A Multi-Party Collaboration Method and System for Airport In-Flight Food Security Based on Blockchain

Abstract

The present invention belongs to the technical field of informatization of civil airports in the civil aviation industry, and discloses a multi-party collaboration method and system for airport airline food security based on blockchain. The method includes: accessing data through interfaces such as Webservice, RestfulAPI, MQ messages, FTP, ODBC, etc., and parsing the above data by using technologies such as XML to data object, JSON to data object, deserialization, etc.; constructing and initializing variable structures of the credible level of airlines, the cumulative service time of airlines for airline food security, the number of flights of airlines for airline food security, the number of historical complaints of airlines for airline food security, and the number of non-payments of airlines for airline food security, and calculating the credible number and credible level of airlines. The present invention takes into account the airline food security collaborative operation structure and the on-chain storage structure that meet requirements such as performance and storage capacity, and realizes the long-term continuous availability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of civil aviation industry civil airport information technology, and in particular to a blockchain-based airport catering multi-party collaboration method and system. Background Art

[0002] In traditional airport catering, there is business collaboration between different airlines and different catering companies and specific departments of catering companies. The airlines give business requirements, the catering companies provide catering services according to the requirements, and finally settle accounts with the airlines on a periodic basis. In this collaboration process, the communication costs of the multiple parties are high due to the lack of a trustworthy basis. Many business operations require repeated multi-step manual confirmation due to credibility issues, and the overall collaboration efficiency is low. At the same time, when the catering company and the airline reconcile and settle accounts at the end, the inconsistency of the information recorded separately by both parties often leads to refusal to pay and wrangling, which affects the normal income of the catering company. Summary of the invention

[0003] In order to overcome the problems existing in the related technologies, the disclosed embodiments of the present invention provide a blockchain-based airport catering service multi-party collaboration method and system. The present invention is aimed at the airport catering service business field, and aims to achieve reliable and efficient collaboration among multiple parties in the catering service scenario by adopting blockchain technology.

[0004] The technical solution is as follows: a blockchain-based multi-party collaborative method for airport catering security, comprising the following steps:

[0005] S1, access flight plans, catering airline history, catering settlement history, contract airlines, historical flight cabin occupancy rate, flight reservations, and aircraft travel time relationship matrix data through Webservice, RestfulAPI, MQ message, FTP, ODBC and other interfaces, and use XML to data object, JSON to data object, deserialization and other technologies to parse the above data, among which flight plan data and flight reservation data are data for the next 3 days, and catering airline history, catering settlement history, and historical flight cabin occupancy rate are data for the past year;

[0006] S2, construct and initialize the variable structure of airline trust level, cumulative service time of airline catering guarantee, number of flights of airline catering guarantee, number of historical complaints of airline catering guarantee, number of refusal of airline catering guarantee, and calculate the trust number and trust level of airline;

[0007] S3, construct the coordinated operation structure variables of airline catering guarantee, construct the on-chain storage structure variables according to the flight trust level, initialize the coordinated operation structure variables of airline catering guarantee and the on-chain storage structure variables according to the flight plan and contract, and perform the on-chain storage structure variable on-chain operation;

[0008] S4. Obtain the demand data such as the catering types, quantities, and additional requirements for different cabins of specific flights of the airline, query the collaborative operation structure variables and the variables stored on the chain, verify the legality of the signature of the cabin catering requirements entered by the airline, and update the collaborative operation structure variables and the variables stored on the chain based on version control;

[0009] S5. Obtain data such as the seat occupancy rate of historical flights, construct the intermediate calculation variable structure for the catering quantity requirements of flight cabins, initialize the seat reservation number data for specific cabins of specific flights, calculate the historical seat occupancy rate of specific cabins of specific flights, calculate the historical complaint rate of specific flights, calculate the mutual relationship between flights during the meal delivery process, calculate the proportion of different cabin meal types in the whole for specific flights, calculate the catering quantities required for different cabins of specific flights, and update the collaborative operation structure variables and the variables stored on the chain;

[0010] S6. Obtain the confirmation status of the catering production department of the airline catering company and the input data for updating its own catering production status, retrieve the collaborative operation structure variables and the variables stored on the chain, verify the legality of the signature of the catering department, and update the catering production status to the collaborative operation structure variables and the variables stored on the chain;

[0011] S7. Obtain the confirmation status of the catering distribution department of the airline catering company and the input data for updating its own catering distribution process record, query the collaborative operation structure variables and the variables stored on the chain, verify the legality of the signature of the airline catering distribution department, and update the catering distribution process record to the collaborative operation structure variables and the variables stored on the chain;

[0012] S8. Obtain the data on the confirmation status of the on-site support of the flight crew for airline catering support, retrieve the collaborative operation structure variables and the variables stored on the chain, verify the legality of the signature of the flight crew, and update the airline catering support completion status to the collaborative operation structure variables and the variables stored on the chain.

[0013] In step S1, access the flight plan, the history of airline catering support, the historical records of airline catering support settlement, contract airlines, the seat occupancy rate of historical flights, flight seat reservations, and the relationship matrix data of aircraft movement time through interfaces such as Webservice, RestfulAPI, MQ messages, FTP, and ODBC. Use technologies such as XML to data object conversion, JSON to data object conversion, and deserialization to parse the above data. After copying the data attributes of the parsed flight plan data, set it as After copying the data attributes of the parsed historical data of airline catering support, set it as After copying the data attributes of the parsed historical records of airline catering support settlement, set it as After copying the data attributes of the parsed contract airline data, set it as , set the parsed historical flight class occupancy rate data as , set the parsed flight reservation data as , set the parsed aircraft position travel time relationship matrix data as , where the flight schedule data and flight reservation data are for the next 3 days, and the historical data of airline catering guarantee, airline catering guarantee settlement history records, and historical flight class occupancy rate are for the past 1 year.

[0014] In step S2, construct and initialize the variable structures of airline credibility level, cumulative service time of airline catering guarantee, number of flights of airline catering guarantee, number of historical complaints of airline catering guarantee, and number of non-payment of airline catering guarantee, and calculate the airline credibility number and credibility level, including:

[0015] S201, construct the variable of airline credibility level structure , which is used to record the credibility levels of different airlines, where the key value is the airline name and the value is the level number; construct the variable of cumulative service time structure of airline catering guarantee , which is used to record the cumulative service time of different airlines, where the key value is the airline name and the value is the cumulative service time in days; construct the variable of the number of flights of airline catering guarantee structure , which is used to record the cumulative number of flights of airline catering guarantee of different airlines, where the key value is the airline name and the value is the cumulative number of flights of airline catering guarantee; construct the variable of the number of historical complaints of airline catering guarantee structure , which is used to record the number of complaint flights of different airlines, where the key value is the airline name and the value is the number of complaint flights; construct the variable of the number of non-payment of airline catering guarantee structure , which is used to record the number of non-payment flights of different airlines, where the key value is the airline name and the value is the number of non-payment flights;

[0016] S202, obtain , traverse , using the airline name in the obtained single contract airline data as the key value and 0 as the value, put the constructed data into , to complete the initialization; using the airline name in the obtained single data as the key value and the cumulative service time in the obtained single data as the value, put the constructed data into , to complete traversal;

[0017] S203, obtain , traverse , obtain the historical data of a single specific in-flight meal service guarantee, use the airline name in the obtained single historical data as the key value, and query , if the returned value is null, put 1 as the value into , if the returned value is not null, perform the operation of updating the returned value by adding 1; if the attribute of whether the airline has filed a complaint in the obtained single in-flight meal service guarantee historical data is yes, use the airline name in the obtained single historical data as the key value, and query , if the returned value is null, put 1 as the value into , if the returned value is not null, perform the operation of updating the returned value by adding 1; complete the traversal ;

[0018] S204, obtain , traverse , obtain a single specific in-flight meal service settlement historical record. If the attribute of whether to refuse payment in the specific record is yes, use the airline name in the obtained single historical record as the key value, and query , if the returned value is null, put 1 as the value into , if the returned value is not null, perform the operation of updating the returned value by adding 1; complete the traversal ;

[0019] S205, obtain , traverse , obtain the specific record, and query , , , in sequence to obtain , , , , where represents the cumulative service time of this airline, represents the cumulative number of in-flight meal service guarantee flights of this airline, represents the number of complaint flights of this airline, represents the number of refused payment flights of this airline, then the airline credibility is calculated as follows:

[0020]

[0021] where are all user-adjustable parameters used to adjust the calculation impact of different elements on the airline credibility.

[0022] Airline credibility level number , the calculation method is as follows:

[0023]

[0024] Put the obtained by calculation as the value into . After the traversal is completed .

[0025] In step S3, construct the collaborative operation structure variable for airline food supply guarantee, construct the on-chain storage structure variable according to the flight trust level, initialize the collaborative operation structure variable and the on-chain storage structure variable according to the flight plan and contract, and perform the on-chain operation of the on-chain storage structure variable, including:

[0026] S301: Construct the collaborative operation structure variable for airline food supply guarantee,

[0027] ;

[0028] Where is the number of flights served by the airline food company represents the collaborative operation structure variable for airline food supply guarantee constructed around the th flight, Where represents the flight number, represents the flight date, represents the airline company, represents the aircraft type, represents the overall flight status, represents the flight route, represents the meal distribution requirements for different cabins entered by the airline company, represents the signature of the meal distribution requirements for different cabins entered by the airline company, used to prove that it is entered for a specific airline company, represents the estimated number of meals required for the flight, represents the signature of the meal department, used to confirm the entered meal distribution requirements and the number of meals, represents the record of the process of the airline food meal department preparing meals, represents the signature confirmation of the airline food distribution department for the process of the airline food meal department preparing meals, represents the record of the meal distribution process of the airline food distribution department, represents the signature confirmation of the airline crew, represents the updated version number, used to solve the possible update loss problem during multi-party writing;

[0029] S302: Construct the storage structure to be put on the chain,

[0030]

[0031] Indicates the on-chain collaborative operation structure variable constructed around the th flight. The structure of is determined by as follows:

[0032]

[0033] S303: Obtain , traverse , obtain the specific single-flight schedule data, obtain , traverse , obtain the specific single contract airline data, and determine whether the airline attribute in the flight schedule data is consistent with the airline attribute in the contract airline data. If they are consistent, obtain , obtain the specific credibility level of the airline with the airline name as the key, construct structure, and assign the flight number, flight date, airline, aircraft type, flight status, and flight route attribute in the flight schedule data to in turn. Set in to 1, put into . Construct the of the corresponding structure according to the obtained specific credibility level of the airline, and assign the flight number, flight date, airline, aircraft type, flight status, and flight route attribute in the flight schedule data to in turn. Set in to 1, put into . After the traversal is completed ;

[0034] S304: Store the data in the blockchain, and store it in the form of a key-value structure on the blockchain . First, establish a trusted link with the blockchain management platform through the access key granted by the held blockchain management platform and the development kit, and then start traversing data to obtain the specific , obtain in and , combine them into a string, call the blockchain write interface, pass in the constructed combined string as the key, as the value, and upload the data to the blockchain. After the traversal is completed .

