CN116166889B - Hotel product screening method, device, equipment and storage medium - Google Patents
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Abstract
The application relates to the field of artificial intelligence and discloses a hotel product screening method, a hotel product screening device, hotel product screening equipment and a hotel product storage medium. The method comprises the following steps: acquiring hotel product data, and extracting first image data containing breakfast and second image data containing house type; processing the first image data by adopting a first image recognition model, recognizing the breakfast quantity in the first image data, processing the second image data by adopting a second image recognition model, and recognizing the house type in the second image data; obtaining the corresponding breakfast price and the house type price, and obtaining the quantity of breakfast, the house type, the breakfast price and the preset score value and the preset weight value corresponding to the house type price; weighting calculation is carried out on the preset score value and the preset weight value, and the score value of each hotel product is obtained; and screening the hotel products according to the score values of the hotel products. Through the mode, the hotel product screening method and the hotel product screening system can solve the problems that in the prior art, hotel product screening results are not objective enough and operation requirements are high.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a hotel product screening method, a hotel product screening device, hotel product screening equipment and a hotel product storage medium.
Background
At present, on the premise of having more comprehensive hotel product data, when screening hotel resources which want to be expanded, scenic spots mainly rely on manpower and limited computer system tools to perform work. Typically, a user screens and sorts data by using a spreadsheet tool such as Excel, and a processed hotel product list is obtained.
However, the screening standards of different commercial expanders in the prior art are not uniform, and personal subjective factor influence in the screening process is difficult to eliminate. In addition, in the process of screening operation in the electronic form tool such as Excel, the operation difficulty is increased correspondingly due to the functional complexity of the electronic form tool such as Excel, the operation level of different personnel and other problems, and the situation that part of operators cannot effectively complete the screening operation is likely to exist, that is, the technical problems of insufficient objective result and higher operation requirement exist in the prior art.
Disclosure of Invention
The application provides a hotel product screening method, a hotel product screening device, hotel product screening equipment and a hotel product storage medium, which can solve the technical problems that the hotel product screening result is not objective enough and the operation requirement is high in the prior art.
In order to solve the technical problems, the application adopts a technical scheme that: the hotel product screening method comprises the following steps:
acquiring hotel product data, and extracting first image data containing breakfast and second image data containing house type;
processing the first image data by adopting a first image recognition model, recognizing the breakfast quantity in the first image data, processing the second image data by adopting a second image recognition model, and recognizing the house type in the second image data;
obtaining the corresponding breakfast price and the house type price, and obtaining the quantity of breakfast, the house type, the breakfast price and the preset score value and the preset weight value corresponding to the house type price;
weighting calculation is carried out on the preset score value and the preset weight value, and the score value of each hotel product is obtained;
and screening the hotel products according to the score values of the hotel products.
According to one embodiment of the present application, the screening the hotel products according to the score value of the hotel products further comprises:
acquiring the heat of the hotel product;
adjusting the score value of the hotel product according to the heat of the hotel product;
and screening the hotel products according to the adjusted score values of the hotel products.
According to one embodiment of the present application, before the hotel product data is obtained and the first image data containing breakfast and the second image data containing the room model are extracted, the method further comprises:
storing the hotel product data in a preset library to be screened;
and periodically updating the hotel product data in the preset library to be screened.
According to one embodiment of the present application, after the screening of the hotel products according to the score values of the hotel products, the method further comprises:
adding the screened hotel products into a preset screened library;
and regularly acquiring the supply quotation of the hotel products in the preset screened library, synchronously updating the price of the hotel products in the preset screened library, and dropping the room state and the room quantity of the hotel products into the SaaS inventory in real time.
According to one embodiment of the present application, after the hotel products to be screened are added to the preset screened library, the method further comprises:
and periodically inquiring the hotel products which are not updated in the preset screened library within preset time, and removing the non-updated hotel products from the preset screened library.
