A hotel building automation system commissioning evaluation method

Abstract

The application provides a hotel building automation system debugging evaluation method, comprising: collecting real-time illumination intensity data and area occupancy state signals, identifying illumination difference by comparing the numerical difference before and after the corridor lighting is improved, determining the illumination overflow intensity according to the difference between the illumination difference and the guest room reference lighting standard difference; classifying the light interference degree according to the illumination overflow intensity and the area occupancy state signals, obtaining the interference level evaluation result, adjusting the corridor lighting partition according to the interference level; obtaining the corridor cleaning demand data, performing area function matching analysis according to the guest room lighting demand level and the corridor cleaning demand data, determining the adjacent area lighting matching group, obtaining the corridor lighting adjustment range and the partition lighting intensity ratio; evaluating the light interference elimination effect and the guest room lighting independence through the lighting uniformity value, and obtaining the illumination intensity data and the occupancy state signals after the adjustment instruction is executed.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for debugging and evaluating a hotel building automation system. Background Technology

[0002] In modern hotel management, building automation systems (BAS) serve as a core technology for intelligent operation, playing a crucial role in improving service quality, optimizing energy utilization, and ensuring customer experience. Particularly in hotel lighting control, the system needs to balance multiple functional requirements to ensure environmental comfort and operational efficiency. However, in practical applications, lighting control often faces dynamic conflicts in complex scenarios, especially in the interaction between public and private spaces. Traditional methods struggle to address the challenges posed by these dynamic changes. Existing lighting control solutions typically rely on preset lighting modes or simple timed switches, making it difficult to adapt to the dynamic needs of different areas and times within the hotel. For example, cleaning operations require high-brightness lighting to ensure cleaning quality, while guests may require a low-light environment in their rooms for rest. This difference in needs leads to mutual interference between the light environments of different areas, particularly in the light interaction between corridors and guest rooms. Existing methods lack the ability to perceive and dynamically adjust real-time scenarios, making it difficult to effectively balance the needs of multiple parties. For instance, when cleaning staff increase the brightness of corridor lighting, strong light may seep into guest rooms through door gaps, disturbing guests' rest. Traditional sensors struggle to capture this weak light transmission and its specific impact on the guest room's lighting environment. More importantly, directly turning on high-brightness lighting would disturb resting guests. Therefore, it is essential to dynamically adjust the corridor lighting zones and brightness distribution based on real-time room occupancy status and guest schedules to ensure that the lighting needs of cleaning operations are met while avoiding light disturbance to resting guests. Furthermore, the independent control of the light environment between areas requires dynamic adjustment of lighting zones based on sensor data. This adjustment must also consider the status of guest room blackout devices and guests' designated "do not disturb" periods. Therefore, how to dynamically adjust lighting zones based on real-time light intensity data and area occupancy status to balance the high brightness requirements of cleaning operations with the blackout requirements of guest rooms, thereby avoiding light interference and maintaining the independence of the light environment in each area, becomes a key issue for building automation systems in hotel lighting control. Summary of the Invention

[0003] This invention provides a method for debugging and evaluating a hotel building automation system, mainly including:

[0004] The system collects illuminance data and area occupancy status signals at the junction of the corridor and guest rooms. It compares the difference in illuminance data before and after corridor lighting upgrades to determine the deviation from the guest room baseline lighting standard, thus obtaining illuminance overflow intensity. Based on the illuminance overflow intensity and the area occupancy status signals, it classifies the degree of light interference, obtains the interference level, and adjusts the corridor lighting zones. It extracts the illuminance overflow intensity exceeding the guest room baseline lighting standard to determine the guest room lighting requirement level. It acquires corridor cleaning requirement data, analyzes area function matching based on the guest room lighting requirement level and corridor cleaning requirement data, determines adjacent area lighting matching groups, and obtains the corridor lighting adjustment range and zone lighting intensity ratio. Based on the corridor lighting adjustment range and zone lighting intensity ratio, it generates dynamic adjustment commands, collects the lighting intensity distribution after executing the dynamic adjustment commands, calculates the illuminance uniformity at each monitoring point, and obtains light transmittance and lighting uniformity values. Based on the lighting uniformity values, illuminance data after executing the dynamic adjustment commands, and occupancy status signals, it determines the light interference elimination status. Based on the illuminance data, occupancy status signals, and guest room lighting requirement levels, it constructs an optimization database for debugging parameters.

[0005] Furthermore, light intensity data and area occupancy status signals are collected at the junction of the corridor and guest rooms. The difference in light intensity data before and after the corridor lighting upgrade is compared to determine the deviation from the guest room baseline lighting standard, thus obtaining the light spillover intensity, including:

[0006] An array of photosensitive sensors is deployed at the junction of the corridor and guest rooms to collect light intensity data from both sides. An infrared sensor collects the area occupancy status signal and records the initial light intensity baseline value over time. Based on the area occupancy status signal, the corridor lighting brightness is increased, and the increased light intensity data is collected. This data is then compared point-by-point with the initial light intensity baseline value to calculate the illuminance difference at each sensor node. The illuminance difference is compared with the guest room baseline lighting standard, and the portion exceeding the standard is extracted as the light overflow value. Based on the sensor node location and the light overflow value, a distance-inverse weighted method is used to calculate the light overflow intensity.

[0007] Furthermore, based on the light spillover intensity and the area occupancy status signal, the degree of light interference is classified to obtain the interference level, and the corridor lighting zones are adjusted, including:

[0008] The light spill intensity is compared with a preset threshold to classify it as mild, moderate, or severe interference. Based on the room occupancy identifier and activity period information in the area occupancy status signal, weighting coefficients are assigned to calculate a weighted interference level value. The weighted interference level value is compared with the interference tolerance threshold corresponding to the room type to assess the interference level. Based on the interference level, the corridor section where the affected room is located is identified, a buffer zone is divided, the upper limit of the lighting intensity and the gradation rate of the buffer zone are adjusted, and the corridor lighting zoning is reconfigured.

[0009] Furthermore, extracting the light spillover intensity exceeding the guest room baseline lighting standard to determine the guest room lighting requirement level includes:

[0010] The ratio of the light spillover intensity to the guest room baseline lighting standard is calculated. Weighting coefficients are assigned based on the distance of the monitoring point from the center of the guest room door, and a weighted summation is used to obtain a comprehensive interference index. If the comprehensive interference index exceeds a preset threshold, the location data of the blackout curtain and the guest do-not-disturb setting data are obtained. Combined with the guest room occupancy status and time period attributes, the guest activity status is determined, and lighting sensitivity weights are assigned. Based on the lighting sensitivity weights and the lighting demand baseline values ​​corresponding to the time period attributes, the adjusted lighting demand value is calculated, and the guest room lighting demand level is determined.

