Customer demand storage and elimination optimization management system adaptive to hotel operation management

Abstract

The invention discloses a customer demand storage and elimination optimization management system adapted to hotel operation management, and relates to the technical field of customer demand intelligent replenishment, the system can identify unregistered and actually used guest rooms by introducing multi-source sensing data such as door lock frequency, light on-off, television use and the like, avoids replenishment delay caused by misjudgment of empty rooms, and improves the replenishment efficiency. And the service response efficiency is improved. By analyzing historical consumption data and customer group features, the system constructs a customer demand marginal utility curve, implements a demand-based replenishment strategy, reduces excessive delivery, and improves the material utilization rate and customer satisfaction. And a matched reusable article identification module can intelligently judge whether check-out articles have backflow conditions or not, qualified products are brought into a microcirculation material warehouse, and low-carbon environmental protection and cost control are realized. The intelligent replenishment robot cooperation module automatically completes replenishment and backflow tasks and feeds back the state in real time based on replenishment priority and path planning, and the replenishment efficiency is improved.

Description

Technical Field

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

[0002] In traditional hotel operation management, the management of guest necessities has always been a difficult problem, especially in terms of guest room replenishment and the recycling and utilization of guest necessities. Most traditional guest room management models rely on manual records and management, and are easily affected by factors such as inaccurate guest room occupancy status, insufficient timeliness of replenishment, 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 formally check in, but due to the needs of activities such as meetings and gatherings, they will actually still use some facilities in the guest room or consume some guest necessities. In this case of "not formally 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 necessities 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. After many guests check out, the remaining guest necessities in the hotel are not effectively recycled and reused, resulting in the inability to reasonably reuse these items, increasing the operation cost of the hotel. The lack of an effective management mechanism for the return and reuse of items has caused 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 necessities 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 necessities adapted to hotel operation management includes: A guest room status perception module, which is used to establish a perception data set based on multi-source information such as the frequency of door lock switches, the opening and closing status of lighting equipment, the TV usage status, and cleaning behavior data, determine whether there is a situation of "not formally 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 the corresponding replenishment instruction ; A marginal utility analysis module for guest necessities, which is used to construct a marginal utility curve for a single guest necessity based on the historical unit consumption and customer group data of each guest necessity, and output the marginal utility weight of the j-th guest necessity And corresponding prediction results are used to generate corresponding replenishment instructions, a first non-default placement strategy, and a second default placement strategy; A reusable item intelligent recognition module is configured to, after a guest checks out, perform weight detection, appearance recognition, and packaging integrity analysis on the remaining guest supplies in the room to construct the integrity coefficient of the j-th guest supply And evaluate, mark and allocate reusable items into the "microcirculation material library", and simultaneously generate corresponding item return decision instructions; An intelligent replenishment robot collaboration module is configured to receive corresponding replenishment instructions and item return decision instructions, and calculate and obtain the replenishment timeliness coefficient of the j-th guest supply by combining the current inventory data set and the room status data And evaluate, generate a 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.

[0007] Preferably, the guest room status perception module includes a first collection unit, a first recognition unit, and a first trigger unit; The first collection unit is configured to collect the switch frequency, opening time period, and opening stay duration of the guest room door lock through an intelligent door lock device connected to the network, 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 ; The average stay duration of a single door opening in the i-th guest room ; The lighting opening and closing rate of the i-th guest room , which is obtained by dividing the total long-term duration of lighting in the i-th guest room by 24 hours; The TV usage rate of the i-th guest room , which is obtained by dividing the total opening duration of the TV in 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 configured to, after normalizing each variable of the perception data set, statistically calculate the value within the range of [0,1], and calculate and obtain the room status perception coefficient of the i-th guest room through the following formula : ; Wherein, is the number of door lock switch times for the i-th normalized guest room weight; is the average stay duration for a single door opening of the i-th normalized guest room weight, is the lighting on-off rate for the i-th guest room weight; is the TV usage rate for the i-th guest room weight, is the intensity of cleaning behavior for the i-th guest room weight; is the wifi-downlink data traffic for the i-th guest room weight; and are both constants, and , represents calculating the room status perception coefficient and weight for the i-th guest room .

[0008] Preferably, 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: 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; 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-regular guest necessities, and the replenishment time limit is within 60 minutes; When , it means no usage status, maintain regular inspections, and there is no replenishment requirement.

