Guest demand item inventory and consumption optimization management system adapted to hotel operation management

Abstract

The present invention discloses an optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management, which relates to the technical field of intelligent replenishment of guest supplies. By introducing multi-source perception data such as door lock frequency, lighting on / off, and TV usage, the system can identify the rooms that are actually used but not registered, avoid replenishment delays caused by "empty room misjudgment", and improve the service response efficiency. By analyzing historical consumption data and customer group characteristics, the system constructs the marginal utility curve of guest supplies and implements the on-demand replenishment strategy to reduce over-investment and improve the material utilization rate and customer satisfaction. The supporting reusable item identification module can intelligently judge whether the items checked out of the room meet the conditions for recycling, include the qualified products in the "microcirculation material library", and achieve low-carbon environmental protection and cost control. The intelligent replenishment robot cooperation module, based on the replenishment priority and path planning, automatically completes the replenishment and recycling tasks and real-time feedback on the status, improving the replenishment efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent replenishment of guest supplies, and more specifically to an optimized management system for the storage and consumption of guest supplies adapted to hotel operation management. Background Art

[0002] In traditional hotel operation management, the management of guest supplies has always been a difficult problem, especially in terms of guest room replenishment and the recycling and utilization of guest supplies. Most traditional guest room management models rely on manual records and management, which are easily affected by factors such as inaccurate guest room occupancy status, insufficient replenishment timeliness, and chaotic management of returned items, thus affecting the service quality and operation efficiency of the hotel.

[0003] First of all, the management of guest room occupancy status is a pain point in traditional hotel management. Since some guests do not officially check in, but due to the needs of activities such as meetings and gatherings, they actually still use some facilities in the guest room or consume some guest supplies. In this situation of "not officially checked in but actually used", traditional systems often fail to accurately identify it, resulting in the inability to timely replenish the required items. For example, some guest rooms may show as vacant, but due to temporary meeting use, the consumption of guest supplies is ignored, causing a lag in subsequent replenishment, affecting the guest experience and the operation efficiency of the hotel.

[0004] In addition, for some unused or still recyclable items in the guest room, traditional management systems often cannot identify and recycle them in a timely manner. Many hotels do not effectively recycle and reuse the remaining guest supplies after guests check out, resulting in these items not being reasonably reused, increasing the operation cost of the hotel. The lack of an effective management mechanism for the return and reuse of items causes waste of resources. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an optimized management system for the storage and consumption of guest supplies adapted to hotel operation management to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: An optimized management system for the storage and consumption of guest supplies adapted to hotel operation management, including:

[0007] A guest room status perception module, which is used to establish a perception data set based on multi-source information such as door lock switch frequency, lighting equipment opening and closing status, TV usage status, and cleaning behavior data, determine whether there is a situation of "not officially checked in but actually used" in the guest room, and generate a room status perception coefficient for the i-th guest room used to trigger a corresponding replenishment instruction ;

[0008] The marginal utility analysis module for customer-demand items is used to construct the marginal utility curve of a single customer-demand item based on the historical unit consumption and customer group data of each customer-demand item, and output the marginal utility weight of the j-th customer-demand item. And the corresponding prediction results, generate the corresponding replenishment instructions, the first non-default placement strategy, and the second default placement strategy;

[0009] The intelligent identification module for reusable items is used to detect the weight, identify the appearance, and analyze the packaging integrity of the remaining customer-demand items in the room after the guest checks out, so as to construct the integrity coefficient of the j-th customer-demand item. And evaluate, mark and allocate the reusable items into the "microcirculation material library", and at the same time generate the corresponding item return decision instructions;

[0010] The intelligent replenishment robot coordination module is used to receive the corresponding replenishment instructions and item return decision instructions, combine the current inventory data set and room status data, and calculate and obtain the replenishment timeliness coefficient of the j-th customer-demand item. And evaluate, generate the first optimization strategy, control the robot to replenish the priority list in descending order, and transmit the replenishment execution status back after reaching the capture point.

[0011] Preferably, the guest room status perception module includes a first acquisition unit, a first identification unit, and a first trigger unit;

[0012] The first acquisition unit is used to collect the switch frequency, opening time period, and opening stay duration of the guest room door lock through the connected intelligent door lock device, and collect the lighting device opening and closing status, TV usage status, and cleaning behavior data, and establish a perception data set.

[0013] The perception data set has a 24-hour cycle and includes:

[0014] The number of door lock switches of the i-th guest room ;

[0015] The average stay duration of a single door opening of the i-th guest room ;

[0016] The lighting opening and closing rate of the i-th guest room , which is obtained by dividing the total long-term lighting duration of the i-th guest room by 24 hours;

[0017] The TV usage rate of the i-th guest room , which is obtained by dividing the total TV opening duration of the i-th guest room by 24 hours;

[0018] The cleaning behavior intensity of the i-th guest room , which is obtained by dividing the total cleaning duration of the i-th guest room Divided by the number of cleaning actions in the i-th guest room Obtain;

[0019] The wifi-downlink data traffic of the i-th guest room ;

[0020] The first recognition unit is used to normalize each variable in the perception data set, make the value fall within the range of [0,1], and calculate and obtain the room state perception coefficient of the i-th guest room through the following formula ;

[0021] ;

[0022] In the formula, is the number of door lock switch times of the i-th guest room after normalization is the weight of is the average stay duration for a single door opening of the i-th guest room after normalization is the weight of is the lighting on-off rate of the i-th guest room is the weight of is the TV usage rate of the i-th guest room is the weight of is the cleaning behavior intensity of the i-th guest room is the weight of is the wifi-downlink data traffic of the i-th guest room is the weight of are all constants, and , means calculated according to weight, the room state perception coefficient of the i-th guest room .

[0023] Preferably, the first trigger unit is used to perform a first classification result according to the value of the room state perception coefficient of the i-th guest room, including:

[0024] When , but there is no check-in information for the guest room → automatically trigger the "suspiciously used room" status, and automatically trigger the first replenishment instruction, including: replenishment of regular guest necessities, including water, towels or toiletries, and the replenishment completion time is 30 minutes;

[0025] When , but there is no check-in information for the guest room → the system automatically triggers the "buffer observation" status and delays automatic replenishment, and automatically triggers the second replenishment instruction, including: replenishment after the buffer observation period, which will be replenished differently according to the usage of the room, including replenishment of night lights, skin care products, sheets and pillows, non-daily guest necessities, and the replenishment time limit is within 60 minutes;

[0026] When , it indicates a non - usage state, maintaining regular inspections and having no replenishment requirements.

