CN114443735B - Hotel data mapping rule generation method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a hotel data mapping rule generation method, a device, equipment and a storage medium, wherein the method comprises the following steps: counting first historical order data of all hotels in the target area based on the room type dimension, and counting second historical order data of all rooms in the target area based on the hotel dimension; predicting according to the first historical order data and a pre-trained first prediction model to obtain predicted house type heat of various house types in a time period to be predicted; predicting to obtain the predicted hotel heat of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model; and setting a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining with a preset rule. According to the invention, different room state information mapping rules are set for each room type of each hotel according to the hotness of the hotel and the room type, so that the pressure of the server is reduced, and the reasonable distribution of system resources is realized.
Description
Technical Field
The present application relates to the field of hotel management technologies, and in particular, to a method, an apparatus, a device, and a storage medium for generating a hotel data mapping rule.
Background
With the rapid development of the internet, the hotel industry is highly online, consumers can book hotels online through various hotel OTA platforms, and the convenient hotel booking mode is more and more accepted by consumers. In order to ensure good experience of consumers, hotel room-state volume price information needs to be synchronized between systems of upstream and downstream suppliers and distributors through the Internet, so that the condition that rooms are over-ordered is avoided.
Generally, synchronous mapping of hotel data mainly comprises two schemes of full data synchronization and incremental data synchronization, wherein the full data synchronization is realized by acquiring all hotel data information of all hotels and synchronizing the hotel data information at one time; incremental data synchronization refers to acquiring and synchronizing only changed hotel data information. However, regardless of full data synchronization or incremental data synchronization, the data size synchronized at one time in a short time is large, the pressure on the server is large, and the time consumed by synchronization is long, which easily causes the platform server to crash.
Disclosure of Invention
The application provides a hotel data mapping rule generation method, device, equipment and storage medium, and aims to solve the problems of large one-time synchronous data volume and unreasonable system resource allocation in the existing hotel data synchronization.
In order to solve the technical problem, the application adopts a technical scheme that: the hotel data mapping rule generation method comprises the following steps: counting first historical order data of all hotels in the target area based on the room type dimension, and counting second historical order data of all rooms in the target area based on the hotel dimension; predicting according to the first historical order data and a pre-trained first prediction model to obtain the predicted house type heat of various house types in a time period to be predicted, and training the first prediction model according to the historical order data of each house type to obtain the predicted house type heat; predicting to obtain the predicted hotel heat of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model, and training the second prediction model according to the historical order data of each hotel to obtain the predicted hotel heat; and setting a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining with a preset rule.
As a further improvement of the application, the method for predicting the heat of the various house types in the time period to be predicted according to the first historical order data and a pre-trained first prediction model comprises the following steps: acquiring first historical trading data and first historical browsing data of each house type of all hotels from the first historical order data; predicting to obtain first predicted transaction data of various house types in a time period to be predicted according to the first historical transaction data and a pre-trained first transaction number prediction model, and training the first transaction number prediction model according to the historical transaction data of each house type to obtain the first predicted transaction data; predicting to obtain first predicted browsing data of various house types in a time period to be predicted according to the first historical browsing data and a pre-trained first browsing number prediction model, and training the first browsing number prediction model according to the historical browsing data of each house type; and calculating a first ratio of the first predicted transaction data and the first predicted browsing data, and taking the first ratio as the predicted house type heat of various house types.
As a further improvement of the application, the predicted hotel hotness of each hotel in the time period to be predicted is predicted according to the second historical order data and a pre-trained second prediction model, and the method comprises the following steps: acquiring second historical trading data and second historical browsing data of all house types of each hotel from the second historical order data; predicting according to the second historical trading data and a pre-trained second trading volume prediction model to obtain second predicted trading data of each hotel in the time period to be predicted, and training the second trading volume prediction model according to the historical trading data of each hotel to obtain the second predicted trading volume data; predicting according to second historical browsing data and a pre-trained second browsing number prediction model to obtain second predicted browsing data of each hotel in a time period to be predicted, and training the second browsing number prediction model according to the historical browsing data of each hotel to obtain the second predicted browsing data; and calculating a second ratio of the second predicted transaction data to the second predicted browsing data, and taking the second ratio as the predicted hotel popularity of each hotel.
As a further improvement of the present application, in combination with a preset rule, a room state information mapping rule for setting a corresponding synchronization frequency for each room type of each hotel in a to-be-predicted time period according to a predicted room type heat degree and a predicted hotel heat degree includes: performing data normalization processing on the predicted house type heat of each house type, and confirming a first preset synchronous frequency corresponding to each house type according to the data after normalization processing; performing data normalization processing on the predicted hotel heat of each hotel, and determining a second preset synchronization frequency corresponding to each hotel according to the data after the normalization processing; accumulating a first preset synchronization frequency and a second preset synchronization frequency corresponding to each room type of each hotel according to a preset weight value to obtain a final synchronization frequency of each room type of each hotel; and setting room state information mapping rules for each type of room of each hotel according to the final synchronization frequency.