[0009] Preferably, the guest necessity usage marginal utility analysis module includes a guest necessity consumption monitoring unit and a marginal utility calculation unit; The guest necessity consumption monitoring unit is used to collect the historical unit consumption and customer group data of each guest necessity; Customer group data includes: guest room reservation data, length of stay in each guest room, number of occupants and customer type; According to the customer group data, construct the marginal utility curve of a single guest necessity, specifically: Use regression analysis or machine learning models to fit the customer group data. Use the x-axis to represent the consumption of individual customer-needed items, and the y-axis to represent the marginal utility, that is, the utility and demand of customers, and plot the marginal utility curve of individual customer-needed items; The marginal utility calculation unit is used to output the marginal utility weight of the j-th customer-needed item based on the customer group data and the marginal utility curve of the individual customer-needed item , and the marginal utility weight of the j-th customer-needed item is calculated and obtained through the following formula: ; In the formula, represents the historical consumption of the j-th customer-needed item, is the customer preference coefficient of the j-th customer-needed item, which is calculated and obtained through the following formula: ; Among them, is the th customer group's demand probability for the j-th customer-needed item, 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; is the daily consumption frequency of the j-th customer-needed item; and are both constants, and represents calculating the marginal utility weight of the j-th customer-needed item according to the and weights , and further normalizing the marginal utility weight of the j-th customer-needed item to make the result range of the marginal utility weight of the j-th customer-needed item within [0, 1], and the expression is: ; In the formula, represents the marginal utility weight of the j-th customer-needed item after normalization, and are respectively the minimum and maximum values of the marginal utility weights of all customer-needed items.

[0010] Preferably, the customer-needed item marginal utility analysis module further includes a second prediction unit; The second prediction unit is used to evaluate the value of the marginal utility weight of the j-th customer-needed item after normalization to obtain a second prediction result, including: When , it indicates that the customer demand for this customer-needed item is strong, and it is necessary to immediately replenish this customer-needed item, and generate a third replenishment instruction, including: replenishing 80%-90% of the current inventory of the j-th customer-needed item; And generate the first non-default placement strategy, including: placing the customer-needed item at the bedside table, desktop, TV cabinet, bedside area or guest room entrance area of the guest room; When , it indicates that the customer demand for the customer-needed item exceeds the expectation; replenish the customer-needed item and generate the fourth replenishment instruction, including: replenishing 50%-70% of the current inventory of the j-th customer-needed item; and generate the second default placement strategy; place the customer-needed item in the regular placement area, and the regular placement area includes: storage cabinets, drawers or refrigerators; When , it indicates that the demand for the customer-needed item does not exceed the expectation, check the inventory of the customer-needed item, and keep the inventory within the safety inventory range, and the safety inventory range is within 20%-30%.

[0011] Preferably, the reusable item intelligent identification module includes an integrity monitoring unit and an integrity evaluation unit; The integrity monitoring unit is used to perform weight detection, appearance recognition, and packaging integrity analysis on the remaining customer-needed items in the room after the guest checks out, so as to construct the integrity coefficient of the j-th customer-needed item , specifically including: S11. Monitor the current weight of the j-th customer-needed item after the guest checks out through a weight sensor and the initial weight to obtain the weight loss coefficient of the j-th customer-needed item : ; S12. Monitor the appearance damage or usage marks of the j-th customer-needed item after the guest checks out through image processing technology, and obtain the appearance damage coefficient of the j-th customer-needed item through the following formula : ; 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; 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 : ; 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; 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 demand item and the packaging damage coefficient of the j-th customer demand item . After dimensionless processing, the integrity coefficient of the j-th customer demand item is calculated through the following weighted formula : ; In the formula, and are respectively the weight loss coefficient , the appearance damage coefficient of the j-th customer demand item, and the packaging damage coefficient of the j-th customer demand item, and the sum of the weights is 1.

[0012] Preferably, the integrity evaluation unit is used to evaluate the integrity coefficient of the j-th customer demand item to obtain an evaluation result, including: When , it means that the customer demand item is not damaged. Mark the customer demand 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"; When , it means that the customer demand item is damaged but has a return value. Mark the customer demand item as "damaged but has repair value" and allocate it to the microcirculation material warehouse to generate the 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 re-use link. If the item cannot fully restore its appearance or function, decide whether to reduce the use frequency or use it in a specific environment; When , it means that the customer demand item is damaged and not suitable for re-use or return, and generate a waste decision instruction, "Discard for disposal, status: severely damaged, not returnable".

