Identity identification and compensation device in data consistency comparison and control method

Abstract

The invention discloses an identity affirmation and compensation device in data consistency comparison and a control method, and belongs to the technical field of data processing. The device comprises a basic data module, a weight configuration module, a data identity affirmation module, a TAC module and a compensation check module. According to the method, key fields and non-key fields are dynamically divided through a weight configuration module, accurate matching is adopted for the key fields, and fuzzy matching based on an editing distance algorithm is adopted for the non-key fields to calculate similarity; then, the average similarity of the fields is calculated according to weight grouping, and the identity affirmation rate percentage is obtained by combining weight weighting; if the affirmation rate exceeds a threshold value, starting consistency comparison, otherwise, transferring to manual review; and when the comparison result is inconsistent, the compensation check module performs delay retry and targeted secondary check on the inconsistent field, so that the problem of false inconsistency caused by data synchronization delay is effectively solved. And meanwhile, a layered matching mechanism is combined with compensation verification, so that the data comparison success rate is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a device and control method for identity identification and compensation in data consistency comparison. Background Technology

[0002] With the continued rapid growth of passenger traffic in China's civil aviation industry, departure systems need to process massive amounts of booking, check-in, and baggage drop-off records. Modern aviation services have fully shifted towards digitalization, including the application of new technologies such as electronic tickets, self-service check-in, and facial recognition, generating a large amount of structured and unstructured data. The normal operation of aviation business highly depends on the accuracy of data, but the surge in data volume and the increase in business complexity have led to severe challenges in data consistency comparison. Due to the lack of effective identity determination standards, similar business data are difficult to accurately identify as the same data entity, resulting in low comparison success rates, high manual review costs, and wasted system resources, directly affecting the operational efficiency of airlines and passenger service experience.

[0003] Currently, the data consistency checking systems commonly used in the industry (such as TravelSky's TACS system) mainly compare the same type of business data (flight data, passenger data, ticket data, seat map data, etc.) stored in various software within the passenger service system's managed service with the standard data in the host system (Departure Control System, or DCS). These systems typically rely on exact matching rules to compare key fields (such as flight number, date, etc.) item by item, and depend on a pre-defined comparison rule base for result verification. Existing technologies usually include three basic modules: data extraction, rule matching, and result output, with the comparison process initiated through scheduled tasks or trigger mechanisms.

[0004] Traditional direct comparison methods have significant shortcomings: First, due to differences in data structure, storage mechanisms, and update frequency between host systems and open systems, relying solely on precise matching of key fields can easily lead to misjudgments due to data synchronization delays or minor format differences. Second, differences in non-critical fields (such as boarding gate information and flight status) are not quantified and incorporated into the evaluation system, resulting in a large amount of "suspected inconsistencies" requiring manual intervention. Furthermore, existing systems lack a secondary verification mechanism for inconsistent data, making it impossible to identify false inconsistencies caused by temporary synchronization delays. Statistics show that the success rate of such methods is only about 85%, with a significant amount of time wasted on invalid comparisons and manual review.

[0005] In summary, existing data consistency comparison technologies, due to the lack of identity verification mechanisms and insufficient fault tolerance, are ill-equipped to meet the challenges of surging civil aviation business data volumes and real-time requirements. There is an urgent need for a mechanism that can intelligently determine data identity, support hierarchical matching rules, and provide compensation verification to significantly improve the comparison success rate and system automation level. Summary of the Invention

[0006] This invention aims to solve the problem of low data comparison success rate in the prior art. By establishing a data identity recognition and consistency rate compensation mechanism, it provides a device and control method for identity recognition and compensation in data consistency comparison, so as to improve comparison efficiency and accuracy.

[0007] This invention is achieved through the following technical solution: In one aspect, the present invention provides a device for identity verification and compensation in data consistency comparison. The device includes a basic data module, a weight configuration module, a data identity verification module, an application data consistency check module (Travelsky Application Checking, or TAC for short), and a compensation check module, wherein: The basic data module is used to provide comparison data and calibration data; The weight configuration module is used to dynamically configure field weights; The data identity verification module is used to calculate the data identity verification rate in a hierarchical manner, including precise matching of key fields and fuzzy matching of non-key fields. The TAC module is used for data consistency comparison and checking; The compensation check module is used to perform a secondary consistency comparison check on inconsistent data.

[0008] In another aspect, the present invention provides a method for identity determination and compensation in data consistency comparison, used to control the aforementioned device for identity determination and compensation in data consistency comparison, the method comprising: S1: The basic data module receives a data comparison request, queries the data to be compared and the calibration data based on the request information, and triggers the data comparison; S2: The weight configuration module assigns field weights based on the data content and business criticality information corresponding to the data to be compared and the calibration data, including key fields, relatively key fields and non-key fields; S3: The data identity determination module performs hierarchical matching on the data based on the data from the weight configuration module and the assigned field weight information to obtain the data similarity of each field, i.e., field similarity. S4: The data identity determination module calculates the average similarity of all fields within the weighted reorganization based on the weights, and calculates the weighted average based on the obtained average similarity and weights to obtain the identity determination percentage of the two sets of data respectively; S5: Based on the percentage of the identity recognition rate of the two sets of data, determine whether the two sets of data can be compared for consistency. S6: If the determination result is that consistency comparison can be performed, the TAC module will perform consistency comparison between the data to be compared and the calibration data according to the data comparison rules and obtain the comparison result; if the determination result is that consistency comparison cannot be performed, it means that the data does not have the sameness, so no comparison will be performed and manual review will be carried out later.

[0009] Furthermore, in S3, the hierarchical matching of the data to obtain the data similarity of each field, i.e., field similarity, specifically includes: S301: Set the weight threshold for key fields, and determine whether a data field is a key field based on the weight information of the data. S302: If the weight of the data is greater than or equal to the weight threshold of the key field, then the data field is the key field. The data identity identification module accurately matches the key field to obtain the key field matching similarity. Furthermore, in S302, the acquisition of the key field matching similarity is specifically as follows: if the field data is completely consistent, the similarity is 1; if it is not completely consistent, the similarity is 0.

[0010] S303: If the weight of the data is less than the weight threshold of the key field, the data identity identification module uses fuzzy matching for the non-key fields to calculate the matching similarity of the non-key fields.

[0011] Furthermore, in S303, the non-key field matching similarity is obtained by calculating field similarity using an edit distance algorithm, where a smaller edit distance indicates that the two strings are more similar. The formula for the edit distance algorithm is as follows:

[0012] In the formula, Indicates similarity between non-key fields; edit distance Represents a string Convert to The minimum number of operations required; This represents the maximum length of the two strings.

[0013] Furthermore, in S4, the step of calculating the average similarity of all fields within the weighted recombination based on weights includes: Group all fields according to their weights, resulting in a total of 100 groups. n Groups, each weighted group contains k One field; Divide the sum of the similarities of all fields under each weight by the number of fields under that weight.

[0014] The expression for calculating the percentage of identity recognition rate is as follows:

[0015] In the formula, Indicates the first j Similarity of each field , For natural numbers, it represents the first natural number. i The total number of fields in each weight group; Indicates the first i The weights of each weight group , is a natural number, representing the total number of weighted reorganizations.

[0016] Furthermore, in S5, the determination of whether the two sets of data can be compared based on the percentage of the identity recognition rate of the two sets of data is as follows: a threshold for the data identity recognition rate is set. If the data identity recognition rate exceeds the set threshold, it is considered that the two sets of data come from the same data and can be compared. Otherwise, a comparison cannot be made.

[0017] Furthermore, in S6, if the determination result indicates that a consistency comparison can be performed, the TAC module performs a consistency comparison between the data to be compared and the calibration data according to the data comparison rules and obtains the comparison result, which also includes: S601: If the comparison results of the data to be compared and the calibration data are consistent, they are considered to be identical, and the data comparison ends; S602: If the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison is performed through the compensation check module. If the second comparison results are consistent, they are considered to be identical and the data comparison ends. Otherwise, no further comparison is performed and a manual review is required later.

[0018] Furthermore, in S602, if the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison is performed through the compensation check module, specifically as follows: S602-1: When the data comparison results are inconsistent, the compensation check module is entered. The compensation check module checks whether there is data that needs to be compensated and checks whether the delay time of the data to be compensated has been reached. S602-2: If the delay time is reached, the compensation check module queries the corresponding flight data and calibration data to be compared based on the set of inconsistent fields, and performs consistency comparison on the queried data according to the comparison rules. During the comparison, only the data corresponding to the inconsistent fields are compared.

[0019] Compared with the prior art, the present invention has the following advantages: 1. This invention divides fields into key fields, relatively key fields, and non-key fields through a weight configuration module. Key fields use exact matching to ensure the core accuracy of the data, while non-key fields use an edit distance algorithm for fuzzy matching and similarity metric calculation. This hierarchical mechanism, combined with weighted average calculation, upgrades the determination of data correlation from absolute matching to probabilistic evaluation, solving the problem of systematic misjudgment caused by slight differences in data format.

[0020] 2. To address the deficiency of traditional systems lacking secondary verification, this invention designs a compensation check module that includes delayed retries and targeted comparison. When initial comparisons show inconsistencies, the system does not immediately transfer the data to manual processing; instead, it first uses a delay mechanism to avoid temporary data synchronization problems. Subsequently, the compensation module performs precise secondary verification only on the inconsistent fields. This design avoids the waste of resources from repeated comparisons of all fields and increases the success rate of civil aviation data comparison from the industry average of 85% to over 90%, significantly reducing the cost of manual review.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for identifying and compensating for identity discrepancies in data consistency comparison according to the present invention. Figure 2 This is a diagram showing the result of the secondary verification mechanism executed by the compensation check module of this invention when the initial data comparison is inconsistent. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1: In this embodiment, a device for identity verification and compensation in data consistency comparison is provided. The device includes a basic data module, a weight configuration module, a data identity verification module, an application data consistency check module (Travelsky Application Checking, or TAC for short), and a compensation check module, wherein: The basic data module is used to provide comparison data and calibration data; The weight configuration module is used to dynamically configure field weights; The data identity verification module is used to calculate the data identity verification rate in a hierarchical manner, including precise matching of key fields and fuzzy matching of non-key fields. The application data consistency check module (TAC module) is used for data consistency comparison and check; The compensation check module is used to perform a secondary consistency comparison check on inconsistent data.

[0026] Example 2: In this embodiment, please refer to Figure 1 This paper provides a method for identity verification and compensation in data consistency comparison, the method comprising the following steps: Step S1: Receive a data comparison request, query the data to be compared and the calibration data based on the request information, and trigger the data comparison; Step S2: The weight configuration module assigns field weights based on the data content and business criticality information corresponding to the data to be compared and the calibration data, including key fields, relatively key fields and non-key fields; Step S3: The data identity determination module performs hierarchical matching on the data based on the data from the weight configuration module and the assigned field weight information to obtain the data similarity of each field, i.e., field similarity. Furthermore, the hierarchical matching of the data to obtain the data similarity of each field, i.e., field similarity, specifically includes: Step S301: Set the key field weight threshold, and determine whether the data field is a key field based on the weight information of the data; Step S302: If the weight of the data is greater than or equal to the weight threshold of the key field, then the data field is the key field. The data identity identification module accurately matches the key field to obtain the key field matching similarity. Furthermore, in step S302, the acquisition of the key field matching similarity is specifically as follows: the similarity is 1 if the field data is completely consistent, and 0 if they are not completely consistent.

[0027] Step S303: If the weight of the data is less than the weight threshold of the key field, the data identity identification module uses fuzzy matching for the non-key fields to calculate the matching similarity of the non-key fields.

[0028] Furthermore, in step S303, the non-key field matching similarity is obtained by calculating field similarity using an edit distance algorithm, where a smaller edit distance indicates greater similarity between the two strings. The formula for the edit distance algorithm is as follows:

[0029] In the formula, Indicates similarity between non-key fields; edit distance Represents a string Convert to The minimum number of operations required; This represents the maximum length of the two strings.

[0030] Step S4: The data identity determination module calculates the average similarity of all fields within the weighted reorganization based on the weights, and calculates the weighted average based on the obtained average similarity and weights to obtain the identity determination percentage of the two sets of data respectively. Furthermore, in step S4: The calculation of the average similarity of all fields within the weighted recombination based on weights includes: Group all fields according to their weights, resulting in a total of 100 groups. n Groups, each weighted group contains k One field; Divide the sum of the similarities of all fields under each weight by the number of fields under the corresponding weight; The expression for calculating the percentage of identity recognition rate is as follows:

[0031] In the formula, Indicates the first j Similarity of each field , For natural numbers, it represents the first natural number. i The total number of fields in each weight group; Indicates the first i The weights of each weight group , is a natural number, representing the total number of weighted reorganizations.

[0032] Step S5: Based on the obtained identity recognition rate percentage of the two sets of data, determine whether the two sets of data can be compared for consistency. Furthermore, the determination of whether the two sets of data can be compared based on the percentage of the identity recognition rate of the two sets of data is as follows: a threshold for the data identity recognition rate is set. If the data identity recognition rate exceeds the set threshold, it is considered that the two sets of data come from the same data and the success rate of comparison is very high, so a consistency comparison can be performed. Otherwise, a consistency comparison cannot be performed.

[0033] Step S6: If the determination result is that consistency comparison can be performed, the TAC module performs consistency comparison between the data to be compared and the calibration data according to the data comparison rules and obtains the comparison result; if the determination result is that consistency comparison cannot be performed, it means that the data do not have the sameness, so no comparison is performed and manual review is required later. Furthermore, in step S6, if the determination result indicates that a consistency comparison can be performed, the TAC module performs a consistency comparison between the data to be compared and the calibration data according to the data comparison rules and obtains the comparison result, which also includes: Step S601: If the comparison results of the data to be compared and the calibration data are consistent, they are considered to be identical, and the data comparison ends; Step S602: If the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison is performed through the compensation check module. If the second comparison results are consistent, they are considered to be identical and the data comparison ends. Otherwise, no further comparison is performed and a manual review is required later.

[0034] Furthermore, in step S602, if the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison is performed through the compensation check module, specifically as follows: Step S602-1: When the data comparison results are inconsistent, the compensation check module is entered. The compensation check module checks whether there is data that needs to be compensated and checks whether the delay time of the data to be compensated has been reached. Step S602-2: If the delay time is reached, the compensation check module queries the corresponding flight data and calibration data to be compared based on the set of inconsistent fields, and performs consistency comparison on the queried data according to the comparison rules. During the comparison, only the data corresponding to the inconsistent fields are compared.

[0035] Experimental example: In this experimental example, based on the method provided by the present invention, the present invention will be further illustrated using specific experimental data, as follows: First, the basic data module sends a request to the DC to query all flight information to be compared, and the DC reports the flight information back to the basic data module in the form of a message; the basic data module sends a request to the DCS to query all baseline flight information, and the DCS reports the flight information back to the basic data module in the form of a message. Secondly, the weight configuration module obtains data from the basic data module and configures weights for fields based on their business criticality, as follows (due to the large number of fields involved, only a few are selected as examples here): Fields: Flight number, flight suffix, and flight date are key fields (weight 0.6), flight status is a relatively key field (weight 0.3), and gate information is a non-key field (weight 0.1).

[0036] Next, the data identity verification module performs stratified matching on the data. Key fields (weight 0.6) are matched precisely, while non-key fields (weights 0.3 and 0.1) are matched fuzzily. After matching, the data similarity for each field (hereinafter referred to as field similarity) is obtained. Then, the intra-group average field similarity is calculated according to the weights, and finally, a weighted average is calculated to obtain the identity verification rate, as shown in the following formula:

[0037] In the formula, Indicates the first j Similarity of each field , For natural numbers, it represents the first natural number. i The total number of fields in each weight group; Indicates the first i The weights of each weight group , is a natural number, representing the total number of weighted reorganizations.

[0038] This involves calculating the similarity of all fields, specifically by calculating field similarity based on fields with different weights, including: A. Key fields (weight 0.6): Perform precise matching on the flight number + flight suffix field and the flight date field. If the field data is completely consistent, the similarity score is 1; if the data is not completely consistent, the similarity score is 0.

[0039] B. Non-critical fields (weights of 0.3 and 0.1): Fuzzy matching is performed on the flight status and gate information fields. The edit distance algorithm is used here; a smaller edit distance indicates greater similarity between the two strings. Field similarity is calculated using the core formula: Similarity = 1 - (Edit Distance / Maximum Length of the Two Strings), i.e.:

[0040] In the formula, edit distance Indicates the string Convert to Minimum number of operations required (including insertion, deletion, and replacement); denominator Take the maximum length of the two strings and use it for the normalization operation to ensure that the similarity value is within a certain range. Within the range.

[0041] For example: If S1 = "Check-in open" and S2 = "Check-in open", the edit distance is 0, and the similarity is 1. 0 / 4 = 1 (perfect match); If S1 = "Check-in open", S2 = "Check-in open", the edit distance is 1, and the similarity is 1. 1 / 5 = 0.8 (partial match).

[0042] The similarity scores for all fields obtained after matching are as follows: Flight number + flight suffix (1102A and 1102A) = 1; Flight dates (March 3, 2020 and March 3, 2025) = 1; Flight status ("Check-in open" and "Check-in open") = 1 1 / 5 = 0.8; Gate information ("C16 / D27 / F15" and "C16 / D27") = 1 - 4 / 11 = 0.636.

[0043] Then, the average field similarity under each weight is calculated, which is the sum of all field similarities under each weight divided by the total number of fields under that weight, including: A. Key fields (weight 0.6): Average field similarity = ((flight number + flight suffix) field similarity + flight date field similarity) / 2 = (1 + 1) / 2 = 1; B. Non-critical fields (weight 0.3): Average field similarity = (flight status field similarity) / 1 = 0.8 / 1 = 0.8; C. Non-critical fields (weight 0.1): Average field similarity = (similarity of boarding gate information fields) / 1 = 1 / 1 = 0.636.

[0044] Finally, the weighted average is calculated based on the weights to obtain the identity recognition rate, i.e.: Identity verification rate = (weight 0.6 average field similarity × 0.6 + weight 0.3 average field similarity × 0.3 + weight 0.1 average field similarity × 0.1) / (0.6 + 0.3 + 0.1) = (1 × 0.6 + 0.8 × 0.3 + 0.636 × 0.1) / (0.6 + 0.3 + 0.1) = 0.9036 = 90.36%.

[0045] If the identity recognition rate reaches 90% or higher, the data is considered to be identical. The TAC module compares the flight information data of DC and DCS according to the flight data comparison rules. If the data comparison is inconsistent, the inconsistent data is sent to the compensation check module for a second consistency comparison.

[0046] If the data is not identical, a manual review will be conducted later.

[0047] In this experimental example, the compensation check module performs a secondary consistency comparison. Specifically, the compensation check module receives information about inconsistent fields, sends a request to the DC (Data Center) to query the flight information to be compared for all inconsistent fields, and the DC reports the flight information back to the compensation check module via a message. It also sends a request to the DCS (Data Center Service) to query the baseline flight information for all inconsistent fields, and the DCS reports the flight information back to the compensation check module via a message. The compensation check module performs a data consistency comparison check on the two sets of data according to the inconsistent field names and data comparison rules, obtaining the two sets of compared data. Based on the data, it outputs the consistency rate for each flight field, such as... Figure 2 As shown; Figure 2 The main presentation showcases the results of the secondary verification mechanism executed by the compensation check module when the initial data comparison is inconsistent. The secondary verification mechanism effectively solves the problem of false inconsistency caused by data synchronization delay in traditional comparison systems, increasing the overall success rate of civil aviation data comparison to over 90% and significantly reducing the cost of manual review.

[0048] In this invention, the technical terms involved are shown in Table 1 below. Table 1 lists the technical terms and their corresponding explanations: Table 1: Explanation of Technical Terms

[0049] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A device for identity verification and compensation in data consistency comparison, characterized in that, The device includes: The basic data module is used to provide comparison data and calibration data; The weight configuration module is used to dynamically configure field weights; The data identity verification module is used to calculate the data identity verification rate in a hierarchical manner, including precise matching of key fields and fuzzy matching of non-key fields; The TAC module is used for data consistency comparison and checking. The compensation check module is used to perform a secondary consistency comparison check on inconsistent data.

2. A method for identity determination and compensation in data consistency comparison, used to control the identity determination and compensation device in data consistency comparison as described in claim 1, characterized in that, The method includes: The basic data module receives a data comparison request, queries the data to be compared and the calibration data based on the request information, and triggers the data comparison. The weight configuration module assigns field weights based on the data content and business criticality information corresponding to the data to be compared and the calibration data, including key fields, relatively key fields and non-key fields; The data identity determination module performs hierarchical matching of the data based on the data from the weight configuration module and the assigned field weight information to obtain the data similarity of each field, i.e., field similarity. The data identity determination module calculates the average similarity of all fields within the weighted reorganization based on the weights, and calculates a weighted average based on the obtained average similarity and weights to obtain the identity determination percentage of the two sets of data respectively. Based on the percentage of the sameness identification rate of the two sets of data, it is determined whether the two sets of data can be compared for consistency. If the determination result indicates that a consistency comparison can be performed, the TAC module will perform a consistency comparison between the data to be compared and the calibration data according to the data comparison rules and obtain the comparison result; if the determination result indicates that a consistency comparison cannot be performed, it means that the data do not have the sameness, so no comparison will be performed and a manual review will be conducted later.

3. The method for identity determination and compensation in data consistency comparison as described in claim 2, characterized in that, The process of performing hierarchical matching on the data to obtain the data similarity for each field includes: Set a weight threshold for key fields, and determine whether a data field is a key field based on the weight information of the data. If the weight of the data is greater than or equal to the weight threshold of the key field, then the data field is the key field. The data identity identification module accurately matches the key field to obtain the key field matching similarity. If the weight of the data is less than the weight threshold of the key field, the data identity identification module uses fuzzy matching for the non-key fields to calculate the similarity of the non-key field matching.

4. The method for identity determination and compensation in data consistency comparison as described in claim 3, characterized in that, The key field matching similarity is obtained as follows: if the field data is completely consistent, the similarity is 1; if the data is not completely consistent, the similarity is 0.

5. The method for identity determination and compensation in data consistency comparison as described in claim 3, characterized in that, The similarity of non-key fields is obtained by calculating field similarity using an edit distance algorithm. A smaller edit distance indicates greater similarity between the two strings. The formula for the edit distance algorithm is as follows: In the formula, Indicates similarity between non-key fields; edit distance Represents a string Convert to The minimum number of operations required; This represents the maximum length of the two strings.

6. The method for identity determination and compensation in data consistency comparison as described in claim 2, characterized in that, The calculation of the average similarity of all fields within the weighted recombination based on weights includes: Group all fields according to their weights, resulting in a total of 100 groups. n Groups, each weighted group contains k One field; Divide the sum of the similarities of all fields under each weight by the number of fields under that weight.

7. The method for identity determination and compensation in data consistency comparison as described in claim 6, characterized in that, The expression for calculating the percentage of identity recognition rate is as follows: In the formula, Indicates the first j Similarity of each field , For natural numbers, it represents the first natural number. i The total number of fields in each weight group; Indicates the first i The weights of each weight group , is a natural number, representing the total number of weighted reorganizations.

8. The method for identity determination and compensation in data consistency comparison as described in claim 2, characterized in that, The determination of whether two sets of data can be compared for consistency based on the obtained identity recognition rate percentage of the two sets of data includes: Set a threshold for the data identity verification rate. If the data identity verification rate exceeds the set threshold, the two sets of data are considered to come from the same data and a consistency comparison can be performed. Otherwise, a consistency comparison cannot be performed.

9. The method for identity determination and compensation in data consistency comparison as described in claim 2, characterized in that, If the determination result indicates that a consistency comparison can be performed, the TAC module performs a consistency comparison between the data to be compared and the calibration data according to the data comparison rules, and obtains the comparison result. This also includes: If the comparison results of the data to be compared and the calibration data are consistent, they are considered to be identical, and the data comparison ends. If the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison will be performed through the compensation check module. If the results of the second comparison are consistent, they are considered to be identical and the data comparison ends. Otherwise, the comparison will not be performed again and will be subject to manual review later.

10. The method for identity determination and compensation in data consistency comparison as described in claim 9, characterized in that, If the comparison results of the data to be compared and the calibration data are inconsistent, a second comparison will be performed through the compensation check module, specifically as follows: When the data comparison results are inconsistent, the system enters the compensation check module. The compensation check module checks whether there is data that needs to be compensated and checks whether the delay time of the data to be compensated has been reached. If the delay time is reached, the compensation check module queries the corresponding flight data and calibration data to be compared based on the set of inconsistent fields, and performs a consistency comparison on the queried data according to the comparison rules. During the comparison, only the data corresponding to the inconsistent fields are compared.

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