According to one embodiment of the present application, the processing the first image data using the first image recognition model, before recognizing the number of breakfast in the first image data, further includes:
pre-constructing the first image recognition model;
acquiring a plurality of image data containing breakfast, and marking the breakfast quantity and breakfast coordinates in the image data;
performing model reasoning training on the first image recognition model according to the plurality of image data obtained by labeling, and calculating a loss function;
and adjusting the model training parameters to enable the loss function to be smaller than a preset value, so as to obtain the trained first image recognition model.
According to one embodiment of the present application, the processing the first image data using a first image recognition model, and recognizing the number of breakfast in the first image data includes:
extracting image features of the first image data by adopting the first image recognition model, wherein the first image recognition model comprises a plurality of convolution layers;
determining an object candidate frame set in the first image data according to image features output by at least two layers of convolution layers in the first image recognition model, wherein the image features at least comprise object categories and positioning coordinates of the object candidate frames;
screening and de-duplicating the object candidate frames in the object candidate frame set according to the object category in the image characteristic and the positioning coordinates of the object candidate frames;
and taking the number of target object candidate frames obtained after screening and de-duplication as the breakfast number in the first image data.
In order to solve the technical problems, the application adopts another technical scheme that: provided is a hotel product screening apparatus, comprising:
the first acquisition module is used for acquiring hotel product data and extracting first image data containing breakfast and second image data containing house type;
the identification module is used for processing the first image data by adopting a first image identification model, identifying the breakfast quantity in the first image data, processing the second image data by adopting a second image identification model, and identifying the house type in the second image data;
the second acquisition module is used for acquiring the corresponding breakfast price and the house type price, and acquiring the breakfast quantity, the house type, the breakfast price and the preset score value and the preset weight value corresponding to the house type price;
the calculation module is used for carrying out weighted calculation on the preset score value and the preset weight value to obtain the score value of each hotel product;
and the screening module is used for screening the hotel products according to the score values of the hotel products.
In order to solve the technical problems, the application adopts a further technical scheme that: there is provided a computer device comprising: the hotel product screening system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the hotel product screening method is realized when the processor executes the computer program.
In order to solve the technical problems, the application adopts a further technical scheme that: there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the hotel product screening method described above.
The beneficial effects of the application are as follows: processing the first image data by adopting a first image recognition model, recognizing the quantity of breakfast in the first image data, processing the second image data by adopting a second image recognition model, recognizing the type of the house in the second image data, and carrying out weighted calculation on the quantity of breakfast, the type of the house, the price of breakfast and the preset score value and the preset weight value corresponding to the type of the house to obtain the score value of each hotel product; the hotel products are screened according to the score values of the hotel products, so that the intellectualization and automation of hotel product screening can be realized, and the technical problems that the hotel product screening result is not objective enough and the operation requirement is high in the prior art are solved.
Drawings
Fig. 1 is a flow chart of a hotel product screening method of a first embodiment of the application;
figure 2 is a flow chart of a hotel product screening method according to a second embodiment of the application;
fig. 3 is a schematic structural view of a hotel product screening apparatus according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a computer device according to an embodiment of the present application;
fig. 5 is a schematic structural view of a computer storage medium according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," and the like in this disclosure are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", and "a third" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. All directional indications (such as up, down, left, right, front, back … …) in embodiments of the present application are merely used to explain the relative positional relationship, movement, etc. between the components in a particular gesture (as shown in the drawings), and if the particular gesture changes, the directional indication changes accordingly. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may 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 may be included in at least one embodiment of the application. The appearances of such phrases 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. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Fig. 1 is a flow chart of a hotel product screening method according to a first embodiment of the application. It should be noted that, if there are substantially the same results, the method of the present application is not limited to the flow sequence shown in fig. 1. As shown in fig. 1, the method comprises the steps of:
step S101: hotel product data is acquired and first image data containing breakfast and second image data containing a house type are extracted.
In step S101, in an application scenario, a provider of a hotel product may take a picture of the hotel product by using an electronic device, and directly upload image data to a preset library to be screened, so that complicated introduction and text editing of the hotel product can be avoided, and the working efficiency and data authenticity of the provider of the hotel product are effectively improved, thereby improving user experience.
