CN116933903A

CN116933903A - Intelligent processing method and system for online hotel reservation

Info

Publication number: CN116933903A
Application number: CN202310863252.6A
Authority: CN
Inventors: 周福才; 罗庆锋
Original assignee: Shenzhen Ketan Technology Co ltd
Current assignee: Shenzhen Ketan Technology Co ltd
Priority date: 2023-07-13
Filing date: 2023-07-13
Publication date: 2023-10-24
Anticipated expiration: 2043-07-13
Also published as: CN116933903B

Abstract

The invention relates to the technical field of intelligent data processing, in particular to an intelligent processing method and system for online hotel reservation. The method comprises the following steps: receiving order creation data through an API interface and inserting the order creation data into preset order queue data; analyzing order creation data from the order queue data to obtain hotel reservation standard data and room reservation type number standard data; the hotel reservation data and the room reservation type number data are aggregated and calculated by utilizing the pre-stored local hotel data of suppliers and the room type data of the suppliers, and hotel and room type aggregation list data of the suppliers are obtained; real-time price checking and price filtering sorting are carried out according to the hotel and house type list data of the aggregated suppliers, and price sorting data of the suppliers are obtained; and carrying out intelligent ordering processing according to the price ordering data of the suppliers to obtain intelligent order processing data. The invention selects the best supplier and house type to place the order, and improves the satisfaction degree and the booking success rate of the user.

Description

Intelligent processing method and system for online hotel reservation

Technical Field

The invention relates to the technical field of intelligent data processing, in particular to an intelligent processing method and system for online hotel reservation.

Background

The intelligent processing method for online hotel reservation refers to processing online hotel reservation flow in an intelligent manner by utilizing computer technology and algorithm, and comprises the steps of order receiving, data analysis, provider data aggregation, real-time price inquiry and intelligent ordering processing, so that reservation efficiency and user experience are improved. The intelligent processing method for online hotel reservation is generally based on a general algorithm and strategy, and is difficult to fully meet the personalized requirements of users. For example, a user may have specific preferences, requirements, or special needs, and existing intelligent processing methods may not adequately take these factors into account, thereby failing to provide an optimal subscription scheme.

Disclosure of Invention

The application provides an intelligent processing method and system for online hotel reservation, which aims to solve at least one technical problem.

The application provides an intelligent processing method for online hotel reservation, which comprises the following steps:

step S1: receiving order creation data through an API interface and inserting the order creation data into preset order queue data;

step S2: analyzing order creation data from the order queue data so as to obtain hotel reservation standard data and room reservation type number standard data;

Step S3: the hotel reservation data and the room reservation number data are aggregated and calculated by utilizing the pre-stored local hotel data and the local room type data of the suppliers, so that hotel and room type aggregation list data of the suppliers are obtained;

step S4: real-time price checking and price filtering sorting are carried out according to the hotel and house type list data of the aggregated suppliers, so that price sorting data of the suppliers are obtained;

step S5: and performing intelligent order placing processing according to the price ordering data of the suppliers, thereby obtaining intelligent order processing data.

According to the invention, the API interface is used for receiving order creation data and inserting the order creation data into the order queue, so that quick processing and management of orders are realized, the booking flow is quickened, and the booking efficiency is improved. And analyzing the order data and performing aggregation calculation, and obtaining aggregation list data of the supplier hotels and the room types by utilizing prestored supplier hotels and room type data. More comprehensive and accurate hotel and room type selection can be provided, and different requirements and preferences of users are met. And according to the aggregated supplier hotel and house type list data, performing real-time price checking and price filtering sorting to obtain supplier price sorting data. The method can help the user to acquire the current latest price information, and filter according to the price policy set by the user, so as to ensure that the user obtains the price of the provider meeting the budget and the preferential price. And according to the price ordering data of the suppliers, adopting an intelligent ordering algorithm to conduct order processing. The algorithm is based on an advanced computer technology, comprehensively considers factors of user requirements, prices and availability, selects the best suppliers and house types for ordering, and improves user satisfaction and reservation success rate.

