Hotel building automatic control system debugging evaluation method

Abstract

The invention provides a hotel building automatic control system debugging evaluation method, which comprises the steps of collecting real-time illumination intensity data and an area occupancy state signal, identifying an illumination difference value by comparing numerical value differences before and after corridor illumination improvement, and determining illumination overflow intensity according to the illumination difference value and a guest room reference illumination standard difference; classifying the light interference degree according to the illumination overflow intensity and the area occupancy state signal to obtain an interference level evaluation result, and adjusting the corridor illumination partition according to the interference level; corridor cleaning demand data are obtained, regional function matching analysis is carried out according to the guest room lighting demand level and the corridor cleaning demand data, adjacent regional lighting matching groups are determined, and the corridor lighting adjustment amplitude and the partition lighting intensity ratio are obtained; and evaluating a light interference elimination effect and guest room illumination independence through the illumination uniformity numerical value, and obtaining illumination intensity data and an occupancy state signal after the adjustment instruction is executed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a hotel building automation system debugging evaluation method. BACKGROUND

[0002] In modern hotel management, building automation system as the core technology to realize intelligent operation, is crucial to improve service quality, optimize energy utilization and protect customer experience. Especially in the field of hotel lighting control, the system needs to balance multiple functional requirements to ensure environmental comfort and operational efficiency. However, in practical application, lighting control often faces dynamic conflicts in complex scenarios, especially in the interaction between public areas and private spaces, traditional methods are difficult to cope with the challenges brought by these dynamic changes. Existing lighting control schemes usually rely on preset lighting modes or simple timing switches, which are difficult to adapt to the dynamic needs of different areas and different time periods in the hotel. For example, cleaning operations require high-brightness lighting to ensure cleaning quality, while guests in the room may need low-light environment to ensure rest. The difference in demand leads to mutual interference of light environment between areas, especially in the light interaction problem between corridors and guest rooms, the existing methods lack the ability to perceive and dynamically adjust the real-time scene, and it is difficult to effectively balance the needs of multiple parties. For example, when cleaning personnel increase the brightness of corridor lighting, strong light may penetrate into the guest room through the door gap, disturbing the rest of the guests, and traditional sensors are difficult to capture such weak light transmittance and its specific impact on the guest room light environment. More importantly, directly turning on high-brightness lighting will affect the resting in-house guests, so it is necessary to combine the real-time occupancy state of the guest room and the guest's sleep time to dynamically adjust the corridor lighting partition range and brightness distribution, ensuring that the lighting needs of cleaning operations are met while avoiding light interference to the resting guests. Further, independent control of light environment between areas requires dynamic adjustment of lighting partitions based on perception data, and such adjustment must take into account the state of the guest room shading device and the guest's set do-not-disturb period. Therefore, how to realize dynamic adjustment of lighting partitions based on real-time light intensity data and area occupancy state between high-brightness demand of cleaning operations and guest room shading demand to avoid light interference and maintain the independence of regional light environment has become a key problem in hotel lighting control of building automation system. SUMMARY

[0003] The present application provides a hotel building automation system debugging evaluation method, mainly comprising:

[0004] The illumination intensity data and the area occupancy state signal at the junction of the corridor and the guest room are collected, the difference of the illumination intensity data before and after the corridor lighting is improved is compared, the deviation from the guest room standard lighting standard is determined, and the illumination overflow intensity is obtained; the light interference degree is classified according to the illumination overflow intensity and the area occupancy state signal, the interference level is obtained, and the corridor lighting partition is adjusted; the illumination overflow intensity exceeding the guest room standard lighting standard is extracted, and the guest room lighting demand level is determined; the corridor cleaning demand data is obtained, the area function matching is analyzed according to the guest room lighting demand level and the corridor cleaning demand data, the adjacent area lighting matching group is determined, the corridor lighting adjustment range and the partition lighting intensity ratio are obtained; the dynamic adjustment instruction is generated according to the corridor lighting adjustment range and the partition lighting intensity ratio, the lighting intensity distribution after the dynamic adjustment instruction is executed is collected, the illumination uniformity of each monitoring point is calculated, and the light transmittance and the illumination uniformity value are obtained; the light interference elimination state is determined according to the illumination uniformity value, the illumination intensity data after the dynamic adjustment instruction is executed, and the occupancy state signal; the debugging parameter optimization database is constructed according to the illumination intensity data, the occupancy state signal and the guest room lighting demand level.

[0005] Further, the illumination intensity data and the area occupancy state signal at the junction of the corridor and the guest room are collected, the difference of the illumination intensity data before and after the corridor lighting is improved is compared, the deviation from the guest room standard lighting standard is determined, and the illumination overflow intensity is obtained, including:

[0006] The photosensitive sensor array is arranged at the junction of the corridor and the guest room, the illumination intensity data of the corridor side and the guest room side is collected, the area occupancy state signal is collected by the infrared sensor, and the initial illumination intensity reference value of the time sequence is recorded; the corridor lighting brightness is triggered according to the area occupancy state signal, the illumination intensity data after the improvement is collected, and the initial illumination intensity reference value is compared point by point, and the illumination difference value of each sensor node is calculated; the illumination difference value is compared with the guest room standard lighting standard, the part exceeding the guest room standard lighting standard is extracted as the illumination overflow value, and the illumination overflow intensity is calculated by using the distance inverse ratio weighting method according to the sensor node position and the illumination overflow value.

[0007] Further, the light interference degree is classified according to the illumination overflow intensity and the area occupancy state signal, the interference level is obtained, and the corridor lighting partition is adjusted, including:

[0008] The light overflow intensity is compared with a preset threshold value, light, medium or heavy interference is marked, a weight coefficient is assigned according to the room occupancy state signal and the activity period information, a weighted interference degree value is calculated, the weighted interference degree value is compared with the interference tolerance threshold value corresponding to the room type, and the interference level is evaluated; according to the interference level, the corridor section where the affected room is located is identified, the buffer area is divided, the upper limit value of the lighting intensity and the gradient rate of the buffer area are adjusted, and the corridor lighting partition is reconfigured.

[0009] Further, the light overflow intensity exceeding the room reference lighting standard is extracted, and the room lighting demand level is determined, including:

[0010] The ratio of the light overflow intensity to the room reference lighting standard is calculated, a weight coefficient is assigned according to the distance of the monitoring point from the center of the room door, and a comprehensive interference index is obtained by weighted summation; if the comprehensive interference index exceeds a preset threshold value, the shade position data and the guest do-not-disturb setting data are obtained, the guest activity state is determined in combination with the room occupancy state and the period attribute, and the lighting sensitivity weight is assigned; according to the lighting sensitivity weight and the lighting demand reference value corresponding to the period attribute, the adjusted lighting demand value is calculated, and the room lighting demand level is determined.