[0035] In step S4, obtain the demand data such as the catering types, quantities, and additional requirements for different cabins of specific flights of the airline, query the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the cabin catering requirements entered by the airline, and update the collaborative operation structure variables and the on-chain storage variables based on version control, including:

[0036] S401: Obtain the data that the airline needs to enter; the airline enters the flight number for which catering is required , the flight date , the catering requirements for different cabins and the signature for the catering requirements ,

[0037] ,

[0038] is the specific catering requirement for different cabins , where represents the specific cabin, represents the meal type, represents the quantity of meals required. When this variable is -1, it means that the airline has not specified the number of people, represents the additional requirements;

[0039] S402: Retrieve the specific and according to the input of the airline. Through and traverse . When the current flight number, date, and are consistent with , the traversal is completed . Take as the variable that meets the requirements, then establish a connection with the blockchain platform, and combine and into a query key, and obtain the specific through the blockchain query interface;

[0040] S403: Verify the data. Use the public key of the specific airline to decrypt to obtain the specific one-way hash value, and then calculate the one-way hash value of . Compare the two. If they are consistent, it passes; otherwise, cancel the data entry of the airline for this time. Update the catering requirement data for different cabins data into and , and respectively put the and in the Increment the attribute by 1;

[0041] S404: Update and data. By and traverse , when the current specific data flight number during traversal is consistent with the date and and , then determine whether the specific data is equal to in minus 1. If it is equal, update the data to the specific data during traversal. If it is not equal, abandon this update. Establish a link with the blockchain platform, combine and into a query key, obtain specific data through the blockchain query interface, and determine whether the specific data is equal to in minus 1. If it is equal, update the data to the specific data during traversal. If it is not equal, abandon this update.

[0042] In step S5, obtain data such as historical flight seat occupancy rates, construct an intermediate calculation variable structure for the number of meal distribution requirements for flight seats, initialize the seat reservation number data for specific flights and specific seats, calculate the historical seat occupancy rate of specific flights and specific seats, calculate the historical complaint rate of specific flights, calculate the mutual relationship between flights during meal delivery, calculate the proportion of different seat meal types in the overall specific flight, calculate the number of meals required for different seats of specific flights, and update the collaborative operation structure variables and the on-chain storage variables, including:

[0043] S501: Obtain historical flight seat occupancy rate data , obtain flight seat reservation data , obtain historical data of airlines providing in-flight meals , obtain flight schedule data , obtain the relationship matrix data of aircraft position travel time , obtain the collaborative operation structure variables for in-flight meal guarantee , obtain the storage structure to be uploaded to the chain ;

[0044] S502: Construct a variable structure , used to store the intermediate calculation values for each flight that needs to calculate the number of meal distribution requirements for different seats of the flight. It is a hash table structure, where the key value is a string composed of the flight number and the flight date, and the value is the structure of , where It is expressed as the number of seats booked for a specific flight and specific cabin class, and is a list structure, where is expressed as a specific cabin class, is expressed as the number of seats booked for a specific cabin class, The historical passenger occupancy rate of a specific flight is a list structure, where is expressed as a specific cabin class, is expressed as the historical passenger occupancy rate of a specific cabin class, is expressed as the historical complaint rate of a specific flight, is expressed as the allocated meal cart mark for a specific flight, used for subsequent calculation marking, is expressed as the auxiliary structure for calculating the allocation of meal carts for a specific flight, and it is a list structure, where is expressed as a specific cabin class, is expressed as the proportion of the same type of meal allocation for the same cabin class in the same meal cart to the whole, represents the number of meal allocations for a specific cabin class;

[0045] S503: Traverse , obtain the specific , get the specific flight number and flight date, and construct a list structure variable containing , traverse , search for data with the same flight number as , and according to the cabin class and the number of seats booked for the specific data, construct the data structure and put it into , until all data with the same flight number as is found, and the traversal ends ; , using the combined string of the flight number and flight date of as the key, construct the structure as the value, and assign to , put the constructed key value and value into , continue to traverse , repeat the operation until the traversal is completed;

[0046] S504: Traverse , obtain the specific , construct a list variable containing the structure, where represents the specific flight cabin class, represents the cumulative number of flights with the same flight and the same cabin class, represents the cumulative passenger occupancy rate of the same flight and the same cabin class, and traverse , query for specific flight schedule data where the flight number is the same as the flight date, and obtain the schedule plan number of the specific flight schedule data , traverse , obtain specific historical data of airline catering guarantee airlines where the flight number is the same and the schedule plan number attribute is the same, traverse , obtain data where the flight number and flight date are the same as those in the specific historical data of airline catering guarantee airlines, and perform cumulative addition of 1 for the corresponding cabin , and perform cumulative addition of the load factor for the corresponding cabin . After the traversal is completed , put the cumulative structured data into , traverse , start calculating the historical average load factor of the specific cabin , the specific calculation method is:

[0047]

[0048] Using the combined string of flight number and flight date as the key, query , obtain , update the average load factor of different cabins in, and continue to traverse , repeat the operation until the traversal is completed;

[0049] S505: Traverse , obtain specific , construct variable , used to represent the total number of historical service flights, construct variable , used to represent the number of flights with complaints among the historical service flights, traverse , query for specific flight schedule data where the flight number is the same as the flight date, and obtain the schedule plan number of the specific flight schedule data , traverse , obtain specific historical data of airline catering guarantee airlines where the flight number is the same and the schedule plan number attribute is the same, perform cumulative addition of 1, and judge whether the airline's complaint attribute in the specific historical data of airline catering guarantee airlines is "yes", if so, then perform cumulative addition of 1. After the traversal is completed , calculate the historical complaint rate , the specific method is as follows:

[0050]

[0051] Using the combined string of flight number and flight date as the key, query to obtain and update the historical complaint rate data, and continue to traverse and repeat the operation until the traversal is completed;

[0052] S506: Traverse to construct a list structure variable that contains the structure where represents the specific cabin class, represents the meal type, represents the quantity of the same meal type on the same meal cart, obtain the specific and construct a variable to represent the number of flights for which the vehicle has loaded meals, initially 0, traverse and query the specific flight schedule data with the same flight number and flight date as to obtain the start time and end time of the in-flight meal service plan in the specific flight schedule data , the aircraft stand, continue to traverse to obtain the start time of the in-flight meal service plan in the specific flight schedule data , the aircraft stand, and traverse based on the two aircraft stand data to obtain the travel time between them . When , it is considered that the requirement for connecting meal distribution can be met. Query using the flight number and flight date as the key value, and update the in the value. Traverse , statistically summarize , , values, perform an increment operation; continue to traverse and continue to make a judgment based on the subsequent flight schedule data. When , it is considered that the same meal cart has reached the loading limit, is a constant value adjusted for the user to control the maximum number of flights that each meal cart can carry meals. End the traversal and end the traversal ; ;

[0053] S507: Obtain the constructed in S506, traverse from the beginning to obtain the specific , obtain the catering requirements entered by the airline , according to the cabin class and meal type in the catering requirements, in query the corresponding quantity of the cabin class type and meal type that meet the requirements, using the flight number and flight date as the combined key, query , obtain the corresponding structural data. Assume that the specific quantity of the same cabin class and meal is , then the proportion of the same cabin class and the same catering type in the same catering truck to the whole is calculated as follows:

[0054]

[0055] Update the calculated and the data of different cabin classes into , complete the update of ; continue to traverse from the beginning , skip the flights that have been marked as having been assigned catering trucks, and repeat the above operations until all flights have been calculated;

[0056] S508: Traverse , obtain the specific , using the flight number and flight date as the key, query , obtain the corresponding structural data. According to the flight number and flight date, query and traverse , obtain the corresponding maximum number of passengers . Assume that the specific quantity of catering for a cabin class is , obtain the historical seat occupancy rate of the specific cabin class from , obtain the historical complaint rate of the flight from , obtain the number of reserved seats for the specific flight and specific cabin class from , obtain the proportion of the same cabin class and the same catering type in the same catering truck to the whole from , obtain the number of reserved seats for the specific flight and specific cabin class from , obtain the proportion of the same cabin class and the same catering type in the same catering truck to the whole from , then the specific calculation method is as follows: , then the specific calculation method is as follows:

[0057]

[0058] Update in , update to in attribute;

[0059] S509: Update the data in and synchronize it to the blockchain. Traverse to obtain the specific . Subsequently, establish a connection with the blockchain platform, and use the flight number and flight date in as the combined query key to obtain the specific through the blockchain query interface. If exists attribute, then update the data in to , and increment the attribute in by 1. Use the flight number and flight date in as the combined query key to obtain the specific data again through the blockchain query interface, and determine whether the specific data is equal to minus 1 in . If they are equal, update the data to the specific data traversed. If they are not equal, abandon this update.