[0011] Furthermore, the location data of the blackout curtain and the guest do-not-disturb setting data are obtained, and combined with the room occupancy status and time period attributes, the guest activity status is determined, and lighting sensitivity weights are assigned, including:

[0012] The percentage of the blackout curtain's opening / closing and the start and end times of the guest do-not-disturb setting data are obtained. Combined with the number of guests and the number of days in the guest room, the guest activity status is determined. If the percentage of the blackout curtain's opening / closing is below a threshold and it falls within the time period of the guest do-not-disturb setting data, the highest lighting sensitivity weight is assigned. If the blackout curtain's opening / closing indicates that it is open and it does not fall within the time period of the guest do-not-disturb setting data, a low lighting sensitivity weight is assigned.

[0013] Furthermore, corridor cleaning requirement data is obtained. Based on the guest room lighting requirement level and corridor cleaning requirement data, the functional matching of areas is analyzed to determine adjacent area lighting matching groups, and the corridor lighting adjustment range and zone lighting intensity ratio are obtained, including:

[0014] Collect data on corridor cleaning needs, including the work location, duration, and area, to determine the overall cleaning lighting demand value. Normalize the guest room lighting demand level and the overall cleaning lighting demand value, and use a clustering algorithm to group them into regional matching sets. Based on the adjacency relationship and lighting demand difference of the regional matching sets, divide the lighting transition zone and calculate the corridor lighting adjustment range. Based on the lighting demand priority and spatial distribution of the regional matching sets, assign weights inversely proportional to the distance from the cleaning operation center to determine the zone lighting intensity ratio.

[0015] Furthermore, based on the corridor lighting adjustment range and the zone lighting intensity ratio, a dynamic adjustment command is generated. The lighting intensity distribution after executing the dynamic adjustment command is collected, and the illuminance uniformity at each monitoring point is calculated to obtain the light transmittance and lighting uniformity values, including:

[0016] Based on the corridor lighting adjustment range and the zone lighting intensity ratio, a dynamic adjustment command sequence containing the target brightness value and execution priority is generated; the dynamic adjustment command sequence is sent through the lighting control bus, the lighting intensity distribution after execution is collected, and a lighting intensity distribution matrix is ​​constructed; based on the lighting intensity distribution matrix, the standard deviation of the illuminance values ​​of adjacent monitoring points is calculated, the illuminance value at the door gap position is extracted as the light transmittance, and the lighting uniformity value is calculated.

[0017] Furthermore, based on the light intensity data, the occupancy status signal, and the guest room lighting requirement level, a database for optimizing debugging parameters is constructed, including:

[0018] Based on the light intensity data and the occupancy status signal, calculate the illuminance difference between the guest room door and the corridor, determine the light interference elimination status, and record the interference elimination time; based on the guest room lighting requirement level and the measured illuminance value, calculate the deviation percentage and generate the illuminance comparison data before and after adjustment; obtain the lighting comfort score from the guest room management terminal and map it to a satisfaction value; based on the illuminance comparison data before and after adjustment, the interference elimination time, and the satisfaction value, construct an optimization record containing scene identifiers and adjustment parameters, group them using a clustering algorithm, extract recommended values, and generate the debugging parameter optimization database.

[0019] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0020] This invention discloses a method for debugging and evaluating a hotel building automation system. Addressing the interference problem caused by light spillage at the junction of hotel corridors and guest rooms, the method collects real-time data on light intensity and area occupancy status, identifies illuminance differences, and assesses the intensity of light spillage. Combining guest room occupancy status, do-not-disturb settings, and cleaning requirements, it dynamically classifies light interference levels and determines guest room lighting needs. This invention generates corridor lighting zone adjustment ranges and intensity ratios through area function matching analysis, dynamically sends adjustment commands to the controller, and monitors the adjusted lighting uniformity and light interference elimination effect through a sensor array. Finally, it establishes a database of optimized debugging parameters to achieve interference elimination, improved guest room lighting independence, and enhanced satisfaction. The most important innovation of this invention lies in the intelligent analysis of light spillage and occupancy status to dynamically match area lighting needs, ensuring precise adjustment and high energy efficiency, thereby improving hotel environmental comfort and management intelligence. Attached Figure Description

[0021] Figure 1 This is a flowchart of a hotel building automation system commissioning and evaluation method according to the present invention.

[0022] Figure 2 This is a schematic diagram of a hotel building automation system commissioning and evaluation method according to the present invention.

[0023] Figure 3 This is another schematic diagram of a hotel building automation system commissioning and evaluation method according to the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0025] like Figure 1-3 This embodiment of a hotel building automation system commissioning and evaluation method may specifically include:

[0026] S101. Collect real-time light intensity data and area occupancy status signals, identify the illuminance difference by comparing the numerical differences before and after the corridor lighting upgrade, and determine the light spillover intensity based on the difference between the illuminance difference and the guest room reference lighting standard.

[0027] An array of photosensitive sensors is arranged at preset intervals above the door frame at the junction of the corridor and guest rooms. Each sensor node collects real-time light intensity data from both the corridor and guest room sides. Simultaneously, an infrared sensor acquires area occupancy status signals, records the timestamp and location information of cleaning personnel entering the corridor, and establishes an initial light intensity benchmark value containing a time series. Based on the area occupancy status signal, cleaning operations are initiated, triggering an increase in corridor lighting brightness. The photosensitive sensor array continuously collects light intensity data after the lighting increase, comparing the increased light intensity data with the initial light intensity benchmark value point-by-point. The illuminance difference at each sensor node location is calculated, and this illuminance difference is compared with a preset guest room benchmark lighting standard. If the illuminance difference exceeds a preset overflow threshold, the portion exceeding the guest room benchmark lighting standard is extracted as the light overflow value for each monitoring point. Based on the spatial location of each sensor node and the light overflow value, the spatial distribution of overflow intensity is calculated using a distance-inverse weighted method, where the weighting coefficient is inversely proportional to the distance from the sensor to the guest room door seam. The weighted overflow values ​​of all monitoring points are then summed to obtain the light overflow intensity.

[0028] Specifically, in one embodiment, the photosensitive sensor array adopts a matrix arrangement, with sensor nodes forming a U-shape along the top edge and both vertical edges of the door frame. Each node contains a bidirectional photosensitive element, facing both the corridor side and the guest room side, enabling bidirectional monitoring of light transmission through the door gap. The infrared sensor and photosensitive sensor are integrated into a single package, using the pyroelectric principle to detect the movement trajectory of cleaning personnel.