Preferably, step S1 is specifically:

step S11: acquiring order creation request data from a client;

step S12: receiving order creation data through an API interface provided by a cloud computing platform;

step S13: analyzing and verifying the data of the order creation data, thereby obtaining order creation verification data;

step S14: and inserting the order creation verification data into preset order queue data.

According to the invention, the order creation data is received through the API interface and is inserted into the order queue data, so that the quick processing and management of orders can be realized, and the order processing efficiency is improved. And analyzing and verifying the order creation data, so that the integrity, the legality and the accuracy of the order data are ensured, and the booking problems and disputes caused by data errors are reduced. The API interface provided by the cloud computing platform is utilized to receive order data, and the order data can be safely transmitted and stored by means of high availability and stability of the cloud computing platform. The order creation verification data is inserted into the preset order queue data, order can be sequentially controlled and managed, order processing according to the sequence is ensured, and order processing efficiency and accuracy are improved.

Preferably, in step S14, an order form insertion calculation formula is adopted for performing data insertion processing, where the order form insertion calculation formula specifically includes:

f (x) is an insertion value corresponding to the order creation verification data, n is the quantity data of the order creation verification data, i is the order item of the order creation verification data, x is the number of the order creation verification data, a is a source channel of the order creation data, b is the priority of the order creation data, c is the amount of the order creation data, d is the commodity quantity of the order creation data, e is the discount rate of the order creation data, f is the validity period of the order creation data, and g is the customer satisfaction score of the order creation data.

The invention constructs an order form insertion calculation formula, and performs insertion sequencing on order form verification data through mathematical operation in the calculation formula. According to the values and interactions of different parameters, order form verification data with higher priority, larger amount, higher discount rate, longer effective period and higher customer satisfaction can occupy higher position in the sorting, so that the orders are preferentially processed. The parameter interactions in the calculation formula can adjust the weights of different parameters in the data insertion process. By adjusting the values and interactions of the different parameters, order form verification data can be more finely ordered and processed, so that orders with specific characteristics are more reasonably processed with priority. Mathematical symbols of the parameters in the calculation formula interact with each other, and personalized processing can be performed according to the characteristics of the order form data. The numerical values and the interaction relation of different parameters can enable the system to conduct intelligent processing on orders according to specific conditions, and user satisfaction and system performance are improved. The number n of the order form verification data represents the number of the order form verification data, and influences the accumulation times of the data in the calculation formula. The order item i of the order form verification data represents the position of the order form verification data in the dataset for indexing and looping computation of the control data. The number x of the order creation verification data represents an identifier of the order creation verification data for uniquely identifying each order creation verification data. The source channel a of the order form data represents the source channel of the order form data and is used for measuring the influence of different channels on the order form verification data. The priority b of the order form data represents the priority of the order form data for determining the weight of the order form verification data during the insertion process. The amount c of the order creation data represents the amount of the order creation data, and influences the importance degree of the order creation verification data in the inserting process. The commodity number d of the order creation data represents the commodity number in the order creation data, and is used for adjusting the insertion value of the order creation verification data. The discount rate e of the order creation data represents the discount rate of the order creation data, and is used to calculate the insertion value of the order creation verification data. The validity period f of the order creation data represents the validity period of the order creation data, and is used for measuring the timeliness of the order creation verification data. The customer satisfaction score g of the order creation data represents the customer satisfaction score of the order creation data, affecting the importance of the order creation verification data in the insertion process. According to the calculation formula, intelligent processing and sorting of order creation verification data can be achieved, personalized insertion processing is conducted on orders according to numerical values of different parameters and interaction relations, and order processing efficiency and user experience are improved. Meanwhile, the calculation formula can also carry out weight adjustment on order data according to different characteristics and requirements, and the accuracy and the priority of order processing are ensured.

Preferably, step S2 is specifically:

step S21: reading the order queue data, thereby obtaining order data;

step S22: carrying out data analysis on the order data so as to obtain order analysis data;

step S23: carrying out hotel booking data extraction and room booking type number data extraction on the order analysis data so as to obtain hotel booking data and room booking type number data;

step S24: carrying out data validity verification on hotel booking data and hotel booking type number data so as to obtain hotel booking verification data and hotel booking type number verification data;

step S25: carrying out data association according to the hotel booking verification data and the hotel booking type number verification data, thereby obtaining hotel booking type number association data;

step S26: and carrying out data standardization processing on the hotel booking room type number associated data so as to obtain hotel booking standard data and hotel booking room type number standard data.

