Car renting platform data arrangement system and method

Abstract

The invention relates to the technical field of intelligent traffic, and discloses a car rental platform data arrangement system and method. According to the invention, data are respectively acquired from a car rental user side and a car rental database through a data acquisition module, and the sorting system is composed of the data acquisition module, a data comparison module, a correctness module, a logic calculation module, a cost calculation module, a data sorting module and an anomaly detection module. The data input by the user comprises a name, a car rental date and cost, the data extracted from the database comprises car rental parameters and cost records, and the data comparison module compares the data input by the user with the data in the database to identify inconsistency and ensure data consistency; the beneficial effects of simultaneously obtaining the data from the car rental user side and the database and carrying out correct comparison to avoid wrong data are achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and in particular to a car rental platform data sorting system and method. Background Art

[0002] For field test operators who often have field test needs, car rental suppliers usually settle car rental fees on a monthly or quarterly basis according to pre-signed contracts, and send the settlement fees and car rental data to the field test operators, who will then calculate and settle the fees. However, for field test operators, since internal settlement is generally based on the projects to which the field test belongs, accounting personnel need to manually select the car rental data of the corresponding projects from the summarized car rental data sent by each car rental supplier, perform corresponding calculations, and then summarize them, thereby achieving the cost settlement of car rental suppliers and projects. However, this method of settling car rental data requires accounting personnel to screen the car rental data corresponding to the project, which makes the processing efficiency of car rental data low; and the manual screening method also has a high error rate, which affects the accuracy of settlement.

[0003] In order to solve the above technical problems, a method and device for settling car rental data with a publication number of CN 114116797A was found through searching, wherein the method for settling car rental data includes: constructing a supplier settlement template table page in an electronic spreadsheet, each settlement factor for car rental fee settlement corresponds to a column, setting a logical calculation formula for the settlement factors with logical relationships; setting the project information of the fees to be settled; obtaining the project car rental data matching the project information from the car rental database according to the set project information; obtaining the car rental parameters matching the settlement factors from the project car rental data according to the settlement factors, and filling the corresponding car rental parameter values ​​into the rows corresponding to the settlement factors; calculating the car rental fees corresponding to the settlement factors according to the logical calculation formula corresponding to the settlement factors and the filled car rental parameter values; and calculating the car rental fees corresponding to each settlement factor in the project information to obtain the project car rental fees corresponding to the project information. The processing efficiency of car rental data can be improved.

[0004] However, this method relies on obtaining accurate and complete data from the car rental database. If there are errors, omissions or inconsistencies in the data in the database, the accuracy of the settlement results will be affected. At the same time, errors and anomalies are inevitable when processing large amounts of data and complex logic. However, the materials do not mention how to handle and record these errors or anomalies. Summary of the invention

[0005] Technical issues solved:

[0006] In view of the deficiencies in the prior art, the present invention provides a car rental platform data sorting system and method, which has the advantages of simultaneously acquiring data from the car rental user terminal and the database and performing correct comparison to avoid erroneous data, and the system regularly sorts data to promptly discover erroneous and abnormal data, thereby solving the problems of the above-mentioned technologies.

[0007] Technical solution:

[0008] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a car rental platform data sorting system, the sorting system comprises a data acquisition module, a data comparison module, a correction module, a logic calculation module, a cost calculation module, a data sorting module and an anomaly detection module;

[0009] The data acquisition module acquires the input car rental information from the car rental user: user name, car rental date and cost, and database data acquisition: extracts relevant car rental parameters and cost records from the car rental database;

[0010] The data comparison module is used to compare the data input by the user with the data extracted from the database to ensure the consistency between the two and identify potential errors in the data: inconsistent car rental fees, missing parameters;

[0011] The correction module is used to automatically correct the identified erroneous data according to the result of the data comparison, or prompt the user to make manual corrections;

[0012] The logic calculation module is used to define the logic calculation formula required for fee settlement according to business requirements;

[0013] The cost calculation module is used to calculate various costs according to the formula defined in the logic calculation module and the extracted car rental parameters;

[0014] The data sorting module is used to set data sorting tasks to be performed regularly to ensure that the data remains up to date;

[0015] The anomaly detection module is used to monitor and detect anomalies in the data in real time, including extreme values ​​and duplicates;

[0016] The extreme value detection expression in the anomaly detection module is:

[0017]

[0018] Where: x i is the i-th value in the data set; L is the lower threshold, which is set according to the statistical characteristics of the data; U is the upper threshold; the formula indicates that if x i If the value of is less than the lower limit L or greater than the upper limit U, then x i are considered extreme values;

[0019] The duplicate detection expression is:

[0020] Duplicate detection is achieved by checking whether there are duplicate elements in the dataset, where: all elements of dataset X are x i :

[0021]

[0022] Where: x i and x j are two elements in the data set; i≠j ensures that the detected element is not a duplicate of itself; the formula indicates that if there is another data element x j So that x i Equal to x j and i is not equal to j, then x i are considered duplicates.

[0023] Preferably, the data acquisition module expression is:

[0024] D A =U I +D C

[0025] in:

[0026] D A It is the data acquisition module;

[0027] U I Enter data for the user;

[0028] D C Collect data for the database;

[0029] User input data U I Contains: User name U N 、Rental Date D and cost C;

[0030] Database collection data C Contains: car rental parameters R P , expense record C R .

[0031] Preferably, the data comparison result formula in the data comparison module is:

[0032] C D =(U I -D C )

[0033] Where: C D Data comparison results indicate the difference between the user input data and the data collected from the database, and calculate the difference between the two to help identify inconsistencies or potential errors;

[0034] U I Enter data for the user, including the user's name, rental date and cost;

[0035] D C Collect data for the database, including car rental parameters and expense records.

[0036] Preferably, the inconsistent car rental fee formula in the data comparison module is expressed as:

[0037] C≠C R

[0038] Where: C is the car rental fee entered by the user; C R Car rental costs recorded for the database;

[0039] The above formula is used to check whether the rental fee entered by the user matches the fee recorded in the database. R , then there is a cost discrepancy.

[0040] Preferably, the corrected result expression in the corrected module is:

[0041] R C =U I +ΔU-ΔD

[0042] Where: R C To correct the results, it means the corrected data; U I is the user input data; ΔU is the correction amount that needs to be added from the user input; ΔD is the correction amount that needs to be deleted from the database;

[0043] The correction expression for inconsistent costs in the correcting module is:

[0044] C corrected =C R

[0045] Where: C corrected is the revised car rental fee; C R The car rental costs recorded in the database;

[0046] The cost C input by the user in the correction expression is consistent with the cost C in the database R If they are inconsistent, the user-entered fee C R Automatically corrected to C R .

[0047] Preferably, the correction expression for the missing parameters of the correcting module is:

[0048] U' I =U I ∪(DC U I )

[0049] D' C =D C ∩U I

[0050] Where: U' I Updated user input data with missing parameters added; D' C The updated database data removes redundant parameters from the database; D C U I A set of parameters that exist in the database but are missing in the user input; D C ∩U I A set of parameters that exist in both the database and user input;

[0051] The step of automatically correcting missing parameters comprises:

[0052] S1, D C Add missing parameters from user input to U I In the I .

[0053] S2, from D C Delete the parameters that exist in the user input but are missing in the database, and get D' C .

[0054] Preferably, the logic calculation module expression is:

[0055] F T =((B+(D×R))×(1-D R ))×(1+T R )

[0056] Among them: F T The final total cost; D R is the discount rate; T R For the tax rate.

[0057] Preferably, the basic cost calculation expression in the cost calculation module is:

[0058] F B =B+(D×R)

[0059] Among them: F B A is the basic fee; B is the basic fee, which is defined in the logical calculation module; D is the number of days or usage of the car rental, which is obtained from the extracted car rental parameters; R is the daily fee or unit fee, which is defined in the logical calculation module.

[0060] Preferably, the expression for regularly executing tasks in the data sorting module is:

[0061] D n =D n-1 +R(T)

[0062] Where: D n The state of the data set in the current state after the nth sorting; D n-1 is the state of the data set after the last sorting; R(T) is the latest data extracted within the time interval T, indicating data updates or new additions. The formula indicates that after each data sorting, the state of the data set is equal to the previous state plus the latest data extracted within the time interval T;

[0063] The constraint expression for keeping the data up to date is:

[0064] D n =D n-1 ∪R(T) and V(D n )=1

[0065] Where: D n =D n-1 ∪R(T) is the union of the current data set equal to the previous state and the latest data extracted; V(D n )=1 to ensure that the data passes integrity verification after being sorted.

