A hotel data processing method and system

Abstract

The present invention discloses a hotel data processing method and system, the method includes: constructing a blockchain based on hotel information services, splitting the hotels distributed in the region into several sub-regions, and the hotels in each sub-region are grouped into a block of the blockchain; each block corresponds to a service node, and different storage nodes are set on the service node according to the access situation of the hotel data in the block, and each hotel corresponds to a storage node; creating a data transaction link to each service node of the blockchain for verification. The system includes a blockchain server, splitting the hotels distributed in the region into several sub-regions, and the hotels in each sub-region are grouped into a block of the blockchain; the block in the blockchain contains a service node, and each service node is provided with different storage nodes, and each hotel corresponds to a storage node. The present invention uses distributed storage and blockchain technology to realize intelligent management of hotel data and personalized customer services.

Description

Technical Field

[0001] The present invention relates to the field of hotel data information services, and in particular to a hotel data processing method and system. Background Art

[0002] With the development of artificial intelligence and information services, more and more hotels are introducing intelligent big data services into their daily operations. In the process of digitalization of the hotel industry, traditional data processing methods face many challenges. On the one hand, the amount of hotel data is huge and the sources are scattered, making it difficult to integrate and analyze it efficiently, resulting in the inability to accurately grasp customer needs and provide personalized services. On the other hand, data security issues are becoming increasingly prominent, and customers' privacy information is at risk of being leaked during storage and transmission, affecting the hotel's reputation and customer trust. Therefore, it is urgent to propose a new hotel data processing method and system. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a hotel data processing method and system, which manages hotel data information in blocks according to the hotel distribution in the region and the consumption characteristics of consumers, reduces the difficulty of data analysis and management, and ensures data security.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0005] A hotel data processing method is provided, comprising:

[0006] Step S1: Build a blockchain based on hotel information services. According to the area of ​​the hotel information service, the hotels distributed in the area are divided into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain;

[0007] Step S2: Each block corresponds to a service node. Different storage nodes are set on the service node according to the access conditions of the hotel data in the block. Each hotel corresponds to a storage node.

[0008] Step S3: When the hotel inputs data to the service node for storage or update, a data transaction link is created to each service node of the blockchain for verification. The service node checks the format, digital signature and access permission rules of the data. If the verification passes, the data is stored in the corresponding service node. If the verification fails, an error is reported to the input end of the hotel data, and the blockchain denies access.

[0009] Further, step S1 includes:

[0010] Step S11: Determine the area where hotel information services are needed, obtain the location information of each hotel in the area, divide the hotels distributed in the area into several sub-areas, and use the business district, station or school in the area as the center of the sub-area, and calculate the correlation coefficient of consumer choice between each hotel and the center of different sub-areas ;

[0011] ;

[0012] in, n is the hotel number in the area. m is the number of the sub-region, k For hotels n To sub-area m The types of transportation available to the center, K For hotels n To sub-area m The number of transport types available to the center, Adopting transportation types for consumers k The moving distance, To determine the moving distance threshold that affects consumer choice, For traffic type k The time spent To determine the time threshold that influences consumer choice, They are the influence weights of moving distance and time spent on the correlation coefficient;

[0013] Step S12: Setting the threshold of the correlation coefficient ;

[0014] like , then generate a hotel n With sub-area m The mapping between , , is the mapping function;

[0015] like , then the hotel n With sub-area m The mapping between them failed;

[0016] Step S13: Calculate hotel n The number of mappings generated with different sub-regions N ,quantity N The initial value of is 0:

[0017] ;

[0018] Step S14: If the quantity , then obtain the correlation coefficient corresponding to each mapping to form a correlation coefficient data set , u is the number of mappings, For the u The correlation coefficient corresponding to the mapping; filter out the correlation coefficient data group The maximum value in , retain the maximum value The corresponding mapping relationship, delete the mapping relationship corresponding to the remaining correlation coefficients;

[0019] If the quantity , the corresponding hotel will be used as a free hotel;

[0020] Step S15: Filter out the closest sub-regions based on the coordinates of the free hotels ;

[0021] ;

[0022] in, e is the ID of the free hotel. The map coordinates of the free hotel in the area of ​​the hotel information service. M is the number of sub-regions in the region, For sub-region The map plane coordinates of the center within the area of ​​the hotel information service;

[0023] Step S16: According to the hotel n With sub-area The mapping relationship between them is retained to obtain the sub-area The best-selling room types of each hotel are mapped out, and the feature tags of each best-selling room type are extracted, and the feature tags include style tags, price tags and promotion tags;