[0027] Preferably, the marginal utility analysis module for customer - required items includes a customer - required item consumption monitoring unit and a marginal utility calculation unit;

[0028] The customer - required item consumption monitoring unit is used to collect the historical unit consumption and customer group data of each customer - required item;

[0029] The customer group data includes: room reservation data, the length of stay in each room, the number of occupants, and the customer type;

[0030] Based on the customer group data, construct the marginal utility curve of a single customer - required item, specifically:

[0031] Use regression analysis or a machine - learning model to fit the customer group data. Use the x - axis to represent the consumption of a single customer - required item, and the y - axis to represent the marginal utility, that is, the utility and demand of customers, and draw the marginal utility curve of a single customer - required item;

[0032] The marginal utility calculation unit is used to output the marginal utility weight of the j - th customer - required item based on the customer group data and the marginal utility curve of a single customer - required item The marginal utility weight of the j - th customer - required item is obtained through the following formula: ;

[0033] In the formula, represents the historical consumption of the j - th customer - required item, is the customer preference coefficient of the j - th customer - required item, which is obtained through the following formula: ;

[0034] where is the th customer group's demand probability for the j - th customer - required item, is the weight of the customer type;

[0035] is the daily consumption frequency of the j - th customer - required item; are all constants, and represents calculating the marginal utility weight of the j - th customer - required item according to the weight, and further normalizing the marginal utility weight of the j - th customer - required item so that the result range of the marginal utility weight of the j - th customer - required item is within [0, 1], and the expression is:

[0036] ;

[0037] In the formula, represents the marginal utility weight of the j-th type of customer demand item after normalization, and are respectively the minimum and maximum values of the marginal utility weights of all customer demand items.

[0038] Preferably, the customer demand item usage marginal utility analysis module further includes a second prediction unit;

[0039] The second prediction unit is used to evaluate the value of the marginal utility weight of the j-th type of customer demand item after normalization, and obtain a second prediction result, including: When

[0040] When , it indicates that the customer demand for this customer demand item is strong and this customer demand item needs to be replenished immediately. A third replenishment instruction is generated, including: replenishing 80%-90% of the current inventory of the j-th type of customer demand item;

[0041] And a first non-default placement strategy is generated, including: placing this customer demand item at the bedside table, desktop, TV cabinet, bedside area or guest room entrance area of the guest room;

[0042] When , it indicates that the customer demand for this customer demand item exceeds the expectation; replenish this customer demand item, and generate a fourth replenishment instruction, including: replenishing 50%-70% of the current inventory of the j-th type of customer demand item; and generating a second default placement strategy; placing this customer demand item in the regular placement area, and the regular placement area includes: storage cabinet, drawer or refrigerator;

[0043] When , it indicates that the demand for this customer demand item does not exceed the expectation. Check the inventory of this customer demand item and keep the inventory within the safety inventory range, and the safety inventory range is within 20%-30%.

[0044] Preferably, the reusable item intelligent identification module includes an integrity monitoring unit and an integrity evaluation unit;

[0045] The integrity monitoring unit is used to perform weight detection, appearance recognition, and packaging integrity analysis on the remaining customer demand items in the room after the guest checks out, so as to construct the integrity coefficient of the j-th customer demand item, specifically including:

[0046] S11. Through a weight sensor, monitor the current weight of the j-th customer demand item after check-out and the initial weight to obtain the weight loss coefficient

[0047] ;

[0048] S12. By means of image processing technology, monitor the appearance damage or usage traces of the j-th customer-needed item after check-out, and obtain the appearance damage coefficient of the j-th customer-needed item through the following formula : ;

[0049] In the formula, represents the area of the damaged part identified in the image recognition result of the j-th customer-needed item; represents the initial total surface area of the j-th customer-needed item;

[0050] S13. Detect the item packaging for any unsealing or damage. Collect whether the packaging is intact through a pressure sensor, and calculate and obtain the packaging damage coefficient of the j-th customer-needed item through the following formula :

[0051] ;

[0052] In the formula, represents the area of the damaged part identified in the image recognition result of the packaging surface of the j-th customer-needed item; represents the initial total surface area of the packaging surface of the j-th customer-needed item;

[0053] S14. Extract the weight loss coefficient of the j-th customer-needed item in S11 - S13, the appearance damage coefficient of the j-th customer-needed item, and the packaging damage coefficient of the j-th customer-needed item. After dimensionless processing, calculate and obtain the integrity coefficient of the j-th customer-needed item through the following weighted formula :

[0054] In the formula, are respectively the weight loss coefficient of the j-th customer-needed item, the appearance damage coefficient

[0055] and the packaging damage coefficient of the j-th customer-needed item, and the sum of the weights is 1.

[0056] Preferably, the integrity evaluation unit is used to evaluate the integrity coefficient of the j-th customer-needed item to obtain an evaluation result, including:

[0056] When , it means that the customer-needed item is not damaged. Mark the customer-needed item as reusable and allocate it to the microcirculation material warehouse, and generate the first item return decision instruction, including: "Return to the microcirculation material warehouse, status: not damaged".

[0057] When , it indicates that the customer-needed item is damaged but has a return value. Mark the customer-needed item as "damaged but repairable" and allocate it to the microcirculation material warehouse, generating a second item return decision instruction, including: "Return to the repair warehouse, status: damaged but repairable"; after repair, the item returns to the normal use state and enters the reuse link. If the item cannot fully restore its appearance or function, decide whether to reduce the usage frequency or use it in a specific environment;

[0058] When , it indicates that the customer-needed item is damaged and not suitable for reuse or return, generating a waste decision instruction, "Discard for disposal, status: severely damaged, non-returnable".

[0059] Preferably, the intelligent replenishment robot collaboration module includes an instruction receiving unit and a priority unit;

[0060] The instruction receiving unit is used to receive the first replenishment instruction, the second replenishment instruction, the third replenishment instruction, the fourth replenishment instruction, the first item return decision instruction and the second item return decision instruction, and combine the current inventory data set and the room status data to calculate and obtain the replenishment timeliness coefficient of the jth customer-needed item through the following formula :

[0061] ;

[0062] ;

[0063] In the formula, represents the replenishment demand quantity of the jth customer-needed item, represents the inventory gap coefficient; represents the expected replenishment response time of the jth customer-needed item; represents the current inventory quantity of the jth customer-needed item, represents the safety inventory quantity of the jth customer-needed item.

