscheduled natural superposition curve predicted in step S103 Perform real-time comparison and calculate the relative deviation rate. When the relative deviation rate Furthermore, if the duration exceeds two sampling periods, an emergency insertion suppression mechanism is dynamically activated: a 200ms hold interval is temporarily inserted into the sequence of commands that have not yet been transmitted, further delaying subsequent commands to actively reduce abnormal peaks; when the relative deviation rate within three consecutive sampling periods... When the error rate drops below 10%, normal scheduling will resume and no additional hold intervals will be inserted.

[0052] Furthermore, the system reserves a soft-start overlap area at the end of the scheduling command sequence. Within this area, the system actively shortens the rated power holding time of some devices that have completed steady-state ramp-up. The specific implementation of this soft-start overlap area is as follows: at the end of the scheduling command sequence... Within a time window (preferably 1 second), for dimming devices and fan coil units that have completed power ramp-up to over 90% of their steady-state value, the system linearly decreases the PWM duty cycle of its drive signal from 100% in a certain manner. The steady-state power of multiple devices is reduced to 95% within a certain time, so that the steady-state power of multiple devices is distributed along the time axis, rather than all concentrated at the same moment to complete the rise; the PWM frequency is set to above 20kHz to eliminate noise in the audible frequency band and lighting flicker that is perceptible to the human eye, ensuring that the above adjustment process does not have a noticeable impact on the guest's environmental experience.

[0053] S105, obtain the calibration status parameters after execution, and update the reference state vector of the currently active scene with the calibration status parameters.

[0054] The acquisition of the calibration status parameters after execution specifically includes: After all peak-shaving scheduling instructions have been executed, the system again reads back the end-physical feedback values ​​of each modular energy-consuming device through the data bus to obtain the calibration status parameter vector after execution. .

[0055] The step of updating the baseline state vector of the currently active scene with calibration state parameters specifically includes: Replace the original reference state vector with this calibration state parameter, and update the reference state vector of the currently active scene, that is: , This ensures that the baseline state vector of the currently active scene is realigned with the actual physical environment of the guest room, guaranteeing that the differential calculations for subsequent scene switching are all based on real physical anchor points.

[0056] When S105 is executed Afterwards, the aforementioned Automatically new Coverage, and by the system with new Replace the corresponding element stored in the scene feature vector library. The original baseline vector enables long-term memory of personalized preferences.

[0057] Simultaneously, the system records key data related to this scene drift event and scheduling execution, including the original peak transient power. Actual peak power after scheduling The start-stop cycle counts for each device are recorded in the long-term health profile database of the RCU host for device aging trend analysis and preventative maintenance early warning. The start-stop cycle count is defined as one complete cycle in which a device switches from a non-zero power state to a zero power state and then switches back from a zero power state to a non-zero power state. During a single scheduling execution, if the power state of a device changes from P>0 to P=0 and then back to P>0, one start-stop cycle is counted.

[0058] In summary, by acquiring the real-time status parameters of each modular energy-consuming device in the guest room and calculating the weighted deviation with the baseline state vector of the currently activated scenario, and combining the dual-condition judgment logic of time inertia delay window and accumulated drift energy to exclude transient disturbances and accurately trigger scenario drift events, the system responds to these events by decoupling the deviation contribution rates of each energy consumption dimension to identify the main drift-causing dimension and generating a correction instruction set. Subsequently, it retrieves the electrical characteristic parameters of the device to be activated (including rated power, load inertia type, power ramp-up time constant, and starting inrush current multiple) to perform transient power superposition prediction, thereby quantifying the peak transient power impact risk when multiple devices execute concurrently. When the peak transient power exceeds the safe bearing baseline, multi-view... The peak-shifting timing optimizer with standard constraints allocates a transmission delay time to each correction instruction, enabling the instructions to be executed sequentially on the time axis, fundamentally eliminating power spike impacts. During execution, the measured power and predicted power are compared in real time through a closed-loop power envelope monitoring thread for dynamic adjustment, and a soft-start overlap area is reserved at the end of the scheduling sequence to smooth the power curve using pulse width modulation. Finally, the physical feedback values ​​at the end of each device are read back as calibration state parameters to update the reference state vector of the currently active scene, ensuring that the system always maintains an accurate mapping of the actual physical environment of the guest room. This achieves full-link closed-loop management from scene drift perception, drift-causing dimension localization, correction instruction generation, transient impact prediction and peak-shifting scheduling execution, to dynamic self-sustaining of the reference vector.