[0029] Specifically, the process of establishing the initial illuminance baseline value involves data collection across multiple time dimensions. Ambient light baseline values ​​are collected during the early morning hours when hotel occupancy is low, and natural light variation curves are collected at different times of the day, forming a baseline value matrix containing timestamps, location identifiers, and three-dimensional illuminance data. Area occupancy status signals are determined by the trigger frequency and duration of infrared sensors. When continuous movement signals are detected and the duration exceeds a preset threshold, cleaning operations are considered to have begun. The illuminance difference is calculated using a point-by-point comparison method. The illuminance data collected by each sensor node after lighting enhancement is subtracted from the baseline value at the corresponding time to obtain the original difference sequence. Then, a moving average filter is used to eliminate transient interference, resulting in a stable illuminance difference value. The preset guest room baseline lighting standard is dynamically set according to the hotel's star rating and room type. The baseline lighting standard for standard suites is 150 lux, and for deluxe suites, it is 200 lux.

[0030] Preferably, the weighting coefficients of the distance-inverse weighting method are calculated based on the Euclidean distance from the sensor node to the center line of the door gap, with the weighting coefficient w = 1 / (d + α), where d is the distance value and α is a smoothing factor to prevent the weight from becoming infinite when the distance is zero. The light spillover values ​​at each monitoring point are multiplied by their corresponding weighting coefficients, summed, and then divided by the sum of the weighting coefficients to obtain the normalized weighted spillover intensity. Spatial distribution uses an interpolation algorithm to extend the discrete monitoring point data into a continuous intensity field, and a bilinear interpolation method is used to estimate the spillover intensity value in the region between sensor nodes.

[0031] In one embodiment, the method can accurately identify the degree of light interference between corridor lighting and guest room lighting, enabling precise adjustment of lighting control and avoiding the impact of cleaning operations on guests' rest.

[0032] S102. Classify the degree of light interference based on the light spillover intensity and area occupancy status signal to obtain the interference level assessment result, and adjust the corridor lighting zoning according to the interference level.

[0033] The interference level is determined by comparing the light spillover intensity value with a preset interference threshold. When the spillover intensity is below the first threshold, it is marked as mild interference; between the first and second thresholds, it is marked as moderate interference; and above the second threshold, it is marked as severe interference. Simultaneously, room occupancy status signals and guest activity time information are extracted from the area occupancy status signal. Occupied rooms are assigned a higher weighting coefficient based on the occupancy status, while vacant rooms are assigned a lower weighting coefficient. The interference level is then multiplied by the weighting coefficient to obtain a weighted interference level value. This weighted interference level value is compared with the interference tolerance threshold corresponding to the room type. The interference tolerance threshold for standard rooms is set as the baseline value, while the thresholds for suites and executive rooms are set as preset percentages of the baseline value. When the weighted interference level value is below 30% of the tolerance threshold, it is assessed as no interference; between 30% and 60%, it is assessed as slight interference; between 60% and 90%, it is assessed as moderate interference; between 90% and 120%, it is assessed as severe interference; and above 120%, it is assessed as extreme interference. This yields the interference level assessment result. Based on the interference level assessment results, the corridor lighting zones are dynamically adjusted. If the interference level is medium or above, the corridor section where the affected guest room is located is identified, and a buffer zone is formed by extending a preset distance to both sides with the guest room door as the center. The lighting equipment in the buffer zone is separated from the original zone, and an independent lighting control zone is established. By adjusting the upper limit of the lighting intensity and the gradient rate parameter of the independent zone, the corridor lighting zones are reconfigured.

[0034] Specifically, in one implementation, the multi-level threshold settings are based on hotel lighting standards and human visual comfort research. The first threshold is set at 50 lux, and the second threshold is set at 100 lux. These two thresholds correspond to the transition point from dark adaptation to light adaptation and the point where significant discomfort occurs, respectively. The weighting coefficients are assigned using a binary judgment mechanism: the weighting coefficient for occupied rooms is set to 1.0, and the weighting coefficient for vacant rooms is set to 0.3. A second weighting is applied based on the guest's activity time: the weighting coefficient for nighttime rest periods is multiplied by 1.5, while the weighting for daytime activity periods remains unchanged. The interference tolerance threshold is set differently according to the room category: the baseline value for standard rooms is set at 80 lux, for suites at 0.75 times the baseline value (60 lux), and for executive rooms at 0.6 times the baseline value (48 lux).

[0035] For example, the five-level evaluation criteria are precisely divided using percentage ranges. When the ratio of the weighted interference level value to the tolerance threshold is below 0.3, it is judged as no interference, at which point the corridor lighting has virtually no impact on the guest rooms; a ratio between 0.3 and 0.6 indicates slight interference, which guests may occasionally notice but does not affect their rest; a ratio between 0.6 and 0.9 indicates moderate interference, which begins to affect the comfort of the guest room environment; a ratio between 0.9 and 1.2 indicates severe interference, which significantly affects the quality of guests' rest; and a ratio exceeding 1.2 indicates extreme interference, which has severely damaged the independence of the guest room's lighting environment. During the ratio calculation process, the timestamp and duration of each evaluation result are recorded in real time for subsequent lighting control optimization.

[0036] Preferably, the buffer zone is divided in a dynamic expansion manner. In the initial state, it extends 2 meters to both sides of the affected guest room door. When the interference level reaches severe or extreme, the extension distance automatically increases to 3 meters to form a larger isolation zone.

[0037] In one embodiment, the lighting control parameters for independent zones include two key parameters: an upper limit for lighting intensity and a gradient rate. The upper limit for lighting intensity is dynamically set according to the level of interference. Under moderate interference, the upper limit is reduced to 70% of the normal value; under severe interference, it is reduced to 50%; and under extreme interference, it is reduced to 30%. The gradient rate controls the rate of change of lighting brightness and is set to change no more than 5% per second to avoid sudden changes that could impact vision. This gradual adjustment achieves a balance between corridor functional lighting and the comfortable environment of guest rooms.

[0038] S103. By extracting the light spillover intensity that exceeds the reference lighting standard, an interference index is obtained. If the interference index exceeds the preset interference threshold, the current position of the blackout curtain and the guest's do-not-disturb setting data are obtained, and the room lighting requirement level is evaluated in combination with the room occupancy status and time period requirements.

[0039] By extracting light spillover intensity values ​​exceeding the baseline lighting standard, the ratio of spillover intensity at each monitoring point to the baseline lighting standard is calculated. A weighting coefficient is determined based on the distance of the monitoring point from the center of the guest room door; the weighting coefficient decreases by a preset attenuation rate for every meter increase in distance. A comprehensive interference index is obtained by weighted summation and dividing by the total weights. This comprehensive interference index reflects the overall interference level of corridor lighting on the guest room. If the comprehensive interference index exceeds a preset interference threshold, the current blackout curtain position data, including the blackout curtain's opening / closing percentage, is obtained through the guest room management interface. Simultaneously, do-not-disturb setting data is extracted from the guest service records, including the do-not-disturb start time, end time, and do-not-disturb level. The do-not-disturb level is divided into two levels: normal do-not-disturb and deep do-not-disturb. The blackout curtain's opening / closing percentage and the do-not-disturb setting data together constitute the guest room privacy protection status parameters. Based on the guest room privacy protection status parameters and the guest room occupancy status obtained from the hotel management database, including the number of guests and the number of stay days, the guest activity status is determined according to preset status judgment rules. If the blackout curtain is closed for more than a preset closing threshold and it is during a do-not-disturb period, the guest is judged to be in a resting state and assigned the highest lighting sensitivity weight. If the blackout curtain is open and it is not during a do-not-disturb period, the guest is judged to be in an active state and assigned a lower lighting sensitivity weight. Based on the lighting sensitivity weight, the current time period attribute is obtained. The lighting demand baseline value corresponding to the time period attribute is multiplied by the lighting sensitivity weight to obtain the adjusted lighting demand value. The adjusted lighting demand value is compared with a preset level classification threshold to determine the guest room lighting demand level, which includes five levels: very low demand, low demand, medium demand, high demand, and very high demand.