As a further improvement of the present application, before counting first historical order data of all hotels in the target area based on the house type dimension and counting second historical order data of all house types in the target area based on the hotel dimension, the method further includes: acquiring address information of all hotels; performing K-Means clustering on all hotels according to the address information to obtain a plurality of clustering clusters; and performing area division on all hotels according to a plurality of clustering clusters, wherein each clustering cluster corresponds to one area.
As a further improvement of the present application, after setting a room state information mapping rule of a corresponding synchronization frequency for each room type of each hotel in a time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree, the method further includes: and when the current time is detected to be within the range of the time period to be predicted, pulling the room state information of each room type of each hotel from the hotel supplier platform according to the room state information mapping rule for synchronization.
As a further improvement of the present application, after the predicted hotel hotness of each hotel in the time period to be predicted is predicted according to the second historical order data and the pre-trained second prediction model, the method further includes: acquiring label information of each hotel; scoring each hotel according to the label information to obtain a scoring value of each hotel; respectively sorting each hotel according to the predicted hotel popularity and the score value to obtain a first sorting based on the predicted hotel popularity and a second sorting based on the score value; confirming confidence of the predicted hotel hotness according to the first sequence and the second sequence; and confirming a preset correction coefficient corresponding to each hotel according to the confidence coefficient of the predicted hotel heat degree, and correcting the predicted hotel heat degree by using the preset correction coefficient.
In order to solve the above technical problem, another technical solution adopted by the present application is: provided is a hotel data mapping rule generating device, including: the acquisition module is used for counting first historical order data of all hotels in a target area based on the dimension of the room type and counting second historical order data of all the rooms in the target area based on the dimension of the hotels; the first prediction module is used for predicting and obtaining the predicted house type heat of various house types in a time period to be predicted according to first historical order data and a pre-trained first prediction model, and the first prediction model is obtained by training according to the historical order data of each house type; the second prediction module is used for predicting and obtaining the predicted hotel heat of each hotel in the time period to be predicted according to second historical order data and a pre-trained second prediction model, and the second prediction model is obtained by training according to the historical order data of each hotel; and the setting module is used for setting a room state information mapping rule of corresponding synchronous frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining with a preset rule.
In order to solve the above technical problem, the present application adopts another technical solution that: there is provided a computer device comprising a processor, a memory coupled to the processor, the memory having stored therein program instructions which, when executed by the processor, cause the processor to perform the steps of the hotel data mapping rule generating method described above.
In order to solve the above technical problem, the present application adopts another technical solution that: a storage medium is provided that stores program instructions that enable the hotel data mapping rule generation method described above.
The beneficial effect of this application is: according to the hotel data mapping rule generation method, the hotness prediction is carried out on the house type hotness and the hotel hotness of the time period to be predicted respectively based on the house type dimension and the hotel dimension to obtain the predicted house type hotness and the predicted hotel hotness, then the house state information mapping rule is set for each house type of each hotel according to the predicted house type hotness and the predicted hotel hotness, so that the multi-dimensional comprehensive assessment of each house type of each hotel is carried out according to the house type hotness and the predicted hotel hotness, then the house state information mapping rules with different synchronous frequencies are set according to the assessment result, the one-time synchronization process is divided into different time points to be synchronized respectively, the system resources can be reasonably utilized, and meanwhile, excessive pressure cannot be caused on a server.
Drawings
Fig. 1 is a flowchart illustrating a hotel data mapping rule generating method according to an embodiment of the present invention;
fig. 2 is a functional module schematic diagram of a hotel data mapping rule generating device according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second" and "third" in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or as implying a number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. All directional indications (such as up, down, left, right, front, and rear … …) in the embodiments of the present application are only used to explain the relative positional relationship between the components, the movement, and the like in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indication is changed accordingly. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a flowchart illustrating a hotel data mapping rule generating method according to an embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
step S101: and counting first historical order data of all hotels in the target area based on the dimension of the room type, and counting second historical order data of all the rooms in the target area based on the dimension of the hotels.
In step S101, counting the first historical order data of all hotels based on the house type dimension means that the historical order data of all hotels in the target area are counted according to the house type, so as to obtain all historical order data of each house type, and obtain the first historical order data. And the second historical order data of all the house types is counted based on the hotel dimension, which means that the historical order data of all the house types of each hotel in the target area is counted, so that the second historical order data of each hotel is obtained.
When historical order data statistics is performed, historical order data of a last period of time is captured, for example, historical order data of a last six months or historical order data of a last three months is captured. The historical order data comprises the transaction data of each room type of all hotels in the target area and also comprises the browsing data of each room type of all hotels.
In this embodiment, in a certain regional scope, the customer is likely to be relatively close to the needs of hotel room type to this can be according to the heat degree of the various room types of this regional scope of demand analysis room type, for example, for the hotel in tourist attraction, because the personnel of tourism are usually a plurality of or group, the customer can be higher than the demand to single room type to the demand of double room type, consequently, the heat degree of double room type in this region will be higher than the heat degree of single room type. Therefore, the house type heat of the region can be analyzed through the house type reservation condition in the region. The hotness of the hotel can be directly reflected by the historical order data of all types of rooms of each hotel.
