Guest room cleaning sequence dynamic adjustment method and system, product and medium

Abstract

The invention relates to a guest room cleaning sequence dynamic adjustment method and system, a product and a medium, and relates to a hotel system, and the method comprises the steps: obtaining the state information and the initial cleaning sequence of each guest room in real time; when the state information changes, whether the initial cleaning sequence needs to be adjusted or not is judged based on a preset judgment model; and when adjustment is needed, the initial cleaning sequence is adjusted according to a preset adjustment model based on the changed state information, and a re-planned cleaning sequence is obtained. The cleaning priority can be adjusted according to the real-time data, and it is ensured that the cleaning task is dynamically planned in the cleaning period.

Description

Technical Field

[0001] The invention relates to the field of hotel systems, and in particular to a method, system, product and medium for dynamically adjusting the cleaning sequence of guest rooms. Background Art

[0002] In existing hotel management, when cleaning guest rooms, a fixed cleaning task list is usually generated in advance before cleaning begins. The list is arranged in a predetermined priority order, and the cleaning staff performs the cleaning tasks one by one according to this fixed order when working.

[0003] However, this fixed-order cleaning method has limitations in actual implementation. If new requirements arise during the cleaning process, such as a customer's temporary request to clean the room immediately, the generated cleaning order cannot be adjusted in real time. This inflexible arrangement may result in important cleaning requirements not being responded to in a timely manner, thereby reducing cleaning efficiency and may have a negative impact on customer satisfaction. Summary of the invention

[0004] In view of the shortcomings of the prior art described above, an object of the present invention is to provide a method, system, product and medium for dynamically adjusting the guest room cleaning order, which can adjust the cleaning priority according to real-time data to ensure dynamic planning of cleaning tasks during the cleaning period.

[0005] To achieve the above purpose, the present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for dynamically adjusting the cleaning order of guest rooms, which adopts the following technical scheme: obtaining the status information and initial cleaning order of each guest room in real time; When the status information changes, whether the initial cleaning order needs to be adjusted is determined based on a preset judgment model; when adjustment is required, the initial cleaning order is adjusted based on the changed status information and according to a preset adjustment model to obtain a re-planned cleaning order.

[0007] Furthermore, in the above-mentioned method for dynamically adjusting the guest room cleaning sequence, when the state information changes, judging whether the initial cleaning sequence needs to be adjusted based on a preset judgment model includes: When a change in status information is detected, the judgment process is triggered; Extracting cleaning demand features from the changed state information, and preprocessing the cleaning demand features to make them meet the input requirements of the judgment model; The judgment model performs calculations based on the input cleaning requirement characteristics and outputs a binary judgment result.

[0008] Furthermore, in the above-mentioned method for dynamically adjusting the guest room cleaning sequence, the judgment model is set as a rule engine.

[0009] Furthermore, in the above-mentioned guest room cleaning sequence dynamic adjustment method, the initial cleaning sequence is adjusted based on the changed state information according to a preset adjustment model to obtain a re-planned cleaning sequence, including: combining the changed state information with the current initial cleaning sequence to obtain the latest task priority and work progress; extracting cleaning demand features from the changed state information, and pre-processing the cleaning demand features to make them meet the input requirements of the adjustment model; The extracted cleaning demand features are input into the adjustment model to recalculate the cleaning priority of each room.

[0010] Furthermore, in the above-mentioned method for dynamically adjusting the cleaning order of guest rooms, the step of inputting the extracted cleaning demand features into the adjustment model to recalculate the cleaning priority of each room includes: Based on the preset decision variables and constraints, the preset objective function is minimized to obtain the cleaning priority; Among them, the decision variables include at least a cleaning order variable and a cleaning task allocation variable, the constraints include at least a priority constraint, and the objective function includes at least the total cleaning time or the total cleaning path length.

[0011] Furthermore, in the above-mentioned method for dynamically adjusting the guest room cleaning sequence, the constraint conditions also include path constraints, time window constraints and / or personnel allocation constraints.

[0012] In a second aspect, the present invention provides a system for dynamically adjusting the cleaning sequence of guest rooms, which adopts the following technical scheme: an acquisition module, which is at least used to acquire the status information and initial cleaning sequence of each guest room in real time; A judgment module, at least used to judge whether the initial cleaning sequence needs to be adjusted based on a preset judgment model when the state information changes; The adjustment module is used to adjust the initial cleaning sequence based on the changed state information and a preset adjustment model to obtain a re-planned cleaning sequence when adjustment is needed.