According to the method, the order analysis data can be extracted through reading and analyzing the order queue data, and the hotel reservation data and the room reservation type number data are further extracted. By analyzing and extracting the data, errors and inaccuracy in the data processing process are reduced, and the accuracy and reliability of the data are improved. And carrying out data validity verification on hotel reservation data and reservation room type number data, so that the validity and effectiveness of the data can be ensured. By verifying the format, range and rule of the data, illegal data are eliminated, the problem caused by error data is reduced, and the quality of the data is ensured. According to the hotel reservation verification data and the room type number verification data, data association is carried out, so that the association relationship between the hotel reservation and the room type number can be established, and more accurate data analysis and processing are provided. Meanwhile, standardized processing is carried out on the hotel room reservation type number associated data, so that the data has a unified format and specification, and subsequent data processing and analysis are convenient. And analyzing, extracting, verifying, correlating and standardizing the order data to obtain hotel reservation standard data and room reservation type number standard data. The processed data has consistent format and specification, improves the operability and consistency of the data, and is convenient for subsequent business processing and system integration.

Preferably, step S24 is specifically:

step S241: acquiring user basic data, wherein the user basic data comprises user name data, user identity card data, user mobile phone number data and user history record data;

step S242: scoring the user credit according to the user basic data, thereby scoring the user credit data;

step S243: carrying out data format verification on hotel booking data and hotel booking type number data so as to obtain hotel booking format verification data and hotel booking type number format verification data;

step S244: carrying out data integrity verification on the hotel booking format verification data and the hotel booking type number format verification data, thereby obtaining hotel booking integrity verification data and hotel booking type number integrity verification data;

step S245: carrying out data uniqueness verification on the hotel booking integrity verification data and the hotel booking type number integrity verification data so as to obtain hotel booking uniqueness verification data and hotel booking type number uniqueness verification data;

step S246: screening the hotel reservation unique verification data and the hotel reservation type number unique verification data by using the user credit scoring data so as to acquire hotel reservation screening data and hotel reservation type number screening data;

Step S247: and carrying out numerical rationality verification on the hotel booking screening data and the booking room type number screening data, thereby obtaining booking hotel verification data and booking room type number verification data.

The invention can evaluate the credit condition of the user by acquiring the user basic data and scoring the credit of the user. This facilitates credit control and risk management for users by the hotel reservation system, improving transaction security and reliability. The accuracy, the integrity and the legality of the data can be ensured by carrying out format verification, integrity verification, uniqueness verification and numerical rationality verification on the hotel reservation data and the room type number data. Meanwhile, the user credit scoring data is combined to screen the reserved hotels and house types, so that the quality and the matching degree of the data are improved. Through the hotel booking verification data and the booking room number verification data in the step S247, verified and screened data can be obtained, and the data has higher credibility and adaptability. This helps the system to intelligently select the most appropriate booking hotel and room style, improving user satisfaction and booking success rate. By verifying and optimizing the data, invalid or illegal data processing is reduced, and the operation efficiency of the system is optimized. Meanwhile, the user can be supported by more accurate and reliable data in the booking process, and user experience and booking effect are improved.

Preferably, in step S242, the user credit score is processed by a user credit score calculation formula, where the user credit score calculation formula specifically includes:

g (y) is a credit score value of a user, y is service time data of user basic data, h is service time data of user basic data, delta is residual effective period of user identification card data, j is service time data of user mobile phone number data, k is the number of user history data, m is the number of negative evaluation in the user history data, l is the number of positive evaluation in the user history data, omega is the number of neutral evaluation in the user history data, o is the number of overdue repayment in the user history data, p is the number of advanced repayment in the user history data, and q is the number of order cancellation in the user history data.