[0013] Preferably, the intelligent replenishment robot collaboration module includes an instruction receiving unit and a priority unit; 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 the replenishment timeliness coefficient of the j-th customer demand item through the following formula : ; ; In the formula, represents the replenishment demand quantity of the j-th customer demand item, represents the inventory gap coefficient; represents the expected replenishment response time for the jth customer demand item; represents the current inventory of the jth customer's desired product, Represents the safety stock of the jth customer demand.

[0014] Preferably, the priority unit is used to preset a tolerance threshold X and set the replenishment timeliness coefficient of the jth customer demand item Compared with the tolerance threshold X, if the replenishment timeliness coefficient of the jth customer demand product , indicating that the replenishment efficiency of the jth customer demand product is unqualified, generating the first optimization strategy, including: increasing the replenishment frequency from once a week to once every two days; increasing the replenishment volume by 15%-20%, shortening the replenishment response time by 20%-30%, and reducing the replenishment timeliness coefficient of the jth customer demand product that exceeds the tolerance threshold X. Generate a priority list in descending order of the value of , control the robot to replenish the priority list in descending order, deliver the required items to the replenishment point along the optimal path based on the path planning algorithm, and report the replenishment execution status after delivery; When the replenishment timeliness coefficient of the jth customer demand product , indicating that the replenishment efficiency of the j-th customer demand product is qualified, and replenishment is carried out according to the current estimated replenishment time and replenishment demand.

[0015] The present invention provides a guest goods storage and consumption optimization management system suitable for hotel operation management. It has the following beneficial effects: (1) This guest supplies inventory optimization management system adapted to hotel operation management can build a guest room status perception model by introducing multi-source behavior perception data such as door lock frequency, light on and off, TV use and cleaning behavior. It can accurately identify guest rooms that are not checked in but are actually temporarily used, thereby effectively compensating for the problem that the traditional system cannot replenish stocks in time when "vacancy misjudgment" occurs, and improving the timeliness of service response and the accuracy of guest supplies supply.

[0016] (2) This customer demand inventory and consumption optimization management system is adapted to hotel operation management. By analyzing the historical unit consumption and customer characteristics (including length of stay, number of people, customer type, etc.), this system constructs the marginal utility curve of each customer demand and quantifies the marginal utility weight, thereby realizing a replenishment strategy guided by the real needs of customers, avoiding blind replenishment or over-delivery, and improving the efficiency of material use and user satisfaction.

[0017] (3)The optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management is equipped with an intelligent identification module for reusable items. It can detect the weight of the remaining guest supplies after check-out, identify the integrity of the packaging and the appearance status, accurately determine whether they meet the conditions for return, and include 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.

[0018] (4)The optimized 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 identification, 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

[0019] Figure 1 It is a schematic flow chart of the optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management of the present invention.

[0020] Figure 2 It is a schematic diagram of the marginal utility curve of a single guest supply taking towel consumables as an example of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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 the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1 Please refer to Figure 1 , the present invention provides an optimized management system for the inventory and consumption of guest supplies adapted to hotel operation management, including: A guest room status perception module, which is used to establish a perception data set based on multi-source information such as the frequency of door lock switches, the on-off 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 generated to generate the corresponding replenishment instructions, the first non-default placement strategy, and the second default placement strategy. The intelligent identification module for reusable items 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. 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. 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 with the 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.

[0023] 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 to 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 customer-demand item supply.

[0024] 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-demand item, quantifies the marginal utility weight, so as to realize a replenishment strategy guided by the real needs of customers, avoid blind replenishment or over-placement, and improve the material use efficiency and user satisfaction.

[0025] The intelligent identification module for reusable items equipped in the system can perform weight detection, packaging integrity and appearance status recognition on the remaining customer-demand items 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 customer-demand items, but also achieves the dual goals of low-carbon environmental protection and cost optimization.

[0026] With the intelligent replenishment robot collaboration module, the system can automatically dispatch robots to execute replenishment and return commands according to the replenishment priority and replenishment timeliness coefficient. The robots deliver supplies to the target guest rooms in the optimized order and transmit 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 the hotel management to transform and upgrade towards the direction of "refinement, intelligence, and greenness".

[0027] Embodiment 2 This embodiment is an explanatory note based on 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; 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 on - off state of lighting equipment, 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 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.

[0028] Table 1 Induction information of the number of door lock switches in the i - th guest room Timestamp Operation type Operation mode 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 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.