[0011] Further, the shade position data and the guest do-not-disturb setting data are obtained, the guest activity state is determined in combination with the room occupancy state and the period attribute, and the lighting sensitivity weight is assigned, including:

[0012] The opening and closing percentage of the shade position data and the start and end time of the guest do-not-disturb setting data are obtained, the guest activity state is determined in combination with the number of occupants and the number of days of the room occupancy state, if the opening and closing percentage of the shade position data is lower than a threshold value and is in the period of the guest do-not-disturb setting data, the highest lighting sensitivity weight is assigned, and if the shade position data indicates opening and is not in the period of the guest do-not-disturb setting data, the low lighting sensitivity weight is assigned.

[0013] Further, corridor cleaning demand data is obtained, and according to the room lighting demand level and the corridor cleaning demand data, the regional function matching is analyzed, the adjacent area lighting matching group is determined, the corridor lighting adjustment range and the partition lighting intensity ratio are obtained, including:

[0014] The work position, time length and area range of collecting the corridor cleaning demand data are determined to determine a comprehensive cleaning lighting demand value; the guest room lighting demand grade and the comprehensive cleaning lighting demand value are normalized, a clustering algorithm is used for grouping to obtain a region matching set; a lighting transition zone is divided according to the adjacency relationship and lighting demand difference of the region matching set, and a corridor lighting adjustment range is calculated; a weight inversely proportional to the distance from the cleaning operation center is allocated according to the lighting demand priority and spatial distribution of the region matching set, and a partition lighting intensity ratio is determined.

[0015] Further, according to the corridor lighting adjustment range and the partition lighting intensity ratio, a dynamic adjustment instruction is generated, the lighting intensity distribution after the dynamic adjustment instruction is executed is collected, the illuminance uniformity of each monitoring point is calculated, the light transmission amount and the lighting uniformity value are obtained, including:

[0016] According to the corridor lighting adjustment range and the partition lighting intensity ratio, a dynamic adjustment instruction sequence containing a target brightness value and an execution priority is generated; the dynamic adjustment instruction sequence is sent through a lighting control bus, the lighting intensity distribution after execution is collected, and a lighting intensity distribution matrix is constructed; according to the lighting intensity distribution matrix, the standard deviation of the illuminance values of adjacent monitoring points is calculated, the illuminance value of the door gap position is extracted as the light transmission amount, and the lighting uniformity value is calculated.

[0017] Further, according to the light intensity data, the occupancy state signal and the guest room lighting demand grade, a debugging parameter optimization database is constructed, including:

[0018] According to the light intensity data and the occupancy state signal, the difference between the illuminance in front of the guest room and the corridor is calculated, the light interference elimination state is determined, and the interference elimination time length is recorded; according to the guest room lighting demand grade and the measured illuminance value, the deviation percentage is calculated, and the illuminance comparison data before and after adjustment is generated; the lighting comfort score is obtained from the guest room management terminal and is mapped into a satisfaction value; according to the illuminance comparison data before and after adjustment, the interference elimination time length and the satisfaction value, an optimization record containing a scene identifier and an adjustment parameter is constructed, a clustering algorithm is used for grouping, a recommended value is extracted, and the debugging parameter optimization database is generated.

[0019] The technical scheme provided by the embodiment of the application can include the following beneficial effects:

[0020] The application discloses a hotel building automatic control system debugging evaluation method, and aims at the interference problem caused by light overflow at the intersection of a hotel corridor and a guest room, identifies an illumination difference value and evaluates light overflow intensity by collecting light intensity and area occupancy state in real time, dynamically classifies light interference levels and determines a guest room lighting demand level in combination with a guest room check-in state, a do-not-disturb setting and cleaning needs. BRIEF DESCRIPTION OF DRAWINGS

[0021] Fig. 1 A flow chart of the hotel building automatic control system debugging evaluation method.

[0022] Fig. 2 A schematic diagram of the hotel building automatic control system debugging evaluation method.

[0023] Fig. 3 Another schematic diagram of the hotel building automatic control system debugging evaluation method. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the application will be clearly and completely described in connection with the drawings in the embodiments of the application. The described embodiments are only some of the embodiments of the application.

[0025] As Figs. 1-3 , the hotel building automatic control system debugging evaluation method can specifically include the following steps.

[0026] S101, real-time light intensity data and area occupancy state signals are collected, an illumination difference value is identified by comparing the numerical difference before and after the corridor lighting is improved, and light overflow intensity is determined according to the difference between the illumination difference value and the guest room reference lighting standard.

[0027] The light sensor array is arranged at a preset interval above the door frame at the intersection of the corridor and the guest room. Each sensor node collects real-time light intensity data on the corridor side and the guest room side, and simultaneously obtains a regional occupancy state signal through an infrared sensor, records the timestamp and position information of the cleaning personnel entering the corridor, and establishes an initial light intensity reference value containing a time sequence. According to the regional occupancy state signal, it is judged that the cleaning work starts, the corridor lighting brightness is triggered to increase, the light sensor array continuously collects the light intensity data after the lighting is increased, the light intensity data after the lighting is increased is compared with the initial light intensity reference value point by point, the illumination difference value of each sensor node position is calculated, and the illumination difference value is compared and analyzed with the preset guest room reference lighting standard. If the illumination difference value exceeds the preset overflow judgment threshold, the part exceeding the guest room reference lighting standard is extracted as the light overflow value of each monitoring point, and the overflow intensity spatial distribution is calculated according to the spatial position of each sensor node and the light overflow value through the inverse distance weighted method, wherein the weight coefficient is inversely proportional to the distance from the sensor to the guest room door gap, and the weighted overflow value of each monitoring point is summed to obtain the light overflow intensity.

[0028] Specifically, in an embodiment, the light sensor array adopts a matrix arrangement scheme, the sensor nodes are arranged in a U-shaped distribution along the upper edge of the door frame and the vertical edges on both sides, each node contains a bidirectional photosensitive element, respectively facing the corridor side and the guest room side, and realizing bidirectional monitoring of the door gap light transmission. The infrared sensor is integrated and packaged with the light sensor, and the moving track of the cleaning personnel is detected through the pyroelectric principle.