Further, before step S101, the method further includes:
1. and acquiring address information of all hotels.
2. And performing K-Means clustering on all hotels according to the address information to obtain a plurality of clustering clusters.
3. And performing area division on all hotels according to a plurality of clustering clusters, wherein each clustering cluster corresponds to one area.
Specifically, after the address information of each hotel is obtained, the geographic position of each hotel is used as one point, k points are randomly selected as initial clustering centers, then the distance from other points to each clustering center is calculated, and the other points are classified into the class where the clustering center closest to the other points is located; and calculating the coordinate average value of all the points in each cluster, taking the average value as a new cluster center, repeatedly clustering the points by using the new cluster center, and adjusting the position of the cluster center until the cluster center does not move in a large range any more or the clustering frequency reaches the preset requirement, thereby finally obtaining a plurality of cluster clusters. The area covered by each cluster is the divided area. After the area division is performed, when the hotel data mapping rule generating method of the embodiment is implemented, a target area is selected from a plurality of divided areas, and then the hotel data mapping rule generating method is executed.
Step S102: and predicting the predicted house type heat of various house types in the time period to be predicted according to the first historical order data and a pre-trained first prediction model, and training the first prediction model according to the historical order data of each house type to obtain the predicted house type heat.
Specifically, after first historical order data are obtained, the first historical order data are input into a first prediction model for prediction, and therefore the predicted house type heat of various house types at a future moment is obtained. Wherein the first prediction model is obtained by training according to the historical order data of each house type.
Further, step S102 specifically includes:
1. and acquiring first historical trading data and first historical browsing data of each house type of all hotels from the first historical order data.
Specifically, the first historical order data comprises first historical transaction data and first historical browsing data, the first historical transaction data refers to the number of times that the client orders the house type from the platform and successfully stays in the house type, and the historical browsing data refers to the number of times that the client clicks and browses the house type on the platform.
2. And predicting to obtain first predicted deal data of various house types in the time period to be predicted according to the first historical deal data and a pre-trained first deal number prediction model, and training the first deal number prediction model according to the historical deal data of each house type to obtain the first predicted deal data.
3. And predicting to obtain first predicted browsing data of various house types in a time period to be predicted according to the first historical browsing data and a pre-trained first browsing number prediction model, and training the first browsing number prediction model according to the historical browsing data of each house type to obtain the first predicted browsing data.
4. And calculating a first ratio of the first predicted transaction data and the first predicted browsing data, and taking the first ratio as the predicted house type heat of various house types.
It should be noted that the ratio of the number of deals and the number of browsing times of each house type can be regarded as the booking success rate of the house type. In this embodiment, after the first predicted deal data and the first predicted browsing data of various house types are obtained, the first ratio of the first predicted deal data and the first predicted browsing data is calculated, and the first ratio is used as reference data reflecting the heat of various house types at a future time to obtain the predicted house type heat.
Step S103: and predicting the predicted hotel heat of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model, and training the second prediction model according to the historical order data of each hotel to obtain the predicted hotel heat.
Specifically, after second historical order data are obtained, the second historical order data are input into a second prediction model for prediction, and therefore the predicted hotel heat of each hotel at a future time is obtained. And the second prediction model is obtained by training according to the historical order data of each hotel.
Further, step S103 specifically includes:
1. and acquiring second historical trading data and second historical browsing data of all house types of each hotel from the second historical order data.
Specifically, the second historical order data includes second historical deal data and second historical browse data, the second historical deal data refers to the number of times that the customer orders the hotel from the platform and successfully check in the hotel, and the historical browse data refers to the number of times that the customer clicks and browses the hotel on the platform.
2. And predicting to obtain second predicted deal data of each hotel in the time period to be predicted according to the second historical deal data and a pre-trained second deal forming prediction model, and training the second deal forming prediction model according to the historical deal forming data of each hotel to obtain the second predicted deal forming data.
3. And predicting to obtain second predicted browsing data of each hotel in the time period to be predicted according to the second historical browsing data and a pre-trained second browsing number prediction model, and training the second browsing number prediction model according to the historical browsing data of each hotel to obtain the second predicted browsing data.
4. And calculating a second ratio of the second predicted transaction data and the second predicted browsing data, and taking the second ratio as the predicted hotel heat of each hotel.
It should be noted that the ratio of the number of deals and the number of views in each hotel can be regarded as the booking success rate of the hotel. In this embodiment, after the second predicted transaction data and the second predicted browsing data of each hotel are obtained, a second ratio of the second predicted transaction data and the second predicted browsing data is calculated, and the second ratio is used as reference data reflecting the hotness of each hotel at a future time to obtain the predicted hotness of the hotel.