[0013] In a third aspect, the present invention provides a readable storage medium, which adopts the following technical solution: The computer program product includes computer instructions, and when the computer instructions are executed by a processor, a method for dynamically adjusting the guest room cleaning sequence as described in any one of the above is implemented.

[0014] In a fourth aspect, the present invention provides a readable storage medium, which adopts the following technical solution: The readable storage medium stores computer instructions, and when the computer instructions are executed by the processor, a method for dynamically adjusting the guest room cleaning sequence as described in any one of the first aspects above is implemented.

[0015] In summary, compared with the prior art, the present invention includes at least one of the following beneficial technical effects: The present invention obtains the guest room status information in real time and combines it with the preset judgment and adjustment model. When the status information changes, it can timely judge and dynamically adjust the initial cleaning order and generate an updated cleaning task list. This dynamic adjustment mechanism greatly improves the flexibility and response speed of the cleaning task, so that the cleaning work can more effectively respond to sudden demands, such as temporary requests from customers or changes in room status, thereby significantly improving cleaning efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flowchart of a specific embodiment of the method for dynamically adjusting the guest room cleaning sequence of the present invention.

[0018] Figure 2 It is a flowchart of another specific embodiment of the method for dynamically adjusting the guest room cleaning sequence of the present invention.

[0019] Figure 3 It is a structural schematic diagram of a specific embodiment of the system for dynamically adjusting the guest room cleaning sequence of the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application. In addition, it should be understood that the specific implementation methods described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0021] It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments of the present application. In addition, in the following embodiments, the description of each embodiment has its own emphasis, and for parts not described in detail in one embodiment, reference can be made to the relevant description of other embodiments.

[0022] The method steps described in the embodiments of the present invention may be executed in the order described in the specific implementation manner, or the execution order of each step may be adjusted according to actual needs and on the premise that the technical problem can be solved, which will not be listed here one by one.

[0023] Reference Figure 1 , a method for dynamically adjusting the cleaning sequence of guest rooms, comprising: S1, obtain the status information and initial cleaning order of each guest room in real time; specifically, the system continuously monitors and obtains the latest status information of each guest room, such as the current check-in status, reservation information, special needs of customers (such as emergency cleaning requests), and room location information, by connecting to the hotel management system (such as the PMS system) in real time. At the same time, the system also obtains the current initial cleaning order, that is, the task list arranged according to pre-set rules or previously generated cleaning plans. These real-time updated status information and initial cleaning order will serve as the basic data input for subsequent judgment and adjustment to ensure that the cleaning tasks can promptly reflect the latest room status and customer needs; S2, when the status information changes, based on the preset judgment model, determine whether the initial cleaning order needs to be adjusted; specifically, continuously monitor and compare the real-time acquired guest room status information with the initial cleaning order, and use a preset judgment model (such as rule-based, threshold judgment or machine learning model) to automatically analyze the changes in status information. When changes in status information (such as room status changes, customers making emergency cleaning requests, etc.) are detected, the judgment model evaluates whether these changes have a significant impact on the current cleaning order, and decides whether the cleaning order needs to be adjusted. If the judgment model believes that the changes are sufficient to affect the task priority, the next step of the order adjustment process is triggered; S3, when adjustment is needed, based on the changed state information, according to the preset adjustment model, the initial cleaning order is adjusted to obtain the re-planned cleaning order; specifically, when the judgment model determines that the cleaning order needs to be adjusted, the system calls the preset adjustment model (such as optimization algorithm or dynamic programming model) to recalculate and optimize the initial cleaning order according to the latest changed state information. The adjustment model will comprehensively consider the changed state information, such as new customer needs, changes in room priority, the current location of the cleaning staff and other factors, and generate an updated cleaning order list. This new cleaning order ensures that the task arrangement can better meet the current actual needs and achieve the optimal balance between cleaning efficiency and customer service.

[0024] The embodiment of the present invention obtains the guest room status information in real time and combines the preset judgment model and adjustment model to intelligently judge whether the cleaning order needs to be adjusted when the status information changes, and timely optimizes the execution order of the cleaning tasks. Its technical effect is to significantly improve the flexibility and responsiveness of the cleaning tasks, so that the system can dynamically adapt to the changing room status and customer needs, ensuring that the cleaning work is carried out in the best order and efficiency, thereby improving the overall operational efficiency and customer satisfaction.