[0064] Preferably, the priority unit is used to preset a tolerance threshold X and compare the replenishment timeliness coefficient of the jth customer-needed item with the tolerance threshold X. If the replenishment timeliness coefficient of the jth customer-needed item, it indicates that the replenishment efficiency of the jth customer-needed item is unqualified, generating a first optimization strategy, including: increasing the replenishment frequency from once a week to once every two days; increasing the replenishment quantity by 15%-20% and shortening the replenishment response time by 20%-30%, and the replenishment timeliness coefficient Generate a priority list in descending order of values, control the robot to replenish the priority list in descending order, based on the path planning algorithm, deliver the required items to the replenishment point along the optimal path, and transmit the replenishment execution status after delivery;

[0065] When the replenishment timeliness coefficient of the j-th customer required item , it indicates that the replenishment efficiency of the j-th customer required item is qualified, and replenishment is carried out according to the current estimated replenishment time and replenishment demand.

[0066] The present invention provides a customer required item inventory and consumption optimization management system adapted to hotel operation management. It has the following beneficial effects:

[0067] (1) For the customer required item inventory and consumption optimization management system adapted to hotel operation management, by introducing multi-source behavior perception data such as door lock frequency, lighting on / off, TV usage, and cleaning behavior, the system can construct a guest room status perception model, accurately identify the guest rooms that are actually temporarily used although not checked in, thus effectively making up for the problem that the traditional system cannot replenish goods in time in the case of "empty room misjudgment", and improving the timeliness of service response and the accuracy of customer required item supply.

[0068] (2) For the customer required item inventory and consumption optimization management system adapted to hotel operation management, by analyzing the historical unit consumption and customer group characteristics (including length of stay, number of people, customer type, etc.), the system constructs the marginal utility curve of each customer required item, quantifies the marginal utility weight, so as to realize the replenishment strategy guided by the real needs of customers, avoid blind replenishment or over-investment, and improve the material use efficiency and user satisfaction.

[0069] (3) For the customer required item inventory and consumption optimization management system adapted to hotel operation management, the intelligent identification module for reusable items equipped in the system can detect the weight of the remaining customer required items after check-out, identify the packaging integrity and appearance status, accurately judge whether they meet the return conditions, and include the qualified materials in the "microcirculation material library". This not only reduces the waste of customer required items, but also achieves the dual goals of low-carbon environmental protection and cost optimization.

[0070] (4)The optimization management system for the inventory and consumption of guest supplies adapted to hotel operation management, with the help of the intelligent replenishment robot collaboration module, can automatically dispatch robots to execute replenishment and return commands according to the replenishment priority and replenishment timeliness coefficient. The robot distributes materials to the target guest rooms in the optimized order and transmits the execution status in real time, realizing unmanned and intelligent management of the entire replenishment process, significantly improving work efficiency and accuracy. The system realizes a full-process closed-loop from perception - analysis - decision - execution - feedback. Through technical paths such as marginal utility modeling, intelligent recognition, and robot collaborative operation, it comprehensively optimizes the consumption management and resource recycling mechanism of hotel guest supplies, helping hotel management to transform and upgrade towards the direction of "refinement, intelligence, and greenness". BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic flowchart of the optimization management system for the inventory and consumption of guest supplies adapted to hotel operation management of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0073] Embodiment 1

[0074] Please refer to Figure 1 , the present invention provides an optimization management system for the inventory and consumption of guest supplies adapted to hotel operation management, including:

[0075] A guest room status perception module, which is used to establish a perception data set based on multi-source information such as the door lock switch frequency, lighting equipment opening and closing status, TV usage status, and cleaning behavior data, determine whether there is a situation of "not registered but actually used" in the guest room, and generate the room status perception coefficient of the i-th guest room for triggering the corresponding replenishment instruction ;

[0076] A marginal utility analysis module for guest supplies usage, which is used to construct the marginal utility curve of a single guest supply based on the historical unit consumption and customer group data of each guest supply, output the marginal utility weight of the j-th guest supply and the corresponding prediction result, and generate the corresponding replenishment instruction, the first non-default placement strategy, and the second default placement strategy;

[0077] The reusable item intelligent recognition module is used to detect the weight, identify the appearance, and analyze the packaging integrity of the remaining guest supplies in the room after the guest checks out, so as to construct the integrity coefficient of the j-th guest supply. And evaluate, mark the reusable items and allocate them into the "microcirculation material library", and at the same time generate corresponding item return decision instructions;

[0078] The intelligent replenishment robot collaboration module is used to receive the corresponding replenishment instructions and item return decision instructions, combine the current inventory data set with the room status data, and calculate and obtain the replenishment timeliness coefficient of the j-th guest supply. And evaluate, generate the first optimization strategy, control the robot to replenish the priority list in descending order, and transmit the replenishment execution status back after delivering to the capture point.

[0079] In this embodiment, by introducing multi-source behavior perception data such as door lock frequency, lighting on / off, TV usage, and cleaning behavior, the system can construct a guest room status perception model, accurately identify the guest rooms that are actually temporarily used although not registered, thus effectively making up for the problem that the traditional system cannot replenish goods in time in the case of "empty room misjudgment", and improving the timeliness of service response and the accuracy of guest supply.

[0080] By analyzing the historical unit consumption and customer group characteristics (including length of stay, number of people, customer type, etc.), the system constructs the marginal utility curve of each type of guest supply, quantifies the marginal utility weight, so as to realize the replenishment strategy guided by the real needs of customers, avoid blind replenishment or over-investment, and improve the material use efficiency and user satisfaction.

[0081] The reusable item intelligent recognition module equipped with the system can detect the weight, identify the packaging integrity and appearance status of the remaining guest supplies after check-out, accurately judge whether they meet the return conditions, and include the qualified materials in the "microcirculation material library". This not only reduces the waste of guest supplies, but also achieves the dual goals of low-carbon environmental protection and cost optimization.