[0029] The lighting on - off rate of the i - th guest room , 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 on - off rate by comparing it with 24 hours. The room light state can also be detected in real time through an illuminance sensor (for redundancy or lighting anomaly judgment).

[0030] The 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; Obtain the total on - time of the TV through the hotel intelligent TV background interface or control terminal and calculate the usage rate. The running state of the TV can also be detected using an infrared remote control state acquisition module or a current sensor.

[0031] Cleaning behavior intensity of the i-th guest room , 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 ; Record the cleaning duration through the time when the service staff enters and leaves the room; or the cleaning cart is equipped with an RFID + positioning module to monitor the cleaning path and duration; with a job punching terminal, the number of cleaning behavior occurrences can be accurately recorded.

[0032] 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.

[0033] The first recognition unit is used to normalize each variable in the perception data set, and then count the values within the range of [0,1], and calculate the room state perception coefficient of the i-th guest room through the following formula :[[]] ; In the formula, is the weight for the number of door lock switchings of the i-th guest room after normalization ; is the weight for the average stay duration of a single door opening of the i-th guest room after normalization ; is the weight for the lighting on-off rate of the i-th guest room ; is the weight for the TV usage rate of the i-th guest room ; is the weight for the cleaning behavior intensity of the i-th guest room ; is the weight for the wifi-downlink data traffic of the i-th guest room ; and are both constants, and , represents calculating the room state perception coefficient of the i-th guest room according to and weights .

[0034] In this embodiment, this module not only relies on the door lock switch data, but also integrates the perception information of multiple dimensions such as lighting on / off status, TV usage, cleaning behavior, wifi-downlink traffic, etc., which can effectively identify scenarios such as "temporary use" and "conference occupation" where items are actually used but not registered for check-in, filling the blind spots in the recognition of traditional hotel room status. The module normalizes variables such as door lock frequency, door opening duration, lighting usage rate, TV usage rate, cleaning behavior intensity and downlink data traffic, and calculates the room status perception coefficient in combination with weights, providing a quantifiable basis for subsequent replenishment, cleaning, resource allocation and other operations, and achieving the intelligent management goal of "data-driven service decision-making". The perception data set is updated in a rolling cycle of 24 hours, which is not only convenient for continuous room status monitoring, but also supports rapid response to situations including temporary entry into the room at night or short-term high-frequency use, enhancing the system's ability to recognize and adapt to real-time room status changes.

[0035] The multi-source data fusion processing method effectively reduces the misidentification caused by single-dimensional anomalies (such as mis-touch of door locks or mis-recording of cleaning). The weighted model assigns corresponding importance to different variables, improving the accuracy and robustness of the system's judgment of the actual status of the guest room.

[0036] Example 3 This example is an explanation of Example 1. Figure 1 Specifically, the first trigger unit is used to determine the room state perception coefficient of the i-th guest room according to The first classification results include: when , but the room has no check-in information → automatically triggers the "suspicious use of room" status, and automatically triggers the first replenishment instruction, including: including the replenishment of regular guest supplies, including water, towels or toiletries, and the replenishment completion time is 30 minutes; 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 usage of the room, including replenishment of night lights, skin care products, bed sheets and pillows, which are not daily necessities for guests, and the replenishment time limit is 60 minutes; when , indicating that it is not in use, regular inspections are maintained, and there is no need for replenishment.

[0037] In this embodiment, when the room status perception coefficient of the i-th room is greater than 0.7, the system will automatically determine that the room has a strong use behavior. Even if the system has no check-in information, it will automatically trigger the "suspicious use of room" state and execute the first replenishment instruction, including: Replenish the standard regular guest supplies for this guest room, including bottled drinking water, towels, toothbrushes, shampoo and bath products, etc.; Control the intelligent replenishment robot to complete the replenishment task of the corresponding items within 30 minutes according to the priority order; After the replenishment is completed, the robot automatically uploads the replenishment status information and feeds it back to the management background for data closed-loop; At the same time, the system automatically issues an abnormal use reminder to prompt the operation staff to further review the use 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, the problems of mis-replenishment and missed replenishment can be effectively avoided. According to the perception degree, regular and non-regular guest supplies are allocated to make the material allocation more intelligent. The delayed decision-making mechanism reduces the operation cost: a buffer mechanism is set for rooms in the intermediate state (0.4 - 0.7) to reduce the ineffective replenishment rate. Realize the unmanned room status abnormal monitoring and replenishment closed-loop management.