The invention constructs a calculation formula of the credit score of the user, and the credit score of the user can be calculated according to the user basic data and the history record data through the calculation formula. The user credit score is used to evaluate the user's credit level and reliability for credit control and risk management in the hotel reservation system. The parameters in the calculation formula interact with each other through the action of mathematical operation symbols. For example, the number k of the user history data and the time data y, h and j are used to influence each other through operations such as product, division and index, and the number l and m of positive and negative face evaluations and overdue repayment o, advanced repayment p and the number q of order cancellation are weighed and offset through operations such as evolution and logarithm. Based on personal information and historical behavior data of the user, each parameter in the calculation formula realizes personalized evaluation and accurate measurement of credit conditions of the user through mathematical operation and weight setting. The combination of the parameters and the operation symbols in the formula can accurately capture the credit characteristics and the behavior patterns of the user, and more accurate and reliable credit scoring data is provided for the hotel reservation system. Through the user credit score calculation formula in step S242, the credit level of the user can be quantitatively evaluated according to the user basic data and the history data, and personalized and accurate credit score data can be provided for the hotel reservation system, so that the beneficial effects of credit control and risk management are realized. The mathematical parameters in the formula are interacted and operated through mathematical symbols, and the credit score of the user is finally determined according to the mutual influence of the weights and the association relation.

Preferably, step S3 is specifically:

step S31: hotel data matching is carried out by utilizing pre-stored local supplier hotel data and reservation hotel data, so that hotel matching data is obtained;

step S32: carrying out house type data matching on the reserved house type number data by utilizing the pre-stored house type data of the local suppliers so as to obtain house type matching data;

step S33: data aggregation is carried out on hotel matching data and room type matching data, so that hotel and room type aggregation data of suppliers are obtained;

step S34: data screening is carried out on the supplier hotel and house type aggregation data by utilizing the user demand data in the order form data, so that supplier hotel and house type screening data are obtained;

step S35: index calculation is carried out on the supplier hotel and room type screening data so as to obtain supplier hotel and room type index data, wherein the index calculation comprises evaluation price calculation, available room number calculation and price fluctuation calculation;

step S36: and carrying out data combination on the supplier hotel and room type screening data and the supplier hotel and room type index data, thereby obtaining supplier hotel and room type aggregation list data.

According to the hotel data matching method and the hotel data matching system, pre-stored supplier hotel data and reservation hotel data are utilized for hotel data matching and room data matching, so that the matching accuracy of the data can be improved. This helps ensure that the hotel and room types booked match the information of the suppliers perfectly, avoiding mismatching or mistakes. And aggregating hotel and house type data of a plurality of suppliers through data aggregation and screening operation, and screening according to user requirements in order form data, so as to obtain screening data of the hotels and the house types of the suppliers. This helps optimize the range of choices for suppliers hotels and room types, providing hotel and room type options that more closely match the needs of the user. The key indexes of the supplier hotel and the room type can be quantified by carrying out index calculation on the supplier hotel and room type screening data, including evaluation price calculation, available room number calculation and price fluctuation calculation. The indexes can be used for evaluating price advantages, room availability and price fluctuation conditions of hotels and rooms of suppliers, and providing more accurate selection and decision basis for users. And carrying out data combination on the supplier hotel and house type screening data and index data to obtain the aggregation list data of the supplier hotel and house type. The hotel and house type information display method is beneficial to comprehensively displaying hotel and house type information of different suppliers, facilitates comparison and selection of users, and improves booking experience and satisfaction of the users.

Preferably, the vendor price ordering data includes first vendor price ordering data and second vendor price ordering data, and step S4 is specifically:

step S41: real-time price inquiry is carried out through the cloud platform according to the hotel and house type list data of the aggregated suppliers, so that real-time price data of the suppliers are obtained;

step S42: price filtering is carried out on the real-time price data of the suppliers according to the order creation data, so that price filtering data of the suppliers are obtained;

step S43: price sorting is carried out on the provider price filtering data, so that first provider price sorting data are obtained;

step S44: and acquiring historical price data of the suppliers, and performing price discount ranking calculation on the price filtering data of the suppliers by utilizing the historical price data of the suppliers so as to acquire second price ranking data of the suppliers.