[0082] With the help of the intelligent replenishment robot collaboration module, the system can automatically dispatch the robot to execute the replenishment and return instructions according to the replenishment priority and replenishment timeliness coefficient. The robot distributes the materials to the target guest rooms in the optimized order and transmits the execution status in real time, realizing the unmanned and intelligent management of the whole replenishment process, and significantly improving the work efficiency and accuracy. The system realizes the full-process closed-loop from perception - analysis - decision - execution - feedback, and comprehensively optimizes the consumption management and resource recycling mechanism of hotel guest supplies through technical paths such as marginal utility modeling, intelligent recognition, and robot collaborative operation, helping the hotel management to transform and upgrade towards the direction of "refinement, intelligence, and greenness".

[0083] Embodiment 2

[0084] This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically, the guest room status perception module includes a first acquisition unit, a first recognition unit, and a first trigger unit;

[0085] The first acquisition unit is used to collect the switching frequency, opening time period, and opening stay duration of the guest room door lock through the connected intelligent door lock device, and collect the opening and closing status of lighting equipment, the usage status of the TV, and cleaning behavior data to establish a perception data set;

[0086] The perception data set has a 24-hour cycle and includes:

[0087] The number of door lock switches in the i-th guest room ; The induction information of the number of door lock switches in the i-th guest room is shown in Table 1.

[0088] Table 1 Induction information of the number of door lock switches in the i-th guest room

[0089] Timestamp Operation type Operation method Operator Result 2025-04-02-20：12 Open the door Card Housekeeping department Success 2025-04-02-20：15 Close the door Automatic induction None Success

[0090] The average stay duration of a single door opening in the i-th guest room ; Record the time difference between each door opening and the next door closing through the intelligent door lock, calculate the stay time of a single door opening, and take the average value.

[0091] The lighting opening and closing rate of the i-th guest room , which is obtained by dividing the total long-term lighting duration of the i-th guest room by 24 hours; Record the total lighting on time through the control system and calculate the lighting opening and closing rate by comparing it with 24 hours. The room light state can also be detected in real time by an illuminance sensor (for redundancy or lighting anomaly judgment).

[0092] The TV usage rate of the i-th guest room , which is obtained by dividing the total TV on duration of the i-th guest room by 24 hours;

[0093] Obtain the total duration of TV opening through the hotel intelligent TV background interface or control terminal to calculate the usage rate. The running state of the TV can also be detected by an infrared remote control state acquisition module or a current sensor.

[0094] The cleaning behavior intensity of the i-th guest room , which is obtained by dividing the total cleaning duration of the i-th guest room by the number of cleaning behavior occurrences in the i-th guest room Obtain; record the cleaning duration through the time when the service staff enters the room + the leaving time; or the cleaning cart is equipped with RFID + positioning module to monitor the cleaning path and duration; combined with an operation check-in terminal, the number of cleaning behaviors can be accurately recorded.

[0095] The wifi-downlink data traffic of the i-th guest room ; record the total downlink data traffic of each guest room terminal device through the AP background; bind the room number through the MAC address and capture the traffic data packet in real time.

[0096] The first recognition unit is used to normalize each variable in the perception data set, and then statistically calculate the value within the range of [0,1], and calculate the room state perception coefficient of the i-th guest room through the following formula :

[0097] ;

[0098] In the formula, is the number of door lock switch times of the i-th guest room after normalization is the weight of is the average stay duration for a single door opening of the i-th guest room after normalization is the weight of is the lighting opening and closing rate of the i-th guest room is the weight of is the TV usage rate of the i-th guest room is the weight of is the cleaning behavior intensity of the i-th guest room is the weight of is the wifi-downlink data traffic of the i-th guest room is the weight of are all constants, and , represents weight, and calculate the room state perception coefficient of the i-th guest room .

[0099] In this embodiment, this module not only relies on door lock data but also integrates multiple sensory information, including lighting status, TV usage, cleaning behavior, and Wi-Fi downlink traffic. This effectively identifies scenarios where items are used outside of check-in, such as "temporary use" and "meeting use," addressing a blind spot in traditional hotel room status recognition. The module normalizes variables such as door lock frequency, door open duration, lighting usage, TV usage, cleaning intensity, and downlink data traffic, and weights them to calculate a room status perception coefficient. This provides a quantifiable basis for subsequent operations such as restocking, cleaning, and resource allocation, achieving the goal of intelligent management driven by data-driven service decisions. The sensory dataset is updated on a 24-hour rolling basis, facilitating continuous room status monitoring and enabling rapid response to incidents such as overnight stays and short periods of high-frequency use, enhancing the system's ability to identify and adapt to real-time room status changes.

[0100] Multi-source data fusion processing effectively reduces misidentifications caused by single-dimensional anomalies (such as accidental door lock activation or misrecorded cleanings). A weighted model assigns appropriate importance to different variables, improving the system's accuracy and robustness in determining the actual guest room status.

[0101] Example 3

[0102] This example is explained in Example 2. Figure 1 Specifically, the first trigger unit is used to determine the room state perception coefficient of the i-th guest room. The first classification results include:

[0103] when , but the room has no check-in information → automatically trigger the "suspicious use of room" status, and automatically trigger the first replenishment instruction, including: including the replenishment of regular guest necessities, such as water, towels or toiletries, and the replenishment completion time is 30 minutes;

[0104] when , but the room has no check-in information → the system automatically triggers the "buffer observation" status, delays automatic replenishment, and automatically triggers the second replenishment instruction, including: Replenishment after the buffer observation period will be different according to the room usage, including replenishing night lights, skin care products, sheets and pillows, which are not daily guest necessities, and the replenishment time limit is completed within 60 minutes;

[0105] when , indicating that it is not in use, and regular inspections are maintained, with no need for replenishment.

[0106] In this embodiment, when the room status perception coefficient of the i-th guest room is greater than 0.7, the system will automatically determine that there is a strong usage behavior in this guest room. Even if there is no check-in information in the system, it will automatically trigger the "suspiciously used room" status and execute the first replenishment instruction, including:

[0107] Replenish standard regular guest supplies to this guest room, including bottled drinking water, towels, toothbrushes, shampoo and bath products, etc.;

[0108] Control the intelligent replenishment robot to complete the replenishment task of corresponding items within 30 minutes according to the priority order;

[0109] After the replenishment is completed, automatically upload the replenishment status information through the robot and feedback it to the management background for data closed-loop;

[0110] At the same time, the system automatically issues an abnormal usage reminder to prompt the operation staff to further review the usage situation of this room, which serves as an important reference basis for the dynamic inspection of the guest room status. By dividing the room status perception coefficient Fi into three intervals for discrimination, it effectively avoids the problems of mis-replenishment and missed replenishment. According to the perception degree, allocate regular and non-daily guest supplies to make the material allocation more intelligent. The delayed decision-making mechanism reduces the operation cost: set a buffer mechanism for rooms in the intermediate state (0.4 - 0.7) to reduce the ineffective replenishment rate. Realize the abnormal monitoring of the unoccupied room status and the closed-loop management of replenishment.

[0111] Embodiment 4

[0112] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the marginal utility analysis module of guest supplies includes a guest supplies consumption monitoring unit and a marginal utility calculation unit;

[0113] The guest supplies consumption monitoring unit is used to collect the historical unit consumption and customer group data of each guest supply;

[0114] The customer group data includes: guest room reservation data, the length of stay in each guest room, the number of occupants, and the customer type;

[0115] According to the customer group data, construct the marginal utility curve of single-item guest supplies, specifically:

[0116] Use regression analysis or machine learning model to fit the customer group data. Use the x-axis to represent the consumption of single-item guest supplies and the y-axis to represent the marginal utility, that is, the utility and demand of customers, and draw the marginal utility curve of single-item guest supplies;

[0117] Marginal utility refers to the satisfaction that a customer obtains from an additional unit of consumer goods. The X-axis represents the consumption quantity, meaning that we are studying the satisfaction of customers at different consumption quantities. As the consumption quantity increases, the utility brought by each additional unit of consumption usually decreases. For example, the first bottle of towel will make the customer feel satisfied, while the second bottle may not have such a strong effect, and the third bottle may even feel redundant. Measured by the consumption quantity, this decreasing trend can be clearly presented;

[0118] The marginal utility calculation unit is used to output the marginal utility weight of the j-th type of customer demand goods based on the customer group data and the marginal utility curve of individual customer demand goods , the marginal utility weight of the j-th type of customer demand goods is calculated and obtained through the following formula: ;

[0119] In the formula, represents the historical consumption quantity of the j-th type of customer demand goods, the customer preference coefficient of the j-th type of customer demand goods is calculated and obtained through the following formula: ;

[0120] where, is the demand probability of the -th customer group for the j-th type of customer demand goods, is the weight of the customer type, and the customer types include: business travelers, family travelers, long-term stay travelers, parent-child travel travelers, vacation travelers, and group travelers;

[0121] is the daily consumption frequency of the j-th type of customer demand goods; are all constants, and represents calculating the marginal utility weight of the j-th type of customer demand goods according to the weight, and further normalizing the marginal utility weight of the j-th type of customer demand goods so that the result range of the marginal utility weight of the j-th type of customer demand goods is within [0, 1], and the expression is:

[0122] ;

[0123] In the formula, represents the marginal utility weight of the j-th type of customer demand goods after normalization, and are respectively the minimum value and the maximum value of the marginal utility weights of all customer demand goods.

[0124] In this embodiment, the marginal utility calculation unit is configured to construct a marginal utility curve for each type of customer-needed item based on customer group data. The x-axis of the marginal utility curve represents the consumption quantity of the customer-needed item, and the y-axis represents the marginal utility value perceived by the customer, that is, the actual demand utility generated by the customer-needed item for the customer. The marginal utility curve can be constructed through a regression analysis model or a machine learning fitting model to quantify the functional relationship between consumption and utility.

[0125] Through such a marginal utility curve, the hotel can obtain a reasonable replenishment strategy for customer-needed items.

[0126] For example, the hotel may find that 1 bottle of shampoo or 1 towel meets the needs of most customers, and excessive consumption (such as more than 3 bottles or 3 towels) has little impact on customer satisfaction and may even waste resources. Therefore, the marginal utility curve can help the hotel adjust its inventory and replenishment strategies according to actual demand, improving resource utilization efficiency.

[0127] By conducting marginal utility analysis on customer-needed items, it is possible to intelligently determine which items should be replenished first based on customer types and usage preferences. The system can automatically adjust the material delivery volume according to usage efficiency to avoid material waste or supply-demand imbalance. It can improve the configuration priority of customer-needed items that are strongly related to customer behavior and needs, enhancing the customer's check-in experience. As more customer group data accumulates, the marginal utility model can be continuously updated to achieve self-learning optimization of strategies.

[0128] Embodiment 5

[0129] This embodiment is an explanatory description based on Embodiment 4. Please refer to Figure 1 Specifically, the marginal utility analysis module for customer-needed item usage further includes a second prediction unit.

[0130] The second prediction unit is configured to evaluate the value of the marginal utility weight of the j-th type of customer-needed item after normalization to obtain a second prediction result, including:

[0131] When , it indicates that the customer demand for this customer-needed item is strong and this customer-needed item needs to be replenished immediately. A third replenishment instruction is generated, including: replenishing 80% - 90% of the current inventory of the j-th type of customer-needed item;

[0132] And a first non-default placement strategy is generated, including: placing this customer-needed item at the bedside table, desktop, TV cabinet, bedside area, or guest room entrance area of the guest room.

[0133] When , indicating that the customer demand for this customer-demand item exceeds expectations; replenish this customer-demand item to generate a fourth replenishment instruction, including: replenishing 50%-70% of the current inventory of the j-th customer-demand item; and generating a second default placement strategy; placing this customer-demand item in the regular placement area, where the regular placement area includes: storage cabinets, drawers, or refrigerators;

[0134] When , indicating that the demand for this customer-demand item does not exceed expectations and the demand is not strong, check the inventory of this customer-demand item and keep the inventory within the safety inventory range, where the safety inventory range is within 20%-30%.

[0135] In this embodiment, the customer-demand item marginal utility analysis module further includes a second prediction unit for intelligently evaluating the value of the marginal utility weight of the j-th customer-demand item after normalization to obtain a second prediction result, and accordingly formulate a replenishment instruction and a customer-demand item placement strategy; the system dynamically adjusts the replenishment ratio according to the normalized marginal utility weight to avoid overstocking or understocking; automatically generates a placement strategy according to the customer demand intensity to enhance the accessibility and visibility of high-efficiency items for customers; in a non-high-demand state, prioritize ensuring the safety inventory to reduce the risk of material backlog; through marginal utility modeling and intelligent judgment, fit the potential customer demands of different customer groups to improve the occupancy satisfaction.