In this embodiment, before hotel product data is acquired, hotel product data acquired in advance by a web crawler is preset in stock to be screened, and updated periodically, so as to acquire newly added hotel products and synchronize the latest data information of existing hotel products. The hotel product data includes at least first image data and second image data, wherein the first image data includes breakfast information and the second image data includes room-type information. Hotel product data may also include one or more of hotel name, hotel star, location, price, service item.
Further, the system performs standardized processing on hotel product data according to preset rules, determines product labels, and configures score values and weight values for each hotel product data according to the product labels. For example, the product labels corresponding to the house type can be single person, double person, suite and the like, the product labels with the house type price between 300 and 400 yuan are economical, the product labels with the house type price between 600 and 800 yuan are comfortable and the like. The product labels corresponding to breakfast quantity can be marked, configured according to requirements and the like, and the product labels corresponding to breakfast price can be free, non-free and the like. According to the embodiment, the weight value, namely the preset weight value, of each product label of the hotel product can be preset according to factors such as the screening standard of the customer, the user experience evaluation and the like, the corresponding score value, namely the preset score value, is calculated according to the preset weight value, the association mapping is carried out on each product label, the corresponding preset weight value and the preset score value, the association mapping relation is formed and stored, the association mapping relation can be directly called later, and the data processing efficiency is effectively improved.
In one embodiment, after the first image data and the second image data are extracted, quality detection may be performed on the image data to clean the image data that is not of acceptable quality. Specifically, the image size, the pixel value and the pixel point mean value in the image data are obtained, the peak signal-to-noise ratio of the image data is calculated according to the image size, the pixel value and the pixel point mean value, the peak signal-to-noise ratio is used as the quality standard of the image data, and the image data with the peak signal-to-noise ratio lower than the preset value is used as the image data with unqualified quality.
Step S102: and processing the first image data by adopting a first image recognition model, recognizing the breakfast quantity in the first image data, processing the second image data by adopting a second image recognition model, and recognizing the house type in the second image data.
In step S102, the first image recognition model is an artificial intelligence model for recognizing the number of breakfast in the first image data. Specifically, extracting image features of first image data by adopting a first image recognition model, wherein the first image recognition model comprises a plurality of convolution layers; determining an object candidate frame set in the first image data according to image features output by at least two layers of convolution layers in the first image recognition model, wherein the image features at least comprise object categories and positioning coordinates of the object candidate frames; screening and de-duplicating the object candidate frames in the object candidate frame set according to the object category in the image characteristics and the positioning coordinates of the object candidate frames; and taking the number of target object candidate frames obtained after screening and de-duplication as the breakfast number in the first image data.
In one embodiment, before the first image data is processed using the first image recognition model to recognize the number of breakfast in the first image data, the method further comprises the steps of:
pre-constructing a first image recognition model; acquiring a plurality of image data containing breakfast, and marking the breakfast quantity and breakfast coordinates in the image data; performing model reasoning training on the first image recognition model according to the plurality of image data obtained by labeling, and calculating a loss function; and adjusting the model training parameters to enable the loss function to be smaller than a preset value, so as to obtain a trained first image recognition model.
The second image recognition model is an artificial intelligent model and is used for recognizing the house type in the second image data. Specifically, a second image recognition model is adopted to recognize second image data, and a target area and confidence degrees of each room type candidate type are obtained, wherein the target area is an area containing an object in the second image data, and the confidence degrees are used for indicating the probability that the object belongs to the corresponding room type candidate type; and comparing the confidence coefficient with a preset confidence coefficient threshold value, and determining a target room type of the object contained in the target area from a plurality of room type candidate types according to the comparison result, wherein the target room type is a room type candidate type with the confidence coefficient larger than the preset confidence coefficient threshold value.
Step S103: and obtaining the corresponding breakfast price and the house type price, and obtaining the quantity of breakfast, the house type, the breakfast price, the preset score value corresponding to the house type price and the preset weight value.