According to the method and the system, real-time price data of the suppliers can be obtained by carrying out real-time price query according to the hotel and house type list data of the aggregated suppliers. This allows the user to obtain up-to-date price information, avoids inaccurate or outdated data due to price variations, and provides more realistic and reliable price information. And price filtering is carried out on the real-time price data of the suppliers according to the order form data, and the supplier data in a price range meeting the requirements of the users can be screened out according to the requirements and budget limit of the users. This helps provide personalized price filtering services that enable users to more quickly find budget-compliant options. The price-filtered supplier data is subjected to price sorting, and the suppliers can be sorted according to the price. This allows the user to clearly understand the price difference of the suppliers, facilitating comparison and selection. The suppliers can be ranked according to the preference degree of the historical prices by acquiring the historical price data of the suppliers and performing price preference ranking calculation on the price-filtered supplier data by utilizing the data. This helps the user find provider options that are relatively low priced or have a higher preference, providing a more economical option.

Preferably, step S5 is specifically:

step S51: performing supplier qualification screening on the supplier price ordering data, thereby obtaining supplier price screening data;

step S52: real-time inventory inquiry is carried out on the price screening data of the suppliers so as to obtain real-time inventory data;

step S53: intelligent pricing is carried out on the real-time inventory data, so that real-time inventory pricing data are obtained;

step S54: making an order decision on the real-time inventory pricing data by using the order creation data, thereby obtaining order decision data;

step S55: and generating orders according to the order placing decision data and updating the state of the orders in real time, thereby acquiring intelligent processing data of the orders.

According to the invention, the supplier qualification screening is carried out according to the supplier price ordering data, and the suppliers can be evaluated and screened in qualification. This helps to increase the reliability and credibility of the suppliers, ensures that qualified and reliable suppliers are selected, and provides high quality services and products to the users. And carrying out real-time inventory inquiry on the price screening data of the suppliers, and acquiring the real-time inventory information of the current suppliers. The user can know the available inventory condition of the suppliers in time, and the situation that the suppliers cannot be successfully subscribed due to insufficient inventory is avoided. Intelligent pricing is performed according to real-time inventory data, and the price of the suppliers can be optimized and adjusted according to supply and demand conditions and market price changes. This helps to achieve a reasonable pricing strategy, making the price more competitive and attractive, providing a more cost-effective option. The order form data is utilized to make an order decision on the real-time inventory pricing data, and the intelligent order decision can be made according to factors of the requirements, budget and supplier conditions of the user. This helps ensure that the user selects the most appropriate suppliers and products and provides a personalized ordering experience. And generating an order according to the order placing decision data, and updating the state information of the order in real time. The method can ensure accurate generation of orders and timely state update, provide timely order feedback and service tracking, and enhance the visibility and transparency of users to the order processing process.

Preferably, an online hotel reservation intelligent processing system comprises:

the order creation data receiving module is used for receiving order creation data through the API interface and inserting the order creation data into preset order queue data;

the order creation data analysis module is used for analyzing the order creation data from the order queue data so as to acquire standard data of a reservation hotel and standard data of a reservation room type number;

the aggregation calculation module is used for carrying out aggregation calculation on the hotel reservation data and the room reservation number data by utilizing the hotel data of the suppliers and the room type data of the suppliers which are prestored locally so as to obtain list data of hotels and rooms of the aggregated suppliers;

the supplier price ordering module is used for carrying out real-time price searching and price filtering ordering according to the aggregated supplier hotel and house type list data so as to obtain supplier price ordering data;

and the intelligent order placing processing module is used for performing intelligent order placing processing according to the price ordering data of the suppliers so as to acquire intelligent order processing data.

The invention has the beneficial effects that: the order form data can be quickly received and inserted into preset order queue data through the API interface. By the aid of the method, timely processing and accuracy of order data can be guaranteed, and order processing efficiency and reliability are improved. And analyzing the order creation data from the order queue data to extract hotel reservation standard data and room reservation type number standard data. The extraction process can ensure the accuracy and consistency of the subscription information and avoid subscription errors or confusion caused by data parsing errors. And the aggregation calculation calculates booking hotel data and booking room type number data by utilizing the pre-stored local supplier hotel data and the supplier room type data so as to acquire supplier hotel and room type aggregation list data. This comprehensive calculation takes into account the data and factors of multiple suppliers, providing the user with a more comprehensive and diverse choice. And carrying out real-time price checking and price filtering sorting according to the hotel and house type list data of the aggregated suppliers, and obtaining price sorting data of the suppliers. The process combines the real-time data query and price filtering strategies, ensures that the user obtains the latest price information, and sorts and screens according to the requirements and preferences of the user. And carrying out intelligent ordering processing according to the price ordering data of the suppliers to obtain intelligent order processing data. This step combines the user requirements, vendor data and price ordering results to intelligently process the order, providing an optimal order decision that meets the user requirements and preferences. The online hotel booking service is more perfect and intelligent, and the user experience, booking success rate and processing capacity of the system are enhanced.