[0136] Embodiment 6

[0137] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the reusable item intelligent identification module includes an integrity monitoring unit and an integrity evaluation unit;

[0138] The integrity monitoring unit is used to comprehensively monitor the integrity of the remaining customer-demand items in the room by hotel staff using visual monitoring equipment installed on the cleaning cart during the room inspection after the guest checks out. Conduct weight detection, appearance recognition, and packaging integrity analysis on the remaining customer-demand items in the room to construct the integrity coefficient of the j-th customer-demand item , specifically including:

[0139] S11. The weight sensor on the cleaning cart monitors the current weight of the j-th customer-demand item after check-out and the initial weight to obtain the weight loss coefficient of the j-th customer-demand item :

[0140] ;

[0141] S12. The image recognition device on the cleaning vehicle will perform appearance detection and scanning on each customer-needed item. Through image processing technology, it monitors the appearance damage or usage traces of the j-th customer-needed item after check-out. The appearance damage coefficient of the j-th customer-needed item is obtained through the following formula :

[0142] ;

[0143] In the formula, represents the area of the damaged part marked in the image recognition result of the j-th customer-needed item; represents the initial total surface area of the j-th customer-needed item;

[0144] S13. Through the pressure sensor and image recognition technology on the cleaning vehicle, the item packaging is detected for whether there is unsealing or damage. The integrity of the packaging is collected through the pressure sensor. The packaging damage coefficient of the j-th customer-needed item is calculated and obtained through the following formula : ;

[0145] In the formula, represents the area of the damaged part marked in the image recognition result of the packaging surface of the j-th customer-needed item; represents the initial total surface area of the packaging surface of the j-th customer-needed item;

[0146] S14. Extract the weight loss coefficient , the appearance damage coefficient of the j-th customer-needed item, and the packaging damage coefficient of the j-th customer-needed item. After dimensionless processing, the integrity coefficient of the j-th customer-needed item is calculated through the following weighted formula : ;

[0147] In the formula, are respectively the weight loss coefficient , the appearance damage coefficient of the j-th customer-needed item, and the packaging damage coefficient of the j-th customer-needed item, and the sum of the weights is 1.

[0148] The integrity evaluation unit is used to evaluate the integrity coefficient of the j-th customer-needed item to obtain an evaluation result, including:

[0149] When , it indicates that the customer-needed item is not damaged. Mark the customer-needed item as reusable and allocate it to the microcirculation material warehouse to generate the first item return decision instruction, including: "Return to the microcirculation material warehouse, status: not damaged";

[0150] When it indicates that the customer-required item is damaged but has a return value. Mark the customer-required item as "damaged but repairable" and allocate it to the microcirculation material warehouse, generating a second item return decision instruction, including: "Return to the repair warehouse, status: damaged but repairable"; after repair, the item returns to the normal use state and enters the reuse link. If the item cannot fully restore its appearance or function, decide whether to reduce the usage frequency or use it in a specific environment;

[0151] When it indicates that the customer-required item is damaged and not suitable for reuse or return, generating a waste decision instruction, "Discard, status: severely damaged, non-returnable".

[0152] In this embodiment, by integrating various sensing means such as weight detection (such as an electronic scale), image recognition (such as a vision camera), and pressure sensing (such as a packaging touch sensor), multi-angle analysis of the usage status of customer-required items is realized; effectively avoiding misjudgment caused by a single index, such as the weight remains unchanged but the packaging has been opened, the appearance has been damaged, etc.; the "weight loss coefficient", "appearance damage coefficient", and "packaging damage coefficient" can be quantitatively output, and the "integrity coefficient" is constructed as a whole, with a complete recognition dimension and a clear data structure. The system automatically normalizes the three damage dimensions and introduces a weighted model to form a unified evaluation index "integrity coefficient"; after integrity evaluation, the "undamaged" or "slightly damaged but repairable" items enter the microcirculation material warehouse or the repair warehouse; reusable items avoid unnecessary discard, saving daily operating expenses, such as towels, slippers, soap boxes, toothbrush holders, tissue boxes / remote control protectors / desktop storage boxes, hangers / trouser racks, etc. do not need to be replaced every time; for repairable items, strategies such as "low-frequency re-investment" and "regional restricted use" are supported to maximize the remaining value of the items.

[0153] Embodiment 7

[0154] This embodiment is an explanatory description based on Embodiment 6. Please refer to Figure 1 specifically, the intelligent replenishment robot collaboration module includes an instruction receiving unit and a priority unit;

[0155] The instruction receiving unit is used to receive the first replenishment instruction, the second replenishment instruction, the third replenishment instruction, the fourth replenishment instruction, the first item return decision instruction, and the second item return decision instruction. Combining the current inventory data set and the room status data, the replenishment timeliness coefficient of the jth customer-required item is calculated through the following formula :

[0156] ;

[0157] ;

[0158] In the formula, represents the replenishment demand quantity of the j-th customer required item, represents the inventory gap coefficient; represents the expected replenishment response time of the j-th customer required item; represents the current inventory quantity of the j-th customer required item, represents the safety inventory quantity of the j-th customer required item.

[0159] Specifically, the priority unit is used to preset a tolerance threshold X, and compare the replenishment timeliness coefficient of the j-th customer required item with the tolerance threshold X. If the replenishment timeliness coefficient of the j-th customer required item, it means that the replenishment efficiency of the j-th customer required item is unqualified, and a first optimization strategy is generated, including: increasing the replenishment frequency from once a week to once every two days; increasing the replenishment quantity by 15%-20%, shortening the replenishment response time by 20%-30%, and generating a priority list in the order of the values of the replenishment timeliness coefficient of the j-th customer required item exceeding the tolerance threshold X from large to small, controlling the robot to replenish according to the priority list from large to small, delivering the required items to the replenishment point according to the optimal path, and transmitting the replenishment execution status after delivery; after the robot completes the replenishment, it automatically transmits the execution status (such as successful delivery, shortage of items, path interruption, etc.), and the system can dynamically correct the inventory data based on this, forming an efficient closed-loop replenishment management process that links material flow, inventory change, and execution feedback.

[0160] When the replenishment timeliness coefficient of the j-th customer required item, it means that the replenishment efficiency of the j-th customer required item is qualified, and replenishment is carried out according to the current expected replenishment time and replenishment demand quantity.

