Product data integration method and system based on business travel platform

Abstract

The invention provides a product data integration method and system based on a business travel platform, and relates to the technical field of data processing. The method comprises the steps of obtaining product data of a business travel platform; making a mapping rule; performing data conversion on the product data according to the mapping rule, and determining conversion data; performing data merging on the conversion data, and determining a merged data set; performing data integration on the merged data set through a Bayesian model, and outputting integrated data; carrying out integrity verification on the integrated data; judging whether the integrated data is complete or not according to an integrity verification result; and if yes, outputting the integrated data, otherwise, re-acquiring the product data of the business travel platform. According to the invention, data from different business travel platforms can be rapidly integrated, and the integrity of the data is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a product data integration method and system based on a business travel platform. Background Art

[0002] The product data integration method based on a business travel platform refers to the unified processing and fusion of product data from different sources, different formats, and different dimensions through technical means, so as to provide accurate product information for users. In a business travel platform, product data usually includes various information such as hotels, air tickets, and travel routes. These data may come from different suppliers, data formats, and source systems. The integration method enables seamless connection and collaborative work between data, thereby improving the accuracy and availability of platform data.

[0003] In recent years, with the continuous expansion of enterprise business, business travel activities have become increasingly frequent, and the proportion of business travel costs in enterprise operating costs has gradually increased. Enterprises urgently need an effective management tool to finely control business travel expenses, such as standardizing employees' booking behaviors, restricting expense standards (such as air ticket class, hotel accommodation price, etc.), and real-time monitoring of expense expenditure situations. A business travel platform can help enterprises achieve reasonable cost control by setting corresponding rules and providing data analysis functions.

[0004] However, in the process of data integration, the existing data integration methods cannot effectively ensure the accuracy, integrity, and consistency of data, which may affect the final integration effect. They usually rely on simple matching or calculation rules and lack a flexible adaptive mechanism to cope with the diversity of data sources. When facing diverse data sets, they cannot accurately complete the integration task, resulting in too low data integration efficiency. Summary of the Invention

[0005] In order to solve the technical problems that in the process of data integration, the existing data integration methods cannot effectively ensure the accuracy, integrity, and consistency of data, which may affect the final integration effect, usually rely on simple matching or calculation rules, lack a flexible adaptive mechanism to cope with the diversity of data sources, and cannot accurately complete the integration task when facing diverse data sets, resulting in too low data integration efficiency, the present invention provides a product data integration method and system based on a business travel platform.

[0006] The technical solutions provided by the embodiments of the present invention are as follows:

[0007] First aspect:

[0008] A product data integration method based on a business travel platform provided by an embodiment of the present invention includes:

[0009] S1: Obtain the product data of the business travel platform;

[0010] S2: Formulate mapping rules;

[0011] S3: According to the mapping rules, perform data conversion on the product data to determine the converted data;

[0012] S4: Perform data merging on the converted data to determine the merged data set;

[0013] S5: Through the Bayesian model, perform data integration on the merged data set and output the integrated data;

[0014] S6: Perform integrity verification on the integrated data;

[0015] S7: According to the integrity verification result, determine whether the integrated data is complete; if so, proceed to step S8, otherwise, return to step S1;

[0016] S8: Output the integrated data.

[0017] Second aspect:

[0018] A product data integration system based on a business travel platform provided by an embodiment of the present invention includes:

[0019] A processor;

[0020] A memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the product data integration method based on the business travel platform described in the first aspect is implemented.

[0021] Third aspect:

[0022] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored, and when the program is executed by a processor, the product data integration method based on the business travel platform described in the first aspect is implemented.

[0023] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include:

[0024] In the embodiment of the present invention, through precise rule mapping and data conversion, it can be ensured that business travel product data from different sources can be in a unified format, eliminating the problem of data inconsistency. The standardized conversion of mapping rules allows the data to be effectively merged and avoids errors caused by format differences. The data sets from different business travel platforms are quickly merged, which can reduce the time for manual processing and merging of data, thereby improving the efficiency and accuracy of data processing. By introducing the Bayesian model, the potential characteristics of the merged data set can be inferred based on historical data and prior knowledge, and the selection of data attributes can be automatically optimized, thereby improving the quality of data integration. Through bilinear mapping technology, automatic verification of data integrity can be achieved, ensuring the security and consistency of data in the entire data integration process, and reducing the risk of data tampering or loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 A flowchart of a product data integration method based on a business travel platform provided by an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of the structure of a product data integration system based on a business travel platform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0030] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0031] In the embodiments of the present invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0032] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0033] Referring to the attached Figure 1 description, a schematic flowchart of a product data integration method based on a business travel platform provided by an embodiment of the present invention is shown.