Further, in this embodiment, the method further includes:
a first predictive model is trained from a first sample set and a first test set constructed from historical order data for each house type.
And training a second prediction model according to a second sample set and a second test set constructed according to the historical order data of each hotel.
The first prediction model comprises a first interaction number prediction model and a first browsing number prediction model, and the second prediction model comprises a second interaction number prediction model and a second browsing number prediction model.
It should be noted that, the prediction models in this embodiment are all constructed based on an autoregressive model of a time sequence, occurrence rules of hotel booking are hidden in the historical transaction times and the historical browsing times of various house types or each hotel, and the prediction models obtained through training of the time sequence of the historical transaction times or the time sequence of the historical browsing times can be used for expressing the rules, so that the transaction times or the browsing times at a future time are accurately predicted.
Further, in a general case, the hotness of the house type is for all hotels, and in a case that the number of hotels is large, the hotness of the house type is less influenced by historical order data of a single hotel, and the hotness of the hotel is for the single hotel, therefore, the hotness of the hotel is greatly influenced by the outside, which easily causes great fluctuation of hotel reservation, for example, the hotel marketing activity may cause the reservation data of the hotel to increase dramatically, and in consideration of that the historical volume of the hotel and the historical browsing volume may cause great fluctuation, so that the predicted hotness of the hotel obtained by analyzing the batch of historical data is not accurate enough, and in order to improve the accuracy of predicting the hotness of the hotel, in this embodiment, after the predicted hotness of the hotel is obtained, the method further includes:
1. and acquiring label information of each hotel.
Specifically, the tag information can perform semantic recognition on the comment information of the client by using a pre-trained semantic recognition algorithm to obtain semantic feature information included in the comment information, and then the semantic feature information is respectively matched with feature information of preset tags one by one to obtain tag information corresponding to the comment information, wherein the tag information includes hardware facilities, services, environments, traffic conditions and the like of the hotel, and the tag information includes tag information such as large room type, clean room, in-place room service, traffic convenience and the like. Preferably, when the comment information of the client is selected, the credit rating of the client is obtained, the comment information given by the client with the high credit rating is selected, the credit rating is set when the client registers, and the comment information is obtained by subsequent evaluation according to the booking condition, the unsubscribing condition, the timely check-in information and the like of the client.
2. And scoring each hotel according to the label information to obtain the score value of each hotel.
Specifically, the hotels are scored by combining label information through a preset scoring system, so that the scoring value of each hotel is obtained.
3. And respectively sorting each hotel according to the predicted hotel popularity and the score value to obtain a first sorting based on the predicted hotel popularity and a second sorting based on the score value.
4. Confirming confidence of the predicted hotel hotness according to the first ranking and the second ranking.
Specifically, the ranking of the current hotel in the first ranking and the ranking in the second ranking are compared, when the difference value between the two is within a preset threshold value, the predicted hotel heat of the current hotel is considered to be credible, and when the difference value between the two is not within the preset threshold value, the predicted hotel heat of the current hotel is considered to be credible.
5. And confirming a preset correction coefficient corresponding to each hotel according to the confidence coefficient of the predicted hotel heat degree, and correcting the predicted hotel heat degree by using the preset correction coefficient.
Specifically, when the ranking of the hotel in the first ranking is higher than that in the second ranking and the difference between the two ranks exceeds a preset threshold, a first preset correction coefficient is selected, and when the ranking of the hotel in the first ranking is lower than that in the second ranking and the difference between the two ranks exceeds the preset threshold, a second preset correction coefficient is selected, wherein the second preset correction coefficient is larger than the first preset correction coefficient. And correcting the predicted hotel heat by using a first preset correction coefficient or a second preset correction coefficient. It should be noted that the first preset correction coefficient is a coefficient between (0,1), and the second preset correction coefficient is a coefficient greater than 1, which is specifically set according to user experience.
Step S104: and setting a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining with a preset rule.
In step S104, after the predicted house type heat degree and the predicted hotel heat degree are obtained, level evaluation is performed on each house type of each hotel from the house type dimension and the hotel dimension, a first preset level corresponding to each house type of each hotel in the house type dimension and a second preset level corresponding to each hotel in the hotel dimension are confirmed, a corresponding first preset synchronization frequency is obtained according to the first preset level, a corresponding second preset synchronization frequency is obtained according to the second preset level, weight accumulation is performed on the first preset synchronization frequency and the second preset synchronization frequency to obtain a final synchronization frequency, the final synchronization frequency is set to be a synchronization frequency of the house state information of each house type of each hotel, and the house state information mapping rule of each house type of each hotel is obtained. Wherein, the corresponding relation between the preset grade and the preset frequency is preset.
Further, step S104 specifically includes:
1. and carrying out data normalization processing on the predicted house type heat of each house type, and confirming a first preset synchronization frequency corresponding to each house type according to the data after normalization processing.