[0066] A method for collating data on a car rental platform, the method comprising the following steps:

[0067] Step 1: Data acquisition: Get car rental information from the user: name, date and cost; extract car rental parameters and cost records from the database;

[0068] Step 2: Data comparison: Compare the data entered by the user with the data in the database to identify inconsistencies;

[0069] Step 3: Data Correction: Automatically correct the identified erroneous data or prompt the user to make manual corrections to solve the problems of inconsistent costs and missing parameters;

[0070] Step 4: Logical calculation: define and apply the logical formula for cost calculation;

[0071] Step 5: Cost calculation: Calculate various costs based on logical formulas and car rental parameters;

[0072] Step 6. Data collation: Perform data collation tasks regularly to keep the data up to date;

[0073] Step 7: Anomaly detection: Real-time monitoring of anomalies in data, including extreme values ​​and duplicates.

[0074] Compared with the prior art, the present invention provides a car rental platform data sorting system and method, which has the following beneficial effects:

[0075] 1. The present invention obtains data from the car rental user terminal and the car rental database respectively through the data acquisition module. The data input by the user includes name, car rental date and fee, and the data extracted from the database includes car rental parameters and fee records. The data comparison module compares the data input by the user with the data in the database to identify inconsistencies. By checking whether the fees and parameters match, it can timely discover and mark potential erroneous data, including inconsistent fees or missing parameters. The correction module automatically corrects the identified erroneous data based on the results of the data comparison, adjusts the fees input by the user to the recorded fees in the database, adds the missing parameters in the database to the user input, or removes redundant parameters from the database to ensure data consistency, thereby achieving the beneficial effect of simultaneously acquiring data from the car rental user terminal and the database and performing correction comparison to avoid erroneous data.

[0076] 2. The present invention sets a regularly executed data sorting task through the data sorting module. This mechanism ensures that the system updates the data set at a predetermined time interval to keep the data up to date. Regularly sorted data sets help to discover and correct errors caused by outdated or missing data. In each data sorting, the system merges the last sorted data set with the newly extracted data. By adding new data to the existing data set, the system can update the data in a timely manner to ensure that the latest information is included. The data set after data sorting will be verified for integrity to ensure that the data still meets the expected integrity standards after sorting. The system can promptly discover and handle problems that may arise during the data sorting process, including data loss or inconsistency, and achieve the beneficial effect of timely discovery of erroneous and abnormal data through system regular data sorting. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a schematic diagram of the system of the present invention; DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] See also Figure 1 , a car rental platform data sorting system, the sorting system consists of a data acquisition module, a data comparison module, a correction module, a logic calculation module, a cost calculation module, a data sorting module and an anomaly detection module;

[0080] The data acquisition module acquires the input car rental information from the car rental user: user name, car rental date and cost, and database data acquisition: extracts relevant car rental parameters and cost records from the car rental database;

[0081] The data comparison module is used to compare the data input by the user with the data extracted from the database to ensure the consistency between the two and identify potential errors in the data: inconsistent car rental fees, missing parameters;

[0082] The correction module is used to automatically correct the identified erroneous data according to the result of the data comparison, or prompt the user to make manual corrections;

[0083] The logic calculation module is used to define the logic calculation formula required for fee settlement according to business requirements;

[0084] The cost calculation module is used to calculate various costs according to the formula defined in the logic calculation module and the extracted car rental parameters;

[0085] The data sorting module is used to set data sorting tasks to be performed regularly to ensure that the data remains up to date;

[0086] The anomaly detection module is used to monitor and detect anomalies in the data in real time, including extreme values ​​and duplicates;

[0087] The extreme value detection expression in the anomaly detection module is:

[0088]

[0089] Where: x i is the i-th value in the data set; L is the lower threshold, which is set according to the statistical characteristics of the data; U is the upper threshold; the formula indicates that if x i If the value of is less than the lower limit L or greater than the upper limit U, then x i are considered extreme values;

[0090] The duplicate detection expression is:

[0091] Duplicate detection is achieved by checking whether there are duplicate elements in the dataset, where: all elements of dataset X are x i :

[0092]

[0093] Where: x i and x j are two elements in the data set; i≠j ensures that the detected element is not a duplicate of itself; the formula indicates that if there is another data element x j So that x iEqual to x j and i is not equal to j, then x i are considered duplicates.