[0038] Embodiment 4 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 Specifically, the marginal utility analysis module for guest supplies includes a guest supply consumption monitoring unit and a marginal utility calculation unit; The guest supply consumption monitoring unit is used to collect the historical unit consumption and customer group data of each guest supply; 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; According to the customer group data, construct the marginal utility curve of single-item guest supplies, specifically: 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; Taking towel consumables as an example, please refer to Figure 2 ; Marginal utility refers to the satisfaction that a customer obtains from an additional unit of consumer goods. The x-axis represents the consumption volume, which means we are studying the satisfaction of customers at different consumption volumes. As the consumption volume 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 utility, and the third bottle may even feel redundant. Measured by the consumption volume, this decreasing trend can be clearly presented; The marginal utility calculation unit is used to output the marginal utility weight of the j-th type of guest supply based on the customer group data and the marginal utility curve of single-item guest supplies The marginal utility weight of the j-th type of guest supply is calculated and obtained through the following formula: ; In the formula, represents the historical consumption of the j-th type of customer demand item, is the customer preference coefficient of the j-th type of customer demand item, which is obtained by calculating through the following formula: ; wherein, is the th customer group's demand probability for the j-th type of customer demand item, is the weight of the customer type, and the customer types include: business travelers, family travelers, long-term staying guests, parent-child travelers, vacation travelers, and group travelers; is the daily consumption frequency of the j-th type of customer demand item; and are both constants, and represents that according to the and weights, 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 is further normalized so that the result range of the marginal utility weight of the j-th type of customer demand item is within [0, 1], and the expression is: ; 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.

[0039] In this embodiment, the marginal utility calculation unit is used to construct the marginal utility curve of each type of customer demand item based on the customer group data. The x-axis of the marginal utility curve represents the consumption amount of the customer demand item, and the y-axis represents the marginal utility value perceived by the customer, that is, the actual demand utility generated by the customer demand 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 (for example, Figure 2 shows an example with towel consumption); Through such a marginal utility curve, the hotel can obtain a reasonable replenishment strategy for customer demand items.

[0040] For example, the hotel may find that 1 bottle of shampoo or 1 towel can meet 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 the inventory and replenishment strategies according to the actual demand and improve the resource utilization efficiency.

[0041] By conducting marginal utility analysis on customer-demand 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 the usage efficiency to avoid material waste or supply-demand imbalance. It can improve the allocation priority of customer-demand 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.

[0042] Example 5 This example is an explanatory note based on Example 1. Please refer to Figure 1 , specifically, the customer-demand item usage marginal utility analysis module further includes a second prediction unit; The second prediction unit is used to evaluate the value of the marginal utility weight of the j-th normalized customer-demand item to obtain a second prediction result, including: When , it indicates that the customer demand for this 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 customer-demand item; and generating a first non-default placement strategy, including: placing this customer-demand item at the bedside table, desktop, TV cabinet, bedside area or guest room entrance area of the guest room; When , it indicates that the customer demand for this item exceeds expectations; replenish this customer-demand item and 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, and the regular placement area includes: storage cabinet, drawer or refrigerator; When , it indicates 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, and the safety inventory range is within 20%-30%.

[0043] In this example, the customer-demand item usage marginal utility analysis module further includes a second prediction unit, which is used to intelligently evaluate the value of the marginal utility weight of the j-th normalized customer-demand item 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 over-replenishment or under-replenishment. It 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, it gives priority to ensuring the safety inventory and reduces the risk of material backlog. Through marginal utility modeling and intelligent judgment, it fits the potential customer demands of different customer groups and improves the check-in satisfaction.