Drawings

Other features, objects and advantages of the application will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

figure 1 illustrates a flow chart of steps of an online hotel reservation intelligent processing method of an embodiment;

FIG. 2 shows a step flow diagram of step S1 of an embodiment;

FIG. 3 shows a step flow diagram of step S2 of an embodiment;

FIG. 4 shows a step flow diagram of step S24 of an embodiment;

FIG. 5 shows a step flow diagram of step S3 of an embodiment;

FIG. 6 shows a step flow diagram of step S4 of an embodiment;

fig. 7 shows a step flow diagram of step S5 of an embodiment.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to fall within the scope of the present application.

Furthermore, the drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

Referring to fig. 1 to 7, the application provides an intelligent processing method for online hotel reservation, which comprises the following steps:

specifically, order form data containing a user name, a date of check-in, and a house type requirement, such as { "name": "Zhang Sanj", "date of check-in": "2023-07-15", "house type requirement": "two-person house" }, is received, for example, through an API interface.

Specifically, for example, order creation data is sent to preset order queue data through an API interface in a JSON format, so that data transmission safety and integrity are ensured.

specifically, for example, order creation data with a user name of "Zhang San", and a date of entry of "2023-07-15" is parsed from the order queue data.

Specifically, for example, the order queue data is analyzed, and hotel reservation standard data such as hotel name, address and star level information and hotel reservation type number standard data such as room type name, description and price information are obtained.

specifically, for example, the booking hotel data and the supplier hotel data are matched and compared by using the pre-stored local supplier hotel data, so that the aggregation list data of the supplier hotel and the house type is obtained.

Specifically, for example, based on the vendor room type data, the reservation room type number data is matched and filtered to obtain the eligible vendor room type list data.

specifically, for example, according to the hotel and house type list data of the aggregated suppliers, the current price information of each supplier is obtained by calling the supplier API to perform real-time price checking.

Specifically, price filtering and sorting are performed on the aggregated provider hotel and room type list data according to the reservation hotel standard data and reservation room type number standard data, for example, to obtain provider price sorting data.

Specifically, for example, according to the vendor price ranking data, an intelligent algorithm and a strategy are applied to make decisions, and an optimal vendor and house type combination is selected to generate intelligent ordering data.

Specifically, optimal order intelligent processing data including reservation information, price, check-in date are generated, for example, based on user demand and provider price ordering data, in combination with offers and inventory conditions.

Preferably, step S1 is specifically:

step S11: acquiring order creation request data from a client;

specifically, an HTTP request containing order creation request data is received, for example, from a client.

Specifically, order creation request data is obtained from a client, for example, through a network communication protocol.

specifically, for example, an order creation API interface provided by the cloud computing platform is called, and order creation data is transferred to the interface for processing.

Specifically, for example, using the SDK provided by the cloud computing platform, a corresponding function is called to receive order creation data.

specifically, JSON parsing is performed on order creation data, for example, verification data of order information, user information, and payment information is extracted.

Specifically, for example, the integrity, format correctness and legality of the order form data are verified, such as checking the necessary filling field, the data type and the length.

Specifically, order form verification data is inserted into a preset message queue, for example, for subsequent processing.

Specifically, the order creation verification data is stored in a database or cache as part of the order queue data, for example.

Preferably, step S2 is specifically:

step S21: reading the order queue data, thereby obtaining order data;

specifically, for example, order queue data is loaded locally or a connection is established with a database storing order queue data, so that data inquiry is performed to obtain order data.

specifically, for example, order data in different formats, such as text, markup language (such as XLM, JSON), binary data, are subjected to data conversion according to a preset format (such as a table, an object sequence, or other specific data structures or formats), so as to obtain order analysis data.

specifically, booking hotel data, including hotel names, addresses, stars, are extracted from the order data, for example.