[0040] Specifically, in one implementation, the comprehensive interference index is calculated using a spatial distance attenuation model. Based on the principle of optical propagation, light intensity decreases inversely with distance. In the specific calculation process, the original overflow intensity value of each monitoring point is first obtained; this value is the difference between the measured light intensity and the standard lighting intensity of the guest room. The weighting coefficient is determined using an exponential decay function, w=e^(-π / 2). (-αd) Where d is the distance from the monitoring point to the center of the guest room door, and α is the attenuation coefficient, dynamically adjusted according to the corridor width and building structure. During weighted summation, the overflow intensity value of each monitoring point is multiplied by its corresponding weight coefficient, summed, and then divided by the sum of all weight coefficients for normalization. The resulting comprehensive interference index ranges from 0 to 1, reflecting the degree of interference of corridor lighting on the overall guest room. The decreasing trend of the weight coefficients follows optical attenuation characteristics; for every 0.5 meters increase in distance from the center of the guest room door, the weight coefficient decreases according to a preset attenuation rate. The attenuation rate is adjusted based on the light reflectance coefficient of the corridor material; the attenuation rate is set to 0.15 for smooth marble floors and 0.25 for carpet materials.

[0041] Specifically, the position data of the blackout curtains is acquired in real time through the position encoder of the smart curtain controller, with an encoder resolution of 1%. The open / closed percentage data of the blackout curtains is transmitted to the building automation host in real time. The Do Not Disturb setting data is stored in a structured format, including four fields: start timestamp, end timestamp, Do Not Disturb level identifier, and trigger source. The normal Do Not Disturb level allows interruptions for urgent matters, while the deep Do Not Disturb level completely blocks all external interference. The trigger source records the activation method of the Do Not Disturb setting, including three types: manual setting by the guest, automatic system triggering, and remote setting by the front desk. Do Not Disturb settings from different sources have different priority weights.

[0042] For example, the guest room privacy protection status parameters are formed through multi-dimensional data fusion, converting the percentage of blackout curtain opening / closing into a blackout level: 0-20% is fully open, 20-50% is partially blackout, 50-80% is mostly blackout, and 80-100% is completely blackout. The "Do Not Disturb" period is compared with the current time to determine if the user is within the "Do Not Disturb" protection period. The two types of data are fused through logical AND operations to form four privacy protection statuses: no protection, light protection, moderate protection, and deep protection.

[0043] In one possible implementation, the state determination rule is based on a multi-condition logic decision tree. The first layer determines the status of the blackout curtain; if the curtain is closed more than 80% of a preset threshold, the process proceeds to the second layer. The second layer determines the "Do Not Disturb" setting; if the current time falls within the "Do Not Disturb" period, the process proceeds to the third layer. The third layer determines the occupancy status; if the room is occupied and the occupancy time exceeds 24 hours, it is determined to be in a resting state, assigned a lighting sensitivity weight of 1.0. If the blackout curtain is open more than 50% and it is not in a "Do Not Disturb" period, it is directly determined to be in an active state, assigned a lighting sensitivity weight of 0.3. States in between are determined by linear interpolation based on the number of days of stay, with a weight of 0.5 for the first day and an increase of 0.1 for each additional day, up to a maximum of 0.8. This determination rule is executed every 30 seconds to ensure real-time reflection of guest status changes.

[0044] Understandably, the time period is divided into five intervals based on the hotel's operational characteristics: the early morning period is 0:00-6:00, the morning period is 6:00-9:00, the daytime period is 9:00-18:00, the evening period is 18:00-22:00, and the late night period is 22:00-24:00. Each time period corresponds to a different baseline lighting requirement value.

[0045] For example, in determining the lighting demand level, the baseline demand value for the current time period is first obtained: 30 lux for early morning and late night, 50 lux for early morning and evening, and 80 lux for daytime. This baseline value is then multiplied by a lighting sensitivity weight to obtain the adjusted lighting demand value. The final level is determined by comparing this value with five threshold values: 0-20 for very low demand, 20-40 for low demand, 40-60 for medium demand, 60-80 for high demand, and above 80 for very high demand. Each level corresponds to a different corridor lighting control strategy, achieving precise lighting management.

[0046] In one embodiment, the above method can accurately assess the actual lighting needs of guest rooms, dynamically adjust lighting control strategies according to guests' daily routines and privacy requirements, and achieve personalized light environment management.

[0047] S104. Obtain corridor cleaning demand data, perform regional function matching analysis based on guest room lighting demand level and corridor cleaning demand data, determine adjacent area lighting matching groups, and obtain corridor lighting adjustment range and zone lighting intensity ratio.

[0048] The system acquires corridor cleaning requirements data, including the starting location of cleaning operations, estimated operation time, cleaning area range, and required lighting intensity. The system collects real-time data on the movement trajectory and work progress of cleaning personnel through a cleaning management terminal. Basic lighting requirements are determined based on different cleaning task types, with the floor cleaning requirement set as a first preset illuminance value and the wall cleaning requirement set as a second preset illuminance value. A comprehensive cleaning lighting requirement value is calculated based on the task type and area characteristics. Area function matching is performed based on the guest room lighting requirement level and the comprehensive cleaning lighting requirement value. The lighting requirement values ​​of each area are normalized and used as feature vectors. A clustering algorithm is used to group areas with similar lighting requirement characteristics. The Euclidean distance between cluster centers represents the degree of difference in requirements between areas, resulting in an area matching set. This set includes corridor sections that can be uniformly controlled and isolated sections that require independent control. Based on the area matching set, the spatial adjacency relationship and lighting demand difference of adjacent areas are extracted. If the difference exceeds a preset compatibility threshold, the adjacent area pair is marked as a conflict area pair. A lighting transition zone is inserted between the conflict area pairs. The lighting intensity within the transition zone decreases from the high-demand area to the low-demand area using a linear interpolation method, thus determining the adjacent area lighting matching group. According to the lighting demand priority and spatial distribution characteristics of each area in the adjacent area lighting matching group, the corridor lighting adjustment range is calculated. The adjustment range is equal to the difference between the cleaning lighting demand value and the upper limit of the lighting threshold corresponding to the lighting demand level of the adjacent guest rooms. Based on the adjustment range and the number of areas in each matching group, the zonal lighting intensity ratio is determined according to the weight allocation principle that is inversely proportional to the distance from the cleaning operation center.