[0029] Specifically, the establishment process of the initial light intensity reference value includes multiple time dimension data collection. The ambient light reference value is collected in the early morning period when the hotel occupancy rate is low, and the natural light variation curve is collected in different time periods during the day to form a reference value matrix containing three-dimensional data of timestamp, position identifier and light intensity. The regional occupancy state signal is judged by the triggering frequency and duration of the infrared sensor. When a continuous movement signal is detected and the duration exceeds a preset threshold, it is determined that the cleaning work starts. The calculation of the illumination difference value uses the point-by-point comparison method. The light intensity data collected by each sensor node after the lighting is increased is subtracted from the corresponding reference value to obtain the original difference sequence, and then the moving average filter is used to eliminate transient interference to obtain the stable illumination difference value. The preset guest room reference lighting standard is dynamically set according to the hotel star rating and the guest room type. The reference lighting standard of a standard suite is 150 lux, and the reference lighting standard of a luxury suite is 200 lux.

[0030] Preferably, the weight coefficient calculation of the distance inverse weighting method is based on the Euclidean distance from the sensor node to the center line of the door gap, and the weight coefficient w = 1 / (d + a), where d is the distance value, and a is a smoothing factor to prevent the weight from being infinite when the distance is zero. The light spill value of each monitoring point is multiplied by the corresponding weight coefficient and then accumulated, and then divided by the sum of the weight coefficients to obtain the normalized weighted spill intensity. The spatial distribution is expanded to a continuous intensity field by an interpolation algorithm, and the bilinear interpolation method is used to estimate the spill intensity value in the area between the sensor nodes.

[0031] In one embodiment, the method can accurately identify the degree of light interference of corridor lighting on guest rooms, achieve precise adjustment of lighting control, and avoid the impact of cleaning operations on guest rest.

[0032] S102, classify the light interference degree according to the light spill intensity and the area occupancy state signal to obtain the interference level evaluation result, and adjust the corridor lighting partition according to the interference level.

[0033] According to the comparison between the light spill intensity value and the preset interference threshold, when the spill intensity is lower than the first threshold, it is marked as light interference, between the first threshold and the second threshold, it is marked as moderate interference, and when it exceeds the second threshold, it is marked as severe interference. At the same time, the room check-in identifier and the guest activity period information in the area occupancy state signal are extracted, a higher weight coefficient is given to the occupied room according to the check-in identifier, and a lower weight coefficient is given to the vacant room. The interference label is multiplied by the weight coefficient to obtain the weighted interference degree value. By comparing the weighted interference degree value with the interference tolerance threshold corresponding to the room type, the interference tolerance threshold of the standard room is set as the reference value, and the interference tolerance threshold of the suite and the executive room is set as the preset proportion of the reference value. When the weighted interference degree value is lower than 30% of the tolerance threshold, it is rated as no interference, between 30% and 60%, it is rated as slight interference, between 60% and 90%, it is rated as moderate interference, between 90% and 120%, it is rated as severe interference, and more than 120%, it is rated as extreme interference, to obtain the interference level evaluation result. According to the interference level evaluation result, the corridor lighting partition is dynamically adjusted. If the interference level is moderate or above, the corridor section where the affected room is located is identified, a buffer area is formed by extending a preset distance to both sides of the room door, the lighting devices in the buffer area are separated from the original partition, and an independent lighting control partition is established. By adjusting the lighting intensity upper limit value and the gradual change rate parameter of the independent partition, the corridor lighting partition is reconfigured.

[0034] Specifically, in an embodiment, the setting of the multi-level threshold is based on hotel lighting standards and human visual comfort research, the first threshold is set to 50 lux, and the second threshold is set to 100 lux, which correspond to the transition point of human eye from dark adaptation to light adaptation and the point of obvious discomfort, respectively. The assignment of the weight coefficient adopts a binary judgment mechanism, the weight coefficient of the occupied guest room is set to 1.0, and the weight coefficient of the vacant guest room is set to 0.3, and meanwhile, according to the guest activity period, the weight coefficient is further weighted by 1.5 in the night rest period, and the original weight is kept unchanged in the daytime activity period. The interference tolerance threshold is differentiated according to the guest room grade, the reference value of the standard room is set to 80 lux, the reference value of the suite is set to 0.75 times of the reference value, i.e. 60 lux, and the reference value of the executive room is set to 0.6 times of the reference value, i.e. 48 lux.

[0035] Exemplarily, the five-level evaluation standard is precisely divided by percentage intervals, when the ratio of the weighted interference degree value to the tolerance threshold is less than 0.3, it is determined as no interference level, at this time the corridor lighting has little effect on the guest room; when the ratio is in the interval of 0.3 to 0.6, it is light interference, the guest may occasionally feel it but it does not affect the rest; when the ratio is in the interval of 0.6 to 0.9, it is moderate interference, which starts to affect the guest room environment comfort; when the ratio is in the interval of 0.9 to 1.2, it is serious interference, which obviously affects the guest rest quality; when the ratio exceeds 1.2, it is extreme interference, which has seriously damaged the independence of the guest room light environment. In the ratio calculation process, the timestamp and duration of each evaluation result are recorded in real time, which are used for subsequent lighting control optimization.

[0036] Preferably, the division of the buffer area adopts a dynamic expansion mode, in the initial state, the affected guest room door is taken as the center to extend 2 meters to both sides, when the interference level reaches serious or extreme, the extension distance is automatically increased to 3 meters, forming a larger isolation interval.

[0037] In an embodiment, the lighting control parameters of the independent partition include two key parameters of lighting intensity upper limit value and gradual change rate, the lighting intensity upper limit value is dynamically set according to the interference level, the upper limit value is reduced to 70% of the normal value in moderate interference, to 50% in serious interference, and to 30% in extreme interference; the gradual change rate controls the change speed of lighting brightness, which is set to not more than 5% per second to avoid sudden change impacting vision, and the balance between corridor functional lighting and guest room comfortable environment is realized through this gradual adjustment.

[0038] S103, an interference index is obtained by extracting the light intensity overflow exceeding the reference lighting standard, if the interference index exceeds the preset interference threshold, the current shade position and guest do-not-disturb setting data are obtained, and the guest room lighting demand level is evaluated in combination with the guest room occupancy state and time period demand.