[0034] The embodiments of the present invention provide a product data integration method based on a business travel platform. This method can be implemented by a product data integration device based on a business travel platform, and the product data integration device based on a business travel platform can be a terminal or a server. The processing flow of the product data integration method based on a business travel platform may include the following steps:

[0035] S1: Obtain product data of the business travel platform.

[0036] Among them, product data usually includes detailed information, price, user evaluation, and inventory of the product.

[0037] It should be noted that by obtaining product data of the business travel platform, the diversity and comprehensiveness of data sources can be ensured, a more accurate and effective product data model can be established, and with rich multi-channel data, the comparison and integration of the same product on different platforms can be realized, improving the accuracy and timeliness of product information, and further supporting the subsequent data analysis and decision-making process.

[0038] In a possible implementation manner, the business travel platform specifically includes: an air ticket reservation platform, a hotel reservation platform, a train ticket reservation platform, a car rental service platform, and a business travel expense management platform.

[0039] S2: Develop mapping rules.

[0040] Among them, the mapping rule refers to the conversion rule defined in the data integration and conversion process to unify the standards and formats of data in different data sources, and is used to convert data from different sources or systems into the same data structure or format, so as to facilitate subsequent processing, analysis, and use.

[0041] It should be noted that by developing mapping rules, the data formats of different platforms can be unified, the problem of inconsistent data formats between platforms can be solved, the integrated data has higher comparability and consistency, data chaos or omission can be avoided, and the data processing efficiency can be improved.

[0042] In a possible implementation, the mapping rules specifically include: price mapping rules, scoring mapping rules, and date format mapping rules.

[0043] The price mapping rule is specifically as follows:

[0044] P USD = P local × ExchangeRate

[0045] Wherein, P USD represents the price data after mapping, with the unit of USD, and P local represents the original price data in the product data, and ExchangeRate represents the exchange rate.

[0046] The scoring mapping rule is specifically as follows:

[0047]

[0048] Wherein, R final represents the scoring data after mapping, and R raw represents the original scoring data in the product data.

[0049] The date format mapping rule is specifically as follows:

[0050] date formatted = convert date(raw date)

[0051] Wherein, raw date represents the original date data in the product data, date formatted represents the date data after mapping, with the format of YYYY - MM - DD, and convert date() represents the function for converting dates.

[0052] Among them, the price mapping rule refers to the rule of converting the original price (such as Plocal) in the product data into a unified currency unit (such as USD). This rule uses the exchange rate (ExchangeRate) for conversion to ensure the consistency and comparability of data across multiple platforms. The scoring mapping rule refers to the rule of converting different scoring criteria adopted on different platforms into a unified scoring standard. The date format mapping rule refers to the rule of converting the date formats in different data sources into a unified format, which can ensure the consistency and correctness of date data across different platforms and systems.

[0053] Specifically, by formulating price mapping rules, scoring mapping rules, and date format mapping rules, it is possible to effectively unify data from different business travel platforms and eliminate errors or ambiguities caused by format differences and unit inconsistencies.

[0054] S3: According to the mapping rules, perform data conversion on the product data to determine the converted data.

[0055] Among them, the conversion data is specifically:

[0056] X final = {ProductID, P USD , R final , date formatted}

[0057] Among them, X final represents the conversion data, and ProductID represents the product ID.

[0058] It should be noted that through the mapping rules, it is possible to ensure the consistency of product data from different sources in terms of format, unit, and standard, enabling the smooth integration and analysis of multi-source data. The standardization of data not only improves the subsequent processing efficiency but also reduces errors and biases caused by data inconsistencies, thereby enhancing the quality and reliability of the integrated data.

[0059] S4: Perform data merging on the conversion data to determine the merged data set.