[0024] Step S17: Obtain the feature labels of free hotels and The feature labels of the best-selling room types of each hotel are mapped out, and the free hotels and sub-regions are calculated. The matching coefficient ;

[0025] Step S18: Setting the threshold of the matching coefficient ;

[0026] like , you will leave the hotel e Mapping to sub-regions Inside;

[0027] like , then filter out the sub-areas Zhongyouli Hotel eThe hotel with the highest matching coefficient ;

[0028] ;

[0029] in, For hotels Free hotels e The matching coefficient between For hotels No. d style tags, For hotels Price tag, For hotels During promotion period t Promotional labels;

[0030] Will Hotel Style tags, price tags and promotional tags used in the operation process are sent to free hotels e , for free hotels e Provide reference for adjusting operation strategy;

[0031] Step S19: All hotels mapped out in each sub-area are taken as a block, and a service node is constructed for each block to perform data storage and services for each block. The service node provides data analysis for the hotels in the block.

[0032] Furthermore, the matching coefficient The calculation method is:

[0033] ;

[0034] in, v For sub-region The hotel number mapped in V For sub-region The number of hotels mapped in d is the style tag number, D is the number of style tags, For hotels v No. d style tags, For free hotels e No. d style tags, is the semantic matching function of the style tag, For hotels v Price tag, For free hotels e Price tag, is the semantic matching function of the price tag, t During the promotion period,T For the promotion period, For hotels v During promotion period t Promotional labels, For free hotels e During promotion period t Promotional labels, is the semantic matching function of the promotion tag, are the influence weights of style, price and promotion on the matching coefficient respectively.

[0035] Furthermore, the size of the storage node in step S2 for:

[0036] ;

[0037] in, The basic size of the storage node, z is the historical data type of the hotel in the block, and Z is the historical data volume of the hotel in the block. For the z data, For data The historical visit frequency, For data Historical visit time, To store the redundancy upper limit coefficient, is the current data type, For the data, For data The preset access frequency, For data The preset access time, is the load factor of the storage node, Indicates rounding up.

[0038] Furthermore, step S3 specifically includes:

[0039] When the hotel enters data When storing or updating data at a service node, a data transaction is created and linked to each service node of the blockchain for verification. The service node checks the data. format, digital signature and access permission rules. If the verification is successful, the data Stored in the corresponding service node, if the verification fails, an error is reported to the input end of the hotel data.

[0040] The methods for setting access permission rules include:

[0041] Step S31: classifying the data types that can be input into the hotel's data input terminal into different sensitivity levels according to the sensitivity level, and generating a sensitivity level label according to the sensitivity level;

[0042] Step S32: The service node The data information contained therein identifies the sensitivity level corresponding to the data content and generates a sensitivity level label corresponding to the data content;

[0043] Step S33: When creating a data transaction link, the hotel's data input terminal generates a sensitivity level label that is allowed by the authority. The service node compares the sensitivity level label corresponding to the data content with the sensitivity level label allowed by the authority. If they are the same, the access permission is passed, otherwise the access permission is not passed, and the blockchain denies access.

[0044] A hotel data processing system is provided to execute the above hotel data processing method, which comprises:

[0045] Blockchain server: splits the hotels distributed in the hotel information service area into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain;

[0046] The blocks in the blockchain contain service nodes, each of which has different storage nodes. Each hotel has a storage node. The storage node provides storage services for hotel information.

[0047] Storage node size for:

[0048] ;

[0049] in, The basic size of the storage node, z is the historical data type of the hotel in the block, and Z is the historical data volume of the hotel in the block. For the z data, For data The historical visit frequency, For data Historical visit time, To store the redundancy upper limit coefficient, is the current data type, For the data, For data The preset access frequency, For data The preset access time, is the load factor of the storage node, Indicates rounding up.

[0050] The beneficial effects of the present invention are as follows: the present invention divides the hotel clusters in a region into multiple separate blocks, and independently manages the hotel information data in the sub-regions through the blocks, thereby reducing the difficulty of managing a large amount of hotel information data. The division of blocks follows the consumption habits of business circles with different consumption forms and consumers, ensuring that each divided block has sufficient richness and completeness in the content of hotel information in different regions. Distributed storage and blockchain technology are used to realize intelligent management of hotel data and personalized customer services, while ensuring the security of transactions between data. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Flow chart of the hotel data processing method. DETAILED DESCRIPTION

[0052] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0053] like Figure 1 As shown, a hotel data processing method includes:

[0054] Step S1: Construct a blockchain based on hotel information services. According to the area of ​​the hotel information service, the hotels distributed in the area are divided into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain. Step S1 specifically includes:

[0055] Step S11: Determine the area where hotel information services are needed, obtain the location information of each hotel in the area, divide the hotels distributed in the area into several sub-areas, and use the business district, station or school in the area as the center of the sub-area, and calculate the correlation coefficient of consumer choice between each hotel and the center of different sub-areas ;

[0056] ;

[0057] in, n is the hotel number in the area. m is the number of the sub-region, k For hotels n To sub-area m The types of transportation available to the center, K For hotels n To sub-area m The number of transport types available to the center, Adopting transportation types for consumers k The moving distance, To determine the moving distance threshold that affects consumer choice, For traffic type k The time spent To determine the time threshold that influences consumer choice, are the weights of the impact of moving distance and time spent on the correlation coefficient; according to the characteristics of different sub-areas, the impact weight It can be changed according to the actual situation, for example: the sub-area is a business district, and the types of consumers are diverse. The sub-area is a station, and consumers pay more attention to the convenience of time, so .

[0058] Step S12: Setting the threshold of the correlation coefficient ; Threshold of correlation coefficient It represents the allowable value of the correlation coefficient. If the correlation coefficient exceeds the threshold, it means that consumers in the sub-area are unlikely to choose this hotel for consumption.

[0059] like , then generate a hotel n With sub-area m The mapping between , , is the mapping function;

[0060] like , then the hotel n With sub-area m The mapping between them failed;

[0061] Step S13: Calculate hotel n The number of mappings generated with different sub-regions N ,quantity N The initial value of is 0:

[0062] ;

[0063] Step S14: If the quantity , then obtain the correlation coefficient corresponding to each mapping to form a correlation coefficient data set , u is the number of mappings, For the u The correlation coefficient corresponding to the mapping; filter out the correlation coefficient data group The maximum value in , retain the maximum value The corresponding mapping relationship, delete the mapping relationship corresponding to the remaining correlation coefficients;

[0064] If the quantity , the corresponding hotel will be used as a free hotel;

[0065] Step S15: Filter out the closest sub-regions based on the coordinates of the free hotels ;

[0066] ;

[0067] in, e is the ID of the free hotel. The map coordinates of the free hotel in the area of ​​the hotel information service. M is the number of sub-regions in the region, For sub-region The map plane coordinates of the center within the area of ​​the hotel information service;

[0068] Step S16: According to the hotel n With sub-area The mapping relationship between them is retained to obtain the sub-area The best-selling room types of each hotel are mapped out, and the feature tags of each best-selling room type are extracted, and the feature tags include style tags, price tags and promotion tags;

[0069] Step S17: Obtain the feature labels of free hotels and The feature labels of the best-selling room types of each hotel are mapped out, and the free hotels and sub-regions are calculated. The matching coefficient ;

[0070] Matching coefficient The calculation method is:

[0071] ;

[0072] in, v For sub-region The hotel number mapped in V For sub-region The number of hotels mapped in d is the style tag number, D is the number of style tags, For hotels v No. d style tags, For free hotels e No. d style tags, is the semantic matching function of the style tag, For hotels v Price tag, For free hotels e Price tag, is the semantic matching function of the price tag, t During the promotion period, T For the promotion period, For hotels v During promotion period t Promotional labels, For free hotels e During promotion period t Promotional labels, is the semantic matching function of the promotion tag, are the weights of the influence of style, price and promotion on the matching coefficient. In general, .

[0073] Step S18: Setting the threshold of the matching coefficient ;Threshold of matching coefficient Indicates the allowable value of the matching coefficient. When the matching coefficient exceeds the threshold, it means that the free hotel is not suitable for the consumption habits of consumers in the sub-area.

[0074] like , you will leave the hotel e Mapping to sub-regions Inside;

[0075] like , then filter out the sub-areas Zhongyouli Hotel e The hotel with the highest matching coefficient ;

[0076] ;

[0077] in, For hotels Free hotels e The matching coefficient between For hotels No. d style tags, For hotels Price tag, For hotels During promotion period t Promotional labels;

[0078] Will Hotel Style tags, price tags and promotional tags used in the operation process are sent to free hotels e , for free hotels e Provide reference for adjusting operation strategy;

[0079] Step S19: All hotels mapped out in each sub-area are taken as a block, and a service node is constructed for each block to perform data storage and services for each block. The service node provides data analysis for the hotels in the block.