[0025] Furthermore, as an embodiment of the present invention, step S2, when the state information changes, based on a preset judgment model, determines whether the initial cleaning order needs to be adjusted, including: when a change in the state information is detected, triggering the judgment process; extracting cleaning requirement characteristics from the changed state information, and preprocessing the cleaning requirement characteristics to make them meet the input requirements of the judgment model; the judgment model calculates according to the input cleaning requirement characteristics, and outputs a binary judgment result.

[0026] Specifically, first, the status information of each guest room is continuously monitored. When a change in status information (such as occupancy status, reservation status, customer needs, etc.) is detected, the judgment process is immediately triggered. Subsequently, key features related to cleaning needs (such as urgent cleaning requests from new customers, major changes in room status, etc.) are extracted from these changed status information. These features may include changes in priority of specific rooms, urgency of cleaning time windows, special cleaning requirements of rooms, etc. The extracted feature data needs to be preprocessed, such as standardization, normalization, or unique hot encoding, to ensure that they meet the input format requirements of the preset judgment model. Next, the preprocessed cleaning demand features are input into the judgment model, which may be based on a rule engine, a machine learning algorithm, or a threshold judgment mechanism. By calculating these features, the model outputs a binary judgment result, namely "needs adjustment" or "no adjustment", to determine whether the current cleaning order needs to be dynamically adjusted. This judgment process ensures that key changes can be identified and responded to in a timely manner when the guest room status information changes, so as to determine whether the execution order of the cleaning operation needs to be optimized.

[0027] Furthermore, as an implementation mode of the present invention, the judgment model is configured as a rule engine.

[0028] For example, when making a judgment based on a rule engine, a rule set is first defined, such as: Rule 1: If the cleaning priority of a room changes (e.g., it is upgraded from a regular room to a VIP room), the cleaning order needs to be adjusted; Rule 2: If a room receives an urgent cleaning request and the room is not in the top 5 tasks in the current cleaning order, the cleaning order needs to be adjusted; Rule 3: If the room status changes from "Vacant" to "Upcoming" and the check-in time is within 1 hour, the cleaning priority of the room will be increased.

[0029] Based on the above rule set example, when the room status information changes, such as: Room 101: The original status was "Vacant", and now the status is changed to "Ready to Check-in", and the check-in time is 30 minutes later; Room 102: The original status was a common room, but now it has been upgraded to a VIP room; Room 103: Received an urgent cleaning request from the client, but is currently ranked 10th in the cleaning order; Extract and preprocess cleaning requirement features: Room 101 features: original priority "Normal", changed status "Upcoming check-in, time window 30 minutes"; Room 102 features: original priority "normal", changed status "VIP room"; Room 103 characteristics: original priority "normal", changed status "urgent cleaning request". These features are processed, such as normalizing the time window into minutes and converting the room type into a numeric value (1 for normal and 2 for VIP), and then matched with the rules in the rule engine.

[0030] Based on the above cleaning requirement feature example, rule matching and calculation are performed: Rule 1 matches: Room 102 is upgraded from ordinary to VIP, which meets Rule 1, so the cleaning order needs to be adjusted to give it priority; Rule 2 matches: Room 103 receives an urgent cleaning request and is not in the first five tasks, which satisfies Rule 2, so its order needs to be advanced; Rule 3 matches: Room 101 changes from "Available" to "Upcoming", and the time window is 30 minutes, which meets Rule 3, so the priority of this room needs to be increased; Then output the binary judgment result: Room 101: Output "Needs Adjustment"; Room 102: Output "Needs Adjustment"; Room 103: Output "Needs Adjustment".

[0031] Through the rule engine, the changed status information can be quickly judged based on the preset rule set, and whether the cleaning order needs to be adjusted. The task priority is automatically adjusted by matching the rules, thereby realizing intelligent and dynamic cleaning management. At the same time, system administrators or relevant personnel can modify, add or delete the rule set in the rule engine at any time according to actual operational needs, changes in business strategies or customer feedback to optimize the judgment process of the cleaning order.