[0060] In step S6, obtain the confirmation status of the meal production department of the airline catering company and update the input data of its own meal production status, retrieve the collaborative operation structure variables and the on-chain storage variables, verify the legality of the meal department signature, and update the meal production status to the collaborative operation structure variables and the on-chain storage variables, including:

[0061] S601: Obtain the data required to obtain the confirmation status of the meal production department of the airline catering company and update its own meal production status; the meal production department of the airline catering company needs to enter the flight number , flight date , the signature for confirming the entered catering requirements and catering quantities, and the meal production process status record . The variable is a string, recording the process status record of the meal production department of the airline catering company for making the meals of this flight. is a string structure, which is the one-way hash calculation of the data and the

[0062] S602: Retrieve the specific and according to the input of the meal production department of the airline catering company, and pass through and Traverse When traversing the current flight number is consistent with the date and and is consistent, the traversal is completed Then, take as the variable that meets the requirements, and then establish a link with the blockchain platform. Combine and into a query key, and obtain the specific through the blockchain query interface;

[0063] S603: Verify the data. Use the public key of the catering production department of the airline catering company to decrypt to obtain the specific one-way hash value, and then calculate the one-way hash value of the combined data of and and . Compare the two. If they are consistent, it passes; otherwise, cancel the data entry of the catering production department of the airline catering company this time. Update the , data entered by the catering production department of the airline catering company into and , and respectively increment the and attributes in by 1;

[0064] S604: Update and data.

[0065] In step S7, obtain the confirmation status of the airline catering company's meal distribution department and update the input data of its own meal distribution process record. Query and obtain the collaborative operation structure variable and the on-chain storage variable, verify the legality of the signature of the airline catering distribution department, and update the meal distribution process record to the collaborative operation structure variable and the on-chain storage variable, including:

[0066] S701: Obtain the data required to obtain the confirmation status of the airline catering company's meal distribution department and update its own meal distribution process record. The airline catering company's meal distribution department needs to enter the flight number for which the confirmation status and the meal distribution process record need to be updated, the flight date , the signature for the signature confirmation of the meal preparation process by the airline catering meal department, the meal distribution process status record , The variable is a string, recording the process status record of the airline catering company's meal distribution department for this flight, is a string structure, which is for One-way hashing calculation of data, and the data obtained by encrypting the one-way hash value with the private key of the catering department of the airline catering company;

[0067] S702: Retrieve the specific and through and traverse When the current flight number is consistent with the date and and the traversal is completed Then, is used as the variable that meets the requirements. Subsequently, a link with the blockchain platform is established, and and are combined into a query key, and the specific is obtained through the blockchain query interface;

[0068] S703: Verify the data. Use the public key of the catering department of the airline catering company to decrypt to obtain the specific one-way hash value, and then calculate the one-way hash value of . Compare the two. If they are consistent, it passes; otherwise, the data entry of the catering department of the airline catering company for this time is cancelled. Update the , data entered by the catering department of the airline catering company into and , and respectively increment the and attributes in by 1;

[0069] S704: Update the and data.

[0070] In step S8, obtain the data of the flight crew of the airline catering guarantee flight to confirm the on-site guarantee status, retrieve and obtain the collaborative operation structure variable and the on-chain storage variable, verify the legality of the flight crew signature, and update the airline catering guarantee completion status to the collaborative operation structure variable and the on-chain storage variable, including:

[0071] S801: Obtain the data of the flight crew of the airline catering guarantee flight to confirm the on-site guarantee status;

[0072] S802: Retrieve the specific and according to the input of the airline, and and traverse When the current Flight number, date, and and are consistent, then the traversal is completed. , take as the variable that meets the requirements, then establish a link with the blockchain platform, and take and to form a query key, and obtain the specific through the blockchain query interface;

[0073] S803: Verify the data, use the public key of the airline to decrypt to obtain the specific one-way hash value, then calculate the one-way hash value of the data, compare the two, if they are consistent, it passes, otherwise cancel the current airline data entry; update the data entered by the airline into and , and respectively increment the and attributes in by 1.

[0074] S804: Update and data.

[0075] Another object of the present invention is to provide a multi-party collaborative system for airport in-flight meal support based on blockchain, which implements the multi-party collaborative method for airport in-flight meal support based on blockchain. The system includes:

[0076] A data parsing module for accessing flight plans, historical records of in-flight meal support airlines, historical records of in-flight meal support settlements, contract airlines, historical flight seat occupancy rates, flight bookings, and relationship matrix data of aircraft position travel times through Webservice, RestfulAPI, MQ messages, FTP, and ODBC interfaces, and parsing the above data using XML to data object, JSON to data object, and deserialization technologies;

[0077] A blockchain operation module for constructing and initializing variable structures of airline trust levels, cumulative service times of in-flight meal support airlines, numbers of flights of in-flight meal support airlines, numbers of historical complaints of in-flight meal support airlines, and numbers of non-payments of in-flight meal support airlines, and calculating airline trust numbers and trust levels;

[0078] An in-flight meal collaboration processing module for constructing variable structures of in-flight meal support collaborative operation structures, constructing variable structures of on-chain storage structures according to flight trust levels, initializing in-flight meal support collaborative operation structure variables and on-chain storage structure variables according to flight plans and contracts, and performing on-chain operations on the on-chain storage structure variables;

[0079] The meal demand estimation module is used to obtain the meal distribution types, quantities, and additional requirement data for different cabins of specific flights of the airline, query the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the cabin meal distribution requirements entered by the airline, and update the collaborative operation structure variables and the on-chain storage variables based on version control;

[0080] The basic data module is used to obtain the historical flight cabin occupancy rate data, construct the intermediate calculation variable structure for the meal quantity requirements of flight cabins, initialize the seat reservation number data for specific cabins of specific flights, calculate the historical occupancy rate of specific cabins of specific flights, calculate the historical complaint rate of specific flights, calculate the mutual relationship between flights during meal delivery, calculate the overall proportion of different cabin meal types of specific flights, calculate the meal quantities required for different cabins of specific flights, and update the collaborative operation structure variables and the on-chain storage variables;

[0081] The data storage and processing module is used to obtain the confirmation status of the meal production department of the airline catering company and the input data for updating its own meal production status, retrieve the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the meal department, and update the meal production status to the collaborative operation structure variables and the on-chain storage variables;

[0082] The interface processing module is used to obtain the confirmation status of the meal distribution department of the airline catering company and the input data for updating its own meal distribution process record, query the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the airline catering distribution department, and update the meal distribution process record to the collaborative operation structure variables and the on-chain storage variables;

[0083] The safety control module is used to obtain the data on the confirmation of the on-site support status by the flight crew for airline catering support flights, retrieve the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the flight crew, and update the airline catering support completion status to the collaborative operation structure variables and the on-chain storage variables.

[0084] In combination with all the above technical solutions, the beneficial effects of the present invention are as follows: data is accessed through interfaces such as Webservice, RestfulAPI, MQ message, FTP, ODBC, etc., and the above data is parsed using technologies such as XML to data object, JSON to data object, and deserialization; variable structures such as the airline's trust level, the cumulative service time of the airline that provides catering services, the number of flights of the airline that provides catering services, the number of historical complaints of the airline that provides catering services, and the number of refusal to pay of the airline that provides catering services are constructed and initialized, and the airline's trust number and trust level are calculated; a catering guarantee collaborative operation structure variable is constructed, and an on-chain storage structure variable is constructed according to the flight's trust level, and the catering guarantee collaborative operation structure variable and the on-chain storage structure variable are initialized according to the flight plan and contract, and an on-chain operation is performed; demand data such as the type, quantity, and additional requirements of catering for different cabins of a specific flight of the airline are obtained, the legitimacy of the signature of the catering requirements entered by the airline, and the collaborative operation structure variables and the on-chain storage variables are updated based on version control; historical flight cabins are obtained occupancy rate and other data, construct the intermediate calculation variable structure of the flight cabin meal quantity demand, initialize the specific flight specific cabin reservation number data, calculate the specific flight specific cabin historical occupancy rate, calculate the specific flight historical complaint rate, calculate the relationship between flights in the meal delivery process, calculate the overall proportion of meal types in different cabins of a specific flight, calculate the number of meals required in different cabins of a specific flight, and update the collaborative operation structure variables and the chain storage variables; obtain the confirmation status of the airline catering company's meal preparation department and update its own meal preparation status input data, verify the legitimacy of the meal department's signature, and update the meal preparation status to the collaborative operation structure variables and the chain storage variables; obtain the confirmation status of the airline catering company's meal preparation department and update its own meal preparation process record input data, verify the legitimacy of the meal preparation department's signature, and update the meal preparation process record to the collaborative operation structure variables and the chain storage variables; obtain the data of the on-site support status confirmed by the crew of the airline catering support flight, verify the legitimacy of the flight crew's signature, and update the airline catering support completion status to the collaborative operation structure variables and the chain storage variables. The present invention takes into account the requirements of performance, storage capacity, etc. for the coordinated operation structure of airline catering guarantee and the storage structure on the chain, thus achieving the long-term and continuous availability of the system. The present invention can provide a credible and unified system foundation for the business collaboration between the business departments of the airline catering company and the airline company, solve the occurrence of communication disputes when the records of both parties are inconsistent, and improve the efficiency of multi-party guarantee collaboration. At the same time, it proposes an airline catering guarantee coordinated operation structure and the storage structure on the chain that take into account the requirements of performance, storage capacity, etc. on the basis of meeting the business needs of users, thus achieving the long-term and continuous availability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure;

[0086] Figure 1 It is a flowchart of a multi - party collaborative method for airport in - flight meal guarantee based on blockchain provided by an embodiment of the present invention;

[0087] Figure 2 It is an example diagram of the collaborative operation structure variables for in - flight meal guarantee provided by an embodiment of the present invention;

[0088] Figure 3 It is a schematic diagram of a multi - party collaborative system for airport in - flight meal guarantee based on blockchain provided by an embodiment of the present invention;

[0089] In the figure: 1. Data parsing module; 2. Blockchain operation module; 3. In - flight meal collaborative processing module; 4. Meal demand estimation module; 5. Basic data module; 6. Data storage and processing module; 7. Interface processing module; 8. Security control module. Detailed implementation manners

[0090] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific implementations disclosed below.

[0091] The innovation of the present invention lies in: focusing on the problems in the traditional civil aviation in - flight meal guarantee business field, such as low multi - party collaborative efficiency, high communication and confirmation costs, and difficult problem accountability and traceability, the present invention makes full use of the anti - tampering and anti - repudiation characteristics of blockchain technology, innovatively introduces blockchain technology, constructs a unified method and system for multi - party trusted collaboration in in - flight meal guarantee. At the same time, it optimizes the in - flight meal guarantee data structure on the chain according to the airline's trusted level to meet the long - term and stable development needs of the in - flight meal guarantee business, realizes the efficient collaboration of in - flight meal guarantee between airlines and in - flight meal companies, reduces the communication costs between multiple parties, reduces the occurrence of non - payment situations, and improves the income of in - flight meal companies.