In step S103, breakfast prices and room-type prices may be provided to the suppliers, and hotel product data includes breakfast prices and room-type prices. The embodiment firstly determines the quantity of breakfast, the type of the house, the price of breakfast and the corresponding product label of the house price, matches the product label with the preset product label, and obtains the corresponding preset score value and the preset weight value according to the matching result and the corresponding product label-preset score value-preset weight value association mapping relation.
Step S104: and carrying out weighted calculation on the preset score value and the preset weight value to obtain the score value of each hotel product.
In step S104, the score value of the same hotel product is the preset score value of the number of breakfast x the preset weight value of the number of breakfast x the preset score value of the room type x the preset weight value of the room type x the preset score value of the price of breakfast x the preset weight value of the price of breakfast x the preset score value of the room type x the preset weight value of the price of room type.
Step S105: and screening the hotel products according to the score values of the hotel products.
In step S105, the score values of the hotel products are arranged in descending order, and one or more hotel products arranged in the front are selected.
Further, in an implementation embodiment, the heat degree of the hotel products may also be obtained, for example, a predetermined number of hotel products and/or a repeated predetermined number of hotel products are used as hotel products with higher heat degrees, the score value of the hotel products is adjusted according to the heat degree of the hotel products, the score value of the hotel products with higher heat degrees is higher, and the hotel products are selected according to the adjusted score value of the hotel products.
According to the hotel product screening method, the first image recognition model is adopted to process the first image data, the breakfast quantity in the first image data is recognized, the second image recognition model is adopted to process the second image data, the room type in the second image data is recognized, and the preset score value and the preset weight value corresponding to the breakfast quantity, the room type, the breakfast price and the room type price are weighted and calculated to obtain the score value of each hotel product; the hotel products are screened according to the score values of the hotel products, so that the intellectualization and automation of hotel product screening can be realized, and the technical problems that the hotel product screening result is not objective enough and the operation requirement is high in the prior art are solved.
Fig. 2 is a flow chart of a hotel product screening method according to a second embodiment of the application. It should be noted that, if there are substantially the same results, the method of the present application is not limited to the flow sequence shown in fig. 2. As shown in fig. 2, the method comprises the steps of:
step S201: hotel product data is acquired and first image data containing breakfast and second image data containing a house type are extracted.
In this embodiment, step S201 in fig. 2 is similar to step S101 in fig. 1, and is not described here again for brevity.
Step S202: and processing the first image data by adopting a first image recognition model, recognizing the breakfast quantity in the first image data, processing the second image data by adopting a second image recognition model, and recognizing the house type in the second image data.
In this embodiment, step S202 in fig. 2 is similar to step S102 in fig. 1, and is not described herein for brevity.
Step S203: and obtaining the corresponding breakfast price and the house type price, and obtaining the quantity of breakfast, the house type, the breakfast price, the preset score value corresponding to the house type price and the preset weight value.
In this embodiment, step S203 in fig. 2 is similar to step S103 in fig. 1, and is not described herein for brevity.
Step S204: and carrying out weighted calculation on the preset score value and the preset weight value to obtain the score value of each hotel product.
In this embodiment, step S204 in fig. 2 is similar to step S104 in fig. 1, and is not described herein for brevity.
Step S205: and screening the hotel products according to the score values of the hotel products.
In this embodiment, step S205 in fig. 2 is similar to step S105 in fig. 1, and is not described herein for brevity.
Step S206: and adding the screened hotel products into a preset screened library.
In step S206, the preset filtered library may be a Mysql database.
Step S207: and regularly acquiring supply quotations of hotel products in the preset screened library, synchronously updating the prices of the hotel products in the preset screened library, and dropping the room states and the room amounts of the hotel products into the SaaS inventory in real time.
In step S207, the supply quotation may be changed according to the market supply demand, and the supply quotation needs to be updated periodically and synchronously to ensure the validity and reliability of the data in the preset screened library.
Further, in one implementation embodiment, referring to fig. 2, after step S206, the method further includes:
step S208: and periodically inquiring the hotel products which are not updated in the preset screened library within the preset time, and removing the hotel products which are not updated from the preset screened library.