[0094] The data acquisition module obtains data from the car rental user terminal and the car rental database. The data entered by the user includes name, rental date and cost, and the data extracted from the database includes rental parameters and cost records, ensuring that the system has a complete data source and can compare user input and system records.

[0095] The data comparison module compares the data entered by the user with the data in the database to identify inconsistencies. By checking whether the charges and parameters match, potential erroneous data, including inconsistent charges or missing parameters, can be discovered and flagged in a timely manner.

[0096] The correction module automatically corrects the identified erroneous data based on the results of data comparison, including:

[0097] Correction of inconsistent fees: Adjust the fees entered by the user to the recorded fees in the database.

[0098] Correction of missing parameters: Add missing parameters in the database to the user input, or remove redundant parameters from the database to ensure data consistency.

[0099] The logical calculation module defines the formulas required for fee settlement to ensure the consistency and accuracy of the calculation method.

[0100] The cost calculation module calculates various costs based on the logical calculation formula and the extracted car rental parameters, thereby avoiding inaccurate cost calculation due to data errors.

[0101] The data collation module updates data regularly to ensure that the data remains up to date and reduce errors caused by outdated data.

[0102] The anomaly detection module monitors anomalies in the data in real time, such as extreme values ​​and duplicates, to further ensure the accuracy and consistency of the data.

[0103] The data sorting module sets up data sorting tasks that are performed regularly. This mechanism ensures that the system updates the data set at a predetermined time interval to keep the data up to date. Regularly sorted data sets help to discover and correct errors caused by outdated or missing data.

[0104] In each data collection, the system merges the last collected data set with the newly extracted data. By adding new data to the existing data set, the system can update the data in a timely manner to ensure that it contains the latest information.

[0105] The data set after data collation will be verified for integrity to ensure that the data still meets the expected integrity standards after collation. Through this verification, the system can promptly detect and handle problems that may arise during the data collation process, such as data loss or inconsistency.

[0106] The anomaly detection module monitors anomalies in the data in real time, including extreme values ​​and duplicates. This real-time detection can quickly identify and process abnormal data to prevent it from affecting the overall data quality of the system.

[0107] Through regular data collation and anomaly detection, the system can detect potential problems in a timely manner when data changes. The regular collation mechanism ensures the accuracy and consistency of the data, while anomaly detection further helps identify and handle abnormal data to avoid problems caused by erroneous data.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A car rental platform data sorting system, characterized in that: The collating system consists of a data acquisition module, a data comparison module, a correction module, a logic calculation module, a cost calculation module, a data collating module and an anomaly detection module; The data acquisition module acquires the input car rental information from the car rental user: user name, car rental date and cost, and database data acquisition: extracts relevant car rental parameters and cost records from the car rental database; The data comparison module is used to compare the data input by the user with the data extracted from the database to ensure the consistency between the two and identify potential errors in the data: inconsistent car rental fees, missing parameters; The correction module is used to automatically correct the identified erroneous data according to the result of the data comparison, or prompt the user to make manual corrections; The logic calculation module is used to define the logic calculation formula required for fee settlement according to business requirements; The cost calculation module is used to calculate various costs according to the formula defined in the logic calculation module and the extracted car rental parameters; The data sorting module is used to set data sorting tasks to be performed regularly to ensure that the data remains up to date; The anomaly detection module is used to monitor and detect anomalies in the data in real time, including extreme values ​​and duplicates; The extreme value detection expression in the anomaly detection module is: Where: x i is the i-th value in the data set; L is the lower threshold, which is set according to the statistical characteristics of the data; U is the upper threshold; the formula indicates that if x i If the value of is less than the lower limit L or greater than the upper limit U, then x i are considered extreme values; The duplicate detection expression is: Duplicate detection is achieved by checking whether there are duplicate elements in the dataset, where: all elements of dataset X are x i : Where: x i and x j are two elements in the data set; i≠j ensures that the detected element is not a duplicate of itself; the formula indicates that if there is another data element x j So that x i Equal to x j and i is not equal to j, then x i are considered duplicates.