[0092] Embodiment 1, as Figure 1 shown, the multi - party collaborative method for airport in - flight meal guarantee based on blockchain provided by an embodiment of the present invention includes:

[0093] S1, access flight plans, historical records of in - flight meal guarantee airlines, historical records of in - flight meal guarantee settlements, contract airlines, historical flight cabin seat occupancy rates, flight seat reservations, and relationship matrix data of aircraft parking times through interfaces such as Webservice, RestfulAPI, MQ messages, FTP, and ODBC, and parse the above data using technologies such as XML to data object, JSON to data object, and deserialization;

[0094] The historical data is the data of the past 1 year. The flight schedule data mainly includes flight number, flight date, airline, aircraft type, aircraft number, route, flight status, abnormal status, aircraft position, planned landing time, actual landing time, planned departure time, actual departure time, schedule plan number, start time of in-flight meal support plan, end time of in-flight meal support plan, maximum number of passengers, etc. The historical data of in-flight meal support airlines mainly includes supported airlines, catering staff, support time, number of catering sub-cabin, signed flight crew, supported flights, whether the airline has complaints, schedule plan number, etc. The historical record data of in-flight meal support settlement mainly includes airlines, settlement time, settlement flights, settlement amount, whether there is non-payment, etc. The data of contract airlines mainly includes airlines, contract start time, contract end time, contract flight rules, cumulative service time, etc. The historical flight cabin seat occupancy rate data mainly includes flight number, flight date, cabin type, cabin seat occupancy rate. The flight reservation data mainly includes flight number, cabin type, number of passengers who have purchased tickets for the cabin. The aircraft position travel time relationship matrix data mainly includes the starting aircraft position number, the ending aircraft position number, and the travel time.

[0095] After the parsed flight schedule data is processed by copying data attributes, it is set as After the parsed historical data of in-flight meal support airlines is processed by copying data attributes, it is set as After the parsed historical record data of in-flight meal support settlement is processed by copying data attributes, it is set as After the parsed data of contract airlines is processed by copying data attributes, it is set as After the parsed historical flight cabin seat occupancy rate data is processed by copying data attributes, it is set as After the parsed flight reservation data is processed by copying data attributes, it is set as After the parsed aircraft position travel time relationship matrix data is processed by copying data attributes, it is set as 。

[0096] S2. Construct and initialize the variable structures of the credible level of airlines, the cumulative service time of in-flight meal support airlines, the number of flights of in-flight meal support airlines, the number of historical complaints of in-flight meal support airlines, and the number of non-payments of in-flight meal support airlines. Calculate the credible number and credible level of airlines. The specific steps are as follows:

[0097] S201. Construct the variable of the credible level structure of airlines for recording the credible levels of different airlines, where the key value is the name of the airline and the value value is the level number; construct the variable of the cumulative service time structure of in-flight meal support airlines , used to record the cumulative service time of different airlines, where the key value is the name of the airline and the value is the cumulative service time in days; construct a structural variable for the number of flights guaranteed by airlines for in-flight meals , used to record the cumulative number of flights guaranteed by airlines for in-flight meals, where the key value is the name of the airline and the value is the cumulative number of flights guaranteed by airlines for in-flight meals; construct a structural variable for the historical complaint number of airlines for in-flight meals , used to record the number of complaint flights of different airlines, where the key value is the name of the airline and the value is the number of complaint flights; construct a structural variable for the non-payment number of airlines for in-flight meals , used to record the number of non-payment flights of different airlines, where the key value is the name of the airline and the value is the number of non-payment flights.

[0098] S202, obtain , traverse , using the airline name in the single contract airline data obtained as the key value and 0 as the value, put the constructed data into , complete the initialization; using the airline name in the single data obtained as the key value and the cumulative service time in the single data obtained as the value, put the constructed data into , complete Traversal.

[0099] S203, obtain , traverse , obtain a single specific historical data of in-flight meal guarantee, using the airline name in the single historical data obtained as the key value, query , if the return is a null value, put 1 as the value into , if the return is not a null value, perform an operation to update the value by adding 1; if the "whether the airline has a complaint" attribute in the single historical data of in-flight meal guarantee obtained is "yes", then using the airline name in the single historical data obtained as the key value, query , if the return is a null value, put 1 as the value into , if the return is not a null value, perform an operation to update the value by adding 1; complete the traversal .

[0100] S204, obtain , traverse , obtain a single specific settlement historical record of in-flight meal guarantee. If the "whether there is non-payment" attribute in the specific record is "yes", then using the airline name in the single historical record obtained as the key value, query , if the return is a null value, put 1 as the value into , if the return is not a null value, perform an update operation of adding 1 to the returned value; complete the traversal .

[0101] S205, obtain , traverse , obtain specific records, and query respectively with the key values in the specific records 、 、 、 , and obtain successively 、 、 、 , where represents the cumulative service time of this airline, represents the cumulative number of flight meal guarantee flights of this airline, represents the number of complaint flights of this airline, represents the number of non - payment flights of this airline, then the airline credibility number is calculated as follows:

[0102]

[0103] where are all user - adjustable parameters, used to adjust the calculation influence of different elements on the airline credibility number.

[0104] Airline credibility level number , is calculated as follows:

[0105]

[0106] The larger the value, the worse the credibility. Put the calculated as the value into , complete the traversal . This step is to give the recommended value of the airline credibility level number, and users can make manual adjustment and intervention according to their needs.

[0107] S3, construct the flight meal guarantee collaborative operation structure variable, construct the on - chain storage structure variable according to the flight credibility level, initialize the flight meal guarantee collaborative operation structure variable and the on - chain storage structure variable according to the flight plan and contract, and perform the on - chain operation of the on - chain storage structure variable. The specific steps are as follows:

[0108] S301: Construct the flight meal guarantee collaborative operation structure variable,

[0109] ;

[0110] Among them is the number of flights served by the in-flight catering company represents the collaborative operation structure variable of in-flight catering support constructed around the th flight, , where represents the flight number, represents the flight date, represents the airline company, represents the aircraft type, represents the overall status of the flight, represents the flight route, represents the catering requirements for different cabins entered by the airline company, represents the signature for entering the catering requirements for different cabins by the airline company, used to prove that it is entered for a specific airline company, represents the estimated number of meals required for the flight, represents the signature of the catering department, used to confirm the entered catering requirements and the number of meals, represents the record of the process of the in-flight catering department preparing meals, represents the signature confirmation of the in-flight catering distribution department for the process of the in-flight catering department preparing meals, represents the record of the meal distribution process of the in-flight catering distribution department, represents the signature confirmation of the airline crew, represents the updated version number, used to solve the possible problem of update loss during multi-party writing; Figure 2 is an example;

[0111] S302: Construct the storage structure to be chained,

[0112]

[0113] represents the collaborative operation structure variable on the chain constructed around the th flight, The structure of is determined as follows:

[0114]

[0115] S303: Obtain , traverse , obtain the specific single-flight plan data, obtain , traverse , obtain the specific single contract airline company data, judge whether the airline company attribute in the flight plan data is consistent with the airline company attribute in the contract airline company data, if consistent, then obtain , obtain the specific credibility level of the airline company with the airline company name as the key, construct Structure, assign the flight number, flight date, airline, aircraft type, flight status, and flight route attributes in the flight schedule data to , and in is set to 1, and is put into , construct a of the corresponding structure according to the specific credibility level of the airline obtained, and assign the flight number, flight date, airline, aircraft type, flight status, and flight route attributes in the flight schedule data to , and in is set to 1, and is put into , and the traversal is completed .

[0116] S304: Store the data in the blockchain. To maintain the efficiency of data retrieval after uploading to the blockchain, the present invention will store in the form of a key-value structure on the blockchain to improve the performance of multi-party reading and writing, and at the same time reduce the problem of update loss caused by multi-party writing of the same data. Specifically, first establish a trusted link with the blockchain management platform through the access key given by the held blockchain management platform and the development package, and then start traversing data to obtain the specific , obtain in and , combine them into a string, call the blockchain writing interface, pass in the constructed combined string as the key, as the value, and upload the data to the blockchain. The traversal is completed .

[0117] S4. Obtain the demand data such as the catering types, quantities, and additional requirements for different cabins of specific flights of the airline, query the collaborative operation structure variables and the variables stored on the chain, verify the legality of the signature of the catering requirements entered by the airline, and update the collaborative operation structure variables and the variables stored on the chain based on version control. The specific steps are as follows:

[0118] S401: Obtain the data that the airline needs to enter; the airline enters the flight number 、flight date 、the catering requirements for different cabins and the signature for the catering requirements ,

[0119] ;

[0120] For the catering requirements of specific different cabins , where represents a specific cabin, represents the type of meal, represents the required quantity of meals. When this variable is -1, it means the airline has not specified the number of people, represents additional requirements, which are custom attributes. If the airline has special requirements, they can be described in this attribute. This structure is consistent with the structure of the same variable in the above collaboration structure, is a string structure, which is the one-way hash calculation of data. At the same time, the one-way hash value is encrypted using the private key of the specific airline to obtain data, which is used to prove that it is indeed the requirement of the specific airline and there is no forgery or tampering.

[0121] S402: Retrieve the specific and according to the input of the airline. Specifically, through and traverse . When the current flight number in the traversal is consistent with the date and and , the traversal is completed . Take as the variable that meets the requirements, and then establish a link with the blockchain platform. Combine and into a query key, and obtain the specific through the blockchain query interface.

[0122] S403: Verify the data. Use the public key of the specific airline to decrypt to obtain the specific one-way hash value, and then calculate the one-way hash value of . Compare the two. If they are consistent, it passes; otherwise, cancel the data entry of the airline this time; update the catering requirement data to and , and respectively increment the and attributes in by 1.

[0123] S404: Update the and data. Through and traverse . When the current specific data flight number in the traversal is consistent with the date and If it is consistent with then determine the specific data traversed currently is equal to in the value minus 1. If it is equal, then update the data to the specific data traversed. If it is not equal, then abandon this update. Establish a link with the blockchain platform and combine and into a query key, and obtain the specific data through the blockchain query interface. Determine whether the specific data is equal to in the value minus 1. If it is equal, then update the data to the specific data traversed. If it is not equal, then abandon this update.