[0060] Specifically, by effectively merging the converted data sources, the product information from different platforms or data sources is unified in a single data set, avoiding data dispersion and duplication.

[0061] In a possible implementation, S4 is specifically:

[0062] Perform data merging on the conversion data through the merge() function in the pandas library to determine the merged data set.

[0063] Among them, Pandas is a powerful open-source data analysis tool library widely used in fields such as data science, data analysis, and machine learning. merge() is a very important function in the pandas library used to merge two data sets based on one or more common columns (or indexes). The merge() function can perform different types of join operations, such as inner join, outer join, left join, right join, etc.

[0064] Specifically, through the merge() function for data merging, it is possible to efficiently process large-scale data, quickly connect the content of different data sources according to the specified fields. The merge() function is flexible and can handle different types of data merging according to different matching methods (such as inner join, outer join, etc.) to ensure the accuracy of the merged result. The pandas library provides powerful data manipulation capabilities, enabling the merged data to be conveniently further cleaned and analyzed, greatly improving the data integration efficiency and accuracy.

[0065] S5: Integrate the merged dataset through a Bayesian model and output the integrated data.

[0066] Among them, the Bayesian model is a class of statistical models based on Bayes' theorem, used to infer uncertain parameters or data, especially suitable for dealing with data with uncertainty. The core idea of the Bayesian model is to calculate the most likely result (posterior probability) by combining prior knowledge and observed data.

[0067] It should be noted that using the Bayesian model for data integration can comprehensively utilize the information of product data, effectively improving the accuracy and consistency of the data. The Bayesian model can provide an optimal estimate in the presence of uncertainty and noise, and at the same time infer the data through the posterior distribution, ensuring the efficiency and scientific nature of the data integration process. When dealing with complex data, it can effectively fuse information and avoid possible errors or biases.

[0068] In a possible implementation, S5 specifically includes:

[0069] S501: Introduce an energy function and set the prior distribution of the Bayesian model.

[0070] Among them, the energy function is a function used to measure the state of the system, usually used in optimization problems. In the Bayesian model, the energy function is related to the probability distribution of the parameters, usually used to describe the fitting degree of the parameters to the data. The minimization of the negative energy function usually corresponds to the maximization of the posterior distribution. The prior distribution is a preliminary assumption about the model parameters in the absence of observed data, reflecting our belief in the parameters before starting data processing, usually based on domain knowledge or prior experience.

[0071] It should be noted that introducing the energy function and setting the prior distribution of the Bayesian model can effectively guide the data integration process. Combining historical data or domain knowledge can provide a more reasonable initial assumption for the data integration process. At the same time, the energy function can help measure the fit between the data and the model parameters, ensuring that accurate parameter estimates can still be provided in a data environment with high uncertainty, improving the accuracy and reliability of data integration.

[0072] The prior distribution is specifically:

[0073] P(θ) ∝ exp(-E(θ))

[0074]

[0075] Among them, P(θ) represents the prior distribution of the regression parameter θ, ∝ represents the proportional symbol, exp represents the exponential function, and E(θ) represents the energy function of the regression parameter θ, Y pdenotes the actual observation value of the \(p\) -th combined data in the combined dataset \(K\), \(K\) p denotes the \(p\) -th combined data in the combined dataset \(K\), \(p = 1,2,\cdots,S\), \(S\) represents the total number of combined data, \(Q()\) represents the kernel function, \(\lambda_1\) represents the hyperparameter of the \(L_1\) regularization term, \(\lambda_2\) represents the hyperparameter of the \(L_2\) regularization term, \(\|\cdot\|_1\) represents the \(L_1\) norm represents the \(L_2\) norm, \(K\) q denotes the \(q\) -th combined data in the combined dataset \(K\), \(l\) represents the length scale of the kernel function, \(\|\cdot\|\) represents the norm

[0076] S502: Define the likelihood function of the Bayesian model:

[0077] The likelihood function is specifically:

[0078] \(P(K|\theta)=N(K;\mu,\sigma)\)

[0079] where \(P(K|\theta)\) represents the likelihood function of the combined dataset \(K\) given the regression parameter \(\theta\), \(\theta\) represents the regression parameter, \(N()\) represents the normal distribution, \(\mu\) represents the mean of the combined dataset, and \(\sigma\) represents the standard deviation of the combined dataset

[0080] Specifically, by defining the likelihood function, the matching degree between the model and the observed data under given parameters can be effectively measured, which helps to estimate the optimal parameters and makes the model more suitable for the actual data. Using the normal distribution as the likelihood function can capture the distribution characteristics of the data and ensure reasonable modeling of the data

[0081] S503: Calculate the posterior distribution of the Bayesian model according to the likelihood function and the prior distribution

[0082] The posterior distribution is specifically:

[0083]

[0084] where \(P(\theta|K)\) represents the posterior distribution of the regression parameter \(\theta\) given the combined dataset \(K\), and \(Z(K)\) represents the partition function given the combined dataset \(K\), which is used to normalize the posterior distribution

[0085] Specifically, the posterior distribution combines the prior information and the observation results of the data, can express the distribution of the model parameters more comprehensively, is more accurate than only using the prior or the likelihood. In the Bayesian model, the posterior distribution can be regarded as the "updated" result of the parameters, making the original belief (prior) adjusted based on the newly observed data

[0086] The partition function is specifically:

[0087] \(Z(K)=\int\exp(-E(\theta))P(K|\theta)d\theta\)

[0088] The partition function Z(K) is a constant term in Bayesian inference that ensures the integral of the posterior distribution is 1 and is calculated by integrating over all possible θ, where d represents the differential operator.

[0089] S504: Determine the data attributes of the merged dataset by maximizing the posterior distribution.

[0090] It should be noted that determining data attributes by maximizing the posterior distribution can effectively identify the most representative and influential features from a large amount of data, reduce redundant data, avoid the impact of noise on the analysis results, improve the accuracy and quality of data integration, and ensure more reliable subsequent decision-making and analysis.

[0091] In the present invention, to simplify the calculation, usually after taking the logarithm of the posterior distribution, maximization is performed. Since the partition function is a constant and can be ignored when maximizing the posterior distribution, the objective function can be set as:

[0092] L(θ) = logP(K|θ) - E(θ)

[0093] where L() represents the objective function.

[0094] After setting the objective function, the gradient descent method is used to maximize the posterior distribution. By calculating the gradient of the objective function and updating the parameter θ according to this gradient. Through continuous iterative optimization, the maximum value of the posterior distribution is finally obtained.

[0095] During the process of data integration, data attributes refer to the key features or variables in the merged dataset, and these features are the basic elements describing the data structure and behavior. For example, in the product data of a business travel platform, common data attributes include price, rating, release date, supplier, user feedback, etc. These attributes play a core role in the process of data integration and analysis and can extract important information about the data.

[0096] S505: Perform data integration on the merged dataset according to the data attributes to determine the integrated data.

[0097] It should be noted that performing data integration according to the data attributes can improve the accuracy and consistency of the data. The integrated dataset avoids duplication and redundancy, and at the same time removes irrelevant or invalid information, enhancing the usability of the data.

[0098] In the present invention, by establishing a Bayesian inference framework, the merged data is integrated using the prior distribution, likelihood function, and posterior distribution. First, the prior distribution of the merged dataset is defined, that is, based on historical data or domain knowledge, preliminary assumptions are made about data attributes (such as price, rating, etc.). By introducing an energy function to control the model complexity and avoid overfitting, and then through the likelihood function, the data is related to the model parameters to form the posterior distribution, that is, the belief about the data attributes is updated based on all the observed data. By maximizing the posterior distribution, the Bayesian model can efficiently integrate information from different data sources, while considering the reliability and uncertainty of each data source, ensuring the accuracy and consistency of the integrated data, and thus obtaining a more accurate data integration result.

[0099] S6: Verify the integrity of the integrated data.

[0100] It should be noted that integrity verification can ensure that the integrated data has not been damaged or tampered with during transmission, storage, and processing. By verifying the data, potential errors or malicious operations can be detected and excluded in a timely manner, thereby improving the accuracy and reliability of the data.

[0101] In a possible implementation manner, S6 specifically includes:

[0102] S601: Calculate the public key of the integrated data:

[0103] Z = αP

[0104] Where Z represents the public key, P represents the generator of the cyclic additive group G1, and α represents the private key.