[0044] Example 6 This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically, the intelligent identification module for reusable items includes an integrity monitoring unit and an integrity evaluation unit; The integrity monitoring unit is used to comprehensively monitor the integrity of the remaining guest supplies in the room during the room inspection process after the guest checks out. The hotel staff uses the visual monitoring equipment installed on the cleaning cart to perform weight detection, appearance recognition, and packaging integrity analysis on the remaining guest supplies in the room to construct the integrity coefficient of the j-th guest supply , specifically including: S11. The weight sensor on the cleaning cart monitors the current weight of the j-th guest supply after the guest checks out and the initial weight to obtain the weight loss coefficient of the j-th guest supply : ; S12. The image recognition device on the cleaning cart performs appearance detection and scanning on each guest supply. Through image processing technology, it monitors the appearance damage or usage marks of the j-th guest supply after the guest checks out. The appearance damage coefficient of the j-th guest supply is obtained through the following formula : ; In the formula, represents the area of the damaged part marked in the image recognition result of the j-th guest supply; represents the initial total surface area of the j-th guest supply; S13. Through the pressure sensor and image recognition technology on the cleaning cart, the item packaging is detected for whether there is unsealing or damage. Whether the packaging is intact is collected through the pressure sensor. The packaging damage coefficient of the j-th guest supply is calculated through the following formula : ; In the formula, represents the area of the damaged part marked in the image recognition result of the packaging surface of the j-th guest supply; represents the initial total surface area of the packaging surface of the j-th guest supply; S14. Extract the weight loss coefficient of the j-th guest supply in S11 - S13 , the appearance damage coefficient of the j-th guest supply, and the packaging damage coefficient of the j-th guest supply. After dimensionless processing, the integrity coefficient of the j-th guest supply is calculated through the following weighted formula : In the formula, and are respectively the weight loss coefficient of the j-th guest supply The appearance damage coefficient of the j-th customer required item and the packaging damage coefficient of the j-th customer required item The weights of which sum to 1.

[0045] 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 the first item return decision instruction, including: "Return to the microcirculation material warehouse, status: not damaged"; When , it indicates that the customer required item is damaged but has a return value. Mark the customer required item as "damaged but has repair value" and allocate it to the microcirculation material warehouse, generating the 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 re-use 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; When , it indicates that the customer required item is damaged and is not suitable for re-use or return, generating a waste decision instruction, "Discard, status: severely damaged, non-returnable".

[0046] 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), a multi-angle analysis of the usage status of customer required items is realized; effectively avoiding misjudgments caused by a single index, such as the weight remaining unchanged but the packaging being opened or the appearance being 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-delivery" and "regional restricted use" are supported to maximize the remaining value of the items.

[0047] Embodiment 7 This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, 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. By combining the current inventory data set and the room status data, the replenishment timeliness coefficient of the j-th customer required item is calculated and obtained through the following formula : ; ; 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.

[0048] Specifically, the priority unit is configured 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 indicates 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 from large to small of the values of the replenishment timeliness coefficient of the j-th customer required item exceeding the tolerance threshold X, controlling the robot to replenish the priority list in the order 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, item shortage, 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.

[0049] 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 expected replenishment time and replenishment demand quantity.

[0050] In this embodiment, by introducing the "replenishment timeliness coefficient" and calculating in combination with multiple parameters such as room status, inventory, expected response time, and shortage quantity, it is possible to more scientifically identify whether replenishment is timely and whether there is a risk of lag, thereby effectively avoiding risks such as out-of-stock of items and service interruption, and improving the customer check-in experience. 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 replenishment urgency level, ensuring to prioritize the urgent and the lacking, and solving the problems of "rigid queuing and untimely response" in traditional replenishment.

[0051] 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 replenishment urgency level, ensuring to prioritize the urgent and the lacking, and solving the problems of "rigid queuing and untimely response" in traditional replenishment. Combining with the path planning algorithm, the robot no longer uses a fixed route for replenishment, but generates an action plan combining the shortest path and the most urgent items according to the real-time priority and the replenishment point location, significantly saving energy, shortening the travel time, and improving the execution efficiency.

[0052] When it is detected that the replenishment efficiency of a certain type of item fails to meet 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.

[0053] The setting of the size of the threshold is 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 group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0054] 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 formulas are set by those skilled in the art according to the actual situation. As described above, this 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 goods inventory and consumption optimization management system suitable for hotel operation management, characterized by: include: The guest room status perception module is used to establish a perception data set based on multi-source information such as door lock switch frequency, lighting equipment on / off status, TV usage status, and cleaning behavior data, to determine whether the guest room is "not checked in but actually used", and to generate the room status perception coefficient of the i-th guest room for triggering the corresponding replenishment instruction. ; The marginal utility analysis module is used for the historical unit consumption and customer group data of each customer demand, to construct the marginal utility curve of each customer demand, and to output the marginal utility weight of the jth customer demand. and corresponding prediction results, generating corresponding replenishment instructions, a first non-default placement strategy, and a second default placement strategy; The reusable item intelligent identification module is used to perform weight detection, appearance recognition and packaging integrity analysis on the remaining guest items in the room after the guest checks out, so as to construct the integrity coefficient of the jth guest item. And evaluate, mark and allocate reusable items into the "microcirculation material library", and generate corresponding item return decision instructions; The intelligent replenishment robot collaborative module is used to receive the corresponding replenishment instructions and item return decision instructions, and calculate the replenishment timeliness coefficient of the jth customer demand item by combining the current inventory data set and room status data. And evaluate, generate the first optimization strategy, control the robot to replenish the priority list in order from large to small, and send back the replenishment execution status after delivering to the capture point.

2. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 1 is characterized in that: The guest room status sensing module includes a first acquisition unit, a first recognition unit and a first trigger unit; The first collection unit is used to collect the switch frequency, door opening time period and door opening duration of the guest room door lock through the networked smart door lock device, and collect the lighting equipment opening and closing status, TV usage status and cleaning behavior data to establish a perception data set; The perception data set is based on a 24-hour cycle and includes: The number of times the door lock of the i-th guest room is opened and closed ; The average length of stay of the i-th guest room when it is opened ; Lighting on / off rate of the i-th guest room , the total long-term lighting duration of the i-th guest room Divide by 24 hours to obtain; TV usage rate of the i-th guest room , based on the total TV on time of the i-th room Divide by 24 hours to obtain; The cleaning behavior intensity of the i-th room , the total cleaning time of the i-th room Divided by the number of cleaning behaviors of the i-th room Get; Wi-Fi-downlink data traffic of the i-th room ; The first recognition unit is used to normalize each variable of the perception data set, count the values ​​within the range of [0,1], and calculate the room state perception coefficient of the i-th guest room through the following formula: : ; In the formula, is the normalized number of door lock openings and closings for the i-th guest room The weight of is the normalized average length of stay for a single door opening of the i-th guest room The weight of is the lighting on / off rate for the i-th guest room The weight of is the TV usage rate for the i-th guest room The weight of is the cleaning behavior intensity for the i-th guest room The weight of is the Wi-Fi downlink data traffic for the i-th room The weight of and are constants, and , Indicates according to and Weight, calculate the room status perception coefficient of the i-th guest room .

3. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 2 is characterized in that: The first trigger unit is used to determine the room state perception coefficient of the i-th guest room. The first classification results include: 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 supplies, including water, towels or toiletries, and the replenishment completion time is 30 minutes; 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 replenishment of night lights, skin care products, bed sheets and pillows, which are not daily guest necessities, and the replenishment time limit is 60 minutes; when , indicating that it is not in use, regular inspections are maintained, and there is no need for replenishment.

4. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 1 is characterized in that: The customer demand product use marginal utility analysis module includes a customer demand product consumption monitoring unit and a marginal utility calculation unit; The customer demand product consumption monitoring unit is used to collect the historical unit consumption and customer group data of each customer demand product; Customer data includes: room reservation data, length of stay in each room, number of guests and customer type; According to customer group data, the marginal utility curve of a single customer demand product is constructed, specifically: Use regression analysis or machine learning models to fit customer data, use the x-axis to represent the consumption of a single customer product, and the y-axis to represent marginal utility, i.e. the customer's utility and demand, to draw a marginal utility curve for a single customer product; The marginal utility calculation unit is used to output the marginal utility weight of the jth customer demand product based on the customer group data and the marginal utility curve of the single customer demand product. , the marginal utility weight of the jth customer demand Calculated by the following formula: ; In the formula, represents the historical consumption of the jth customer demand product, is the customer preference coefficient of the jth customer demand product, which is calculated by the following formula: ; in, It is The probability of a customer group demanding the jth customer product, is the weight of the customer type, which includes business travelers, family travelers, long-term travelers, family travelers, vacation travelers and group travelers; Daily consumption frequency of the jth customer demand; and are constants, and Indicates according to and Weight, calculate the marginal utility weight of the jth customer demand And the marginal utility weight of the jth customer demand is A further normalization step is to make the marginal utility weight of the jth customer demand The result range is in [0, 1], and the expression is: ; In the formula, represents the normalized marginal utility weight of the jth customer demand product, and are the minimum and maximum marginal utility weights of all customer needs respectively.

5. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 4 is characterized in that: The customer demand product use marginal utility analysis module further includes a second prediction unit; The second prediction unit is used to normalize the marginal utility weight of the jth customer demand product The value of is evaluated to obtain the second prediction result, including: when , indicating that the customer demand for the product is strong and needs to be replenished immediately, generating a third replenishment instruction, including: replenishing 80%-90% of the current inventory of the jth customer demand product; and generating a first non-default placement strategy, including: placing the guest item on a bedside table, a desktop, a TV cabinet, a bedside area, or a guest room entrance area; when , indicating that the customer demand for the customer demand product exceeds expectations; replenish the customer demand product and generate a fourth replenishment instruction, including: replenish 50%-70% of the current inventory of the jth customer demand product; and generate a second default placement strategy; place the customer demand product in a conventional placement area, which includes: a storage cabinet, a drawer or a refrigerator; when , indicating that the demand for the customer product has not exceeded expectations. Check the inventory of the customer product and keep the inventory within the safety stock range, which is between 20% and 30%.

6. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 1 is 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 perform weight detection, appearance recognition, and packaging integrity analysis on the remaining guest items in the room after the guest checks out, so as to construct the integrity coefficient of the jth guest item. , including: S11. Monitor the current weight of the jth guest's items after check-out through the weight sensor With initial weight The difference between , obtain the weight loss coefficient of the jth customer's desired item : ; S12. Use image processing technology to monitor the appearance damage or use traces of the jth guest item after check-out, and obtain the appearance damage coefficient of the jth guest item through the following formula : ; In the formula, represents the area of ​​the damaged part identified in the j-th customer demand product image recognition result; represents the initial total surface area of ​​the jth customer's needs; S13. Check the packaging of the item to see if it has been opened or damaged. Use the pressure sensor to collect information about whether the packaging is intact. Use the following formula to calculate the packaging damage coefficient of the jth customer's item: : ; In the formula, represents the area of ​​the damaged portion of the packaging surface of the jth customer item identified in the image recognition result; represents the initial total surface area of ​​the packaging surface of the jth customer's desired product; S14. Extract the weight loss coefficient of the jth customer's required item in S11-S13 , the appearance damage coefficient of the jth customer demand and the packaging damage coefficient of the jth customer's item , after dimensionless processing, the integrity coefficient of the jth customer demand is calculated by the following weighted formula : ; In the formula, and are the weight loss coefficients of the jth customer's desired items , the appearance damage coefficient of the jth customer demand and the packaging damage coefficient of the jth customer's item The weights of , and the sum of the weights is 1.

7. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 6 is characterized in that: The integrity evaluation unit is used to evaluate the integrity coefficient of the jth customer demand item. The assessment results include: when , indicating that the customer's required goods are not damaged, the customer's required goods are marked as reusable, and are allocated to the microcirculation material warehouse, and the first item return decision instruction is generated, including: "return to the microcirculation material warehouse, status: not damaged"; when , indicating that the customer's item is damaged but has return value, the customer's item is marked as "damaged but repairable" and allocated to the microcirculation material warehouse, generating the second item return decision instruction, including: "return to the repair warehouse, status: damaged but repairable"; after the repair, the item returns to normal use and enters the reuse stage; when , indicating that the customer's item is damaged and not suitable for reuse or return, and generates a discard decision instruction, "discard processing, status: severely damaged, not recyclable".

8. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 1 is characterized in that: The intelligent replenishment robot collaborative module includes an instruction receiving unit and a priority unit; 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 calculate the replenishment timeliness coefficient of the jth customer demand item by combining the current inventory data set and the room status data through the following formula : ； ； In the formula, represents the replenishment demand for the jth customer's product, represents the inventory gap coefficient; represents the expected replenishment response time for the jth customer demand item; represents the current inventory of the jth customer's desired product, The safety stock quantity of the jth customer's required product.

9. The guest goods storage and consumption optimization management system adapted to hotel operation management according to claim 8 is characterized in that: The priority unit is used to preset a tolerance threshold X and set the replenishment timeliness coefficient of the jth customer demand item Compared with the tolerance threshold X, if the replenishment timeliness coefficient of the jth customer demand product , indicating that the replenishment efficiency of the jth customer demand product is unqualified, generating the first optimization strategy, including: increasing the replenishment frequency from once a week to once every two days; increasing the replenishment volume by 15%-20%, shortening the replenishment response time by 20%-30%, and reducing the replenishment timeliness coefficient of the jth customer demand product that exceeds the tolerance threshold X. Generate a priority list in descending order of the value of , control the robot to replenish the priority list in descending order, deliver the required items to the replenishment point along the optimal path based on the path planning algorithm, and report the replenishment execution status after delivery; When the replenishment timeliness coefficient of the jth customer demand product , indicating that the replenishment efficiency of the j-th customer demand product is qualified, and replenishment is carried out according to the current estimated replenishment time and replenishment demand.

Images