[0049] Specifically, in one implementation, cleaning demand data is obtained through multi-source information fusion. The cleaning management terminal is equipped with ultra-wideband positioning tags to track the precise location of cleaning personnel in the corridor in real time, with positioning accuracy down to the centimeter level. The cleaning task type is marked through the task selection interface on the terminal, including four types: deep floor cleaning, wall stain removal, ceiling dusting, and routine maintenance.

[0050] Specifically, the calculation of the comprehensive cleaning lighting requirements uses a weighted summation method, setting a base illuminance value based on the visual requirements of different cleaning tasks. For deep floor cleaning, which requires identifying fine stains, the first preset illuminance value is set in the 300-350 lux range; the second preset illuminance value for wall stain treatment is set in the 200-250 lux range. When multiple cleaning tasks are performed simultaneously, the maximum illuminance requirement for each task is taken as the base value, and then adjusted according to the reflectivity of the materials in the cleaning area. The correction factor is 1.2 for dark carpet areas and 0.9 for light marble areas; the adjusted value is the comprehensive cleaning lighting requirement.

[0051] It should be noted that the normalization of the feature vectors adopts the min-max normalization method, which maps the lighting demand values ​​of each region to the range of 0 to 1, eliminating the influence of different dimensions on the clustering results.

[0052] For example, the clustering algorithm implementation process includes three stages: feature extraction, distance calculation, and grouping iteration. In the feature extraction stage, the lighting demand value, spatial location coordinates, and functional attributes of each corridor segment are encoded and combined into a three-dimensional feature vector. Distance calculation uses weighted Euclidean distance, with the weights for the lighting demand dimension set to 0.5, the spatial location dimension to 0.3, and the functional attribute dimension to 0.2. During the grouping iteration process, the initial cluster centers are selected using the K-means++ method to ensure the dispersion of the initial center points. The iteration process continues until the movement distance of the cluster centers is less than a preset convergence threshold, typically converging after 15-20 iterations. In the resulting clustering results, the difference in lighting demand within the same category does not exceed 15%, which can be used as a unified control unit.

[0053] Preferably, the identification of conflict areas is achieved by comparing the difference in lighting requirements between adjacent areas with a preset compatibility threshold. The compatibility threshold is dynamically set according to the functional type of the area. The compatibility threshold for the area in front of the guest room door is set to 50 lux, and the compatibility threshold for the elevator lobby area is set to 80 lux.

[0054] In one possible implementation, the lighting transition zone is set up using a multi-segment linear interpolation method, establishing a transition zone with a width of 2-3 meters between conflicting areas. The transition zone is divided into 5 interpolation nodes. The first node maintains the lighting intensity of the high-demand area, the fifth node reduces the lighting intensity to the low-demand area, and the middle three nodes are set according to an arithmetic progression. Each node corresponds to a set of independently controllable lighting fixtures, and a smooth transition is achieved by adjusting the brightness of each set of fixtures. The interpolation calculation formula is Li = Lh - (Lh - Ll) × i / n, where Li is the lighting intensity of the i-th node, Lh is the lighting intensity of the high-demand area, Ll is the lighting intensity of the low-demand area, and n is the total number of nodes.

[0055] Understandably, the calculation of corridor lighting adjustment range needs to comprehensively consider the actual needs of cleaning operations and the tolerance limits of guest rooms. The upper limit of the lighting threshold corresponding to the guest room lighting demand level is obtained by looking up a table: 100 lux for very low demand level, 150 lux for low demand level, 200 lux for medium demand level, and 250 and 300 lux for high demand and very high demand levels, respectively.

[0056] For example, the distance-inverse weighting principle is based on the physical attenuation law of light intensity. The weight calculation formula is weight wi = 1 / (di+1), where di is the distance from the i-th area to the cleaning operation center. Adding 1 is to avoid division by zero when the distance is zero. The lighting intensity ratio of each area is determined after weight normalization to ensure that the total lighting power does not exceed the preset upper limit.

[0057] In one embodiment, the above-mentioned method of matching regional functions and lighting intensity ratios achieves a dynamic balance between the lighting needs of cleaning operations and the comfort needs of guest rooms, thereby minimizing interference with the guest room environment while meeting the visual requirements of cleaning operations.

[0058] S105. Based on the corridor lighting adjustment range and the zonal lighting intensity ratio, determine the dynamic adjustment command for the corridor lighting brightness adjustment range and adjustment timing, and obtain the lighting intensity distribution after executing the adjustment command. Extract the light transmittance by measuring the illuminance uniformity of each monitoring point, and evaluate the lighting uniformity value by analyzing the light transmittance.

[0059] Based on the corridor lighting adjustment range and the intensity ratio of zoned lighting, a dynamic adjustment command sequence is constructed. Each command includes four parameters: target lighting area number, target brightness value, gradient duration, and execution priority. When cleaning staff are detected entering a specific area and the risk of guest room interference is below a preset threshold, the adjustment timing is determined based on the current time and guest room occupancy status, and commands are sorted according to priority to form a command queue. The command queue is sent to the corridor lighting controller via the lighting control bus. After parsing the command parameters, the controller adjusts the drive current of each zone lighting fixture and gradually changes the output power according to the specified gradient duration. During execution, the response time and actual output value of each control node are recorded to obtain command execution feedback data containing execution completion flags and actual brightness values. After confirming the adjustment is complete based on the command execution feedback data, the actual lighting intensity values ​​of each monitoring point are collected through a pre-arranged array of photosensitive sensors to construct a lighting intensity distribution matrix. Each element in the matrix represents the illuminance value of a monitoring point. The standard deviation of the illuminance values ​​of adjacent monitoring points is calculated to assess the local illuminance uniformity, and the illuminance value of the door gap position sensor is extracted as the light transmittance. Based on the light transmittance and illumination intensity distribution matrix, the illumination uniformity value is obtained by dividing the standard deviation of all elements in the matrix by the average value. If the illumination uniformity value exceeds the preset uniformity threshold, the brightness difference between adjacent areas is adjusted, the gradient change rate is reduced, and the illumination intensity of each area is redistributed to obtain an illumination uniformity evaluation result that meets the uniformity requirements.