[0039] The illumination overflow intensity value exceeding the reference lighting standard is extracted, the ratio of the overflow intensity of each monitoring point to the reference lighting standard is calculated, the weight coefficient is determined according to the distance of the monitoring point from the center of the guest room door, the weight coefficient decreases by a preset attenuation rate for each additional meter of distance, the comprehensive interference index is obtained by using the weighted summation and then dividing by the total weight, and the comprehensive interference index reflects the overall interference degree of the corridor lighting on the guest room. If the comprehensive interference index exceeds the preset interference threshold, the current shade position data is obtained through the guest room management interface, including the opening and closing percentage of the shade, and the do-not-disturb setting data is extracted from the guest service record, including the do-not-disturb start time, end time and do-not-disturb level, the do-not-disturb level is divided into two levels of normal do-not-disturb and deep do-not-disturb, and the shade opening and closing percentage and the do-not-disturb setting data jointly constitute the guest room privacy protection state parameter. According to the guest room privacy protection state parameter, in combination with the guest room occupancy state obtained from the hotel management database, including the number of occupants and the occupancy days, the guest activity state is judged through the preset state judgment rule, if the shade is closed for more than a preset closing threshold and is in a do-not-disturb period, it is judged as a rest state and is given the highest lighting sensitivity weight, and if the shade is opened and is not in a do-not-disturb period, it is judged as an active state and is given a lower lighting sensitivity weight. Based on the lighting sensitivity weight, the current period attribute is obtained, the lighting demand reference value corresponding to the period attribute is multiplied by the lighting sensitivity weight to obtain an adjusted lighting demand value, the adjusted lighting demand value is compared with a preset grade division threshold to determine the guest room lighting demand level, and the guest room lighting demand level includes five levels of very low demand, low demand, medium demand, high demand and very high demand.

[0040] Specifically, in an embodiment, the calculation of the comprehensive interference index adopts a spatial distance attenuation model, based on the optical propagation principle, the illumination intensity decreases inversely with the distance. In the specific calculation process, the original overflow intensity value of each monitoring point is first obtained, which is the difference between the measured illumination intensity and the guest room reference lighting standard. The determination of the weight coefficient adopts an exponential attenuation function, w=e (-αd) where d is the distance of the monitoring point from the center of the guest room door, and a is the attenuation coefficient, which is dynamically adjusted according to the corridor width and the building structure. When weighted summation is performed, the overflow intensity value of each monitoring point is multiplied by the corresponding weight coefficient and then accumulated, and then normalized by dividing by the sum of all weight coefficients, to obtain a comprehensive interference index value ranging from 0 to 1, reflecting the overall interference degree of the corridor lighting on the guest room. The decreasing law of the weight coefficient follows the optical attenuation characteristics, and the weight coefficient decreases by a preset attenuation rate for each additional 0.5 meters from the center of the guest room door, and the attenuation rate is adjusted according to the light reflection coefficient of the corridor material, with the attenuation rate set to 0.15 for smooth marble floor and 0.25 for carpet material.

[0041] Specifically, the shade position data is acquired in real time by the position encoder of the smart curtain controller, the encoder has a resolution of 1% accuracy, and the opening and closing percentage data of the shade is transmitted to the building automation host in real time. The do not disturb setting data adopts a structured storage method, including four fields of start timestamp, end timestamp, do not disturb level identifier and trigger source. The normal do not disturb level allows emergency interruptions, and the deep do not disturb level completely shields all external interference. The trigger source records the starting mode of the do not disturb setting, including guest manual setting, system automatic triggering and front desk remote setting, and the do not disturb settings of different sources have different priority weights.

[0042] Exemplarily, the guest room privacy protection state parameter is formed by multi-dimensional data fusion, and the opening and closing percentage of the shade is converted into a shade level, 0-20% is completely opened, 20-50% is partially shaded, 50-80% is mostly shaded, and 80-100% is completely shaded. The do not disturb period is compared with the current time to determine whether it is in the do not disturb protection period. The two types of data are fused by logical AND operation to form four privacy protection states: no protection state, light protection state, medium protection state and deep protection state.

[0043] In one possible implementation, the state determination rule is implemented based on a multi-condition logical decision tree. The first layer determines the shade state, and when the shade is closed more than a preset closing threshold of 80%, the second layer is entered. The second layer determines the do not disturb setting, and if the current time is in the do not disturb period, the third layer is entered. The third layer determines the check-in state, and if the guest room is in the checked-in state and the check-in time is more than 24 hours, it is determined as the rest state, and the lighting sensitivity weight is 1.0. If the shade is opened more than 50% and it is not in the do not disturb period, it is directly determined as the activity state, and the lighting sensitivity weight is 0.3. The state between the two is linearly interpolated according to the number of check-in days, the weight of the first day is 0.5, and the weight increases by 0.1 for each additional day, and the highest does not exceed 0.8. The determination rule is executed every 30 seconds to ensure real-time reflection of the change of the guest state.

[0044] It can be understood that the time period attribute is divided into five intervals according to the hotel operation characteristics, the early morning period is 0:00-6:00, the early 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 period corresponds to a different baseline lighting demand value.

[0045] For example, in the process of determining the lighting demand level, the reference demand value corresponding to the current period is first obtained. The reference value is 30 lux in the early morning and late night period, 50 lux in the early morning and late evening period, and 80 lux in the daytime period. The reference value is multiplied by the lighting sensitivity weight to obtain the adjusted lighting demand value. The final level is determined by comparing with the five level thresholds. 0-20 is extremely low demand, 20-40 is low demand, 40-60 is medium demand, 60-80 is high demand, and above 80 is extremely high demand. Each level corresponds to a different corridor lighting control strategy, achieving precise lighting management.

[0046] In an embodiment, the actual lighting demand of the guest room can be accurately evaluated by the above method, the lighting control strategy is dynamically adjusted according to the work and rest habits and privacy demand of the guest, and personalized light environment management is realized.

[0047] S104, obtain corridor cleaning demand data, and perform regional function matching analysis according to the guest room lighting demand level and the corridor cleaning demand data to determine an adjacent regional lighting matching group, and obtain a corridor lighting adjustment range and a partitioned lighting intensity ratio.

[0048] The corridor cleaning demand data includes the cleaning operation starting position, the expected operation time length, the cleaning area range, and the required lighting intensity value. The movement trajectory and operation progress of the cleaning personnel are collected in real time through a cleaning management terminal. The basic lighting demand value is determined according to different cleaning task types, wherein the ground cleaning demand illuminance is a first preset illuminance value, and the wall surface cleaning demand illuminance is a second preset illuminance value. The comprehensive cleaning lighting demand value is calculated according to the operation type and the regional characteristics. According to the guest room lighting demand level and the comprehensive cleaning lighting demand value, the regional function matching is performed. The lighting demand values of the regions are normalized and taken as feature vectors. A clustering algorithm is used to group regions with similar lighting demand characteristics. The Euclidean distance between the clustering centers represents the demand difference degree between the regions. A regional matching set is obtained, which includes corridor segments that can be uniformly controlled and isolated segments that need to be independently controlled. Based on the regional matching set, the spatial adjacency relationship and the lighting demand difference value of adjacent regions are extracted. If the difference value exceeds a preset compatibility threshold, the adjacent region pair is marked as a conflict region pair. An illumination transition zone is inserted between the conflict region pair. The lighting intensity in the transition zone decreases from the high demand region to the low demand region in a linear interpolation manner. The adjacent regional lighting matching group is determined. According to the lighting demand priority and the spatial distribution characteristics of each region in the adjacent regional 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 adjacent guest room lighting demand level. According to the adjustment range and the number of regions in each matching group, the partitioned lighting intensity ratio is determined according to the weight distribution principle in inverse proportion to the distance from the cleaning operation center.