[0161] In this embodiment, by introducing the "replenishment timeliness coefficient" and combining multi-parameters such as room status, inventory, expected response time, and gap quantity, it is possible to more scientifically identify whether the replenishment is timely and whether there is a risk of lag, so as to effectively avoid risks such as out-of-stock of items and service interruption, and improve the customer check-in experience. By comparing with the set tolerance threshold X, the system automatically judges whether the replenishment efficiency meets the standard, and sorts the customer required items with "unqualified replenishment efficiency" according to the urgency of replenishment, ensuring that the urgent ones are handled first and the slow ones later, and the shortages are replenished first and the surpluses later, solving the problems of "rigid queuing and untimely response" in traditional replenishment.

[0162] By comparing with the set tolerance threshold X, the system automatically determines whether the replenishment efficiency meets the standard, and sorts the customer-demand items with "unqualified replenishment efficiency" according to the urgency of replenishment, ensuring that the urgent ones are handled first and the lacking ones are replenished before the surplus ones, thus solving the problems of "rigid queuing and untimely response" in traditional replenishment. Combining with the path planning algorithm, the robot no longer replenishes goods along a fixed route, but generates an action plan that combines the shortest path and the most urgent items according to the real-time priority and the location of replenishment points, significantly saving energy, shortening the travel time, and improving the execution efficiency.

[0163] When it is detected that the replenishment efficiency of a certain type of item has not met the standard for a long time, the system actively adjusts the replenishment frequency and quantity, and even optimizes the replenishment response mechanism, demonstrating a self-optimization ability based on efficiency feedback, avoiding manual passive adjustment and response lag.

[0164] The size of the threshold is set for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0165] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by those skilled in the art according to the actual situation. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A guest needs product inventory and consumption optimization management system adapted to hotel operation management, characterized in that, including: The guest room status perception module is used to establish a perception data set based on multi-source information such as the door lock switch frequency, the opening and closing status of lighting equipment, the TV usage status, and cleaning behavior data, determine whether there is a situation of "not registered but actually used" in the guest room, and generate the room status perception coefficient of the i-th guest room for triggering the corresponding replenishment instruction ; The marginal utility analysis module for customer-demand items is used to construct the marginal utility curve of a single customer-demand item based on the historical unit consumption and customer group data of each customer-demand item, and output the marginal utility weight of the j-th customer-demand item. And the corresponding prediction results are used to generate the corresponding replenishment instructions, the first non-default placement strategy, and the second default placement strategy. The marginal utility analysis module for customer-needed items includes a customer-needed item consumption monitoring unit and a marginal utility calculation unit; The customer-needed item consumption monitoring unit is used to collect the historical unit consumption and customer group data of each customer-needed item; The customer group data includes: room reservation data, the occupancy duration of each room, the number of occupants, and the customer type; According to the customer group data, a marginal utility curve for a single customer-needed item is constructed, specifically: Using regression analysis or a machine learning model to fit the customer group data, with the x-axis representing the consumption of a single customer-needed item and the y-axis representing the marginal utility, that is, the utility and demand of the customer, to plot the marginal utility curve of a single customer-needed item; The marginal utility calculation unit is configured to output the marginal utility weight of the j-th type of customer demand item based on the customer group data and the marginal utility curve of the individual customer demand item , the marginal utility weight of the j-th type of customer demand item is calculated and obtained through the following formula: ; Wherein, represents the historical consumption of the j-th type of customer required product, is the customer preference coefficient of the j-th type of customer required product, and is obtained by calculating through the following formula: ; Among them, is the demand probability of the j-th customer need for the th customer group, is the weight of the customer type; is the daily consumption frequency of the j-th type of customer demand item; are all constants, and represents according to weight, the marginal utility weight of the j-th type of customer demand item is calculated , and the marginal utility weight of the j-th type of customer demand item further normalization steps are taken to make the marginal utility weight of the j-th type of customer demand item fall within the range of [0, 1], and the expression is: ; In the formula, represents the marginal utility weight of the j-th type of customer demand product after normalization, and are respectively the minimum and maximum values of the marginal utility weights of all customer demand products; The intelligent identification module for reusable items is used to detect the weight, identify the appearance, and analyze the packaging integrity of the remaining guest supplies in the room after the guest checks out, so as to construct the integrity coefficient of the j-th guest supply. And evaluate, mark and allocate the reusable items into the "microcirculation material library", and at the same time generate the corresponding item return decision instruction; The intelligent replenishment robot collaboration module is used to receive corresponding replenishment instructions and item return decision instructions, combine the current inventory data set and room status data, and calculate and obtain the replenishment timeliness coefficient of the jth customer-demand item. And evaluate and generate the first optimization strategy to control the robot to replenish the priority list in descending order and transmit the replenishment execution status back after reaching the capture point.

2. The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management according to claim 1, characterized in that The room status perception module includes a first collection unit, a first identification unit, and a first trigger unit; The first collection unit is used to collect the switch frequency, opening time period, and opening stay duration of the room door lock through the connected intelligent door lock device, and collect the lighting device opening and closing status, TV usage status, and cleaning behavior data to establish a perception data set; The perception data set has a 24-hour cycle and includes: The number of times the door lock of the i-th guest room is switched ; Average residence duration for a single door opening in the i-th guest room ; The lighting on-off rate of the i-th guest room , which is obtained by dividing the total long-term duration of the lighting in the i-th guest room by 24 hours; TV usage rate of the i-th guest room , obtained by dividing the total TV on-time of the i-th guest room by 24 hours; The cleaning behavior intensity of the i-th guest room , which is obtained by dividing the total cleaning duration of the i-th guest room by the number of cleaning behavior occurrences in the i-th guest room ; The wifi-downlink data traffic of the i-th guest room ; The first recognition unit is used to normalize each variable in the perception data set, so that the values are statistically within the range of [0, 1], and calculate the room state perception coefficient of the i-th guest room through the following formula : ； Wherein, is the number of door lock switchings for the i-th normalized guest room 's weight; is the average stay duration for a single door opening for the i-th normalized guest room 's weight, is the lighting on-off rate for the i-th guest room 's weight, is the TV usage rate for the i-th guest room 's weight, is the cleaning behavior intensity for the i-th guest room 's weight, is the wifi-downlink data traffic for the i-th guest room 's weight; are all constants, and , represents that according to weight, the room state perception coefficient for the i-th guest room is calculated .