[0060] Specifically, in one implementation, the construction of the dynamic adjustment instruction sequence adopts a hierarchical encoding mechanism, with each instruction encoded into a 32-bit data packet according to a fixed format. The target lighting area number occupies 8 bits, identifying 256 independent control areas; the target brightness value occupies 10 bits, providing 1024 levels of brightness adjustment accuracy; the gradient duration occupies 8 bits, supporting gradient processes from 0.1 seconds to 25.5 seconds; the execution priority occupies 4 bits, defining 16 priority levels, with the remaining 2 bits used as check bits. The instruction sequence is ordered according to spatial proximity and functional relevance, with adjustment instructions for adjacent areas arranged consecutively to reduce the visual impact of sudden lighting changes. When the execution time windows of multiple instructions overlap, instructions with higher priority are executed first, and instructions with the same priority are executed in the order of area numbers.

[0061] Specifically, the timing of adjustments is determined by considering three dimensions: the location of cleaning staff, the risk of disturbance to guest rooms, and the system load status. The location of cleaning staff is obtained through real-time positioning, and pre-adjustment is triggered when they enter within 5 meters of the target area; the risk of disturbance to guest rooms is calculated based on the current time, guest room occupancy status, and historical complaint records; the system load status reflects the number of adjustment tasks currently being executed.

[0062] It should be noted that the lighting control bus adopts the RS485 communication protocol, with a baud rate of 115200bps, supports multi-point communication and broadcast mode, and each controller has a unique address identifier to ensure accurate delivery of commands.

[0063] Preferably, the drive current is adjusted using pulse width modulation (PWM) technology, with the modulation frequency set above 20kHz to avoid flickering perceptible to the human eye. The gradual dimming process employs an S-curve rather than a linear change, with slow changes at the beginning and end, and faster changes in the middle stage, conforming to the human eye's adaptation to changes in light. The power adjustment range is from 10% to 100% of the rated power, with an adjustment accuracy of 0.1% per level, achieving smooth, stepless dimming.

[0064] For example, the photosensitive sensor array is deployed according to the principle of grid arrangement, with a monitoring point set every 3 meters in the corridor and an additional monitoring point on each side of the guest room door to form a high-density monitoring network.

[0065] In one possible implementation, the construction and analysis of the lighting intensity distribution matrix includes three stages: data acquisition, outlier handling, and spatial interpolation. During data acquisition, each sensor node samples synchronously at a frequency of 10Hz. After each acquisition, median filtering is performed on five consecutive samples to eliminate transient interference. Outlier handling uses the 3σ criterion, marking data points deviating from the mean by more than three standard deviations as outliers and replacing them with the mean of neighboring points. Spatial interpolation uses Kriging interpolation, estimating the illuminance values ​​at locations without sensors based on known monitoring point illuminance values ​​and spatial correlation, thus forming a continuous lighting intensity distribution field. Each element in the matrix contains not only the illuminance value but also a timestamp, sensor status flags, and data quality indicators, providing complete data support for subsequent analysis.

[0066] Understandably, the extraction of light transmittance focuses on the light leakage at the door gap. The difference in illuminance between the sensors on both sides of the door gap directly reflects the degree of light transmittance. When the difference exceeds the preset threshold, it indicates that there is a significant light penetration phenomenon.

[0067] For example, the calculation of lighting uniformity uses statistical methods. First, the arithmetic mean of the illuminance values ​​at all monitoring points is calculated as a baseline. Then, the standard deviation of each point from the mean is calculated. The ratio of these two values ​​is the coefficient of variation. The smaller the coefficient, the more uniform the lighting. When the coefficient of variation exceeds 0.3, a gradient optimization program is initiated. By adjusting the brightness difference between adjacent areas, the illuminance distribution is made smoother. The optimization process uses an iterative approach, with each adjustment not exceeding 10% of the current value to avoid oscillations caused by over-adjustment.

[0068] In one embodiment, gradient optimization adjustment is achieved by redistributing the lighting intensity of each region. While keeping the total power constant, the output of high-brightness regions is reduced and the output of low-brightness regions is increased, thereby achieving a balanced lighting distribution.

[0069] S106. Evaluate the light interference elimination effect and the independence of guest room lighting by measuring the lighting uniformity value, and obtain the light intensity data and occupancy status signal after executing the adjustment command.

[0070] By comparing the lighting uniformity value with a preset uniformity standard, when the uniformity value is lower than the standard threshold, it is determined that the light interference has been eliminated. The illuminance difference between the lighting areas in front of the guest room door and the corridor lighting area is extracted, and the illuminance value sequences of the two areas at the same time are calculated using Pearson correlation. If the correlation coefficient is lower than the preset independence threshold, the guest room lighting independence is confirmed to meet the standard, and the light interference elimination effect evaluation value and independence evaluation value are obtained. Based on the light interference elimination effect evaluation value and independence evaluation value, the real-time monitoring mode of the sensor array is activated, and the sampling interval is set to a preset time period. Each sensor node synchronously collects the light intensity data of the current location, including the real-time illuminance value and illuminance change rate, and simultaneously acquires the occupancy status signal of the infrared sensor. The occupancy status signal includes the presence of personnel, movement direction, and dwell time. The light intensity data and occupancy status signal are time-aligned and spatially mapped to construct a spatiotemporal data matrix. The rows of the matrix represent different monitoring points, and the columns represent the sampled values ​​at different times. By gradually sliding a fixed-length time window, the data change trend is extracted. When a sudden change in light intensity or occupancy status is detected, the adjusted real-time light intensity data and occupancy status signal are output.

[0071] Specifically, in one implementation, Pearson correlation calculation is achieved by extracting the illuminance value sequences of the guest room door area and the corridor lighting area within the same time period. Each sequence contains 30 continuously collected data points. The covariance of the two sequences is calculated by dividing the product of their respective standard deviations to obtain the correlation coefficient, which ranges from -1 to 1.

[0072] It should be noted that the independence assessment adopts a dual judgment mechanism. When the absolute value of the correlation coefficient is less than 0.3, it indicates that the lighting changes in the two areas are basically independent. At the same time, combined with the stability judgment of the illuminance difference, if the fluctuation range of the difference remains within the preset range, the independence is confirmed to meet the standard.

[0073] Specifically, the sampling interval in the real-time monitoring mode is dynamically adjusted according to the hotel's operating hours: 5 seconds during the day, 10 seconds at night, and 30 seconds in the early morning, balancing monitoring accuracy and system resource consumption. Each sensor node maintains consistency in sampling time through a time synchronization protocol.

[0074] For example, the construction process of the spatiotemporal data matrix includes three stages: data preprocessing, time alignment, and spatial mapping. The preprocessing stage removes outliers and imputes missing values ​​in the raw data. Time alignment achieves millisecond-level synchronization through a unified timestamp format. Spatial mapping establishes a correspondence between the physical location coordinates of the sensors and the rows and columns of the matrix. Each row of the matrix represents the time-series data of a monitoring point, and each column represents a snapshot of the spatial distribution of all monitoring points at a specific time, forming a complete spatiotemporal data representation.

[0075] Preferably, the length of the sliding window is set to include 10 consecutive sampling points. The window slides forward by one sampling point each time. Within each window, the mean, variance, and rate of change of illuminance are calculated. Data mutations are identified by comparing the statistical characteristics of adjacent windows. When the rate of change exceeds 30% of a preset threshold, it is marked as a mutation event.