[0032] Further, as an embodiment of the present invention, refer to Figure 2 In step S3, based on the changed state information and according to a preset adjustment model, the initial cleaning sequence is adjusted to obtain a re-planned cleaning sequence, including: S31, combining the changed status information with the current initial cleaning order to obtain the latest task priority and work progress; S32, extracting cleaning demand features from the changed state information, and preprocessing the cleaning demand features to make them meet the input requirements of the adjustment model; S33, inputting the extracted cleaning demand features into the adjustment model to recalculate the cleaning priority of each room.

[0033] Specifically, in step S31, the changed status information (such as a new emergency cleaning request, a room status changed from "vacant" to "soon to be occupied", etc.) is combined with the current initial cleaning order. This process includes integrating the new status information into the current task list and updating the task priority of each room, such as adjusting the cleaning priority of certain rooms or inserting new cleaning tasks. At the same time, the execution progress of the current cleaning task (such as which tasks have been completed and which tasks are in progress) is recorded to ensure that the latest cleaning order generated can seamlessly connect the current working status, avoid repeated cleaning or missed cleaning, and ensure that the cleaning work continues to proceed efficiently and smoothly.

[0034] Step S32 extracts key features related to cleaning requirements from the changed status information, including urgent cleaning requests for rooms, new customer requirements, changes in room status (such as from "vacant" to "coming soon"), VIP customer check-in, etc. The extracted feature data needs to be preprocessed, such as standardization, normalization, encoding, etc., to ensure that these features meet the input format and requirements of the adjustment model. For example, the urgency of the room is converted into a numerical representation, or customer requirements are classified and encoded into different feature vectors. The preprocessed feature data is sorted and becomes the input of the adjustment model so that the model can accurately recalculate the cleaning priority of each room.

[0035] Step S33 inputs the preprocessed cleaning demand features into the adjustment model, which can be selected as an optimization algorithm, dynamic programming, or machine learning model. Based on the input feature data, the adjustment model re-evaluates the cleaning priority of each room, taking into account factors such as emergency needs, room status, and the current task progress of the cleaning staff, and calculates the priority score of each room. Based on these scores, the model generates a new cleaning order list, putting high-priority tasks in front to ensure that the most urgent and important rooms are cleaned first. Finally, the adjustment model outputs an optimized cleaning order to replace the initial cleaning order to ensure that the cleaning tasks are performed in the optimal order.

[0036] Furthermore, as an embodiment of the present invention, step S33, inputs the extracted cleaning demand characteristics into the adjustment model to recalculate the cleaning priority of each room, including: based on preset decision variables and constraints, minimizing a preset objective function to obtain the cleaning priority; wherein, the decision variables include at least a cleaning order variable and a cleaning task allocation variable, the constraints include at least a priority constraint, and the objective function includes at least the total cleaning time or the total cleaning path length.

[0037] Specifically, the extracted and preprocessed cleaning demand features are input into the adjustment model, and the adjustment model recalculates the cleaning priority of each room based on the preset decision variables, constraints and objective functions. Decision variables include cleaning order variables (i.e., the order in which the rooms are cleaned) and cleaning task allocation variables (i.e., which cleaning staff the cleaning task is assigned to). The adjustment model will follow the constraints during the calculation process, such as the priority constraint to ensure that emergency rooms are cleaned first. The objective function is the core of system optimization, such as minimizing the total cleaning time or minimizing the total cleaning path length. The model uses an optimization algorithm to minimize the objective function and derive a new cleaning priority order to ensure that the cleaning work is carried out with the highest efficiency while meeting all constraints.

[0038] Furthermore, as an implementation mode of the present invention, the constraint conditions also include path constraints, time window constraints and / or personnel allocation constraints.

[0039] For example, the adjustment model may be selected as Integer Linear Programming (ILP) to optimize the room cleaning sequence. ILP is a mathematical method for optimization problems, which is suitable for dealing with scenarios that require satisfying multiple constraints and optimizing a certain objective function.

[0040] Based on the integer linear programming, the preset decision variables are first defined, and the decision variables include cleaning order variables and cleaning task allocation variables, where: Cleaning up sequential variables: Define a binary variable x ij , indicating whether cleaning task i is completed before task j. If x ij =1, then task i is completed before task j, otherwise x ij =0; Clean up the task and assign variables: define another binary variable y ik , indicating whether cleaning task i is performed by cleaning staff k. If y ik =1, then task i is performed by person k, otherwise y ik =0.