[0049] Specifically, in an embodiment, the cleaning demand data is obtained by multi-source information fusion, and the cleaning management terminal is equipped with an ultra-wideband positioning tag to track the accurate position of the cleaning personnel in the corridor in real time, with a positioning accuracy of centimeter level. The cleaning operation type is marked through a task selection interface on the terminal, including four types of ground deep cleaning, wall stain treatment, ceiling dust removal, and regular maintenance.

[0050] Specifically, the calculation of the comprehensive cleaning lighting demand value adopts a weighted superposition method, and a basic illuminance value is set according to the visual requirements of different cleaning tasks. The first preset illuminance value for ground deep cleaning is set to the interval of 300-350 lux, and the second preset illuminance value for wall stain treatment is set to the interval of 200-250 lux. When multiple cleaning tasks are performed simultaneously, the maximum value of the illuminance demand of each task is taken as the basic value, and then the basic value is corrected according to the material reflectivity of the cleaning area. The correction coefficient of dark carpet area is 1.2, and the correction coefficient of light marble area is 0.9. The corrected value is the comprehensive cleaning lighting demand value.

[0051] It should be noted that the normalization processing of the feature vector adopts the minimum-maximum standardization method, which maps the lighting demand values of each area to the interval of 0 to 1, eliminating the influence of different dimensions on the clustering results.

[0052] By way of example, the implementation process of the clustering algorithm includes three stages of feature extraction, distance calculation, and grouping iteration. In the feature extraction stage, the lighting demand value, spatial position coordinate, and functional attribute of each corridor section are combined into a three-dimensional feature vector. The weighted Euclidean distance is used for distance calculation, and the weights of the lighting demand dimension, the spatial position dimension, and the functional attribute dimension are set to 0.5, 0.3, and 0.2, respectively. In the grouping iteration process, the initial clustering center is selected by the K-means++ method to ensure the dispersion of the initial center points. The iteration process continues until the moving distance of the clustering center is less than the preset convergence threshold, usually 15-20 iterations. In the formed clustering result, the lighting demand difference of the areas in the same category does not exceed 15%, which can be used as a unified control unit.

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

[0054] In a possible implementation, the setting of the lighting transition zone adopts a multi-segment linear interpolation method, and a transition zone with a width of 2-3 meters is established between the conflict area pairs. The transition zone is divided into 5 interpolation nodes, the first node maintains the lighting intensity of the high demand area, the fifth node is reduced to the lighting intensity of the low demand area, and the middle three nodes are set according to the equal difference decreasing rule. Each node corresponds to a group of independently controllable lighting lamps, and the smooth transition is realized by adjusting the brightness of each group of lamps. The interpolation calculation formula is Li=Lh-(Lh-Ll)×i / n, wherein 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] It can be understood that the calculation of the corridor lighting adjustment range needs to comprehensively consider the actual needs of the cleaning operation and the tolerance limit of the guest room. The upper limit of the lighting threshold corresponding to the lighting demand level of the guest room is obtained by looking up the table, the upper limit corresponding to the extremely low demand level is 100 lux, the upper limit corresponding to the low demand level is 150 lux, the upper limit corresponding to the medium demand level is 200 lux, and the upper limit corresponding to the high demand and extremely high demand levels is 250 and 300 lux respectively.

[0056] For example, the implementation of the distance inverse weight allocation principle is based on the physical attenuation law of illumination intensity, and the weight calculation formula is weight wi=1 / (di+1), wherein di is the distance from the i th area to the cleaning operation center, and 1 is added to avoid zero division error when the distance is zero. The lighting intensity ratio of each area is determined by weight normalization, which ensures that the total lighting power does not exceed the preset upper limit.

[0057] In an embodiment, by using the above-mentioned area function matching and lighting intensity matching method, the dynamic balance between the cleaning operation lighting demand and the guest room comfort demand is realized, the visual requirements of the cleaning operation are met, and the interference to the guest room environment is minimized.

[0058] S105, according to the corridor lighting adjustment range and the partition lighting intensity matching, a dynamic adjustment instruction of the corridor lighting brightness adjustment range and the adjustment time is determined, and the lighting intensity distribution after the execution of the adjustment instruction is obtained, the light transmittance is extracted by measuring the illuminance uniformity of each monitoring point, and the lighting uniformity value is evaluated by the light transmittance analysis.

[0059] According to the corridor lighting adjustment amplitude and the partition lighting intensity ratio, a dynamic adjustment instruction sequence is constructed, each instruction contains four parameters of target lighting area number, target brightness value, gradual change time length and execution priority. When it is detected that the cleaning personnel enters a specific area and the guest room interference risk is lower than the preset threshold, the adjustment opportunity is determined according to the current time and the guest room occupancy state, and the instruction queue is formed according to the priority order. The instruction queue is sent to the corridor lighting controller through the lighting control bus, and the controller adjusts the driving current of each partition lighting lamp after analyzing the instruction parameters, gradually changes the output power according to the specified gradual change time length, records the response time and actual output value of each control node during the execution process, and obtains the instruction execution feedback data containing the execution completion flag and the actual brightness value. After confirming the adjustment completion according to the instruction execution feedback data, the actual lighting intensity value of each monitoring point is collected through the pre-arranged photosensitive sensor array 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 evaluate the local illuminance uniformity, and the illuminance value of the door gap position sensor is extracted as the light transmission amount. Based on the light transmission amount and the lighting intensity distribution matrix, the lighting uniformity value is obtained by dividing the standard deviation of all elements in the matrix by the average value, and if the lighting uniformity value exceeds the preset uniformity threshold, the brightness difference between adjacent areas is adjusted, the gradient change rate is reduced, and the lighting intensity of each area is redistributed to obtain the lighting uniformity evaluation result that meets the uniformity requirement.