[0076] In one embodiment, mutation detection employs a dual-threshold determination method, setting a high threshold to identify significant mutations and a low threshold to capture gradual changes. The two types of events are recorded and output separately to form a complete dynamic monitoring result.

[0077] S107. Based on the light intensity data and occupancy status signal, determine whether the light interference has been eliminated. If it has been eliminated, obtain the comparison data of illuminance before and after adjustment, interference elimination time, and guest room satisfaction feedback based on the matching degree between the guest room lighting demand level and the actual lighting environment, and establish a database for optimizing debugging parameters.

[0078] Interference status is determined based on illuminance data and occupancy status signals. The difference between the illuminance value at the monitoring point in front of the guest room door and the corridor lighting intensity is calculated. If the difference is less than the preset interference elimination threshold and the duration exceeds the stability time requirement, the light interference is determined to be eliminated, and the timestamp of interference elimination and the corresponding lighting control parameters are recorded. Based on the interference elimination determination result, various indicators of the guest room lighting requirement level and the actual lighting environment are obtained, including the target illuminance value, the measured illuminance value, and illuminance uniformity. The matching degree is determined by calculating the percentage deviation between the measured value and the target value. The initial illuminance data before adjustment and the stable illuminance data after adjustment are extracted to form an illuminance comparison dataset before and after adjustment. Based on the illuminance comparison dataset before and after adjustment, the time interval from the start of adjustment to interference elimination is calculated as the interference elimination time. At the same time, the lighting comfort rating submitted by the guest through the room control panel is obtained from the guest room management terminal. The rating is mapped to a satisfaction value according to a preset conversion rule, and the satisfaction value is associated with and stored with the lighting adjustment parameters. An optimization record is constructed using the interference elimination time, satisfaction value, and lighting adjustment parameters. Each record includes a scene identifier, initial illuminance, adjustment parameters, elimination time, and satisfaction rating. The optimization records of similar scenes are grouped using the K-means clustering algorithm, and the adjustment parameter with the highest satisfaction in each group is extracted as the recommended value. A debugging parameter optimization database containing scene classification index and parameter recommended values ​​is established.

[0079] Specifically, in one implementation, interference state determination is achieved through multi-level data analysis. First, real-time illuminance values ​​are collected from a monitoring point located at the center of the door frame, 1.5 meters above the ground. Data is collected 10 times per second, and the median is taken as the current illuminance value. Corridor lighting intensity is obtained from the corridor lighting sensor closest to the guest room door, and the attenuation coefficient of light transmission through the door gap is considered when calculating the difference between the two. When the difference remains below a preset interference elimination threshold for 60 consecutive seconds, the system determines that the light interference has been eliminated. The threshold is dynamically adjusted according to the guest room type: 30 lux for standard rooms, 20 lux for suites, and 15 lux for executive suites. Timestamps are recorded with millisecond accuracy, including three key time points: the start time of determination, the time when a stable state is reached, and the final confirmation time.

[0080] It should be noted that the recorded lighting control parameters contain multi-dimensional information, covering the dimming ratio, gradation rate, execution delay, and priority settings for each zone. These parameters are stored in a structured form, which facilitates subsequent data analysis and parameter optimization.

[0081] Specifically, the matching degree is calculated using a weighted scoring method. The deviation between the target illuminance value and the measured illuminance value accounts for 40% of the weight, illuminance uniformity accounts for 35%, and response time accounts for 25%. The deviation percentage is calculated using a formula: the deviation rate equals the absolute value of the difference between the measured value and the target value divided by the target value and then multiplied by 100%. When the deviation rate is below 10%, the score for this item is full; when the deviation rate is between 10% and 20%, the score decreases linearly; when it exceeds 20%, the score is zero.

[0082] For example, in the process of constructing the illuminance comparison dataset before and after adjustment, the system continuously collects illuminance data for 30 seconds before adjustment begins as an initial baseline, and calculates its mean and standard deviation. After adjustment is completed and stabilized, another 30 seconds of data is collected as the final state. The two sets of data are compared using time series analysis methods to extract the magnitude of change, adjustment rate, and stability indicators.

[0083] Preferably, the conversion of satisfaction ratings follows a five-level mapping rule. Guests give ratings through the five-star rating system on the room control panel, with 1 star corresponding to 20 points, 2 stars to 40 points, 3 stars to 60 points, 4 stars to 80 points, and 5 stars to 100 points. The system also records the time of rating submission and the rating modification history.

[0084] In one possible implementation, the K-means clustering algorithm comprises four stages: data preprocessing, initialization, iterative optimization, and result verification. The preprocessing stage standardizes the optimized records, mapping data of different dimensions to a uniform scale. Initialization uses the K-means++ method to select initial cluster centers, ensuring the dispersion of the centers. During iteration, each record is assigned to the nearest cluster center based on Euclidean distance, and the centroids of each cluster are recalculated as new cluster centers. Iteration continues until the distance the cluster centers have moved is less than 0.01 or the number of iterations reaches 100. The number of clusters K is determined using the elbow rule; by calculating the sum of squares within groups under different K values, the K value corresponding to the inflection point is selected. Each cluster represents a typical lighting scene, such as a "late-night rest scene," a "daytime cleaning scene," or an "evening transition scene."

[0085] Understandably, the five fields of the optimization record form a complete scene description. The scene identifier is uniquely identified by a combination of timestamp and regional code. The initial illuminance record shows the ambient light level before adjustment. The adjustment parameters contain detailed information on all control commands. The elimination duration reflects the system response efficiency. The satisfaction evaluation reflects the quality of user experience.

[0086] For example, the database's indexing mechanism employs a two-tier structure. The first tier uses a hash index based on scenario type for fast location; the second tier uses a B+ tree index based on time order to support range queries. Each scenario type stores the 100 most recent optimized records, with any excess automatically archived. During a query, the system first matches the current scenario type, then finds the top 5 records with the highest satisfaction levels within that type, and calculates the weighted average of their adjustment parameters as the recommendation parameters.

[0087] In one embodiment, the parameter recommendation strategy is dynamically adjusted based on the credibility of historical data. New scenarios use parameters from similar scenarios as initial values, and the parameters are gradually optimized as data accumulates. When a scenario has more than 20 optimization records and an average satisfaction score of more than 85 points, it is marked as a mature parameter and given priority for use.