Specifically, for example, order data is parsed, and reservation type number data including type name, description, price is extracted.

Specifically, the check-in hotel data is verified, including the validity of hotel names and the validity of addresses.

Specifically, for example, verifying the validity of the pre-booking house number data ensures that the house number is present and valid.

specifically, for example, data association is performed according to hotel reservation verification data and room reservation type number verification data, and corresponding hotels and room types are associated.

Specifically, the hotel reservation data and the room reservation type number data are associated through a data matching algorithm, for example, so that the corresponding hotel and room type are matched correctly.

Specifically, for example, standardized processing is performed on the hotel reservation room type number association data, and room type names and price units are unified to obtain hotel reservation standard data.

Specifically, for example, the house type number associated data of the reserved hotel is subjected to standardization processing, so that the information such as house type names, prices, descriptions and the like are ensured to meet the unified standard, and standard data of the reserved hotel is generated.

Preferably, step S24 is specifically:

specifically, for example, user basic data including information of last name "Zhang san", identification card number "123456789", mobile phone number "22222222222" and history data is acquired.

Specifically, for example, user basic data including information of a name of "Lifour", an identification card number of "9876543210", a mobile phone number of "11111111111" and history data is obtained from a user system.

specifically, for example, user credit scoring is performed according to user basic data, and credit scoring data is obtained as 80.

Specifically, the credit score of the user is calculated, for example, by a credit score model, resulting in credit score data of 75.

Specifically, for example, data format verification is performed on hotel booking data, so that fields such as hotel names, addresses, star levels and the like are ensured to meet predetermined format requirements.

Specifically, for example, the format of the pre-booking house number data is verified, ensuring that the house number is a valid string and that a specific naming rule is satisfied.

specifically, for example, data integrity verification is performed on reservation hotel format verification data, ensuring that the fields of the hotel name, address are both present and non-empty.

Specifically, for example, verifying the integrity of the pre-order house number format verifies that the fields of the house number, house name, etc. are valid.

specifically, for example, data uniqueness verification is performed on the reservation hotel integrity verification data, ensuring that the hotel name is unique in the dataset.

In particular, for example, verifying the uniqueness of the pre-ordered house number integrity verification data ensures that the house number is unique in the dataset.

specifically, hotel data with a credit score greater than 70 is selected, for example, by screening the reservation hotel unique verification data according to the user credit score data.

Specifically, the reservation house type number uniqueness verification data is screened, for example, with user credit score data, and house type number data with a credit score greater than 80 is selected.

Specifically, for example, the value rationality verification is performed on the hotel reservation screening data and the hotel reservation type number screening data, so that the price is ensured to be reasonable within a certain range.

Specifically, for example, the rationality of the values of the hotel reservation screening data and the room reservation number screening data is verified, and the values of the price and the room number are ensured to be in accordance with a preset reasonable range.

Preferably, step S3 is specifically:

specifically, hotel data matching is performed, for example, by using pre-stored supplier hotel data and reservation hotel data, and the matched hotel data is obtained therefrom.

Specifically, the conforming hotel data is matched and obtained, for example, by comparing pre-stored vendor hotel data with specific fields of reservation hotel data.

specifically, the reservation house type number data is matched, for example, by using pre-stored vendor house type data, and the matched house type data is obtained.

Specifically, for example, matching is performed based on the pre-reservation house type number data and pre-stored vendor house type data, and the house type data that meets the conditions is extracted.

specifically, for example, the matched hotel data and room type data are aggregated to generate provider hotel and room type aggregated data.

Specifically, for example, hotel matching data and room type matching data are combined to generate aggregated result data of a provider hotel and a room type.

specifically, for example, the data of the hotel and the house type aggregation of the suppliers are screened according to the data of the user demands in the order form data, and the data meeting the user demands is selected.

Specifically, for example, user demand information in an order is utilized to screen the hotel and house type aggregation data of the suppliers, and data meeting the user demand is screened out.

specifically, for example, evaluation price calculation is performed on the provider hotel and house type screening data, and the evaluation price of each provider is calculated from the price and the scoring index.