[0080] Step S2: Each block corresponds to a service node. Different storage nodes are set on the service node according to the access conditions of the hotel data in the block. Each hotel corresponds to a storage node. The size of the storage node for:

[0081] ;

[0082] in, The basic size of the storage node, z is the historical data type of the hotel in the block, and Z is the historical data volume of the hotel in the block. For the z data, For data The historical visit frequency, For data Historical visit time, To store the redundancy upper limit coefficient, is the current data type, For the data, For data The preset access frequency, For data The preset access time, is the load factor of the storage node, Indicates rounding up.

[0083] Step S3: When the hotel inputs data to the service node for storage or update, a data transaction link is created to each service node of the blockchain for verification. The service node checks the format, digital signature and access permission rules of the data. If the verification passes, the data is stored in the corresponding service node. If the verification fails, an error is reported to the input end of the hotel data, and the blockchain denies access.

[0084] Step S3 specifically includes:

[0085] When the hotel enters data When storing or updating data at a service node, a data transaction is created and linked to each service node of the blockchain for verification. The service node checks the data. format, digital signature and access permission rules. If the verification is successful, the data Stored in the corresponding service node, if the verification fails, an error is reported to the input end of the hotel data.

[0086] The methods for setting access permission rules include:

[0087] Step S31: classifying the data types that can be input into the hotel's data input terminal into different sensitivity levels according to the sensitivity level, and generating a sensitivity level label according to the sensitivity level;

[0088] Step S32: The service node The data information contained therein identifies the sensitivity level corresponding to the data content and generates a sensitivity level label corresponding to the data content;

[0089] Step S33: When creating a data transaction link, the hotel's data input terminal generates a sensitivity level label that is allowed by the authority. The service node compares the sensitivity level label corresponding to the data content with the sensitivity level label allowed by the authority to see if they are the same. If they are the same, the access permission is granted, otherwise the access permission is not granted.

[0090] A hotel data processing system, which executes the above hotel data processing method, comprises:

[0091] Blockchain server: splits the hotels distributed in the hotel information service area into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain;

[0092] The blocks in the blockchain contain service nodes, each of which has different storage nodes. Each hotel has a storage node. The storage node provides storage services for hotel information.

[0093] Storage node size for:

[0094] ;

[0095] in, The basic size of the storage node, z is the historical data type of the hotel in the block, and Z is the historical data volume of the hotel in the block. For the z data, For data The historical visit frequency, For data Historical visit time, To store the redundancy upper limit coefficient, is the current data type, For the data, For data The preset access frequency, For data The preset access time, is the load factor of the storage node, Indicates rounding up.

[0096] The present invention divides the hotel clusters in a region into multiple separate blocks, and independently manages the hotel information data in the sub-regions through the blocks, thereby reducing the difficulty of managing a large amount of hotel information data. The division of blocks follows the consumption habits of business circles with different consumption forms and consumers, ensuring that each divided block has sufficient richness and completeness in the content of hotel information in different regions. Distributed storage and blockchain technology are used to realize intelligent management of hotel data and personalized customer services, while ensuring the security of transactions between data.