[0088] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for debugging and evaluating a hotel building automation system, characterized in that, include: Collect light intensity data and area occupancy status signals at the junction of the corridor and guest rooms, compare the difference in light intensity data before and after the corridor lighting is improved, determine the deviation from the guest room reference lighting standard, and obtain the light spillover intensity; Based on the light spill intensity and the area occupancy status signal, the degree of light interference is classified to obtain the interference level, and the corridor lighting zones are adjusted accordingly. Extract the light spillover intensity that exceeds the guest room baseline lighting standard to determine the guest room lighting requirement level; obtain corridor cleaning requirement data, analyze the area function matching based on the guest room lighting requirement level and corridor cleaning requirement data, determine the lighting matching group of adjacent areas, and obtain the corridor lighting adjustment range and zone lighting intensity ratio; Based on the corridor lighting adjustment range and the zone lighting intensity ratio, a dynamic adjustment command is generated. The lighting intensity distribution after executing the dynamic adjustment command is collected, and the illuminance uniformity of each monitoring point is calculated to obtain the light transmittance and lighting uniformity values. Based on the lighting uniformity values, the light intensity data after executing the dynamic adjustment command, and the occupancy status signal, the light interference elimination status is determined. Based on the light intensity data, the occupancy status signal, and the guest room lighting requirement level, an optimization database of debugging parameters is constructed.

2. The hotel building automation system commissioning and evaluation method according to claim 1, characterized in that, The process involves collecting light intensity data and area occupancy status signals at the junction of the corridor and guest rooms, comparing the difference in light intensity data before and after the corridor lighting upgrade, determining the deviation from the guest room baseline lighting standard, and obtaining the light spillover intensity, including: An array of photosensitive sensors is deployed at the junction of the corridor and guest rooms to collect light intensity data from both sides. An infrared sensor collects the area occupancy status signal and records the initial light intensity baseline value over time. Based on the area occupancy status signal, the corridor lighting brightness is increased, and the increased light intensity data is collected. This data is then compared point-by-point with the initial light intensity baseline value to calculate the illuminance difference at each sensor node. The illuminance difference is compared with the guest room baseline lighting standard, and the portion exceeding the standard is extracted as the light overflow value. Based on the sensor node location and the light overflow value, a distance-inverse weighted method is used to calculate the light overflow intensity.

3. The hotel building automation system commissioning and evaluation method according to claim 1, characterized in that, The step of classifying the degree of light interference based on the light spill intensity and the area occupancy status signal, obtaining the interference level, and adjusting the corridor lighting zones includes: The light spill intensity is compared with a preset threshold to classify it as mild, moderate, or severe interference. Based on the room occupancy identifier and activity period information in the area occupancy status signal, weighting coefficients are assigned to calculate a weighted interference level value. The weighted interference level value is compared with the interference tolerance threshold corresponding to the room type to assess the interference level. Based on the interference level, the corridor section where the affected room is located is identified, a buffer zone is divided, the upper limit of the lighting intensity and the gradation rate of the buffer zone are adjusted, and the corridor lighting zoning is reconfigured.

4. The method for debugging and evaluating a hotel building automation system according to claim 1, characterized in that, The step of extracting the light spillover intensity that exceeds the guest room baseline lighting standard and determining the guest room lighting requirement level includes: The ratio of the light spillover intensity to the guest room baseline lighting standard is calculated. Weighting coefficients are assigned based on the distance of the monitoring point from the center of the guest room door, and a weighted summation is used to obtain a comprehensive interference index. If the comprehensive interference index exceeds a preset threshold, the location data of the blackout curtain and the guest's do-not-disturb setting data are obtained. Combined with the guest room occupancy status and time period attributes, the guest's activity status is determined, and lighting sensitivity weights are assigned. Based on the lighting sensitivity weights and the lighting demand baseline values ​​corresponding to the time period attributes, the adjusted lighting demand values ​​are calculated, and the guest room lighting demand level is determined.

5. The hotel building automation system commissioning and evaluation method according to claim 4, characterized in that, The location data of the blackout curtain and the guest do-not-disturb setting data are obtained. Combined with the room occupancy status and time period attributes, the guest activity status is determined, and lighting sensitivity weights are assigned, including: The percentage of the blackout curtain's opening / closing and the start and end times of the guest do-not-disturb setting data are obtained. Combined with the number of guests and the number of days in the guest room, the guest activity status is determined. If the percentage of the blackout curtain's opening / closing is below a threshold and it falls within the time period of the guest do-not-disturb setting data, the highest lighting sensitivity weight is assigned. If the blackout curtain's opening / closing indicates that it is open and it does not fall within the time period of the guest do-not-disturb setting data, a low lighting sensitivity weight is assigned.

6. The method for debugging and evaluating a hotel building automation system according to claim 1, characterized in that, The process of acquiring corridor cleaning requirement data, analyzing regional functional matching based on the guest room lighting requirement level and corridor cleaning requirement data, determining adjacent area lighting matching groups, and obtaining the corridor lighting adjustment range and zone lighting intensity ratio includes: Collect data on corridor cleaning needs, including the work location, duration, and area, to determine the overall cleaning lighting demand value. Normalize the guest room lighting demand level and the overall cleaning lighting demand value, and use a clustering algorithm to group them into regional matching sets. Based on the adjacency relationship and lighting demand difference of the regional matching sets, divide the lighting transition zone and calculate the corridor lighting adjustment range. Based on the lighting demand priority and spatial distribution of the regional matching sets, assign weights inversely proportional to the distance from the cleaning operation center to determine the zone lighting intensity ratio.

7. The hotel building automation system commissioning and evaluation method according to claim 1, characterized in that, The process involves generating dynamic adjustment commands based on the corridor lighting adjustment range and the zone lighting intensity ratio, collecting the lighting intensity distribution after executing the dynamic adjustment commands, calculating the illuminance uniformity at each monitoring point, and obtaining light transmittance and lighting uniformity values, including: Based on the corridor lighting adjustment range and the zone lighting intensity ratio, a dynamic adjustment command sequence containing the target brightness value and execution priority is generated; the dynamic adjustment command sequence is sent through the lighting control bus, the lighting intensity distribution after execution is collected, and a lighting intensity distribution matrix is ​​constructed; based on the lighting intensity distribution matrix, the standard deviation of the illuminance values ​​of adjacent monitoring points is calculated, the illuminance value at the door gap position is extracted as the light transmittance, and the lighting uniformity value is calculated.

8. The method for debugging and evaluating a hotel building automation system according to claim 1, characterized in that, The step of constructing an optimization database for debugging parameters based on the light intensity data, the occupancy status signal, and the guest room lighting requirement level includes: Based on the light intensity data and the occupancy status signal, calculate the illuminance difference between the guest room door and the corridor, determine the light interference elimination status, and record the interference elimination duration; based on the guest room lighting requirement level and the measured illuminance value, calculate the deviation percentage and generate illuminance comparison data before and after adjustment; obtain the lighting comfort score from the guest room management terminal and map it to a satisfaction value; based on the illuminance comparison data before and after adjustment, the interference elimination duration, and the satisfaction value, construct an optimization record containing scene identifiers and adjustment parameters, group them using a clustering algorithm, extract recommended values, and generate the debugging parameter optimization database.

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