[0105] S602: Generate a pseudo-random number corresponding to the integrated data.

[0106] Among them, the pseudo-random number is a sequence of numbers generated by an algorithm. It seems to have randomness, but in fact, it can be reproduced through a specific algorithm. Although the pseudo-random number has random properties, its generation process is deterministic, so it can be precisely controlled in a computer.

[0107] It should be noted that generating a pseudo-random number can introduce unpredictability in the data integrity verification and security processing, while maintaining the reproducibility of the calculation. The pseudo-random number can ensure consistency and security in operations such as data encryption and signature verification. Using the pseudo-random number not only improves the efficiency of data processing but also ensures high reliability of the generated results during the verification process.

[0108] S603: Calculate the proof value of the integrated data according to the pseudo-random number, public key, and hash signature:

[0109]

[0110] Among them, R, μ′, and η all represent proof values, and v i represents the pseudo-random number corresponding to the i-th segment data in the integrated data, where i = 1, 2, …, c, and c represents the total number of data segments, H(M i ) represents the hash value of the i-th segment data in the integrated data M, and δ i represents the hash tag of the i-th segment data in the integrated data M.

[0111] Among them, the hash signature processes the data through a hash algorithm to generate a unique identifier (hash value) of a fixed length, and encrypts it with a private key to ensure the integrity and authenticity of the data source. The proof value is a set of calculated values used to verify the integrity of the data after integration. It usually includes multiple parts and ensures that the data has not been tampered with through encryption and mathematical verification.

[0112] It should be noted that by combining the pseudo-random number, public key, and hash signature to calculate the proof value of the integrated data, it can ensure that the data has not been tampered with during transmission and storage, and uses encryption technology to ensure the integrity, authenticity, and consistency of the data. By verifying the proof value, the platform can effectively prevent data tampering and improve the security and trust of the data.

[0113] S604: Perform integrity verification on the integrated data according to the proof value through bilinear mapping.

[0114] Among them, bilinear mapping: is a mathematical operation, a mapping defined between two groups, with distributive and linear properties, and is commonly used in encryption and data verification, such as public key verification and data integrity check in cryptography.

[0115] Specifically, by using bilinear mapping for integrity verification, it can efficiently and securely confirm whether the data has been tampered with during the integration process or transmission process. Bilinear mapping has mathematical verifiability and non-forgeability, which can ensure the security and efficiency of the verification process, enhance the reliability of data verification, especially when dealing with sensitive data, it can effectively prevent data tampering and ensure the data accuracy and credibility of the platform.

[0116] In a possible implementation manner, the generation method of the hash signature specifically includes:

[0117] Establish a bilinear mapping:

[0118] e: G1×G1→G2

[0119] Among them, e represents the bilinear mapping, G1 represents a cyclic additive group, and G2 represents a cyclic multiplicative group.

[0120] Among them, G1 is usually a cyclic additive group, usually composed of structures such as elliptic curves. In G1, elements can be obtained through addition operations. G2 is usually a cyclic multiplicative group, and the elements of G2 are combined through multiplication operations.

[0121] Determine the private key for the integrated data.

[0122] In the present invention, the private key is a random integer.

[0123] Perform a hash calculation on the integrated data to determine the hash value.

[0124] In the present invention, performing a hash calculation on the integrated data means using a hash algorithm (SHA-256) to process the data and convert it into a hash value of a fixed length. The hash value is the unique identifier of the data content. Any minor change to the data will cause a change in the hash value. Hash calculation can efficiently generate the unique identifier of the data, which is used to verify whether the data has been tampered with. By comparing the hash values, the integrity of the data can be quickly detected to ensure that the data has not changed during storage and transmission. The irreversibility of the hash value guarantees data security. Even if the data content changes slightly, the hash value will change significantly, thus providing an efficient integrity verification mechanism.

[0125] Using the bilinear mapping, generate the hash signature of the integrated data according to the private key and the hash value:

[0126]

[0127] where δ i represents the hash tag of the i-th fragment data in the integrated data M, and H(M i ) represents the hash value of the i-th fragment data in the integrated data M.