The data normalization processing refers to converting data into a number within a range of [0,1], specifically, performing data normalization processing on the predicted house type heat of each house type, confirming a first target preset level corresponding to each house type according to the data after normalization processing, and setting a first target synchronization frequency corresponding to the first target preset level as a first preset synchronization frequency of each house type. The first target preset level corresponds to the first target synchronization frequency one to one.
2. And carrying out data normalization processing on the predicted hotel heat of each hotel, and confirming a second preset synchronization frequency corresponding to each hotel according to the data after the normalization processing.
Specifically, data normalization processing is performed on the predicted hotel hotness of each hotel, a second target preset level corresponding to each hotel is confirmed according to the data after normalization processing, and a second target synchronization frequency corresponding to the second target preset level is set as a second preset synchronization frequency of each hotel. The second target preset levels correspond to the second target synchronization frequencies one to one.
3. And accumulating the first preset synchronization frequency and the second preset synchronization frequency corresponding to each room type of each hotel according to a preset weight value to obtain the final synchronization frequency of each room type of each hotel.
It should be noted that the preset weight value is preset according to the experience of the user. The preset weight value corresponding to the first preset synchronization frequency obtained according to the predicted house type heat is preferably 40%, and the preset weight value corresponding to the second preset synchronization frequency obtained according to the predicted hotel heat is preferably 60%. The reason for the design is that the predicted hotel hotness is aimed at the hotel, and the predicted room type hotness is aimed at one room type of all hotels, the circle layer ranges evaluated by the predicted hotel hotness and the circle layer ranges evaluated by the predicted hotel type hotness are different, and the predicted hotel hotness can reflect the booking situation of the hotel to a certain extent, so that the preset weight value corresponding to the first preset synchronization frequency is preferably lower than the preset weight value corresponding to the second preset synchronization frequency.
Specifically, after the first preset synchronization frequency and the second preset synchronization frequency are obtained, weight accumulation is performed according to respective corresponding preset weight values, that is, the final synchronization frequency is the preset weight value corresponding to the first preset synchronization frequency + the second preset synchronization frequency.
4. And setting room state information mapping rules for each type of room of each hotel according to the final synchronization frequency.
Further, after step 104, the method further comprises: and when the current time is detected to be within the range of the time period to be predicted, pulling the room state information of each room type of each hotel to the hotel supplier platform according to the room state information mapping rule for synchronization.
Specifically, after the room state information mapping rule of each room type of each hotel in the time period to be predicted is obtained, if the current time is within the range of the time period to be predicted, the room state information of each room type of each hotel is synchronized according to the room state information mapping rule. It should be noted that, by dividing the future time into a plurality of time periods to be predicted, and then generating the room state information mapping rule of the next time period to be predicted when the current time period is reached, the room state information mapping rule of each time period to be predicted is obtained according to the latest historical order data analysis and planning, and the room state information mapping rule is set after comprehensive prediction evaluation is performed on the basis of two dimensions of the room type and the hotel, so that a reasonable room state information mapping rule can be set for each room type of each hotel, and not only can the timely synchronization of the room state information of each room type of each hotel be ensured, but also the system resources can be reasonably utilized. According to the hotel data mapping rule generation method, the room type heat degree and the hotel heat degree of the time period to be predicted are respectively predicted based on the room type dimension and the hotel dimension to obtain the predicted room type heat degree and the predicted hotel heat degree, the room state information mapping rule is set for each room type of each hotel according to the predicted room type heat degree and the predicted hotel heat degree, therefore, multi-dimensional comprehensive evaluation is conducted on each room type of each hotel from the room type dimension and the hotel dimension, the room state information mapping rules with different synchronous frequencies are set according to the evaluation result, the one-time synchronization process is divided into different time points to be respectively synchronized, system resources can be reasonably utilized, and meanwhile, excessive pressure cannot be caused on a server.
Fig. 2 is a schematic functional module diagram of a hotel data mapping rule generating apparatus according to an embodiment of the present invention. As shown in fig. 2, the hotel data mapping rule generating apparatus 20 includes an obtaining module 21, a first prediction module 22, a second prediction module 23, and a setting module 24.
The acquisition module 21 is configured to count first historical order data of all hotels in a target area based on a house type dimension, and count second historical order data of all house types in the target area based on the hotel dimension;
the first prediction module 22 is used for predicting and obtaining the predicted house type heat of various house types in a time period to be predicted according to the first historical order data and a pre-trained first prediction model, and the first prediction model is obtained by training according to the historical order data of each house type;
the second prediction module 23 is configured to predict, according to second historical order data and a second pre-trained prediction model, a predicted hotel hotness of each hotel in a time period to be predicted, where the second prediction model is obtained by training according to historical order data of each hotel;
and the setting module 24 is configured to set a room state information mapping rule of a corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat and the predicted hotel heat in combination with a preset rule.