[0060] Specifically, in an embodiment, the construction of the dynamic adjustment instruction sequence adopts a hierarchical encoding mechanism, and each instruction is encoded into a 32-bit data packet in a fixed format. The target lighting area number occupies 8 bits, which can identify 256 independent control areas; the target brightness value occupies 10 bits, which provides 1024 levels of brightness adjustment accuracy; the gradual change time length occupies 8 bits, which supports a gradual change process of 0.1 seconds to 25.5 seconds; the execution priority occupies 4 bits, which defines 16 priority levels, and the remaining 2 bits are used as check bits. The instruction sequence is sorted according to spatial proximity and functional correlation, and the adjustment instructions of adjacent areas are arranged continuously to reduce the visual impact of lighting mutations. When the execution time windows of multiple instructions overlap, the instructions with higher priority are executed first, and the instructions with the same priority are executed in order of area number.

[0061] Specifically, the determination of the adjustment opportunity considers factors in three dimensions: the position of the cleaning personnel, the guest room interference risk and the system load state. The position of the cleaning personnel is obtained by real-time positioning, and the pre-adjustment is triggered when it enters the target area within 5 meters; the guest room interference risk is calculated according to the current time, the guest room occupancy state and the historical complaint records; the system load state reflects the number of adjustment tasks currently being executed.

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

[0063] Preferably, the adjustment of the driving current is realized by pulse width modulation technology, the modulation frequency is set to 20kHz or above to avoid flicker perceived by the human eye. The gradual change process adopts S-shaped curve instead of linear change, the starting and ending stages change slowly, and the middle stage changes faster, which conforms to the adaptation characteristics of the human eye to light changes. The power regulation range is from 10% to 100% of the rated power, and the regulation accuracy of each stage is 0.1%, realizing smooth stepless dimming.

[0064] Illustratively, the photosensitive sensor array is deployed according to the principle of grid arrangement, one monitoring point is set every 3 meters in the corridor, and one monitoring point is added on each side of the guest room door, forming a high-density monitoring network.

[0065] In one possible implementation, the construction and analysis process of the lighting intensity distribution matrix includes three stages of data acquisition, outlier processing and spatial interpolation. In the data acquisition stage, each sensor node samples synchronously, the sampling frequency is 10Hz, and after each acquisition, the median filtering of 5 consecutive samples is performed to eliminate transient interference. The 3σ criterion is used for outlier processing, and the data points deviating from the mean value by more than 3 times the standard deviation are marked as outliers and replaced by the mean value of adjacent points. The Kriging interpolation method is used for spatial interpolation, and according to the known illumination value and spatial correlation of the monitoring points, the illumination value of the position where the sensor is not arranged is estimated to form a continuous lighting intensity distribution field. Each element in the matrix not only contains the illumination value, but also contains the time stamp, sensor state flag and data quality index, providing complete data support for subsequent analysis.

[0066] It can be understood that the extraction of the amount of light transmission focuses on the light leakage at the door gap position, and the illumination difference of the sensors on both sides of the door gap directly reflects the degree of light transmission. When the difference exceeds the preset threshold, it indicates that there is obvious light penetration phenomenon.

[0067] For example, the calculation of the lighting uniformity value adopts statistical method, first calculates the arithmetic mean of the illumination values of all monitoring points as the reference, then calculates the standard deviation of each point deviating from the mean value, and the ratio of the two is the coefficient of variation. The smaller the coefficient is, the more uniform the lighting is. When the coefficient of variation exceeds 0.3, the gradient optimization program is started, and the brightness difference of the adjacent area is adjusted to make the illumination distribution more gentle. The optimization process adopts iterative method, and the adjustment amplitude of each time does not exceed 10% of the current value, to avoid oscillation caused by excessive adjustment.

[0068] In an embodiment, the gradient optimization adjustment is achieved by redistributing the lighting intensity of each region, reducing the output of high-brightness regions and increasing the output of low-brightness regions to balance the lighting distribution while maintaining the total power.

[0069] S106, evaluate the light interference elimination effect and the guest room lighting independence through the lighting uniformity value, obtain the light intensity data and the occupancy state signal after executing the adjustment instruction.

[0070] By comparing the lighting uniformity value with the 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 value of the guest room door area and the corridor lighting area is extracted, the Pearson correlation is calculated for the illuminance value sequences of the two areas at the same time, if the correlation coefficient is lower than the preset independence threshold, it is confirmed that the guest room lighting independence meets the standard, the light interference elimination effect evaluation value and the independence evaluation value are obtained. According to the light interference elimination effect evaluation value and the independence evaluation value, the real-time monitoring mode of the sensor array is started, the sampling interval is set to the preset time period, each sensor node synchronously collects the light intensity data of the current position, including the real-time illuminance value and the illuminance change rate, and the occupancy state signal of the infrared sensor is obtained. The occupancy state signal includes the personnel existence mark, the moving direction and the stay time. The light intensity data and the occupancy state signal are time-aligned and space-mapped to construct a space-time data matrix, the rows of the matrix represent different monitoring points, and the columns represent sampling values at different times. Through setting a fixed length time window to gradually slide and extract data trends, when the light intensity or the occupancy state is detected to be mutated, the adjusted real-time light intensity data and the occupancy state signal are output.

[0071] Specifically, in an embodiment, the Pearson correlation calculation is achieved by extracting the illuminance value sequences of the guest room door area and the corridor lighting area in the same time period, each sequence contains 30 consecutive data points, the covariance of the two sequences is calculated, and the product of the standard deviations is obtained. The correlation coefficient is obtained, the value range is between-1 and 1.

[0072] It should be noted that the independence evaluation adopts a double judgment mechanism, when the absolute value of the correlation coefficient is less than 0.3, it indicates that the lighting changes of the two regions are basically independent, and the stability of the illuminance difference value is judged at the same time, if the difference value fluctuation range remains in the preset range, the independence is confirmed to meet the standard.

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

[0074] Exemplarily, the construction process of the spatio-temporal data matrix includes three links of data preprocessing, time alignment and space mapping. The preprocessing stage removes outliers and fills in missing values of the original data, the time alignment realizes millisecond-level synchronization through unified timestamp format, and the space mapping establishes the corresponding relationship between the matrix rows and columns according to the physical position coordinates of the sensors. Each row of the matrix represents the time series data of a monitoring point, and each column represents the spatial distribution snapshot of all monitoring points at a specific time, forming a complete spatio-temporal data representation.