In step S208, the preset time may be adjusted according to the actual requirement, for example, if the hotel product in the screened library is not updated for one month, which indicates that the hotel product is invalid, the un-updated hotel product is removed from the screened library, and the system does not need to track the quotation information of the hotel product.
According to the hotel product screening method disclosed by the second embodiment of the application, on the basis of the first embodiment, the data validity and reliability can be ensured by regularly tracking and updating the related data of the hotel products in the preset screened library.
Fig. 3 is a schematic structural view of a hotel product screening apparatus according to an embodiment of the present application. As shown in fig. 3, the apparatus 30 includes a first acquisition module 31, an identification module 32, a second acquisition module 33, a calculation module 34, and a screening module 35.
The first acquiring module 31 is configured to acquire hotel product data, and extract first image data containing breakfast and second image data containing a house type;
the recognition module 32 is configured to process the first image data by using the first image recognition model, recognize the number of breakfast in the first image data, process the second image data by using the second image recognition model, and recognize the type of the house in the second image data;
the second obtaining module 33 is configured to obtain a corresponding breakfast price and a corresponding house type price, and obtain a preset score value and a preset weight value corresponding to the number of breakfast, the house type, the breakfast price and the house type price;
the calculating module 34 is configured to perform weighted calculation on the preset score value and the preset weight value, so as to obtain a score value of each hotel product;
the screening module 35 is configured to screen hotel products based on the scoring values of the hotel products.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the application. As shown in fig. 4, the computer device 40 includes a processor 41 and a memory 42 coupled to the processor 41.
The memory 42 stores program instructions for implementing the hotel product screening method of any of the embodiments described above.
The processor 41 is configured to execute program instructions stored in the memory 42 to screen hotel products.
The processor 41 may also be referred to as a CPU (Central Processing Unit ). The processor 41 may be an integrated circuit chip with signal processing capabilities. The processor 41 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf 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. 5, fig. 5 is a schematic structural diagram of a computer storage medium according to an embodiment of the present application. The computer storage medium according to the embodiment of the present application stores a program file 51 capable of implementing all the methods described above, where the program file 51 may be stored in the form of a software product in the computer storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) 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 computer storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes, or a terminal device such as a computer, a server, a mobile phone, a tablet, or the like.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The foregoing is only the embodiments of the present application, and therefore, the patent scope of the application is not limited thereto, and all equivalent structures or equivalent processes using the descriptions of the present application and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the application.
Claims (9)
1. A hotel product screening method, comprising:
acquiring hotel product data, and extracting first image data containing breakfast and second image data containing house type;
processing the first image data by adopting a first image recognition model, recognizing the breakfast quantity in the first image data, processing the second image data by adopting a second image recognition model, and recognizing the house type in the second image data;
obtaining the corresponding breakfast price and the house type price, and obtaining the quantity of breakfast, the house type, the breakfast price and the preset score value and the preset weight value corresponding to the house type price;
weighting calculation is carried out on the preset score value and the preset weight value, and the score value of each hotel product is obtained;
screening the hotel products according to the score values of the hotel products;
the processing of the first image data by using a first image recognition model, and the recognition of the breakfast quantity in the first image data comprises the following steps:
extracting image features of the first image data by adopting the first image recognition model, wherein the first image recognition model comprises a plurality of convolution layers;
determining an object candidate frame set in the first image data according to image features output by at least two layers of convolution layers in the first image recognition model, wherein the image features at least comprise object categories and positioning coordinates of the object candidate frames;
screening and de-duplicating the object candidate frames in the object candidate frame set according to the object category in the image characteristic and the positioning coordinates of the object candidate frames;
taking the number of target object candidate frames obtained after screening and de-duplication as the breakfast number in the first image data;
and processing the second image data by adopting a second image recognition model, wherein recognizing the room type in the second image data comprises the following steps:
identifying the second image data by adopting the second image identification model to obtain a target area and confidence degrees of each room type candidate type, wherein the target area is an area containing an object in the second image data, and the confidence degrees are used for indicating the probability that the object belongs to the corresponding room type candidate type;
comparing the confidence level with the preset confidence level threshold;
and determining a target room type of the object contained in the target area from a plurality of room type candidate types according to a comparison result, wherein the target room type is the room type candidate type with the confidence degree larger than the preset confidence degree threshold value.