[0124] S5: Obtain data such as historical flight seat occupancy rates, construct an intermediate calculation variable structure for the number of meal distribution requirements for flight seats, initialize the number of seat reservations for specific flights and specific seats, calculate the historical seat occupancy rate of specific flights and specific seats, calculate the historical complaint rate of specific flights, calculate the relationship between flights during meal delivery, calculate the proportion of different meal types for different seats of specific flights in the overall proportion, calculate the number of meals required for different seats of specific flights, update the collaborative operation structure variables and the variables stored on the chain. The specific steps are as follows:

[0125] S501: Obtain historical flight seat occupancy rate data , obtain flight seat reservation data , obtain historical data of airlines providing in-flight meals , obtain flight schedule data , obtain the data of the relationship matrix of aircraft position travel time , obtain the collaborative operation structure variables for in-flight meal guarantee , obtain the storage structure to be uploaded to the chain .

[0126] S502: Construct a variable structure to store the intermediate calculation values for each flight that needs to calculate the number of meal distribution requirements for different seats. It is a hash table structure, where the key value is a string composed of the flight number and the flight date, and the value is the structure of which represents the number of seat reservations for specific flights and specific seats, and is a list structure of which represents the specific seat, represents the number of seat reservations for the specific seat, the historical seat occupancy rate of the specific flight, and is a list structure of which represents the specific seat, Expressed as the historical occupancy rate of a specific cabin, Expressed as the historical complaint rate of a specific flight, Expressed as the allocated meal truck mark for a specific flight, used for subsequent calculation marking, Expressed as the calculation auxiliary structure for meal truck allocation of a specific flight, which is A list structure of Expressed as a specific cabin, Expressed as the proportion of the same meal type in the same cabin of the same meal truck in the whole, Expressed as the number of meal allocations for a specific cabin;

[0127] S503: Traverse , obtain the specific , get the specific flight number and flight date, and construct a list structure variable containing , traverse , search for data with the same flight number as , and according to the cabin and seat reservation number of the specific data, construct The data structure of , and put it into , until all data with the same flight number as are found, and the traversal ends , using the combined string of the flight number and flight date of as the key, construct The structure as the value, and assign to , and put the constructed key value and value into , and continue to traverse , and repeat the operation until the traversal is completed.

[0128] S504: Traverse , obtain the specific , and construct a list variable containing The structure of , where Expressed as the specific flight cabin, Expressed as the cumulative number of flights with the same flight and the same cabin, Expressed as the cumulative occupancy rate of the same flight and the same cabin, traverse , query the specific flight schedule data with the same flight number and flight date as , and obtain the schedule plan number of the specific flight schedule data , traverse , obtain the specific airline historical data for in-flight meal service guarantee with the same flight number as and the same schedule plan number attribute as , and traverse Retrieve data that is consistent with the flight numbers and flight dates in the historical data of the specific airline for in-flight meal service, and perform a cumulative addition of 1 for the corresponding cabin class, and a cumulative addition of the load factor for the corresponding cabin class. After traversal is completed Put the cumulative structured data into After traversing start calculating the average historical load factor for the specific cabin class The specific calculation method is as follows:

[0129]

[0130] Using the combination string of flight number and flight date as the key, query to obtain and update the average load factor for different cabin classes in Continue traversing and repeat the operation until traversal is completed.

[0131] S505: Traverse to obtain the specific Construct a variable to represent the total number of historical service flights, and construct a variable to represent the number of flights with complaints among the historical service flights. Traverse and query the specific flight schedule data that is consistent with the flight number and flight date, and obtain the schedule plan number of the specific flight schedule data After traversing obtain the specific airline historical data for in-flight meal service that is consistent with the flight number and whose schedule plan number attribute is consistent with Perform a cumulative addition of 1 operation. Check whether the "whether the airline has a complaint" attribute in the specific airline historical data for in-flight meal service is "yes". If so, perform a cumulative addition of 1 operation. After traversal is completed calculate the historical complaint rate The specific method is as follows:

[0132]

[0133] Using the combination string of flight number and flight date as the key, query to obtain and update the historical complaint rate data. Continue traversing ​​, repeat the operation until the traversal is completed.

[0134] S506: Traverse , construct a list structure variable containing the structure , where represents a specific cabin class, represents the meal type, represents the quantity of the same meal type on the same meal cart, obtain the specific , construct a variable , representing the number of flights for which the vehicle has loaded meals, initially 0, traverse , query the specific flight schedule data with the same flight number and flight date as , obtain the start time and end time of the in-flight meal support plan in the specific flight schedule data , the aircraft stand, continue to traverse , obtain the start time of the in-flight meal support plan of the specific flight schedule data , the aircraft stand, traverse according to the two aircraft stand data , obtain the travel time between them , when , it is considered that the connection meal distribution requirement can be met, query with the flight number and flight date as the key value , update the in the value, traverse , count and summarize , , , values, perform an increment operation. Continue to traverse , continue to make judgments based on the subsequent flight schedule data , when , it is considered that the same meal cart has reached the loading limit, is a constant value that can be adjusted by the user to control the maximum number of flights that each meal cart can carry meals. End the traversal , end the traversal .

[0135] S507: Obtain the constructed in S506, traverse from the beginning , obtain the specific , obtain the meal distribution requirements entered by the airline , according to the cabin class and meal type in the meal distribution requirements, query in the quantity corresponding to the eligible cabin class and meal type, use the flight number and flight date as the combined key, query , obtain the corresponding Structural data. Assume the number of the same type of meals in a specific cabin is , then the proportion of the same meal type in the same cabin of the same meal cart in the whole is The calculation method is as follows:

[0136]

[0137] Put the calculated data of different cabins and the data update of different cabin types into , and complete the update of . Continue to traverse from the beginning , skip the flights that have been marked as having been assigned meal carts, and repeat the above operations until all flights have been calculated.

[0138] S508: Traverse , obtain the specific , use flight number and flight date as the key to query , and obtain the corresponding structural data. According to flight number and flight date, query and traverse , obtain the corresponding maximum number of passengers . Assume the number of meal allocations in a specific cabin is . Obtain the historical seat occupancy rate of the specific cabin from , obtain the historical flight complaint rate from , obtain the number of seat reservations for the specific flight and specific cabin from , obtain the proportion of the same meal type in the same cabin of the same meal cart from , obtain the number of seat reservations for the specific flight and specific cabin from , obtain the proportion of the same meal type in the same cabin of the same meal cart from , obtain the proportion of the same meal type in the same cabin of the same meal cart from in the whole, then , then the specific calculation method is as follows:

[0139]

[0140] Update in , and update to in the attribute;

[0141] S509: Synchronize the data update in to the blockchain. Traverse , obtain the specific , then establish a connection with the blockchain platform, and use flight number and flight date as the combined query key, and obtain the specific one through the blockchain query interface , if there is an attribute, then update the data in to , and increment the attribute in by 1. Use the flight number and flight date of as the combined query key, and obtain the specific data through the blockchain query interface again. Judge whether the specific data is equal to the value in minus 1. If they are equal, update the data to the specific data traversed. If they are not equal, abandon this update.

[0142] S6. Obtain the confirmation status of the catering production department of the airline catering company and update the input data of its own catering production status. Retrieve the collaborative operation structure variables and the on-chain storage variables, verify the legality of the signature of the catering department, and update the catering production status to the collaborative operation structure variables and the on-chain storage variables. The specific steps are as follows:

[0143] S601: Obtain the input data required to obtain the confirmation status of the catering production department of the airline catering company and update its own catering production status. Specifically, the catering production department of the airline catering company needs to enter the flight number , flight date , the signature for confirming the entered catering requirements and catering quantities , and the catering production process status record . The variable is a string, recording the process status record of the airline catering company's catering production department for making the meals of this flight. is a string structure, which is the one-way hash calculation of the data and combined data. At the same time, the data obtained by encrypting the one-way hash value with the private key of the airline catering company's catering production department is used to prove that it is indeed confirmed by the airline catering company's catering production department and there is no forgery or tampering.

[0144] S602: Retrieve the specific and according to the input of the catering production department of the airline catering company. Specifically, traverse and . When the current flight number and date during traversal are consistent with and and , the traversal is completed . Then As a variable that meets the requirements, a link to the blockchain platform is then established, and and are combined into a query key, and specific is obtained through the blockchain query interface.

[0145] S603: Verify the data. Use the public key of the catering production department of the airline catering company to decrypt to obtain the specific one-way hash value, and then calculate the and combined data of the one-way hash value, compare the two, if they are consistent, it passes, otherwise cancel the data entry of the airline catering company's catering production department for this time; update the , data entered by the airline catering company's catering production department into and , and respectively increment the and in by 1.

[0146] S604: Update the and data. The processing method is the same as that of S404.

[0147] S7: Obtain the confirmation status of the airline catering company's meal distribution department and update the input data of its own meal distribution process record, query and obtain the collaborative operation structure variable and the on-chain storage variable, verify the legality of the signature of the airline catering distribution department, and update the meal distribution process record to the collaborative operation structure variable and the on-chain storage variable. The specific steps are as follows:

[0148] S701: Obtain the data required to obtain the confirmation status of the airline catering company's meal distribution department and update its own meal distribution process record. Specifically, the airline catering company's meal distribution department needs to enter the flight number required to confirm the status and update the meal distribution process record, the flight date , the signature for signature confirmation of the meal preparation process by the airline catering meal department, and the meal distribution process status record . The variable is a string, recording the process status record of the airline catering company's meal distribution department for this flight. is a string structure, which is the one-way hash calculation of data, and at the same time, the one-way hash value is encrypted using the private key of the airline catering company's meal distribution department to obtain data, which is used to prove that it is indeed confirmed by the airline catering company's meal distribution department and there is no forgery or tampering.

[0149] S702: Retrieve the specific and . Specifically, through and traverse . When the current flight number is consistent with the date and and , the traversal is completed . Take as the variable meeting the requirements, then establish a link with the blockchain platform, and take and to form a query key, and obtain the specific through the blockchain query interface.

[0150] S703: Verify the data. Use the public key of the in-flight meal company's meal distribution department to decrypt to obtain the specific one-way hash value, then calculate the one-way hash value of . Compare the two. If they are consistent, it passes; otherwise, cancel the data entry of the in-flight meal company's meal distribution department this time; update the , data entered by the in-flight meal company's meal distribution department into and , and respectively increment the and attributes in by 1.

[0151] S704: Update the and data. The processing method is the same as that of S404.

[0152] S8. Obtain the data of the flight crew of the in-flight meal guarantee flight to confirm the on-site guarantee status, retrieve the collaborative operation structure variable and the on-chain storage variable, verify the legality of the flight crew signature, and update the in-flight meal guarantee completion status to the collaborative operation structure variable and the on-chain storage variable. The specific steps are as follows:

[0153] S801: Obtain the data of the flight crew of the in-flight meal guarantee flight to confirm the on-site guarantee status. This part of the data will be signed and confirmed by the flight crew using the airline's business system. The airline's business system will input the flight number , flight date , and the confirmation signature of the on-site in-flight meal guarantee status . is in string structure, which is the one-way hash calculation of data. At the same time, the data obtained by encrypting the one-way hash value using the airline's private key is used to prove that it is indeed confirmed by the airline and there is no forgery or tampering.

[0154] S802: Retrieve specific and according to the input of the airline. Specifically, through and traverse When the current flight number during traversal is consistent with the date and and it is considered that the traversal is completed Then, take as the variable that meets the requirements. Subsequently, establish a link with the blockchain platform, and combine and into a query key, and obtain specific through the blockchain query interface.

[0155] S803: Verify the data. Use the public key of the airline to decrypt to obtain a specific one-way hash value, and then calculate the one-way hash value of the data. Compare the two. If they are consistent, it passes; otherwise, cancel the current airline data entry. Update the data entered by the airline into and and increment the and attributes in by 1 respectively.

[0156] S804: Update and the data. The processing method is the same as that in S404.

[0157] Example 2, as Figure 3 shown, the multi-party collaborative system for airport in-flight meal guarantee based on blockchain provided by the embodiment of the present invention includes a data parsing module 1, a blockchain operation module 2, an in-flight meal collaborative processing module 3, a meal demand estimation module 4, a basic data module 5, a data storage and processing module 6, an interface processing module 7, and a security control module 8;

[0158] The data parsing module 1 is used to access flight plans, historical records of in-flight meal guarantee airlines, historical records of in-flight meal guarantee settlements, contract airlines, historical flight seat occupancy rates, flight seat reservations, and relationship matrix data of aircraft position travel times through Webservice, RestfulAPI, MQ messages, FTP, and ODBC interfaces, and parse the above data using XML to data object, JSON to data object, and deserialization technologies;

[0159] The blockchain operation module 2 is used to construct and initialize the variable structures of the airline's trust level, the cumulative service time of the airline for in-flight meal service, the number of flights of the airline for in-flight meal service, the historical complaint number of the airline for in-flight meal service, and the number of non-payment of the airline for in-flight meal service, and calculate the airline's trust number and trust level;

[0160] The in-flight meal coordination processing module 3 is used to construct the variable structure of the in-flight meal service coordination operation, construct the variable structure of the on-chain storage structure according to the flight trust level, initialize the variable structure of the in-flight meal service coordination operation and the variable structure of the on-chain storage structure according to the flight plan and contract, and perform the on-chain operation on the variable structure of the on-chain storage structure;

[0161] The meal demand estimation module 4 is used to obtain the meal distribution types, quantities, and additional requirement data for different cabins of the airline's specific flights, query the coordination operation variable and the on-chain storage variable, verify the legality of the signature of the meal distribution requirements entered by the airline for the cabin, and update the coordination operation variable and the on-chain storage variable based on version control;

[0162] The basic data module 5 is used to obtain the historical seat occupancy rate data of the flight cabins, construct the intermediate calculation variable structure of the meal distribution quantity requirements for the flight cabins, initialize the seat reservation number data for the specific cabins of the specific flights, calculate the historical seat occupancy rate of the specific cabins of the specific flights, calculate the historical complaint rate of the specific flights, calculate the mutual relationship between flights during the meal delivery process, calculate the proportion of different meal types in the overall for the specific cabins of the specific flights, calculate the quantity of meals required for different cabins of the specific flights, and update the coordination operation variable and the on-chain storage variable;

[0163] The data storage and processing module 6 is used to obtain the confirmation status of the meal production department of the in-flight meal company and the input data for updating its own meal production status, retrieve and obtain the coordination operation variable and the on-chain storage variable, verify the legality of the signature of the meal department, and update the meal production status to the coordination operation variable and the on-chain storage variable;

[0164] The interface processing module 7 is used to obtain the confirmation status of the meal distribution department of the in-flight meal company and the input data for updating its own meal distribution process record, query and obtain the coordination operation variable and the on-chain storage variable, verify the legality of the signature of the in-flight meal distribution department, and update the meal distribution process record to the coordination operation variable and the on-chain storage variable;

[0165] The security control module 8 is used to obtain the data on the confirmation status of the on-site guarantee by the flight crew for the in-flight meal service flights, retrieve and obtain the coordination operation variable and the on-chain storage variable, verify the legality of the signature of the flight crew, and update the in-flight meal service completion status to the coordination operation variable and the on-chain storage variable.

[0166] As can be seen from the above embodiments, the present invention proposes a method and system for multi-party collaboration in airport airline catering support based on blockchain technology, which can significantly improve the efficiency of trusted business collaboration between airlines and catering companies, provide a unified multi-party collaboration system for airline catering support, effectively reduce the occurrence of payment refusals caused by inconsistent accounting between the two parties in the traditional method. At the same time, based on the trusted level of the airline, the present invention can give an optimized method for the on-chain storage structure that takes into account both the blockchain performance and the storage capacity on the basis of meeting the requirements of airline catering business collaboration, so that the system can run stably for a long time without worrying too much about the poor blockchain performance caused by a large storage capacity.

[0167] The present invention is oriented to the field of civil aviation airline catering support business, directly addressing the problems existing in traditional airline catering support, such as low multi-party collaboration efficiency and high cost of lack of mutual trust communication confirmation, and can reduce the occurrence of payment refusals in airline catering support, significantly improving the revenue level of catering companies. Currently, there are 281 transport airports and 82 transport airlines in the national civil aviation industry. Many airlines and airports have their own airline catering support subsidiaries, which can all be promoted as customers, presenting a large market space and expected to generate significant commercial benefits. At the same time, after the system of the present invention is put into production, it can bring revenue increase to catering companies. Taking a catering company at an airport with a throughput of 20 million in China as an example, it will cause a payment refusal loss of more than 2 million yuan per year just for catering support for a large airline. After the system of the present invention is launched, the phenomenon of payment refusal will be significantly reduced.

[0168] The present invention makes full use of the anti-tampering and anti-repudiation characteristics of blockchain technology, proposes a method and system for multi-party collaboration in airport airline catering support based on blockchain technology, constructs a method for efficient and trusted collaboration of multi-party airline catering support including airlines and catering companies, and gives an optimized method for the on-chain storage structure that takes into account both the blockchain performance and the storage capacity, so that the system can run stably for a long time without worrying too much about the poor blockchain performance caused by a large storage capacity. The present invention combines the specific business characteristics of airline catering support, deeply applies blockchain technology, and optimizes the structure according to the business, filling the technical gap in this business field.

[0169] In the traditional civil aviation in-flight meal service guarantee business field, the cooperation methods of each participating entity rely on relatively primitive methods. Airlines first sign service contracts with in-flight meal companies, stipulating the details of in-flight meal services. During the subsequent specific in-flight meal guarantee process for flights, airlines will first send the requirements for specific in-flight meal guarantees to in-flight meal companies by phone, instant messaging, etc. when the flight is in the planning stage. Then, the scheduling department of the in-flight meal company drives the meal production department to produce meals based on these requirements, the estimated number of passengers, and the situation of other flights that need to be guaranteed, and drives the meal distribution department to distribute meals according to the flight dynamics. After the meal distribution is completed, the meal distributor will confirm and sign with the crew of the specific flight to complete the in-flight meal service guarantee for the flight. During this guarantee process, the information transmission methods relied on by different participating entities are diverse. Understanding the entire process of in-flight meal guarantee requires repeated information transmission, resulting in low collaboration efficiency. At the same time, due to the use of non-standard information transmission methods and the lack of a credible basis among multiple parties, it is often difficult to quickly investigate and confirm the responsibilities between airlines and in-flight meal companies and among different departments of in-flight meal companies when problems such as flight meal distribution delays, distribution errors, and inconsistent ledgers on both sides occur. This often leads to the refusal of payment by airlines for specific flights, reducing the revenue of in-flight meal companies. After adopting the technical solution of the present invention, there will be a credible collaboration system based on blockchain technology between in-flight meal companies and airlines. Different airlines uniformly and standardly enter the in-flight meal guarantee requirements, and the crew of the airlines conducts the final service confirmation and can view the in-flight meal guarantee situation of each flight in real time through the system. In-flight meal companies can also unify the collaboration among different internal departments, thereby improving the collaboration efficiency of multi-party in-flight meal guarantees. At the same time, because all collaboration data is credibly stored based on blockchain technology, it is possible to quickly determine responsibilities based on the mutually trusted data in case of problems, thereby significantly reducing the phenomenon of airlines' refusal of payment and increasing the revenue of in-flight meal companies.