[0075] Preferably, the length of the sliding window is set to contain 10 consecutive sampling points, the window slides forward by one sampling point each time, the mean, variance and change rate of the illumination are calculated in each window, the data mutation is identified by comparing the statistical features of adjacent windows, and the mutation event is marked when the change rate exceeds 30% of the preset threshold.

[0076] In one embodiment, the mutation detection adopts a double-threshold decision method, a high threshold is set to identify significant mutations, and a low threshold is set to capture gradual changes, the two types of events are recorded and output respectively, and a complete dynamic monitoring result is formed.

[0077] S107, according to the light intensity data and the occupancy state signal, it is judged whether the light interference is eliminated, if eliminated, according to the matching degree of the guest room lighting demand level and the actual lighting environment, the illumination comparison data before and after adjustment, the interference elimination time length and the guest room satisfaction feedback are obtained, and a debugging parameter optimization database is established.

[0078] The interference state is determined according to the light intensity data and the occupancy state signal. The difference between the illumination value of 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 stable duration requirement, it is determined that the light interference has been eliminated, and the timestamp of interference elimination and the corresponding lighting control parameter are recorded. Based on the interference elimination determination result, the guest room lighting demand level and the indicators of the actual lighting environment are obtained, including the target illumination value, the measured illumination value and the illumination uniformity. The matching degree is determined by calculating the deviation percentage of the measured value and the target value. The initial illumination data before adjustment and the stable illumination data after adjustment are extracted to form the illumination comparison data set before and after adjustment. According to the illumination comparison data set before and after adjustment, the time interval from the start of adjustment to the elimination of interference is calculated as the interference elimination duration. At the same time, the lighting comfort score submitted by the guest through the room control panel is obtained from the guest management terminal. The score is mapped to a satisfaction value according to the preset conversion rule, and the satisfaction value is associated and stored with the lighting adjustment parameter. The interference elimination duration, the satisfaction value and the lighting adjustment parameter are used to construct an optimization record. Each record contains scene identification, initial illumination, adjustment parameter, elimination duration and satisfaction evaluation. The optimization records of similar scenes are grouped by K-means clustering algorithm. 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 recommendation value is established.

[0079] Specifically, in an embodiment, the interference state determination is realized by multi-level data analysis. First, the real-time illumination value of the monitoring point in front of the guest room door is collected. The monitoring point is located at the center of the door frame, 1.5 meters high from the ground. 10 data are collected per second and the median is taken as the current illumination value. The corridor lighting intensity is obtained by the closest corridor lighting sensor to the guest room door. The difference is calculated considering the attenuation coefficient of the door gap light transmission. When the difference remains below the preset interference elimination threshold for 60 seconds, the system determines that the light interference has been eliminated. The threshold is dynamically adjusted according to the type of guest room. The threshold for standard room is set to 30 lux, for suite room to 20 lux, and for executive suite room to 15 lux. The timestamp is recorded to the millisecond level, including the start time, the stable state time and the final confirmation time.

[0080] It should be noted that the record of lighting control parameters contains multi-dimensional information, covering the dimming ratio, gradual change rate, execution delay and priority setting of each partition. These parameters are stored in a structured form, which facilitates subsequent data analysis and parameter optimization.

[0081] Specifically, the calculation of the matching degree adopts the weighted scoring method, the deviation of the target illuminance value and the measured illuminance value accounts for 40% of the weight, the illuminance uniformity accounts for 35% of the weight, and the response time accounts for 25% of the weight. The deviation percentage is calculated by the formula, and the deviation rate is equal to the absolute value of the difference between the measured value and the target value divided by the target value and multiplied by 100%. When the deviation rate is less than 10%, the score is full marks; when the deviation rate is between 10% and 20%, the score decreases linearly; when the deviation rate exceeds 20%, the score is zero.

[0082] Exemplarily, in the construction process of the pre-adjustment and post-adjustment illuminance contrast data set, the system continuously collects 30 seconds of illuminance data as the initial reference before the adjustment starts, and calculates the average value and standard deviation thereof. After the adjustment is completed and stabilized, 30 seconds of data are collected as the final state. The two sets of data are compared by time series analysis method to extract the change amplitude, adjustment rate and stability index.

[0083] Preferably, the conversion of the satisfaction score follows a five-level mapping rule. The guest gives a score through a five-star evaluation system of the room control panel, 1 star corresponds to 20 points, 2 stars correspond to 40 points, 3 stars correspond to 60 points, 4 stars correspond to 80 points, and 5 stars correspond to 100 points. The system also records the time of score submission and the history of score modification.

[0084] In one possible implementation, the implementation of the K-means clustering algorithm includes four stages of data preprocessing, initialization, iterative optimization and result verification. The preprocessing stage performs standardization processing on the optimization records, and maps data of different dimensions to a unified scale. The initialization adopts the K-means++ method to select the initial clustering center, and ensures the dispersion of the center point. In the iteration process, each record is assigned to the nearest clustering center according to the Euclidean distance, and then the centroid of each class is recalculated as the new clustering center. The iteration continues until the moving distance of the clustering center is less than 0.01 or the number of iterations reaches 100. The elbow rule is used to determine the number of clusters K, which selects the K value corresponding to the inflection point by calculating the within-group sum of squares under different K values. Each cluster represents a typical lighting scene, such as "night rest scene", "daytime cleaning scene" and "evening transition scene".

[0085] It can be understood that the five fields of the optimization record form a complete scene description. The scene identification is uniquely identified by the combination of the time stamp and the area code. The initial illuminance record is the ambient light level before adjustment. The adjustment parameter contains detailed information of all control instructions. The elimination duration reflects the response efficiency of the system. The satisfaction evaluation reflects the quality of user experience.

[0086] For example, the index mechanism of the database adopts a double-layer structure, the first layer establishes a hash index according to the scene type to realize fast positioning, and the second layer establishes a B+ tree index according to time sequence to support range query. The latest 100 optimization records are stored under each scene type, and the excess part is automatically archived. When querying, the current scene type is matched first, then the top 5 records with the highest satisfaction are found under the type, and the weighted average value of the adjustment parameters is calculated as the recommended parameters.

[0087] In an embodiment, the parameter recommendation strategy is dynamically adjusted according to the credibility of historical data. The parameters of similar scenes are used as initial values for new scenes, and the parameters are gradually optimized as data accumulates. When the optimization records of a scene exceed 20 and the average satisfaction exceeds 85 points, the scene is marked as a mature parameter, and the parameter is preferentially recommended for use.