Claims

1. A hotel data processing method, characterized in that: include: Step S1: Build a blockchain based on hotel information services. According to the area of ​​the hotel information service, the hotels distributed in the area are divided into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain; Step S2: Each block corresponds to a service node. Different storage nodes are set on the service node according to the access conditions of the hotel data in the block. Each hotel corresponds to a storage node. Step S3: When the hotel inputs data to the service node for storage or update, a data transaction link is created to each service node of the blockchain for verification. The service node checks the format, digital signature and access permission rules of the data. If the verification passes, the data is stored in the corresponding service node. If the verification fails, an error is reported to the input end of the hotel data, and the blockchain denies access. The size of the storage node in step S2 for: ； in, The basic size of the storage node, z is the historical data type of the hotel in the block, and Z is the historical data volume of the hotel in the block. For the z data, For data The historical visit frequency, For data Historical visit time, To store the redundancy upper limit coefficient, is the current data type, For the data, For data The preset access frequency, For data The preset access time, is the load factor of the storage node, Indicates rounding up; The step S1 comprises: Step S11: Determine the area where hotel information services are needed, obtain the location information of each hotel in the area, divide the hotels distributed in the area into several sub-areas, and use the business district, station or school in the area as the center of the sub-area, and calculate the correlation coefficient of consumer choice between each hotel and the center of different sub-areas ; ； in, n is the hotel number in the area. m is the number of the sub-region, k For hotels n To sub-area m The types of transportation available to the center, K For hotels n To sub-area m The number of transport types available to the center, Adopting transportation types for consumers k The moving distance, To determine the moving distance threshold that affects consumer choice, For traffic type k The time spent To determine the time threshold that influences consumer choice, They are the influence weights of moving distance and time spent on the correlation coefficient; Step S12: Setting the threshold of the correlation coefficient ; like , then generate a hotel n With sub-area m The mapping between , , is the mapping function; like , then the hotel n With sub-area m The mapping between them failed; Step S13: Calculate hotel n The number of mappings generated with different sub-regions N ,quantity N The initial value of is 0: ； Step S14: If the quantity , then obtain the correlation coefficient corresponding to each mapping to form a correlation coefficient data set , u is the number of mappings, For the u The correlation coefficient corresponding to the mapping; filter out the correlation coefficient data group The maximum value in , retain the maximum value The corresponding mapping relationship, delete the mapping relationship corresponding to the remaining correlation coefficients; If the quantity , the corresponding hotel will be used as a free hotel; Step S15: Filter out the closest sub-regions based on the coordinates of the free hotels ; ； in, e is the number of the free hotel, The map coordinates of the free hotel within the area of ​​the hotel information service. M is the number of sub-regions in the region, For sub-region The map plane coordinates of the center within the area of ​​the hotel information service; Step S16: According to the hotel n With sub-area The mapping relationship between them is retained to obtain the sub-area The best-selling room types of each hotel are mapped out, and the feature tags of each best-selling room type are extracted, and the feature tags include style tags, price tags and promotion tags; Step S17: Obtain the feature labels of free hotels and The feature labels of the best-selling room types of each hotel are mapped out, and the free hotels and sub-regions are calculated. The matching coefficient ; Step S18: Setting the threshold of the matching coefficient ; like , you will leave the hotel e Mapping to sub-regions Inside; like , then filter out the sub-areas Zhongyouli Hotel e The hotel with the highest matching coefficient ; in, For hotels Free hotels e The matching coefficient between For hotels No. d style tags, For hotels Price tag, For hotels During promotion period t Promotional labels, are the weights of the influence of style, price and promotion on the matching coefficient, D is the number of style tags, d is the style tag number, T For the promotion period, t During the promotion period, For free hotels e No. d style tags, For free hotels e Price tag, For free hotels e During promotion period t Promotional labels; Will Hotel Style tags, price tags and promotional tags used in the operation process are sent to free hotels e , for free hotels e Provide reference for adjusting operation strategy; Step S19: All hotels mapped out in each sub-area are taken as a block, and a service node is constructed for each block to perform data storage and services for each block. The service node provides data analysis for the hotels in the block.

2. The hotel data processing method according to claim 1, characterized in that: The matching coefficient The calculation method is: ； in, v For sub-region The hotel number mapped in V For sub-region The number of hotels mapped in d is the style tag number, D is the number of style tags, For hotels v No. d style tags, For free hotels e No. d style tags, is the semantic matching function of the style tag, For hotels v Price tag, For free hotels e Price tag, is the semantic matching function of the price tag, t During the promotion period, T For the promotion period, For hotels v During promotion period t Promotional labels, For free hotels e During promotion period t Promotional labels, is the semantic matching function of the promotion tag, are the influence weights of style, price and promotion on the matching coefficient respectively.

3. The hotel data processing method according to claim 1, characterized in that: The step S3 specifically includes: When the hotel enters data When storing or updating data at a service node, a data transaction is created and linked to each service node of the blockchain for verification. The service node checks the data. format, digital signature and access permission rules. If the verification is successful, the data Stored on the corresponding service node, if the verification fails, an error is reported to the input end of the hotel data, and the blockchain denies access; The methods for setting access permission rules include: Step S31: classifying the data types that can be input into the hotel's data input terminal into different sensitivity levels according to the sensitivity level, and generating a sensitivity level label according to the sensitivity level; Step S32: The service node The data information contained therein identifies the sensitivity level corresponding to the data content and generates a sensitivity level label corresponding to the data content; Step S33: When creating a data transaction link, the hotel's data input terminal generates a sensitivity level label that is allowed by the authority. The service node compares the sensitivity level label corresponding to the data content with the sensitivity level label allowed by the authority. If they are the same, the access permission is passed, otherwise the access permission is not passed.

4. A hotel data processing system, executing the hotel data processing method according to any one of claims 1 to 3, characterized in that: include: Blockchain server: splits the hotels distributed in the hotel information service area into several sub-areas, and the hotels in each sub-area are grouped into a block of the blockchain; The blocks in the blockchain include service nodes, each of which is provided with different storage nodes, and each hotel corresponds to a storage node; The storage node provides storage service for hotel information.

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