2. The hotel product screening method of claim 1, wherein the screening the hotel products according to the score value of the hotel products further comprises:
acquiring the heat of the hotel product;
adjusting the score value of the hotel product according to the heat of the hotel product;
and screening the hotel products according to the adjusted score values of the hotel products.
3. The hotel product screening method of claim 1, wherein prior to obtaining hotel product data and extracting the first image data comprising breakfast and the second image data comprising a house type, further comprising:
storing the hotel product data in a preset library to be screened;
and periodically updating the hotel product data in the preset library to be screened.
4. The hotel product screening method of claim 1, further comprising, after the screening of the hotel product based on the score value of the hotel product:
adding the screened hotel products into a preset screened library;
and regularly acquiring the supply quotation of the hotel products in the preset screened library, synchronously updating the price of the hotel products in the preset screened library, and dropping the room state and the room quantity of the hotel products into the SaaS inventory in real time.
5. The hotel product screening method of claim 4, wherein after the screened hotel products are added to a pre-set screened library, further comprising:
and periodically inquiring the hotel products which are not updated in the preset screened library within preset time, and removing the non-updated hotel products from the preset screened library.
6. The hotel product screening method of claim 1, wherein the processing the first image data using a first image recognition model, prior to recognizing the number of breakfast in the first image data, further comprises:
pre-constructing the first image recognition model;
acquiring a plurality of image data containing breakfast, and marking the breakfast quantity and breakfast coordinates in the image data;
performing model reasoning training on the first image recognition model according to the plurality of image data obtained by labeling, and calculating a loss function;
and adjusting the model training parameters to enable the loss function to be smaller than a preset value, so as to obtain the trained first image recognition model.
7. A hotel product screening apparatus, comprising:
the first acquisition module is used for acquiring hotel product data and extracting first image data containing breakfast and second image data containing house type;
the identification module is used for processing the first image data by adopting a first image identification model, identifying the breakfast quantity in the first image data, processing the second image data by adopting a second image identification model, and identifying the house type in the second image data;
the second acquisition module is used for acquiring the corresponding breakfast price and the house type price, and acquiring the breakfast quantity, the house type, the breakfast price and the preset score value and the preset weight value corresponding to the house type price;
the calculation module is used for carrying out weighted calculation on the preset score value and the preset weight value to obtain the score value of each hotel product;
the screening module is used for screening the hotel products according to the score values of the hotel products;
the processing of the first image data by using a first image recognition model, and the recognition of the breakfast quantity in the first image data comprises the following steps:
extracting image features of the first image data by adopting the first image recognition model, wherein the first image recognition model comprises a plurality of convolution layers;
determining an object candidate frame set in the first image data according to image features output by at least two layers of convolution layers in the first image recognition model, wherein the image features at least comprise object categories and positioning coordinates of the object candidate frames;
screening and de-duplicating the object candidate frames in the object candidate frame set according to the object category in the image characteristic and the positioning coordinates of the object candidate frames;
taking the number of target object candidate frames obtained after screening and de-duplication as the breakfast number in the first image data;
and processing the second image data by adopting a second image recognition model, wherein recognizing the room type in the second image data comprises the following steps:
identifying the second image data by adopting the second image identification model to obtain a target area and confidence degrees of each room type candidate type, wherein the target area is an area containing an object in the second image data, and the confidence degrees are used for indicating the probability that the object belongs to the corresponding room type candidate type;
comparing the confidence level with the preset confidence level threshold;
and determining a target room type of the object contained in the target area from a plurality of room type candidate types according to a comparison result, wherein the target room type is the room type candidate type with the confidence degree larger than the preset confidence degree threshold value.
8. A computer device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the hotel product screening method of any of claims 1-6 when the computer program is executed by the processor.
9. A computer storage medium having stored thereon a computer program, which when executed by a processor implements the hotel product screening method of any of claims 1-6.
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