[0128] In a possible implementation manner, S604 is specifically:

[0129] According to the following formula, perform integrity verification on the integrated data:

[0130] e(η, P)·e(μ′ + R, P) = e(P, P)

[0131] where e represents the bilinear mapping, R, μ′, and η all represent proof values, and P represents the generator of the cyclic additive group G1.

[0132] In the present invention, when the above formula holds, it proves that the integrated data is complete. When the above formula is incomplete, it proves that the integrated data is incomplete.

[0133] It should be noted that by using bilinear mapping to verify the integrity of the integrated data, it is possible to efficiently ensure that the data has not been tampered with. The mathematical properties of bilinear mapping guarantee the accuracy and security of the verification process. Among them, the proof value serves as the encrypted verification value of the data, and through comparison, the integrity of the data can be quickly detected. Using bilinear mapping not only improves the efficiency of data verification but also strengthens the non-forgeability of the data, ensuring the security and reliability of the platform when processing sensitive data.

[0134] S7: According to the integrity verification result, determine whether the integrated data is complete. If so, proceed to step S8; otherwise, return to step S1.

[0135] It should be noted that judging whether the data is complete according to the integrity verification result can effectively ensure that the integrated data has not been tampered with or damaged. If the verification fails, the system will return to step S1 to re-obtain the data and process it, which can avoid the further use of incorrect data, ensure the accuracy and reliability of the entire data process, and prevent analysis deviations or decision-making mistakes caused by incomplete or incorrect data.

[0136] S8: Output the integrated data.

[0137] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0138] In the embodiments of the present invention, through precise rule mapping and data conversion, it is possible to ensure that the business travel product data from different sources can be in a unified format, eliminating the problem of data inconsistency. The standardized conversion of the mapping rules enables the effective merging of data and avoids errors caused by format differences. Quickly merging the data sets from different business travel platforms can reduce the time for manual processing and merging of data, thereby improving the efficiency and accuracy of data processing. By introducing the Bayesian model, it is possible to infer the potential features of the merged data set based on historical data and prior knowledge, and automatically optimize the selection of data attributes, thereby improving the quality of data integration. Through bilinear mapping technology, it is possible to realize the automatic verification of data integrity, ensure the security and consistency of the data during the entire data integration process, and reduce the risk of data tampering or loss.

[0139] Refer to the attached Figure 2 illustrates the structural schematic diagram of a product data integration system based on a business travel platform provided by the present invention.

[0140] The present invention also provides a product data integration system 20 based on a business travel platform, which is applied to the above-mentioned product data integration method based on a business travel platform, and includes:

[0141] A processor 201.

[0142] Memory 202 stores computer-readable instructions thereon. When the computer-readable instructions are executed by the processor 201, a product data integration method based on a business travel platform as in the method embodiment is implemented.

[0143] The product data integration system 20 based on a business travel platform provided by the present invention can execute the above-mentioned product data integration method based on a business travel platform and achieve the same or similar technical effects. To avoid repetition, the present invention will not elaborate further.

[0144] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0145] In the embodiment of the present invention, through precise rule mapping and data conversion, it can be ensured that business travel product data from different sources can have a unified format, eliminating the problem of data inconsistency. The standardized conversion of the mapping rules enables the data to be effectively merged and avoids errors caused by format differences. Quickly merging data sets from different business travel platforms can reduce the time for manual processing and merging data, thereby improving the efficiency and accuracy of data processing. By introducing a Bayesian model, it is possible to infer the potential features of the merged data set based on historical data and prior knowledge, automatically optimizing the selection of data attributes, thereby improving the quality of data integration. Through bilinear mapping technology, it is possible to achieve automatic verification of data integrity, ensuring the security and consistency of data throughout the data integration process, and reducing the risk of data tampering or loss.

[0146] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0147] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0148] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0149] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0150] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or plural.

[0151] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0152] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0153] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated herein.

[0154] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0155] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, the functional units in various embodiments of the present invention can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0157] When the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0158] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the product data integration method based on a business travel platform as described in the method embodiment.

[0159] The computer-readable storage medium provided by the present invention can implement the steps and effects of the product data integration method based on the business travel platform in the above method embodiment. To avoid repetition, the present invention will not elaborate further.