[0041] Next, preset constraints are set. The constraints in this embodiment include priority constraints, path constraints, time window constraints, and personnel allocation constraints, where: Priority constraints: used to ensure that rooms with higher priority are completed before rooms with lower priority. For the priority order, the following constraints can be defined: x ij =1 if priority of i>p; Path constraint: used to ensure that the cleaners clean along the optimal path and reduce the total moving distance or time. This constraint can be modeled using a distance matrix or a time matrix: x ij ·t ij ≤T max ; Time window constraints: If some tasks must be completed within a specific time, you can add time window constraints. For example, if task i must be completed within time period T: Starttime of i+Cleaning time of i≤T; Head allocation constraint: used to ensure that each cleaning task is assigned to a specific cleaner and that the workload of each cleaner does not exceed its capacity: ∑ k y ik =1 for each task i; ∑ i y ik ≤Max tasks for cleaner k.

[0042] Then, set the objective function. Minimizing the objective function includes minimizing the total cleaning time, where: Minimize total cleaning time: The objective function can be set to minimize the total cleaning time or the total path length. For example, the objective function can be defined as: Among them, dij represents the distance (or time) cost between task i and task j.

[0043] Based on the above decision variables, constraints and objective function, mathematical modeling tools (such as Python's PuLP, Gurobi, or Cplex) can be used to build an ILP model of the objective function and constraints. The model is solved using an ILP solver to obtain the optimal solution. Thus, the optimal updated sweeping order is calculated based on the input initial sweeping order and the updated state information after the state change.

[0044] For example, the code to minimize the objective function is as follows:

[0045] Through the above method, the ILP model can dynamically adjust the cleaning order according to the changing status information to ensure that the overall cleaning efficiency and customer satisfaction are optimized.

[0046] The embodiment of the invention also discloses a system for dynamically adjusting the guest room cleaning sequence.

[0047] Reference Figure 3 The room cleaning sequence dynamic adjustment system includes an acquisition module 1, a judgment module 2 and an adjustment module 3.

[0048] The acquisition module 1 is connected to the hotel management system (such as the PMS system) in real time to continuously monitor and collect the latest status information of each guest room, including but not limited to the current occupancy status of the room (such as vacant, occupied, about to be occupied, etc.), reservation information (such as reservation time, customer type), special needs of customers (such as VIP service requests, emergency cleaning requests), and room location information. In addition, the acquisition module 1 also obtains the current initial cleaning order, which is based on the previously set rules or generated task list. Through the acquisition and integration of these real-time data, the acquisition module 1 provides basic data support for the dynamic adjustment of the system, ensuring the accuracy and timeliness of subsequent judgments and adjustments.

[0049] The judgment module 2 continuously monitors the real-time status information received from the acquisition module 1, and immediately triggers the preset judgment model for analysis when it detects a change in the status information (such as a change in the room status, a new customer request, etc.). The judgment model can be based on a rule engine, a threshold judgment, or a machine learning algorithm. The judgment module 2 first extracts and preprocesses the changed status information to meet the input requirements of the model. Then, the processed feature data is input into the judgment model, and a binary judgment result is obtained through the model calculation, that is, "needs adjustment" or "no adjustment is required". This result determines whether the system needs to make further adjustments to the current initial cleaning order, thereby ensuring that the cleaning task can respond to the changing guest room status and customer needs in real time.

[0050] When the judgment module 2 determines that the cleaning order needs to be adjusted, the adjustment module 3 will receive the latest status information and the initial cleaning order, and recalculate and optimize the cleaning order through the preset adjustment model. The adjustment model may be based on an optimization algorithm, dynamic programming, or a machine learning model. First, the adjustment module 3 extracts and preprocesses the key features that affect the cleaning order (such as emergency cleaning requests, customer priorities, etc.), and then inputs these features into the adjustment model. Based on these input data, the adjustment model combines decision variables (such as cleaning order and task allocation) and constraints (such as minimizing the total cleaning time or path length) to calculate the new cleaning priority for each room, and finally generates an updated cleaning order list to ensure that the cleaning tasks are carried out in the optimal order and respond to new needs in a timely manner.