[0059] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: First, by constructing a weighted deviation perception mechanism between the scene baseline vector and real-time state parameters, scene drift events are accurately determined, overcoming the blind spot of the disconnect between traditional system logic and physical reality. Then, by decoupling contribution, the main effect dimension causing drift is located and correction instructions are generated. Subsequently, load electrical characteristic parameters are introduced to predict the transient power superposition of the correction instructions. When an impact risk is identified, the instructions are scheduled in a staggered time sequence, fundamentally eliminating transient power spikes caused by concurrent instructions from multiple devices, ensuring power supply safety and equipment electrical lifespan. Finally, the scene baseline is updated based on the corrected calibration state, ensuring the system always maintains an accurate mapping of the actual guest room environment. This embodiment, through the above closed-loop process, produces the following technical effects: Through the synergistic effect of steps S101 to S105, the accurate capture of scene drift events, the safe execution of correction instructions, and the true anchoring of scene reference vectors are achieved, effectively ensuring the smoothness of multi-scene switching processes and the consistency of guest environment experience. With the baseline vector restored to its true anchorage, the failure of the existing energy-saving comparison strategy based on total budgeted energy consumption caused by inaccurate sampling base points can be fundamentally eliminated, thereby indirectly restoring the effectiveness of the global energy consumption control strategy and providing a reliable underlying data foundation for the hotel's refined energy-saving management.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart hotel management method based on an RCU host scenario, characterized in that, include: S101: Obtain the real-time status parameters of each modular energy-consuming device in the guest room, construct the baseline state vector of the currently activated scene, calculate the weighted deviation between the real-time status parameters and the baseline state vector, and determine the scene drift event. S102, in response to the scene drift event, performs contribution decoupling analysis on the real-time state parameters, identifies at least one dimension whose deviation contribution rate meets the preset conditions as the main effect dimension of drift, and generates a set of correction instructions for the corresponding modular energy-consuming equipment. S103, retrieve the electrical characteristic parameters of the device to be operated, perform transient power superposition prediction on the correction instruction set based on the electrical characteristic parameters, and obtain the peak transient power of the correction instruction set under concurrent execution conditions; S104 When the peak transient power exceeds the safe bearing baseline to the extent that the preset risk conditions are met, the correction instruction set is staggered and then sent to each modular energy-consuming device for execution.

2. The smart hotel management method based on RCU host scenario as described in claim 1, characterized in that, The calculation of the weighted deviation between the real-time state parameters and the reference state vector includes: The weighted scene deviation D(t) at time t is defined as: ， Where n represents the total number of energy consumption dimensions participating in scenario management. Let be the real-time state parameters of the i-th dimension at time t. This represents the baseline state value for the i-th dimension in the currently active scenario. This represents the pre-set scene sensitivity weight coefficient for the i-th dimension in the currently active scene. This represents the normalized scaling factor for the current i-th dimension.

3. The smart hotel management method based on the RCU host scenario as described in claim 2, characterized in that, The determination of a scene drift event specifically includes: Set the time inertia delay window The cumulative drift energy defined within the window for: ， A scene drift event is determined to have occurred when all of the following conditions are met: The instantaneous deviation D(t) at the current moment is greater than the first preset threshold. ; The accumulated drift energy Greater than the second preset threshold .