[0160] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include:

[0161] In the embodiments of the present invention, through precise rule mapping and data conversion, it can be ensured that business travel product data from different sources can have a unified format, eliminating the problem of data inconsistency. The standardized conversion of the mapping rules enables the effective merging of data and avoids errors caused by format differences. Quickly merging data sets from different business travel platforms can reduce the time for manual data processing and merging, thereby improving the efficiency and accuracy of data processing. By introducing the Bayesian model, it is possible to infer the potential features of the merged data set based on historical data and prior knowledge, and automatically optimize the selection of data attributes, thereby improving the quality of data integration. Through bilinear mapping technology, it is possible to automatically verify the integrity of the data, ensuring the security and consistency of the data throughout the data integration process, and reducing the risk of data tampering or loss.

[0162] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

[0163] The following points need to be explained:

[0164] (1) The accompanying drawings of the embodiments of the present invention only relate to the structures involved in the embodiments of the present invention, and other structures can refer to the general design.

[0165] (2) For clarity, in the accompanying drawings used to describe the embodiments of the present invention, the thickness of layers or regions is enlarged or reduced, that is, these drawings are not drawn to actual scale. It can be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element can be "directly" on or under the other element or there can be intervening elements.

[0166] (3) Without conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0167] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A product data integration method based on a business travel platform, characterized in that: include: S1: Obtain product data from the business travel platform; S2: Formulate mapping rules; S3: performing data conversion on the product data according to the mapping rule to determine conversion data; S4: merging the converted data to determine a merged data set; S5: performing data integration on the merged data set through a Bayesian model, and outputting integrated data; S6: Verify the integrity of the integrated data; S7: judging whether the integrated data is complete according to the integrity verification result; if so, proceeding to step S8; otherwise, returning to step S1; S8: Output the integrated data.

2. The product data integration method based on the business travel platform according to claim 1 is characterized in that: The business travel platform specifically includes: air ticket booking platform, hotel booking platform, train ticket booking platform, car rental service platform and travel expense management platform.

3. The product data integration method based on the business travel platform according to claim 1 is characterized in that: The mapping rules specifically include: price mapping rules, rating mapping rules and date format mapping rules.

4. The product data integration method based on the business travel platform according to claim 1 is characterized in that: The S4 is specifically: The transformed data is merged by using the merge() function in the pandas library to determine the merged data set.

5. The product data integration method based on the business travel platform according to claim 1, characterized in that: The S5 specifically includes: S501: Introducing an energy function and setting the prior distribution of the Bayesian model; S502: defining the likelihood function of the Bayesian model; S503: Calculating the posterior distribution of the Bayesian model according to the likelihood function and the prior distribution; S504: Determine the data attributes of the merged data set by maximizing the posterior distribution; S505: Perform data integration on the merged data set according to the data attributes, and output the integrated data.

6. The product data integration method based on the business travel platform according to claim 1 is characterized in that: The S6 specifically includes: S601: Calculate the public key of the integrated data; S602: Generate a pseudo-random number corresponding to the integrated data; S603: Calculate the proof value of the integrated data according to the pseudo-random number, the public key and the hash signature; S604: According to the proof value, the integrity of the integrated data is verified through bilinear mapping.

7. The product data integration method based on the business travel platform according to claim 6 is characterized in that: The generation method of the hash signature specifically includes: Create a bilinear map: e:G1×G1→G2; Among them, e represents a bilinear map, G1 represents a cyclic additive group, and G2 represents a cyclic multiplicative group; determining a private key for the integrated data; Performing hash calculation on the integrated data to determine a hash value; The bilinear mapping is used to generate a hash signature of the integrated data according to the private key and the hash value.

8. The product data integration method based on the business travel platform according to claim 6, characterized in that: The S604 is specifically as follows: The integrity of the integrated data is verified according to the following formula: e(η,P)·e(μ′+R,P)=e(P,P); Among them, e represents a bilinear map, R, μ′ and η all represent proof values, and P represents the generator of the cyclic additive group G1.

9. A product data integration system based on a business travel platform, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the product data integration method based on the business travel platform as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the product data integration method based on a business travel platform as described in any one of claims 1 to 8 is implemented.

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