[0051] The system for dynamically adjusting the cleaning order of guest rooms described in the embodiment of the present invention collects and monitors the status information and initial cleaning order of each guest room in real time through the acquisition module 1, and uses the judgment module 2 to intelligently analyze the status changes based on the preset judgment model to timely judge whether the cleaning order needs to be adjusted; when adjustment is required, the adjustment module 3 optimizes and reorders the initial cleaning order based on the latest status information and adjustment model. Through this dynamic adjustment mechanism, the system can flexibly and quickly respond to changes in guest room status and customer needs, significantly improve the efficiency and accuracy of cleaning tasks, ensure that cleaning work is carried out in the optimal order, and thus improve overall operational efficiency and customer satisfaction.

[0052] The embodiment of the present invention also discloses a computer program product.

[0053] A computer program product, comprising a computer program, which, when executed by a processor, implements the steps of a method for dynamically adjusting the cleaning order of a guest room described in any one of the above embodiments. Computer-readable storage media may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc. The computer program includes a computer program code. The computer program code may be in source code form, object code form, an executable file, or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying a computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0054] The embodiment of the present invention also discloses a readable storage medium.

[0055] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for dynamically adjusting the cleaning order of a guest room described in any one of the above embodiments. The computer-readable storage medium may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc. The computer program includes a computer program code. The computer program code may be in source code form, object code form, an executable file, or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying a computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), and a software distribution medium, etc.

[0056] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0057] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processing module, or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically adjusting the guest room cleaning sequence, characterized in that: include: Obtain status information and initial cleaning sequence of each guest room in real time; When the status information changes, it is determined whether the initial cleaning order needs to be adjusted based on a preset judgment model; when adjustment is required, the initial cleaning order is adjusted based on the changed status information and according to a preset adjustment model to obtain a re-planned cleaning order.

2. The method for dynamically adjusting the guest room cleaning sequence according to claim 1, characterized in that: When the state information changes, judging whether the initial cleaning sequence needs to be adjusted based on a preset judgment model includes: When a change in status information is detected, the judgment process is triggered; Extracting cleaning demand features from the changed state information, and preprocessing the cleaning demand features to make them meet the input requirements of the judgment model; The judgment model performs calculations based on the input cleaning requirement characteristics and outputs a binary judgment result.

3. The method for dynamically adjusting the guest room cleaning sequence according to claim 1 or 2, characterized in that: The judgment model is configured as a rule engine.

4. The method for dynamically adjusting the guest room cleaning sequence according to claim 1, characterized in that: The adjusting the initial cleaning sequence based on the changed state information according to a preset adjustment model to obtain a re-planned cleaning sequence includes: Combining the changed state information with the current initial cleaning order to obtain the latest task priority and work progress; extracting cleaning demand features from the changed state information, and preprocessing the cleaning demand features to make them meet the input requirements of the adjustment model; The extracted cleaning demand features are input into the adjustment model to recalculate the cleaning priority of each room.

5. The method for dynamically adjusting the guest room cleaning sequence according to claim 4, characterized in that: The step of inputting the extracted cleaning demand features into the adjustment model to recalculate the cleaning priority of each room includes: Based on the preset decision variables and constraints, the preset objective function is minimized to obtain the cleaning priority; Among them, the decision variables include at least a cleaning order variable and a cleaning task allocation variable, the constraints include at least a priority constraint, and the objective function includes at least the total cleaning time or the total cleaning path length.

6. The method for dynamically adjusting the guest room cleaning sequence according to claim 5, characterized in that: The constraint conditions also include path constraints, time window constraints and / or personnel allocation constraints.

7. A system for dynamically adjusting the cleaning order of guest rooms, characterized in that: The system comprises: An acquisition module, at least used to acquire the status information and initial cleaning sequence of each guest room in real time; A judgment module, at least used to judge whether the initial cleaning sequence needs to be adjusted based on a preset judgment model when the state information changes; The adjustment module is used to adjust the initial cleaning sequence based on the changed state information and a preset adjustment model to obtain a re-planned cleaning sequence when adjustment is needed.

8. A computer program product, characterized in that The computer program product includes computer instructions, and when the computer instructions are executed by a processor, a method for dynamically adjusting the guest room cleaning sequence according to any one of claims 1 to 6 is implemented.

9. A readable storage medium, characterized in that: The readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, a method for dynamically adjusting the guest room cleaning sequence according to any one of claims 1 to 6 is implemented.

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