Optionally, the first prediction module 22 performs an operation of obtaining the predicted house type heat of each house type of the time period to be predicted according to the first historical order data and a pre-trained first prediction model, specifically including: acquiring first historical trading data and first historical browsing data of each house type of all hotels from the first historical order data; predicting to obtain first predicted deal data of various house types in a time period to be predicted according to the first historical deal data and a pre-trained first deal number prediction model, and training the first deal number prediction model according to the historical deal data of each house type to obtain the first predicted deal data; predicting to obtain first predicted browsing data of various house types in a time period to be predicted according to the first historical browsing data and a pre-trained first browsing number prediction model, and training the first browsing number prediction model according to the historical browsing data of each house type to obtain the first predicted browsing data; and calculating a first ratio of the first predicted transaction data and the first predicted browsing data, and taking the first ratio as the predicted house type heat of various house types.
Optionally, the second prediction module 23 performs an operation of predicting the predicted hotel hotness of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model, and the operation specifically includes: acquiring second historical trading data and second historical browsing data of all house types of each hotel from the second historical order data; predicting according to the second historical trading data and a pre-trained second trading volume prediction model to obtain second predicted trading data of each hotel in the time period to be predicted, and training the second trading volume prediction model according to the historical trading data of each hotel to obtain the second predicted trading volume data; predicting according to second historical browsing data and a pre-trained second browsing number prediction model to obtain second predicted browsing data of each hotel in a time period to be predicted, and training the second browsing number prediction model according to the historical browsing data of each hotel to obtain the second predicted browsing data; and calculating a second ratio of the second predicted transaction data and the second predicted browsing data, and taking the second ratio as the predicted hotel heat of each hotel.
Optionally, the setting module 24 performs, in combination with the preset rule, an operation of setting a room state information mapping rule of a corresponding synchronization frequency for each room type of each hotel in the to-be-predicted time period according to the predicted room type heat degree and the predicted hotel heat degree, where the operation specifically includes: performing data normalization processing on the predicted house type heat of each house type, and confirming a first preset synchronous frequency corresponding to each house type according to the data after normalization processing; performing data normalization processing on the predicted hotel heat of each hotel, and determining a second preset synchronization frequency corresponding to each hotel according to the data after the normalization processing; accumulating a first preset synchronization frequency and a second preset synchronization frequency corresponding to each room type of each hotel according to a preset weight value to obtain a final synchronization frequency of each room type of each hotel; and setting room state information mapping rules for each type of room of each hotel according to the final synchronization frequency.
Optionally, before the obtaining module 21 performs the operation of counting first historical order data of all hotels in the target area based on the house type dimension and counting second historical order data of all houses in the target area based on the hotel dimension, the obtaining module is further configured to: acquiring address information of all hotels; performing K-Means clustering on all hotels according to the address information to obtain a plurality of clustering clusters; and performing area division on all hotels according to a plurality of clustering clusters, wherein each clustering cluster corresponds to one area.
Optionally, after the setting module 24 performs the operation of setting the room state information mapping rule of the corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree, the setting module is further configured to: and when the current time is detected to be within the range of the time period to be predicted, pulling the room state information of each room type of each hotel from the hotel supplier platform according to the room state information mapping rule for synchronization.
Optionally, after the second prediction module 23 performs an operation of predicting the hotel hotness of each hotel in the time period to be predicted according to the second historical order data and a second pre-trained prediction model, the method further includes: acquiring label information of each hotel; scoring each hotel according to the label information to obtain a scoring value of each hotel; respectively sorting each hotel according to the predicted hotel popularity and the score value to obtain a first sorting based on the predicted hotel popularity and a second sorting based on the score value; confirming confidence of the predicted hotel hotness according to the first sequence and the second sequence; and confirming a preset correction coefficient corresponding to each hotel according to the confidence coefficient of the predicted hotel heat degree, and correcting the predicted hotel heat degree by using the preset correction coefficient.
For other details of the technical solutions implemented by the modules in the hotel data mapping rule generating device in the above embodiments, reference may be made to the description of the hotel data mapping rule generating method in the above embodiments, and details are not described here again.
It should be noted that, in this specification, each embodiment is described in a progressive manner, and each embodiment focuses on differences from other embodiments, and portions that are the same as and similar to each other in each embodiment may be referred to. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown in fig. 3, the computer device 60 includes a processor 61 and a memory 62 coupled to the processor 61, wherein the memory 62 stores program instructions, and when the program instructions are executed by the processor 61, the processor 61 executes the steps of the hotel data mapping rule generating method according to any one of the above embodiments.
The processor 61 may also be referred to as a CPU (Central Processing Unit). The processor 61 may be an integrated circuit chip having signal processing capabilities. The processor 61 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a storage medium according to an embodiment of the invention. The storage medium of the embodiment of the present invention stores program instructions 71 capable of implementing all the methods described above, where the program instructions 71 may be stored in the storage medium in the form of a software product, and include several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or computer equipment, such as a computer, a server, a mobile phone, and a tablet.
In the several embodiments provided in the present application, it should be understood that the disclosed computer apparatus, device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit. The above embodiments are merely examples and are not intended to limit the scope of the present disclosure, and all modifications, equivalents, and flow charts using the contents of the specification and drawings of the present disclosure or those directly or indirectly applied to other related technical fields are intended to be included in the scope of the present disclosure.
Claims (9)
1. A hotel data mapping rule generating method is characterized by comprising the following steps:
counting first historical order data of all hotels in a target area based on the dimension of the room type, and counting second historical order data of all the rooms in the target area based on the dimension of the hotels;
predicting according to the first historical order data and a pre-trained first prediction model to obtain predicted house type heat of various house types in a time period to be predicted, wherein the first prediction model is obtained by training according to the historical order data of each house type;
predicting and obtaining the predicted hotel heat of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model, wherein the second prediction model is obtained by training according to the historical order data of each hotel;
setting a room state information mapping rule of corresponding synchronous frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining a preset rule;
the combination preset rule sets a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree, and the method comprises the following steps:
performing data normalization processing on the predicted house type heat of each house type, and determining a first preset synchronization frequency corresponding to each house type according to the data after normalization processing;
performing data normalization processing on the predicted hotel heat of each hotel, and confirming a second preset synchronization frequency corresponding to each hotel according to the data after normalization processing;
accumulating a first preset synchronization frequency and a second preset synchronization frequency corresponding to each room type of each hotel according to a preset weight value to obtain a final synchronization frequency of each room type of each hotel;
and setting the room state information mapping rule for each room type of each hotel according to the final synchronization frequency.
2. The hotel data mapping rule generating method according to claim 1, wherein the predicting room type heat of various room types in the time period to be predicted according to the first historical order data and a pre-trained first prediction model comprises:
acquiring first historical transaction data and first historical browsing data of each type of room of all hotels from the first historical order data;
predicting to obtain first predicted transaction data of various house types in the time period to be predicted according to the first historical transaction data and a pre-trained first transaction number prediction model, wherein the first transaction number prediction model is obtained by training according to the historical transaction data of each house type;
predicting to obtain first predicted browsing data of various house types in the time period to be predicted according to the first historical browsing data and a pre-trained first browsing number prediction model, wherein the first browsing number prediction model is obtained by training according to the historical browsing data of each house type;
and calculating a first ratio of the first predicted transaction data to the first predicted browsing data, and taking the first ratio as the predicted house type heat of various house types.
3. The hotel data mapping rule generating method of claim 1, wherein the predicting hotness of each hotel in the time period to be predicted according to the second historical order data and a second pre-trained prediction model comprises:
acquiring second historical trading data and second historical browsing data of all house types of each hotel from the second historical order data;
predicting to obtain second predicted transaction data of each hotel in the time period to be predicted according to the second historical transaction data and a pre-trained second transaction data prediction model, wherein the second transaction data prediction model is obtained by training according to the historical transaction data of each hotel;
predicting to obtain second predicted browsing data of each hotel in the time period to be predicted according to the second historical browsing data and a pre-trained second browsing number prediction model, wherein the second browsing number prediction model is obtained by training according to the historical browsing data of each hotel;
and calculating a second ratio of the second predicted transaction data and the second predicted browsing data, and taking the second ratio as the predicted hotel heat of each hotel.
4. The hotel data mapping rule generating method of claim 1, wherein before counting first historical order data of all hotels in a target area based on a house type dimension and counting second historical order data of all hotel types in the target area based on a hotel dimension, the method further comprises:
acquiring address information of all hotels;
performing K-Means clustering on all the hotels according to the address information to obtain a plurality of clustering clusters;
and performing area division on all hotels according to the plurality of cluster clusters, wherein each cluster corresponds to one area.
5. The hotel data mapping rule generating method according to claim 1, wherein after setting a room status information mapping rule of a corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree, the method further comprises:
and when the current time is detected to be within the range of the time period to be predicted, pulling the room state information of each room type of each hotel to a hotel supplier platform according to the room state information mapping rule for synchronization.
6. The hotel data mapping rule generating method according to claim 1, wherein after the predicted hotel hotness of each hotel in the time period to be predicted is obtained through prediction according to the second historical order data and a second pre-trained prediction model, the method further comprises:
acquiring label information of each hotel;
scoring each hotel according to the label information to obtain a scoring value of each hotel;
sorting each hotel according to the predicted hotel popularity and the score value respectively to obtain a first sorting based on the predicted hotel popularity and a second sorting based on the score value;
confirming confidence of the predicted hotel popularity according to the first ranking and the second ranking;
and confirming a preset correction coefficient corresponding to each hotel according to the confidence coefficient of the predicted hotel heat degree, and correcting the predicted hotel heat degree by using the preset correction coefficient.