Specifically, for example, the number of available rooms in the provider hotel and room type screening data is calculated, and the number of available rooms per provider is counted and calculated.

Specifically, for example, the provider hotel and house type screening data and the provider hotel and house type index data are combined to generate aggregate list data of the provider hotel and house type.

Specifically, for example, the supplier hotel and house type screening data and index data are integrated to form an aggregate list of the supplier hotel and house type, and the aggregate list is provided for subsequent processing and analysis.

specifically, real-time price data of suppliers A, B and C are obtained by performing real-time price query according to aggregated supplier hotel and room type list data, for example, through a cloud platform interface.

Specifically, real-time price information of suppliers X, Y and Z is queried according to aggregated supplier hotel and room type listing data, for example, using a real-time price querying function provided by a cloud platform.

specifically, for example, according to the price requirement in the order form data, price filtering is performed on the real-time price data of the suppliers, and the price data of the suppliers meeting the price requirement is screened out.

Specifically, the provider real-time price data is filtered, for example, according to price limits specified in the order, and provider price data meeting price requirements is selected.

specifically, for example, the provider price filter data is price-sorted, and the price data of the providers A, B and C are arranged in order from low to high.

Specifically, the prices of suppliers X, Y and Z are ranked, for example, according to the supplier price filter data, ranking suppliers from lowest price to highest price.

Specifically, for example, historical price data of suppliers A, B and C are acquired, and price preference ranking calculation is performed on the supplier price filter data using the historical price data, generating second supplier price ranking data.

Specifically, for example, according to the historical price data of the suppliers X, Y and Z, price preference ranking calculation is performed on the supplier price filtering data, so as to obtain second supplier price ranking data.

Preferably, step S5 is specifically:

specifically, for example, according to the supplier qualification information in the supplier price sorting data, suppliers meeting the requirements are screened out, and the supplier price screening data is generated.

Specifically, for example, according to the supplier qualification evaluation index in the supplier price sorting data, the suppliers are subjected to qualification screening, so as to obtain supplier price screening data.

specifically, for example, real-time inventory inquiry is performed through a system interface, and inventory condition data of suppliers A, B and C are acquired.

Specifically, for example, with a real-time inventory query function provided by the system, real-time inventory data is acquired according to the supply conditions of suppliers X, Y and Z.

specifically, for example, according to real-time inventory data and market demand conditions, intelligent pricing calculation is performed to obtain pricing data of the real-time inventory.

Specifically, intelligent pricing data for suppliers A, B and C is calculated based on real-time inventory and market conditions, for example, using intelligent pricing algorithms in the system.

specifically, for example, according to the demand information in the order creation data, the real-time inventory pricing data is subjected to order placement decision making, and order placement decision making data is generated.

Specifically, for example, according to the requirements in the order and the real-time inventory pricing data, the order decision is made, and the order decision data is obtained.

Specifically, for example, an order is generated according to the order decision data, and the order state is updated in real time through the system, so that order intelligent processing data are obtained.

Specifically, for example, an order is generated according to the order decision data, and the state of the order is updated in real time through the system, so that the intelligent order processing data is obtained.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The intelligent processing method for online hotel reservation is characterized by comprising the following steps:

2. The method according to claim 1, wherein step S1 is specifically:

step S11: acquiring order creation request data from a client;

3. The method according to claim 2, wherein the data insertion process is performed using an order form insertion calculation formula in step S14, wherein the order form insertion calculation formula is specifically:

4. The method according to claim 1, wherein step S2 is specifically:

step S21: reading the order queue data, thereby obtaining order data;

5. The method according to claim 4, wherein step S24 is specifically:

6. The method according to claim 5, wherein the user credit score in step S242 is processed by a user credit score calculation formula, wherein the user credit score calculation formula is specifically:

7. The method according to claim 1, wherein step S3 is specifically:

8. The method according to claim 1, wherein the vendor price ordering data comprises first vendor price ordering data and second vendor price ordering data, step S4 being specifically:

9. The method according to claim 1, wherein step S5 is specifically:

10. An intelligent processing system for online hotel reservations, comprising: