Hotel anti-flying management method and system based on power consumption data
The hotel electricity consumption data is collected through smart meter and socket sensors, feature extraction and classification model training is carried out, and the electricity consumption behavior of non-hotel staff is automatically judged and early warning is output, which solves the problems of reduced returns and inaccurate data caused by the hotel flying phenomenon, and improves supervision efficiency and accuracy.
Patent Information
- Application Number
- CN202411762052.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-03
- Publication Date
- 2025-03-28
AI Technical Summary
The flying room phenomenon in the hotel industry has led to a decrease in hotel revenue and inaccurate operating data, the existing manual inspection efficiency is low and the regulatory loopholes are large.
The power consumption data of hotel staff is collected through smart meter and socket sensors, feature extraction and classification model training is carried out, the power consumption behavior of non-hotel staff is automatically judged, and the fly room warning is output to the remote monitoring platform.
It has realized automated supervision, improved the efficiency and accuracy of hotel detection of flying house behavior, and timely output early warnings to prevent losses.
Smart Images
Figure CN119722100A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of communication technology, and in particular to a management method and system for anti-flying rooms in hotels. Background Art
[0002] In the hotel industry, the phenomenon of room-flying refers to the behavior of waiters seeking personal gain through improper means. This behavior will not only reduce hotel revenue, but also affect the accuracy of the hotel's operating data reports. Room-flying behavior includes but is not limited to false check-outs, room charges not being recorded in the PMS, and keeping room charges privately. These behaviors will cause the income that should belong to the hotel to flow into personal pockets, seriously damaging the hotel's economic interests.
[0003] However, currently, manual inspection is generally used to check whether there is any phenomenon of flying rooms. However, the efficiency of manual supervision is relatively low, and manual inspection is not easy to be discovered, resulting in loss of hotel benefits. Summary of the invention
[0004] Based on this, in response to the above technical problems, a management method and system for hotel anti-flying rooms based on electricity consumption data are provided to solve the technical problems that the existing technology has low efficiency of manual inspections and large supervision loopholes for flying rooms when conducting hotel flying room inquiries.
[0005] In a first aspect, a method for managing hotel anti-flying rooms based on electricity consumption data, the method comprising:
[0006] Obtaining electricity usage data sent by smart meters and sensors installed on sockets in hotel rooms when a large number of different hotel staff enter guest rooms to work; the electricity usage data includes: voltage data at different time periods;
[0007] Extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set;
[0008] Training a pre-built classification model according to the feature vector set to obtain a trained behavior classification model;
[0009] The registration status of hotel rooms is obtained, and unregistered rooms are taken as target rooms. The target power consumption data sequence of the smart meter in the target room and the sensor installed on the socket of the target room for a set time length is obtained, and the target power consumption data sequence is input into the behavior classification model. If the working power consumption of non-hotel staff is obtained, a room flight warning is output to the remote monitoring platform.
[0010] In the above scheme, optionally, the obtaining of electricity usage data sent by smart meters and sensors installed on sockets in hotel rooms when a large number of different hotel staff enter guest rooms to work also includes: cleaning and standardizing the electricity usage data of the smart meters when the hotel staff are working, and removing outliers and noise.
[0011] In the above scheme, optionally, extracting features from the electricity consumption data of the smart meter of the hotel staff when they are working to obtain a feature vector set includes: extracting time domain features, frequency domain features and time-frequency domain features.
[0012] In the above scheme, optionally, the target electricity consumption data sequence is input into a behavior classification model, and if it is obtained that the electricity consumption is for the working of the hotel staff, a prompt indicating that the electricity consumption of the target guest room is for the working staff is output to the remote monitoring platform.
[0013] In the above scheme, optionally, after outputting the room flight warning to the remote monitoring platform, the method further includes: receiving a room flight confirmation instruction from the remote monitoring platform, and sending a power-off instruction to the power distribution system of the target guest room.
[0014] In the above solution, optionally, the electricity consumption data also includes: current data, power data, and electricity consumption data in different time periods.
[0015] In the above scheme, optionally, the method further includes: obtaining power consumption data of entertainment electrical equipment in the target guest room, and if the power consumption data of the entertainment electrical equipment exceeds a certain threshold, outputting a room flight warning to a remote monitoring platform.
[0016] In a second aspect, a management system for hotel anti-flying rooms based on electricity consumption data, the system comprising:
[0017] Historical work data acquisition module: used to acquire power consumption data of smart meters and smart devices sent by multiple sensors installed in guest rooms when a large number of different hotel staff enter the guest rooms to work; the power consumption data includes: voltage signals at different time periods;
[0018] Feature extraction module: used for extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set;
[0019] A classification model building module: used to train a pre-built classification model according to the feature vector set to obtain a trained behavior classification model;
[0020] Judgment module: used to obtain the registration status of hotel rooms and take unregistered rooms as target rooms; obtain the target power consumption data sequence of the set time length of multiple sensors set in the target rooms, input the target power consumption data sequence into the behavior classification model, and output a room flight warning to the remote monitoring platform if the working power consumption of non-hotel staff is obtained.
[0021] In a third aspect, a computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a method for managing hotel anti-flying rooms based on electricity consumption data as described in the first aspect are implemented.
[0022] In a fourth aspect, a computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of a method for managing hotel anti-flying rooms based on electricity consumption data as described in the first aspect above.
[0023] This application has at least the following beneficial effects:
[0024] This application collects the power consumption data of the smart meter and the sensor set on the socket of the hotel room when the historical hotel staff enters the guest room to work, extracts the features of the power consumption data of the hotel staff when working to obtain a feature vector set, trains the pre-built classification model according to the feature vector set to obtain a trained behavior classification model, and when the registration status of the hotel is unregistered, the unregistered guest room is used as the target guest room, and if the target guest room is in the power-taking state, the target power consumption data sequence of the smart meter in the guest room of the set time length and the sensor set on the socket of the target guest room hotel is obtained, and the target power consumption data sequence is input into the behavior classification model, and if the working power consumption of non-hotel staff is obtained, the flying room warning is output to the remote monitoring platform. In this way, the judgment of flying room can be automatically carried out, the supervision efficiency is improved, and the flying room warning can be accurately and timely carried out. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Figure 1 A flowchart of a method for managing hotel anti-flying rooms based on electricity consumption data is provided for one embodiment of the present application. DETAILED DESCRIPTION
[0026] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0027] In one embodiment, Figure 1 As shown, a management method for hotel anti-flying rooms based on electricity consumption data is provided, comprising the following steps:
[0028] Step S1: obtaining power consumption data sent by smart meters and sensors installed on sockets in hotel rooms when a large number of different hotel staff members enter guest rooms to work; the power consumption data includes: voltage data at different time periods;
[0029] Step S2: extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set;
[0030] Step S3: training the pre-built classification model according to the feature vector set to obtain a trained behavior classification model;
[0031] Step S4: Obtain the registration status of hotel rooms, and take unregistered rooms as target rooms; obtain the target power consumption data sequence of the smart meter in the target room and the sensor installed on the socket of the target room for a set time length, and input the target power consumption data sequence into the behavior classification model. If the working power consumption of non-hotel staff is obtained, a room flight warning is output to the remote monitoring platform.
[0032] In this embodiment, generally, hotel staff will use equipment such as vacuum cleaners and cleaning machines when cleaning the room, and the use of these equipment will lead to an increase in electricity consumption. Therefore, by analyzing the change in electricity consumption in a specific time period, it can be identified whether a vacuum cleaner is used in the room, thereby judging whether it is electricity used by cleaning personnel. At the same time, by collecting the electricity consumption data of the sensor set on the guest room plug, because the electricity used when using the vacuum cleaner generally needs to be plugged into the plug in the room, therefore, obtaining the electricity consumption data of the sensor set on the guest room plug can reflect whether it is electricity used by cleaning personnel. Therefore, the peak value, trough value, mean, variance and standard deviation of the power consumption, voltage and current in a specific time period can be extracted to construct a behavior classification model. At the same time, generally speaking, sanitation cleaning is generally carried out within a specified time, and the model is constructed by collecting the features in the time domain. Thus, these features are extracted to construct a behavior classification model, thereby realizing the judgment of whether the electricity is not used by the cleaning personnel. If it is not the cleaning personnel, the flying room warning is output to the remote monitoring platform.
[0033] In the above-mentioned management method for preventing flying rooms in hotels based on electricity consumption data, by collecting the electricity consumption data of the smart meter and the sensor set on the socket of the hotel room when the historical hotel staff enter the guest room to work, the electricity consumption data of the hotel staff at work is extracted to obtain a feature vector set, and the pre-built classification model is trained according to the feature vector set to obtain a trained behavior classification model. When the registration status of the hotel is unregistered, the unregistered guest room is used as the target guest room. If the target guest room is in the power-taking state, the target electricity consumption data sequence of the smart meter in the guest room of the set time length and the sensor set on the socket of the target guest room hotel is obtained, and the target electricity consumption data sequence is input into the behavior classification model. If the working electricity consumption of non-hotel staff is obtained, the flying room warning is output to the remote monitoring platform. In this way, the judgment of flying rooms can be automatically carried out, the supervision efficiency is improved, and the flying room warning can be accurately and timely carried out.
[0034] In one embodiment, the acquisition of electricity usage data sent by smart meters and sensors installed on sockets in hotel rooms when a large number of different hotel staff enter guest rooms to work also includes: cleaning and standardizing the electricity usage data of the smart meters when the hotel staff are working, and removing outliers and noise.
[0035] In this embodiment, after the electricity consumption data is acquired, preprocessing is performed, including row cleaning, standardization, and removal of outliers and noise to ensure data quality so as to correctly construct a behavior classification model.
[0036] In one embodiment, extracting features from the electricity consumption data of the smart meter of the hotel staff when they are working to obtain a feature vector set includes: extracting time domain features, frequency domain features, and time-frequency domain features.
[0037] In one embodiment, the target electricity usage data sequence is input into a behavior classification model. If it is obtained that the electricity usage is for hotel staff at work, a prompt indicating that the electricity usage of the target guest room is for staff at work is output to a remote monitoring platform.
[0038] In this embodiment, if it is determined through the behavior classification model that the electricity is used by hotel staff, a response can be made to the monitoring platform to determine again whether it is the staff who are using electricity.
[0039] In one embodiment, after outputting the room flight warning to the remote monitoring platform, the method further includes: receiving a room flight confirmation instruction from the remote monitoring platform, and sending a power-off instruction to the power distribution system of the target guest room.
[0040] In this embodiment, after a room-fleeing warning is output to the remote end, after verification by the remote end, a room-fleeing confirmation instruction is received, and a power-off process is sent to the power distribution system of the room to prevent further consumption of hotel profits.
[0041] In one embodiment, the electricity consumption data further includes: current data, power data, and electricity consumption data in different time periods.
[0042] In one embodiment, the method further includes: obtaining power consumption data of entertainment electrical equipment in the target guest room, and if the power consumption data of the entertainment electrical equipment exceeds a certain threshold, outputting a room departure warning to a remote monitoring platform.
[0043] In this embodiment, the hotel staff generally do not use the hotel entertainment appliances for a long time. Therefore, if someone is found to use the entertainment appliances according to the electricity consumption data, it must be a case of room flight. Entertainment appliances include: televisions, stereos, etc.
[0044] In one embodiment, a management system for hotel anti-flying rooms based on electricity consumption data is provided, the system comprising:
[0045] Historical work data acquisition module: used to acquire power consumption data of smart meters and smart devices sent by multiple sensors installed in guest rooms when a large number of different hotel staff enter the guest rooms to work; the power consumption data includes: voltage signals at different time periods;
[0046] Feature extraction module: used for extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set;
[0047] A classification model building module: used to train a pre-built classification model according to the feature vector set to obtain a trained behavior classification model;
[0048] Judgment module: used to obtain the registration status of hotel rooms and take unregistered rooms as target rooms; obtain the target power consumption data sequence of the set time length of multiple sensors set in the target rooms, input the target power consumption data sequence into the behavior classification model, and output a room flight warning to the remote monitoring platform if the working power consumption of non-hotel staff is obtained.
[0049] For the specific definition of a management system for a hotel anti-flying room based on electricity consumption data, please refer to the definition of a management method for a hotel anti-flying room based on electricity consumption data in the above text, which will not be repeated here. Each module in the above-mentioned management system for a hotel anti-flying room based on electricity consumption data can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
[0050] In one embodiment, a computer program product is also provided, including a computer program / instruction. When the computer program / instruction is executed by a processor, it involves all or part of the process in the above-mentioned embodiment method.
[0051] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0052] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0053] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.
Claims
1. A management method for hotel anti-flying rooms based on electricity consumption data, characterized in that: The method comprises: Obtaining electricity usage data sent by smart meters and sensors installed on sockets in hotel rooms when a large number of different hotel staff enter guest rooms to work; the electricity usage data includes: voltage data at different time periods; Extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set; Training a pre-built classification model according to the feature vector set to obtain a trained behavior classification model; The registration status of hotel rooms is obtained, and unregistered rooms are taken as target rooms. The target power consumption data sequence of the smart meter in the target room and the sensor installed on the socket of the target room for a set time length is obtained, and the target power consumption data sequence is input into the behavior classification model. If the working power consumption of non-hotel staff is obtained, a room flight warning is output to the remote monitoring platform.
2. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: After obtaining the power usage data sent by the smart meter and the sensors installed on the sockets in the hotel rooms when a large number of different hotel staff enter the guest rooms to work, the method also includes: cleaning and standardizing the power usage data of the smart meter when the hotel staff are working, and removing abnormal values and noise.
3. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: Extracting features from the electricity consumption data of the smart meter of the hotel staff when they are working to obtain a feature vector set includes: extracting time domain features, frequency domain features, and time-frequency domain features.
4. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: The target electricity consumption data sequence is input into the behavior classification model. If it is obtained that the electricity consumption is for the working of the hotel staff, a prompt indicating that the electricity consumption of the target guest room is for the working staff is output to the remote monitoring platform.
5. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: After outputting the room-flying warning to the remote monitoring platform, the method further includes: receiving a room-flying confirmed instruction from the remote monitoring platform, and sending a power-off instruction to the power distribution system of the target guest room.
6. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: The electricity consumption data also includes: current data, power data, and electricity consumption data in different time periods.
7. The management method of hotel anti-flying rooms based on electricity consumption data according to claim 1 is characterized in that: The method further includes: obtaining power consumption data of entertainment electrical equipment in the target guest room, and if the power consumption data of the entertainment electrical equipment exceeds a certain threshold, outputting a room flight warning to a remote monitoring platform.
8. A hotel anti-flying room management system based on electricity consumption data, characterized in that: The system comprises: Historical work data acquisition module: used to acquire power consumption data of smart meters and smart devices sent by multiple sensors installed in guest rooms when a large number of different hotel staff enter the guest rooms to work; the power consumption data includes: voltage signals at different time periods; Feature extraction module: used for extracting features from the electricity consumption data of the hotel staff at work to obtain a feature vector set; A classification model building module: used to train a pre-built classification model according to the feature vector set to obtain a trained behavior classification model; Judgment module: used to obtain the registration status of hotel rooms and take unregistered rooms as target rooms; obtain the target power consumption data sequence of the set time length of multiple sensors set in the target rooms, input the target power consumption data sequence into the behavior classification model, and output a room flight warning to the remote monitoring platform if the working power consumption of non-hotel staff is obtained.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.
10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.