7. A hotel data mapping rule generating device, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for counting first historical order data of all hotels in a target area based on the dimension of the rooms and counting second historical order data of all the rooms in the target area based on the dimension of the hotels;
the first prediction module is used for predicting and obtaining the predicted house type heat of various house types in a time period to be predicted according to the first historical order data and a pre-trained first prediction model, and the first prediction model is obtained by training according to the historical order data of each house type;
the second prediction module is used for predicting and obtaining the predicted hotel heat of each hotel in the time period to be predicted according to the second historical order data and a pre-trained second prediction model, and the second prediction model is obtained by training according to the historical order data of each hotel;
the setting module is used for setting a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining a preset rule;
the setting module executes an operation of setting a room state information mapping rule of corresponding synchronization frequency for each room type of each hotel in the time period to be predicted according to the predicted room type heat degree and the predicted hotel heat degree by combining with a preset rule, and specifically comprises the following steps: performing data normalization processing on the predicted house type heat of each house type, and determining a first preset synchronization frequency corresponding to each house type according to the data after normalization processing; performing data normalization processing on the predicted hotel heat of each hotel, and confirming a second preset synchronization frequency corresponding to each hotel according to the data after normalization processing; accumulating the first preset synchronization frequency and the second preset synchronization frequency corresponding to each room type of each hotel according to a preset weight value to obtain a final synchronization frequency of each room type of each hotel; and setting room state information mapping rules for each room type of each hotel according to the final synchronization frequency.
8. A computer device, characterized in that the computer device comprises a processor, a memory coupled to the processor, wherein program instructions are stored in the memory, which program instructions, when executed by the processor, cause the processor to perform the steps of the hotel data mapping rule generating method as claimed in any one of claims 1 to 6.
9. A storage medium characterized by storing program instructions capable of implementing the hotel data mapping rule generation method as claimed in any one of claims 1 to 6.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933465A (en) * | 2015-05-12 | 2015-09-23 | 携程计算机技术（上海）有限公司 | Data interconnecting method and system on hotel management platform |
| WO2018018199A1 (en) * | 2016-07-24 | 2018-02-01 | 严映军 | Shortest distance-based hotel reservation method and room reservation system |
| CN107885884A (en) * | 2017-12-01 | 2018-04-06 | 深圳市天下房仓科技有限公司 | A kind of hotel's method of data synchronization, system and storage medium |
| CN111861801A (en) * | 2020-07-21 | 2020-10-30 | 携程计算机技术（上海）有限公司 | Hotel full room prediction method, system, equipment and storage medium |
| CN113139667A (en) * | 2021-05-07 | 2021-07-20 | 深圳他米科技有限公司 | Hotel room recommendation method, device, equipment and storage medium based on artificial intelligence |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784648A (en) * | 2018-12-14 | 2019-05-21 | 北京三快在线科技有限公司 | Scheduling resource distributing method, device, electronic equipment and readable storage medium storing program for executing |
| CN109784848B (en) * | 2018-12-29 | 2021-08-27 | 南京意博软件科技有限公司 | Hotel order processing method and related product |
| US20210117998A1 (en) * | 2019-10-21 | 2021-04-22 | Oracle International Corporation | Artificial Intelligence Based Room Personalized Demand Model |
| CN110992566A (en) * | 2019-11-20 | 2020-04-10 | 四川研宝科技有限公司 | Unmanned hotel system and self-service check-in and check-out method |
| CN111445046A (en) * | 2020-03-18 | 2020-07-24 | 携程计算机技术（上海）有限公司 | Hotel reservation information processing method and system, electronic equipment and storage medium |
-
2022
- 2022-01-27 CN CN202210102830.XA patent/CN114443735B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933465A (en) * | 2015-05-12 | 2015-09-23 | 携程计算机技术（上海）有限公司 | Data interconnecting method and system on hotel management platform |
| WO2018018199A1 (en) * | 2016-07-24 | 2018-02-01 | 严映军 | Shortest distance-based hotel reservation method and room reservation system |
| CN107885884A (en) * | 2017-12-01 | 2018-04-06 | 深圳市天下房仓科技有限公司 | A kind of hotel's method of data synchronization, system and storage medium |
| CN111861801A (en) * | 2020-07-21 | 2020-10-30 | 携程计算机技术（上海）有限公司 | Hotel full room prediction method, system, equipment and storage medium |
| CN113139667A (en) * | 2021-05-07 | 2021-07-20 | 深圳他米科技有限公司 | Hotel room recommendation method, device, equipment and storage medium based on artificial intelligence |
Non-Patent Citations (3)
| Title |
|---|
| 《Network Aware Traffic Adaptation for Cloud Games》;Richard Ewelle Ewelle 等;《IEEE》;20140526;第1-8页 * |
| 《基于云的酒店在线预订系统设计及实现》;胡光华;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;20170315;I138-2862 * |
| 一种航空公司机票+酒店打包推荐方法的研究和实现;李雄清等;《电子测试》;20200815(第16期);第54-56页 * |
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Denomination of invention: Method, device, device, and storage medium for generating hotel data mapping rules Effective date of registration: 20231025 Granted publication date: 20220913 Pledgee: Industrial and Commercial Bank of China Limited Shenzhen Nanshan Sub branch Pledgor: SHENZHEN TIANXIA FANGCANG TECHNOLOGY CO.,LTD. Registration number: Y2023980062595 |