[0088] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can make equivalent replacements or changes to the technical range disclosed in the present application according to the technical solution and inventive concept of the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for commissioning evaluation of a hotel building automation system, the method comprising: The method comprises the following steps: Collecting light intensity data and area occupancy state signals at the intersection of the corridor and the guest room, comparing the difference of the light intensity data before and after the corridor lighting is improved, determining the deviation from the guest room baseline lighting standard, and obtaining the light overflow intensity; Classifying the light interference degree according to the light overflow intensity and the area occupancy state signal, obtaining the interference level, and adjusting the corridor lighting partition; Extracting the light overflow intensity exceeding the guest room baseline lighting standard to determine the guest room lighting demand level; obtaining corridor cleaning demand data, and analyzing the area function matching according to the guest room lighting demand level and the corridor cleaning demand data to determine the adjacent area lighting matching group, and obtaining the corridor lighting adjustment range and the partition lighting intensity ratio; According to the corridor lighting adjustment range and the partition lighting intensity ratio, a dynamic adjustment instruction is generated, the lighting intensity distribution after the dynamic adjustment instruction is executed is collected, the illumination uniformity of each monitoring point is calculated, the light transmission amount and the lighting uniformity value are obtained; according to the lighting uniformity value, the light intensity data and the occupancy state signal after the dynamic adjustment instruction is executed, the light interference elimination state is determined; according to the light intensity data, the occupancy state signal and the guest room lighting demand level, a debugging parameter optimization database is constructed.

2. The method of claim 1, wherein, The method comprises the following steps: Arranging a photosensitive sensor array at the intersection of the corridor and the guest room, collecting the light intensity data on the corridor side and the guest room side, collecting the area occupancy state signal through an infrared sensor, and recording the initial light intensity reference value of the time sequence; triggering the corridor lighting brightness to be improved according to the area occupancy state signal, collecting the light intensity data after the improvement, comparing the initial light intensity reference value point by point, and calculating the illumination difference value of each sensor node; comparing the illumination difference value with the guest room baseline lighting standard, extracting the part exceeding the guest room baseline lighting standard as the light overflow value, and calculating the light overflow intensity by using the distance inverse ratio weighting method according to the sensor node position and the light overflow value.

3. The method of claim 1, wherein, The method comprises the following steps: Comparing the light overflow intensity with a preset threshold value to mark light, medium or heavy interference, assigning a weight coefficient according to the guest room check-in identifier and activity period information in the area occupancy state signal, calculating the weighted interference degree value; comparing the weighted interference degree value with the interference tolerance threshold corresponding to the guest room type to evaluate the interference level; according to the interference level, identifying the corridor section where the affected guest room is located, dividing the buffer area, adjusting the upper limit value and the gradual change rate of the lighting intensity of the buffer area, and reconfiguring the corridor lighting partition.

4. The method of claim 1, wherein, The method comprises the following steps: The ratio of the light spill intensity to the guest room reference lighting standard is calculated, a weight coefficient is assigned according to the distance of the monitoring point from the center of the guest room door, and a weighted sum is used to obtain a comprehensive interference index; if the comprehensive interference index exceeds a preset threshold, the shade position data and the guest Do Not Disturb setting data are obtained, the guest activity state is determined in combination with the guest room occupancy state and the time period attribute, and a lighting sensitivity weight is assigned; according to the lighting sensitivity weight and the lighting demand reference value corresponding to the time period attribute, an adjusted lighting demand value is calculated, and the guest room lighting demand level is determined.

5. The method of claim 4, wherein, The obtaining of the shade position data and the guest Do Not Disturb setting data, the determination of the guest activity state in combination with the guest room occupancy state and the time period attribute, and the assignment of the lighting sensitivity weight include: The opening and closing percentage of the shade position data and the start and end time of the guest Do Not Disturb setting data are obtained, the guest activity state is determined in combination with the number of occupants and the number of days of the guest room occupancy state, and if the opening and closing percentage of the shade position data is below a threshold and is in the time period of the guest Do Not Disturb setting data, the highest lighting sensitivity weight is assigned; if the shade position data indicates that it is open and is not in the time period of the guest Do Not Disturb setting data, a low lighting sensitivity weight is assigned.

6. The method of claim 1, wherein, The obtaining of the corridor cleaning demand data, the analysis of regional function matching according to the guest room lighting demand level and the corridor cleaning demand data, and the determination of the adjacent area lighting matching group to obtain the corridor lighting adjustment range and the partitioned lighting intensity ratio include: The work position, duration and area range of the corridor cleaning demand data are collected to determine a comprehensive cleaning lighting demand value; the guest room lighting demand level and the comprehensive cleaning lighting demand value are normalized, and a clustering algorithm is used for grouping to obtain a regional matching set; according to the adjacency relationship and lighting demand difference of the regional matching set, a lighting transition zone is divided, and the corridor lighting adjustment range is calculated; according to the lighting demand priority and spatial distribution of the regional matching set, a weight inversely proportional to the distance from the cleaning work center is assigned to determine the partitioned lighting intensity ratio.

7. The method of claim 1, wherein, The generation of a dynamic adjustment instruction according to the corridor lighting adjustment range and the partitioned lighting intensity ratio, the collection of the lighting intensity distribution after the dynamic adjustment instruction is executed, the calculation of the illuminance uniformity of each monitoring point, and the obtaining of the transmittance and lighting uniformity values include: A dynamic adjustment instruction sequence containing a target brightness value and an execution priority is generated according to the corridor lighting adjustment range and the partitioned lighting intensity ratio; the dynamic adjustment instruction sequence is sent through a lighting control bus, the lighting intensity distribution after execution is collected, and a lighting intensity distribution matrix is constructed; according to the lighting intensity distribution matrix, the standard deviation of the illuminance values of adjacent monitoring points is calculated, the illuminance value of the door gap position is extracted as the transmittance, and the lighting uniformity value is calculated.

8. The method of claim 1, wherein, The construction of a debugging parameter optimization database according to the light intensity data, the occupancy state signal and the guest room lighting demand level includes: According to the light intensity data and the occupancy state signal, a difference value of the light intensity before and after the guest room door is calculated, a light interference elimination state is determined, and an interference elimination duration is recorded; according to the guest room lighting demand level and the measured light intensity value, a deviation percentage is calculated, and the before-and-after adjustment light intensity comparison data is generated; a lighting comfort score is obtained from a guest room management terminal and is mapped to a satisfaction value; according to the before-and-after adjustment light intensity comparison data, the interference elimination duration and the satisfaction value, an optimization record containing scene identification and adjustment parameters is constructed, a clustering algorithm is used for grouping, a recommended value is extracted, and the debugging parameter optimization database is generated.

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