2. A car rental platform data sorting system according to claim 1, characterized in that: The data acquisition module expression is: D A =U I +D C in: D A It is the data acquisition module; U I Enter data for the user; D C Collect data for the database; User input data U I Contains: User name U N 、Rental Date D and cost C; Database collection data C Contains: car rental parameters R P , expense record C R .

3. A car rental platform data sorting system according to claim 2, characterized in that: The data comparison result formula in the data comparison module is: C D =(U I -D C ) Where: C D Data comparison results indicate the difference between the user input data and the data collected from the database, and calculate the difference between the two to help identify inconsistencies or potential errors; U I Enter data for the user, including the user's name, rental date and cost; D C Collect data for the database, including car rental parameters and expense records.

4. A car rental platform data sorting system according to claim 3, characterized in that: The inconsistent car rental fee formula in the data comparison module is as follows: C≠C R Where: C is the car rental fee entered by the user; C R Car rental costs recorded for the database; The above formula is used to check whether the rental fee entered by the user matches the fee recorded in the database. R , then there is a cost discrepancy.

5. A car rental platform data sorting system according to claim 4, characterized in that: The expression of the corrected result in the corrected module is: R C =U I +ΔU-ΔD Where: R C To correct the results, it means the corrected data; U I is the user input data; ΔU is the correction amount that needs to be added from the user input; ΔD is the correction amount that needs to be deleted from the database; The correction expression for inconsistent costs in the correcting module is: C corrected =C R Where: C corrected is the revised car rental fee; C R The car rental costs recorded in the database; The cost C input by the user in the correction expression is consistent with the cost C in the database R If they are inconsistent, the user-entered fee C R Automatically corrected to C R .

6. A car rental platform data sorting system according to claim 5, characterized in that: The correction expression for the missing parameters of the correct module is: U' I =U I ∪(D C U I ) D’ C =D C ∩U I Among them: U' I Updated user input data with missing parameters added; D' C The updated database data removes redundant parameters from the database; D C U I A set of parameters that exist in the database but are missing in the user input; D C ∩U I A set of parameters that exist in both the database and user input; The step of automatically correcting missing parameters comprises: S1, D C Add missing parameters from user input to U I In the II . S2, from D C Delete the parameters that exist in the user input but are missing in the database, and get D' C .

7. A car rental platform data sorting system according to claim 6, characterized in that: The logic calculation module expression is: F T =((B+(D×R))×(1-D R ))×(1+T R ) Among them: F T The final total cost; D R is the discount rate; T R For the tax rate.

8. The car rental platform data sorting system according to claim 1, characterized in that: The basic cost calculation expression in the cost calculation module is: F B =B+(D×R) Among them: F B A is the basic fee; B is the basic fee, which is defined in the logical calculation module; D is the number of days or usage of the car rental, which is obtained from the extracted car rental parameters; R is the daily fee or unit fee, which is defined in the logical calculation module.

9. The car rental platform data sorting system according to claim 1, characterized in that: The expression for regularly executing tasks in the data sorting module is: D n =D n-1 +R(T) Where: D n The state of the data set in the current state after the nth sorting; D n-1 is the state of the data set after the last sorting; R(T) is the latest data extracted within the time interval T, indicating data updates or new additions. The formula indicates that after each data sorting, the state of the data set is equal to the previous state plus the latest data extracted within the time interval T; The constraint expression for keeping the data up to date is: D n =D n-1 ∪R(T) and V(D n )=1 Where: D n =D n-1 ∪R(T) is the current data set equal to the union of the previous state and the latest data extracted; V(D n )=1 to ensure that the data passes integrity verification after being sorted.

10. A method for collating data on a car rental platform, characterized in that: The tidying method is used in the tidying system according to claim 1, and the tidying method comprises the following steps: Step 1: Data acquisition: Get car rental information from the user: name, date and cost; extract car rental parameters and cost records from the database; Step 2: Data comparison: Compare the data entered by the user with the data in the database to identify inconsistencies; Step 3: Data Correction: Automatically correct the identified erroneous data or prompt the user to make manual corrections to solve the problems of inconsistent costs and missing parameters; Step 4: Logical calculation: define and apply the logical formula for cost calculation; Step 5: Cost calculation: Calculate various costs based on logical formulas and car rental parameters; Step 6. Data collation: Perform data collation tasks regularly to keep the data up to date; Step 7: Anomaly detection: Real-time monitoring of anomalies in data, including extreme values ​​and duplicates.

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