3. The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management according to claim 2, wherein, The first trigger unit is used to perform a first classification result according to the value of the room status perception coefficient of the i-th guest room, including: the value, including: When , but there is no check-in information for the guest room → Automatically trigger the "suspicious use of room" status and automatically trigger the first replenishment order, including: replenishment of regular guest supplies, including water, towels or toiletries, and the replenishment completion time is 30 minutes; When , but there is no check-in information for the guest room → The system automatically triggers the "buffer observation" status and delays automatic replenishment, and automatically triggers the second replenishment instruction, including: replenishment after the buffer observation period will be different according to the usage of the room, including replenishing night lights, skin care products, sheets and pillows, which are not daily necessities for guests. The replenishment time limit is to complete within 60 minutes; When , it indicates a non-usage state, maintaining regular inspections and having no replenishment requirements.

4. The optimized management system for the storage and consumption of guest supplies adapted to hotel operation management according to claim 1, characterized in that The marginal utility analysis module for customer-needed items further includes a second prediction unit; The second prediction unit is configured to evaluate the value of the marginal utility weight of the j-th type of customer demand item after normalization, and obtain a second prediction result, including: the value, and obtain a second prediction result, including: When , it indicates that the customer demand for this item is strong and the item needs to be replenished immediately, generating a third replenishment order, including: replenishing 80%-90% of the current inventory of the j-th customer demand item; And generate a first non-default placement strategy, including: placing the customer-needed item at the bedside table, desktop, TV cabinet, bedside area, or room entrance area of the room; When , it indicates that the customer demand for this customer-demand item exceeds the expectation; replenish this customer-demand item, generate a fourth replenishment instruction, including: replenishing 50% - 70% of the current inventory of the j-th customer-demand item; and generating a second default placement strategy; place this customer-demand item in the regular placement area, and the regular placement area includes: storage cabinets, drawers or refrigerators; When , it indicates that the demand for this customer-required item does not exceed the expectation. Check the inventory of this customer-required item and keep the inventory within the safety inventory range, and the safety inventory range is within 20% - 30%.

5. The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management according to claim 1, characterized in that, The reusable item intelligent identification module includes an integrity monitoring unit and an integrity assessment unit; The integrity monitoring unit is used to detect the weight, identify the appearance, and analyze the packaging integrity of the remaining guest supplies in the room after the guest checks out, so as to construct the integrity coefficient of the j-th guest supply , specifically including: S11. Monitor the current weight of the j-th customer-needed item after check-out through a weight sensor and the initial weight to obtain the weight loss coefficient of the j-th customer-needed item : ； S12. Through image processing technology, monitor the appearance damage or usage traces of the j-th customer required item after check-out, and obtain the appearance damage coefficient of the j-th customer required item through the following formula :[[]]END]] ； In the formula, represents the area of the damaged part marked in the recognition result of the j-th customer required product image; represents the initial total surface area of the j-th customer required product; S13. Detect the item packaging for any unsealing or damage. Collect whether the packaging is intact through a pressure sensor, and calculate the packaging damage coefficient of the j-th customer-required item using the following formula :[[]]END]] ； Wherein, represents the area of the damaged part identified in the image recognition result on the packaging surface of the j-th customer required product; represents the initial total surface area of the packaging surface of the j-th customer required product; S14. Extract the weight loss coefficient of the j-th customer required product among S11 - S13 , the appearance damage coefficient of the j-th customer required product and the packaging damage coefficient of the j-th customer required product . After dimensionless processing, obtain the integrity coefficient of the j-th customer required product through the following weighted formula ; ; In the formula, is the weight loss coefficient of the j-th customer required product , the appearance damage coefficient of the j-th customer required product and the packaging damage coefficient of the j-th customer required product respectively, and the sum of the weights is 1.

6. The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management according to claim 5, wherein The integrity evaluation unit is used to evaluate the integrity coefficient of the j-th customer required item and obtain an evaluation result, including: When , it indicates that the customer-required item is not damaged. Mark the customer-required item as reusable and allocate it to the microcirculation material warehouse, generating a first item return decision instruction, including: "Return to the microcirculation material warehouse, status: not damaged". When indicates that the customer-required item is damaged but has a return value. Mark the customer-required item as "damaged but repairable" and allocate it to the microcirculation material warehouse, generating a second item return decision instruction, including: "Return to the repair warehouse, status: damaged but repairable"; after repair, the item resumes its normal use state and enters the reuse process; When , it indicates that the customer-required item is damaged and not suitable for reuse or return, generating a waste disposal decision instruction of "Discard, Status: Seriously Damaged, Not Returnable".

7. The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management according to claim 1, characterized in that The intelligent replenishment robot collaboration module includes an instruction receiving unit and a priority unit; The instruction receiving unit is configured to receive a first replenishment instruction, a second replenishment instruction, a third replenishment instruction, a fourth replenishment instruction, a first item return decision instruction, and a second item return decision instruction, and calculate and obtain the replenishment timeliness coefficient of the j-th customer-demand item by the following formula in combination with the current inventory data set and the room status data :[[]]END]] ； ； In the formula, represents the replenishment demand quantity of the j-th customer required item, represents the inventory gap coefficient; represents the expected replenishment response time of the j-th customer required item; represents the current inventory level of the j-th customer required item, represents the safety stock of the j-th customer required item.

8. The optimized management system for the storage and consumption of guest supplies adapted to hotel operation management according to claim 7, characterized in that The priority unit is used to preset a tolerance threshold X and the replenishment timeliness coefficient of the j-th customer demand item is compared with the tolerance threshold X. If the replenishment timeliness coefficient of the j-th customer demand item , it indicates that the replenishment efficiency of the j-th customer demand item is unqualified, and a first optimization strategy is generated, including: increasing the replenishment frequency from once a week to once every two days; increasing the replenishment quantity by 15%-20%, shortening the replenishment response time by 20%-30%, and generating a priority list in descending order of the values of the replenishment timeliness coefficients of the j-th customer demand items exceeding the tolerance threshold X, controlling the robot to replenish the priority list in descending order, delivering the required items to the replenishment point according to the optimal path, and transmitting back the replenishment execution status after delivery; ​ When the replenishment timeliness coefficient of the j-th customer demand item indicates that the replenishment efficiency of the j-th customer demand item is qualified, replenishment is carried out according to the current estimated replenishment time and replenishment demand quantity.

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