4. The smart hotel management method based on the RCU host scenario as described in claim 3, characterized in that, The contribution decoupling analysis of the real-time state parameters specifically includes: Define the deviation contribution rate of the i-th dimension. for: ， in, This represents the actual offset of that dimension at time t. This represents the pre-set scene sensitivity weight coefficient for the i-th dimension in the current active scene, where n is the total number of energy consumption dimensions participating in scene management; Contribution rate of each dimension Sort the data in descending order from high to low, and then accumulate them sequentially. Select the top few dimensions whose cumulative contribution rate reaches the preset percentage and mark them as the main effect dimensions of the drifting.

5. The smart hotel management method based on the RCU host scenario as described in claim 4, characterized in that, The generation of the correction instruction set for the corresponding modular energy-consuming equipment specifically includes: Based on the main dimensions of the drift-causing effect and their corresponding actual deviation values Generate a set of correction instructions to be executed. Where q is the number of devices to be acted upon, each The target state correction value corresponding to the i-th modular energy-consuming device to be activated.

6. The smart hotel management method based on the RCU host scenario as described in claim 5, characterized in that, The process of retrieving the electrical characteristic parameters of the device to be operated specifically includes: Retrieve the electrical characteristic parameter sets of all modular energy-consuming devices to be activated from the equipment management database. For the i-th device to be activated, its electrical characteristic parameter set... Defined as: ， in, This represents the rated steady-state power of the i-th device; Indicates the load inertia type label for the i-th device; The power ramp-up time constant of the i-th device is defined as the time window required from the moment the execution command is issued until the actual power reaches 90% of the steady-state value; The starting inrush current multiple of the i-th device is defined as the ratio of the peak current at startup to the rated steady-state current.

7. The smart hotel management method based on the RCU host scenario as described in claim 6, characterized in that, The method of performing transient power superposition prediction on the modified instruction set based on electrical characteristic parameters to obtain the peak transient power of the modified instruction set under concurrent execution conditions specifically includes: The modified instruction set is decomposed into actions, the state change amount of each device is calculated, and instructions with a state change amount less than a preset dead zone threshold are marked as virtual instructions and removed, while the remaining instructions are marked as valid entity instructions. For the remaining p valid entity instructions after filtering, a natural superposition prediction curve of concurrent execution power is constructed on the time axis t. : ， Where p represents the number of valid entity instructions remaining after filtering. , Let be the rated steady-state power of the i-th device. For unit step function, This is the multiple of the starting inrush current of the i-th device. Let be the power ramp-up time constant of the i-th device; extract the peak value of the natural superposition prediction curve as the peak transient power. .

8. The smart hotel management method based on the RCU host scenario as described in claim 7, characterized in that, The method for determining the preset risk conditions is as follows: Define a transient impact failure factor Ψ to quantify the extent to which the current peak transient power exceeds the safe load-bearing baseline: ,in, To ensure the safety of the baseline, Peak transient power; When Ψ>0, the preset risk condition is determined to be met.

9. The smart hotel management method based on the RCU host scenario as described in claim 8, characterized in that, The staggered timing scheduling of the modified instruction set specifically includes: introducing a staggered timing optimizer with multi-objective constraints to calculate the optimal instruction issue delay matrix; An optimal launch delay time is assigned to each valid entity instruction using a multi-objective constraint optimizer. This results in the superimposed actual power curve after scheduling. The peak value was suppressed below the safe bearing baseline; The objective function of the optimizer is: ， in, This represents the instantaneous power contribution of the i-th device at time t after the delay is applied.

10. The smart hotel management method based on the RCU host scenario as described in claim 9, characterized in that, The optimizer uses a greedy sequential decision algorithm to solve the problem. Each device to be activated will be configured according to its power ramp-up time constant. Arrange in descending order from largest to smallest; Under the premise of meeting preset constraints, the earliest available idle time slot on the current time axis is matched for each device in turn; finally, the optimal delay matrix is ​​output. , Let be the optimal transmit delay time for the i-th valid entity instruction.

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