[0170] As described above, it is only a relatively preferred specific implementation manner 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, any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A blockchain-based multi-party collaborative method for airport catering security, characterized in that: The method comprises the following steps: S1, access flight plans, airline catering history, catering settlement history, contract airlines, historical flight cabin occupancy rate, flight booking, and aircraft travel time relationship matrix data, and use XML to data object, JSON to data object, and deserialization technology to parse the above data; S2, construct and initialize the variable structure of airline trust level, cumulative service time of airline catering guarantee, number of flights of airline catering guarantee, number of historical complaints of airline catering guarantee, number of refusal of airline catering guarantee, and calculate the trust number and trust level of airline; S3, construct the coordinated operation structure variables of airline catering guarantee, construct the on-chain storage structure variables according to the flight trust level, initialize the coordinated operation structure variables of airline catering guarantee and the on-chain storage structure variables according to the flight plan and contract, and perform the on-chain storage structure variable on-chain operation; The airline's trustworthy number alc is calculated as follows: Where du represents the airline's cumulative service time, co represents the airline's cumulative number of flights with guaranteed food, pl represents the number of flights with complaints, pr represents the number of flights with refused payment, and B, E, and F are all user-adjustable parameters used to adjust the impact of different elements on the calculation of the airline's trustworthiness number. The airline's trustworthiness level, al, is calculated as follows: S4, obtains the catering type, quantity, and additional requirements of different cabins on specific flights of airlines, queries the collaborative operation structure variables and the variables stored on the chain, verifies the legitimacy of the signature of the airline's entry of cabin catering requirements, and updates the collaborative operation structure variables and the variables stored on the chain based on version control; S5, obtain historical flight cabin occupancy rate data, construct the intermediate calculation variable structure of the flight cabin meal quantity demand; initialize the specific flight specific cabin booking number data, calculate the specific flight specific cabin historical occupancy rate, calculate the specific flight historical complaint rate, calculate the relationship between flights during the meal delivery process, calculate the overall proportion of meal types in different cabins of the specific flight, calculate the number of meals required for different cabins of the specific flight, and update the collaborative operation structure variables and the on-chain storage variables; The specific calculation method for the number of meals sfoc required for different cabins on a specific flight is as follows: Among them, mapc represents the maximum number of passengers, shlf represents the historical occupancy rate of a specific cabin, sren represents the number of people who have booked seats in a specific cabin on a specific flight, shcr represents the historical complaint rate of the flight, and sscc represents the proportion of the same catering type in the same cabin on the same dining car. S6, obtain the confirmation status of the catering company's catering department and update its own catering status input data, retrieve and obtain the collaborative operation structure variables and the on-chain storage variables, verify the legitimacy of the catering department's signature, and update the catering status to the collaborative operation structure variables and the on-chain storage variables; S7, obtain the confirmation status of the catering department of the airline catering company and update the input data of its own catering process record, query and obtain the collaborative operation structure variable and the on-chain storage variable, verify the legitimacy of the signature of the catering department, and update the catering process record to the collaborative operation structure variable and the on-chain storage variable; S8, obtain the data of the on-site catering status confirmed by the flight crew, retrieve the collaborative operation structure variables and the on-chain storage variables, verify the legitimacy of the flight crew signature, and update the catering completion status to the collaborative operation structure variables and the on-chain storage variables.

2. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S1, the above data is parsed by using XML to data object, JSON to data object and deserialization technology, including: setting the parsed flight plan data as flights_list after data attribute replication processing, setting the parsed airline meal guarantee airline history data as hdispatchs_list after data attribute replication processing, setting the parsed airline meal guarantee settlement history record data as hsettles_list after data attribute replication processing, setting the parsed contract airline data as contracts_list after data attribute replication processing, setting the parsed historical flight cabin occupancy rate data as hloads_list after data attribute replication processing, setting the parsed flight booking data as bookings_list after data attribute replication processing, and setting the parsed stand travel time relationship matrix data as standtimes_list after data attribute replication processing.

3. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S2, the variable structures of airline trust level, cumulative service time of airline catering guarantee, number of flights of airline catering guarantee, number of historical complaints of airline catering guarantee, and number of refusal of airline catering guarantee are constructed and initialized, and the airline trust number and trust level are calculated, including: S201, construct an airline trust level structure variable airlevel_map, which is used to record the trust levels of different airlines; construct an airline catering guarantee airline cumulative service time structure variable airduration_map, which is used to record the cumulative service time of different airlines; construct an airline catering guarantee airline flight number structure variable aircount_map, which is used to record the cumulative number of airline catering guarantee flights of different airlines; construct an airline catering guarantee airline historical complaint number structure variable aircomplaint_map, which is used to record the number of complaint flights of different airlines; construct an airline catering guarantee airline refusal number structure variable airprotest_map, which is used to record the number of refusal flights of different airlines; S202, obtain contracts_list, traverse contracts_list, use the airline name in the obtained single contract airline data as the key value, use 0 as the value value, put the constructed data into airlevel_map, and complete initialization; use the airline name in the obtained single data as the key value, use the accumulated service time in the obtained single data as the value value, put the constructed data into airduration_map, and complete the contracts_list traversal; S203, obtain hdispatchs_list, traverse hdispatchs_list, obtain a single specific airline catering guarantee historical data, use the airline name in the obtained single historical data as the key value, query aircount_map, if the return value is null, put 1 as the value into aircount_map, if the return value is not null, perform an update operation by adding 1 to the returned value; if the airline complaint attribute in the obtained single airline catering guarantee historical data is yes, use the airline name in the obtained single historical data as the key value, query aircomplaint_map, if the return value is null, put 1 as the value into aircomplaint_map, if the return value is not null, perform an update operation by adding 1 to the returned value; complete traversal of hdispatchs_list; S204, obtain hsettles_list, traverse hsettles_list, obtain a single specific airline meal guarantee settlement history record, if the whether to refuse payment attribute in the specific record is yes, then use the airline name in the obtained single history record as the key value, query airprotest_map, if the return value is null, put 1 as the value value into airprotest_map, if the return value is not null, then perform an update operation by adding 1 to the return value; complete traversal of hsettles_list; S205, obtain airlevel_map, traverse airlevel_map, obtain specific records, and use the key values ​​in the specific records to query airduration_map, aircount_map, aircomplaint_map, and airprotest_map respectively, and obtain du, co, pl, and pr in sequence, where du represents the cumulative service time of the airline, co represents the cumulative number of flights with guaranteed meals of the airline, pl represents the number of flights complained about by the airline, and pr represents the number of flights refused to pay by the airline. The airline's credible number alc is calculated as follows: Among them, B, E, and F are user-adjustable parameters, which are used to adjust the impact of different elements on the calculation of the airline's credibility number; The airline's trustworthiness level, al, is calculated as follows: Put the calculated al as value into airlevel_map and traverse to complete airlevel_map.

4. The blockchain-based airport catering guarantee multi-party collaboration method according to claim 1 is characterized in that: In step S3, the flight catering support collaborative operation structure variables are constructed, including: S301: Construct the catering support collaborative operation structure variable coos_list = (coo1, coo2, ..., coo i ,...,coo m ), i = 1, 2, ..., m, where m is the number of flights served by the catering company, coo i represents the coordinated operation structure variable of the airline catering support built around the ith flight, coo i =(no, da, ai, ar, st, al, ad, as, pc, mc, ml, tc, tl, uc, vn), where no represents the flight number, da represents the flight date, ai represents the airline, ar represents the aircraft type, st represents the overall status of the flight, al represents the airline's trust level, ad represents the catering requirements for different cabins entered by the airline, as represents the signature of the airline entering the catering requirements for different cabins, used to prove that it is entered for a specific airline, pc represents the estimated number of meals required for the flight, mc represents the signature of the catering department, used to confirm the entered catering requirements and the number of meals, ml represents the record of the catering department preparing meals, tc represents the signature confirmation of the catering department's preparation of meals, tl represents the record of the catering department's meal preparation process, uc represents the signature confirmation of the airline crew, and vn represents the update version number, used to solve the update loss problem that may exist when multiple parties write; S302: Construct the storage structure cochs_list that needs to be chained = (coch1, coch2, ..., coch j , ..., coch h ); j = 1, 2, ..., h, coch i Represents the on-chain collaborative operation structure variable built around the j-th flight. The structure of coch is determined by al, as follows: S303: Get flighTs_list, traverse flighTs_list, get specific single flight plan data, get contracts_list, traverse contracts_list, get specific single contract airline data, determine whether the airline attributes in the flight plan data are consistent with the airline attributes in the contract airline data, if they are consistent, get airlevel_map, use the airline name as the key to get the specific trust level of the airline, build the coo structure, assign the flight number, flight date, airline, aircraft type, flight status, and flight route attributes in the flight plan data to coo in turn, set vn in coo to 1, put coo into coos_list, construct the corresponding structure coch according to the specific trust level of the obtained airline, assign the flight number, flight date, airline, aircraft type, flight status, and flight route attributes in the flight plan data to coch in turn, set vn in coch to 1, put coch into cochs_list, and complete the traversal of flights_list; S304: Store the cochs_list data in the blockchain, and store coch in the form of a key-value structure on the blockchain. First, establish a trusted link with the blockchain management platform through the access key and development kit granted by the blockchain management platform, and then start traversing the cochs_list data, obtain the specific coch, obtain the no and da in coch, combine them into a string, call the blockchain write interface, pass in the constructed combined string as the key, coch as the value, upload the data to the blockchain, and complete the traversal of cochs_list.

5. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S4, the catering type, quantity, and additional requirements of different cabins of the airline's specific flights are obtained, the collaborative operation structure variables and the chain storage variables are queried, and the legitimacy of the signature of the airline's entry of the cabin catering requirements is verified, including: S401: Obtain the data that the airline needs to enter; the airline enters the flight number no, flight date da, catering requirements ad for different cabins, and signature as for the catering requirements, ad = (adi1, adi2, ..., adi k , ..., adi o ), k = 1, 2, ..., o, adi is the specific catering requirements for different cabins, adi = (shs, met, pec, rem), where shs represents the specific cabin, met represents the meal type, pec represents the number of meals required, when this variable is -1, it means that the airline has not specified the number of people, and rem represents additional requirements; S402: Retrieve specific coo and coch according to the input of the airline, traverse coos_list through no and da, and when the current coo flight number and date and no and da are consistent, the traversal of coos_list is completed, and coo is used as a variable that meets the requirements. Then, a link with the blockchain platform is established, and no and da are combined as a query key to obtain specific coch through the blockchain query interface; S403: Verify the as data, decrypt as with the public key of the specific airline, obtain the specific one-way hash value, and then calculate the one-way hash value of ad, compare the two, if they are consistent, pass, otherwise cancel the airline data entry; update the ad data of the catering requirements of different cabins to coo and coch, and add 1 to the vn attribute in coo and coch respectively; S404: Update coo and coch data, traverse coos_list through no and da, when the current specific data traversed, the flight number and date are consistent with no and da, then determine whether the current traversed specific data vn is equal to the vn value in coo minus 1, if equal, update the coo data to the traversed specific data, if not equal, abandon the update; establish a link with the blockchain platform, combine no and da as a query key, obtain specific data through the blockchain query interface, determine whether the specific data vn is equal to the vn value in coch minus 1, if equal, update the coch data to the traversed specific data, if not equal, abandon the update.

6. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S5, historical flight cabin occupancy rate data is obtained, and the intermediate calculation variable structure of the flight cabin catering quantity demand is constructed, including: S501: Get historical flight cabin load factor data hloads_list, get flight booking data bookings_list, get historical data of airlines providing catering services hdispatchs_list, get flight schedule data flights_list, get stand travel time relationship matrix data standtimes_list, get catering service collaborative operation structure variables coos_list, and get storage structure cochs_list that needs to be uploaded to the chain; S502: Construct a variable structure passnum_map to store the intermediate calculation value of each flight that needs to calculate the number of meals required for different cabins on the flight. The structure is a hash table, the key value is a string consisting of the flight number and the flight date, and the value value is a structure of (ren, hlf, hcr, scf, scc, sfoc); S503: traverse coos_list, obtain specific coo, obtain specific flight number and flight date, construct list structure variable shsppcs_list containing (shs, ppc), traverse bookings_list, find data consistent with coo flight number, construct (sgs, ppc) data structure and put it into sgsppcs_list according to the cabin space and number of bookings of specific data, until all data consistent with coo flight number are found, end traversal of bookings_list, use the combined string of coo flight number and flight date as key, construct (ren, hlf, hcr, scf, scc) structure as value, assign ren to sgsppcs_list, put the constructed key value and value into passnum_map, continue to traverse coos_list, repeat the operation until the traversal is completed; S504: traverse coos_list, obtain the specific coo, construct a list variable shsafnahls_list containing a (shs, afn, ahl) structure, traverse flights_list, query the specific flight plan data that is consistent with the flight number and flight date of coo, obtain the flight schedule number snum of the specific flight plan data, traverse hdispatchs_list, obtain the specific flight catering airline historical data that is consistent with the flight number of coo and the flight schedule number attribute is consistent with snum, traverse hloads_list, obtain the data that is consistent with the flight number and flight date of the specific flight catering airline historical data, add 1 to the afn of the corresponding cabin, and add 1 to the ahl of the corresponding cabin. Accumulate the passenger load factor, traverse hdispatchs_list, put the accumulated (shs, afn, ahl) structure data into shsafnahls_list, traverse shsafnahls_list, and start calculating the historical passenger load factor average hls of the specific cabin. The specific calculation method is: Use the combined string of coo's flight number and flight date as the key, query passnum_map, get (ren, hlf, hcr, scf, scc), update the average passenger load factor of different cabins in hlf, continue to traverse coos_list, and repeat the operation until the traversal is completed; S505: traverse coos_list, obtain the specific coo, construct variable hflc, obtain the flight schedule number snum of the specific flight schedule data, traverse hdispatchs_list, obtain the specific airline catering service historical data that is consistent with the flight number of coo and the flight schedule number attribute is consistent with snum, perform hflc plus 1 operation, determine whether the airline complaint attribute in the specific airline catering service historical data is yes, if yes, perform hcoc plus 1 operation, traverse hdispatchs_list to complete, and calculate the historical complaint rate hcr. The specific method is as follows: Use the combined string of coo's flight number and flight date as the key, query passnum_map, get (ren, hlf, hcr, scf), update the hcr historical complaint rate data, continue to traverse coos_list, and repeat the operation until the traversal is completed; S506: traverse coos_list, construct a list structure variable shsmettocs_list containing the structure (shs, met, toc), where shs represents a specific cabin, met represents a meal type, and toc represents the number of meals of the same type in the same dining car, obtain the specific coo, and construct a variable cffc; S507: Get the shsmettocs_list constructed in S506, traverse coos_list from the beginning, get the specific coo, get the catering demand ad entered by the airline, and query the corresponding number of cabin types and meal types that meet the requirements in shsmettocs_list according to the cabin and meal type in the catering demand. Use the flight number and flight date of coo as the combination key, query passnum_map, and get the corresponding (ren, hlf, hcr, scf, scc) structure data. Assuming that the number of meals of the same type in the specific cabin is toc, the top calculation method for the proportion of the same catering type in the same cabin of the same dining car is: S508: traverse coos_list, obtain the specific coo, use the coo flight number and flight date as the key, query passnum_map, obtain the corresponding (ren, hlf, hcr, scf, scc, sfoc) structure data, query and traverse flights_list according to the coo flight number and flight date, obtain the corresponding maximum passenger volume mapc, assume that the number of meals for a specific cabin is sfoc, obtain the historical passenger load factor shlf of the specific cabin from hlf, obtain the historical complaint rate shcr of the flight from hcr, obtain the number of seats reserved for a specific cabin of a specific flight sren from ren, and obtain the proportion sscc of the same meal type in the same cabin of the same dining car from scc. The specific calculation method of sfoc is as follows: Update sfoc in (ren, hlf, hcr, scf, scc, sfoc), and update (ren, hlf, hcr, scf, scc, sfoc) to the pc attribute in coo; S509: Update and synchronize the data in coos_list to the blockchain, traverse coos_list, obtain the specific coo, and then establish a link with the blockchain platform.

7. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S6, the confirmation status of the food preparation department of the airline catering company is obtained and the input data of the food preparation status of the airline catering company is updated, including: S601: Obtain the data that the catering company's catering production department needs to input to confirm the status and update its own catering production status; the catering company's catering production department needs to enter the flight number no, flight date da, signature mc for confirming the entered catering requirements and catering quantity, and catering production process status record ml, where the ml variable is a string that records the process status record of the catering company's catering production department preparing the flight's meals, and mc is a string structure, which is a one-way hash calculation of the ad data and the pc data combined, and the one-way hash value is encrypted using the private key of the catering company's catering production department; S602: Retrieve specific coo and coch according to the input of the catering production department of the airline catering company, traverse coos_list through no and da, and when the current coo flight number and date and no and da are consistent, the traversal of coos_list is completed, and coo is used as a variable that meets the requirements. Then, a link with the blockchain platform is established, and no and da are combined as a query key to obtain specific coch through the blockchain query interface; S603: Verify the mc data, use the public key of the catering company's food production department to decrypt mc, obtain a specific one-way hash value, and then calculate the one-way hash value of the ad and pc combined data, compare the two, if they are consistent, pass, otherwise cancel the data entry of the catering company's food production department; update the mc and ml data entered by the catering company's food production department to coo and coch, and add 1 to the vn attribute in coo and coch respectively; S604: Update coo and coch data.

8. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S7, the confirmation status of the catering department of the airline catering company is obtained and the input data of the catering process record of the airline catering company is updated, and the collaborative operation structure variables and the on-chain storage variables are queried and obtained, including: S701: Obtain the data required for the catering department of the airline catering company to confirm the status and update its own catering process records; S702: Specific coo and coch are retrieved based on the input from the catering department of the airline catering company. S703: Verify the tc data, use the public key of the catering department of the airline catering company to decrypt tc, obtain the specific one-way hash value, and then calculate the one-way hash value of mc, compare the two, if they are consistent, pass, otherwise cancel the data entry of the catering department of the airline catering company; update the tc and tl data entered by the catering department of the airline catering company to coo and coch, and add 1 to the vn attribute in coo and coch respectively; S704: Update coo and coch data.

9. The blockchain-based airport catering multi-party collaboration method according to claim 1 is characterized in that: In step S8, the legitimacy of the flight crew signature is verified, and the completion status of the flight catering guarantee is updated to the collaborative operation structure variable and the on-chain storage variable, including: S801: Obtain the data of the on-site catering status confirmed by the flight crew of the catering service flight; S802: Retrieve specific coo and coch according to the input of the airline, traverse coos_list through no and da, and when the current coo flight number and date and no and da are consistent, the traversal of coos_list is completed, and coo is used as a variable that meets the requirements. Then, a link with the blockchain platform is established, and no and da are combined as a query key to obtain specific coch through the blockchain query interface; S803: Verify the uc data, decrypt the uc using the airline's public key to obtain a specific one-way hash value, then calculate the one-way hash value of the tl data, compare the two, and pass if they are consistent, otherwise cancel the airline data entry; update the uc data entered by the airline to coo and coch, and add 1 to the vn attribute in coo and coch respectively; S804: Update coo and coch data.

10. A blockchain-based airport catering multi-party collaborative system, characterized by: The system implements the blockchain-based airport catering guarantee multi-party collaboration method as described in any one of claims 1-9, and the system includes: Data parsing module (1), used to access flight schedule, history of airline catering guarantee, history of airline catering guarantee settlement, contract airline, historical flight cabin occupancy rate, flight booking, and aircraft seat travel time relationship matrix data, and parse the above data using XML to data object, JSON to data object, and deserialization technology; Blockchain operation module (2), used to construct and initialize the variable structures of airline trust level, cumulative service time of airline catering guarantee, number of flights of airline catering guarantee, number of historical complaints of airline catering guarantee, number of refusal of airline catering guarantee, and calculate the trust number and trust level of airline catering guarantee; The in-flight catering collaborative processing module (3) is used to construct the in-flight catering collaborative operation structure variables; The meal demand estimation module (4) is used to obtain the meal type, quantity, and additional requirements of different cabins of the airline's specific flights, query the collaborative operation structure variables and the on-chain storage variables, and verify the legitimacy of the signature of the airline's entry of the cabin meal demand; Basic data module (5), used to obtain historical flight cabin occupancy rate data and construct an intermediate calculation variable structure for flight cabin catering quantity requirements; The data storage and processing module (6) is used to obtain the confirmation status of the food preparation department of the airline catering company and update its own food preparation status input data; The interface processing module (7) is used to obtain the confirmation status of the catering department of the airline catering company and update the input data of its own catering process record, and query and obtain the collaborative operation structure variables and the chain storage variables; The safety control module (8) is used to verify the legitimacy of the flight crew's signature and update the completion status of the flight catering guarantee to the collaborative operation structure variables and the on-chain storage variables.

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