Privacy computing system, method and application for total driving time of online car-hailing driver

Abstract

The application discloses a privacy calculation system and method for total driving time of a network car driver and application thereof, a time sequence database, real-time synchronization of driver dispatching data of a corresponding network car platform; a privacy calculation center end receives a driver working time query request; a plurality of privacy calculation access ends receive the privacy calculation request sent by the privacy calculation center end, form a calculation task and send the calculation task to the time sequence database; the time sequence database reads corresponding data and calculates based on the calculation task; the privacy calculation access end reads the calculation result of the time sequence database; the calculation result is sent to the privacy calculation center end; the privacy calculation center end accumulates the calculation result sent by each privacy calculation access end to obtain the total working time of the driver in a specific period. The application realizes the calculation of the total driving time of the network car driver across platforms, and protects the privacy data and the business secrets of the network car company through privacy calculation.

Description

Technical Field

[0001] This invention relates to the field of privacy computing technology, and in particular to a privacy computing system, method and application for calculating the total driving time of ride-hailing drivers. Background Technology

[0002] With societal development, people's travel options have become increasingly diverse, including private cars, subways, and buses. Thanks to the rapid development of the internet, ride-hailing services have also become an increasingly important mode of transportation. However, while providing convenience, ride-hailing services also pose significant risks. For example, to prevent fatigue driving, a single ride-hailing platform typically only allows a specific vehicle to operate continuously for 3-4 hours, or cumulatively for no more than 8 hours. Exceeding these limits will result in no further orders being assigned to that vehicle. However, due to the existence of multiple ride-hailing platforms, drivers can register on multiple platforms. When the operating time limit on one platform is reached and no more orders can be assigned, the driver can switch to another platform. This can lead to drivers being continuously fatigued, endangering not only the personal safety of the driver and passengers but also the safety of pedestrians and other vehicles on the road.

[0003] Currently, drivers' data is usually stored in a database. To determine a driver's operating time, various time-related data need to be extracted from the database and calculated to determine the operating time, which results in slow data processing efficiency. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a privacy calculation system, method, and application for the total driving time of ride-hailing drivers, thereby improving data processing efficiency.

[0005] To achieve the above objectives, the present invention provides a privacy calculation system for the total driving time of ride-hailing drivers, including a privacy calculation center and several distributed calculation terminals;

[0006] The computing terminal includes a privacy computing access terminal and a time-series database;

[0007] The privacy computing access terminal receives a privacy computing request for driver working time within a specific time period sent by the privacy computing center, forms a computing task related to time series data and sends it to the time series database, reads the computing results from the time series database, determines intermediate data based on the computing results, and sends the intermediate data to the privacy computing center.

[0008] The time-series database synchronizes in real time the driver dispatch data and driver driving data of the corresponding ride-hailing platform; it reads the corresponding time-series data and performs calculations based on the calculation task to form the calculation result;

[0009] The privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for a specific time period to several privacy computing access terminals; based on the intermediate data sent by each privacy computing access terminal, it determines the total working time of the driver within the specific time period.

[0010] Furthermore, the privacy computing access terminal receives a privacy computing request to determine the data extraction rules and data computing algorithm information related to the time series data, forms a computing task, and sends the computing task to the time series database.

[0011] The time-series database receives computing tasks from the privacy computing access terminal to extract time-series data according to data extraction rules, and performs calculations on the time-series data according to data computing algorithm information to form calculation results, which are then fed back to the privacy computing access terminal.

[0012] The privacy computing access terminal receives the computing results from the time-series database, encrypts and transforms the computing results to form intermediate data, and then feeds the intermediate data back to the privacy computing center.

[0013] Furthermore, the step of performing calculations on the time series data according to the data calculation algorithm information to form calculation results includes:

[0014] The algorithm calls a function based on the data calculation information to perform calculations on the time series data and generate the calculation results.

[0015] Further, the step of extracting time-series data according to data extraction rules and performing calculations on the time-series data according to data calculation algorithm information to form calculation results includes:

[0016] According to the first data extraction rule, extract time series data related to the operational status, and perform calculations on the time series data according to the data calculation algorithm information to determine the first calculation result;

[0017] According to the second data extraction rule, extract the time series data related to low-speed operation in the empty state, and calculate the time series data according to the data calculation algorithm information to determine the second calculation result;

[0018] According to the third data extraction rule, extract the time series data related to high-speed operation in the empty state, and calculate the time series data according to the data calculation algorithm information to determine the third calculation result;

[0019] The calculation result is determined based on the first calculation result, the second calculation result, and the third calculation result.

[0020] Furthermore, the privacy computing center obtains intermediate data from at least one privacy computing access point and performs calculations on the intermediate data from at least one privacy access point to determine the total working time.

[0021] Determine whether the total working time exceeds the fatigue threshold to confirm the fatigue analysis results.

[0022] Furthermore, the privacy computing access terminal and time series database of each computing terminal use the same container (Docker); or the time series database is provided by the ride-hailing platform.

[0023] The second aspect provides a method for calculating the privacy of a ride-hailing driver's total driving time, including:

[0024] The time-series database synchronizes driver dispatch data and driver driving data with the corresponding ride-hailing platform in real time;

[0025] The privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for a specific time period to several privacy computing access terminals.

[0026] Several privacy computing access terminals receive the privacy computing request sent by the privacy computing center, form computing tasks related to time series data, and send them to the time series database;

[0027] The time-series database reads and calculates corresponding data based on the computing task;

[0028] The privacy computing access terminal reads the calculation results from the time-series database, determines intermediate data based on the calculation results, and sends the intermediate data to the privacy computing center.

[0029] The privacy computing center determines the driver's total working time within a specific time period based on the intermediate data sent by each privacy computing access terminal.

[0030] The third aspect provides a method for dispatching ride-hailing drivers. After a driver goes online on the ride-hailing platform and / or before dispatching a ride-hailing driver, the privacy calculation method for the total driving time of the ride-hailing driver is used to obtain the driver's total working time within a specific time period. When the total working time exceeds the fatigue threshold, dispatching rides to the driver is prohibited.

[0031] The fourth aspect provides an electronic device, including a processor and a memory, wherein the memory is used to store program instructions, and the processor is used to invoke the program instructions to execute the privacy calculation method for the total driving time of ride-hailing drivers.

[0032] The fifth aspect provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, contains a privacy calculation method for the total driving time of a ride-hailing driver.

[0033] The above-described technical solution of the present invention has the following beneficial technical effects:

[0034] (1) This invention realizes the calculation of the total driving time of ride-hailing drivers across platforms, and protects privacy data and the trade secrets of ride-hailing companies through privacy calculation.

[0035] (2) This invention distributes some computing tasks to the time series database through the privacy computing access terminal, and takes advantage of the characteristics of the time series database to quickly compute time series data, which greatly improves the efficiency of privacy and security computing processing.

[0036] (3) The privacy computing access terminal interacts with the time series database by using privacy query. Even if the time series database itself has the risk of data leakage, it can still protect privacy data and the trade secrets of ride-hailing companies during the query process. Attached Figure Description

[0037] Figure 1 These are schematic diagrams illustrating the configuration of a ride-hailing driver's total driving time privacy calculation system provided in some embodiments;

[0038] Figure 2 A flowchart of a method for calculating the privacy of total driving time for ride-hailing drivers is provided for some embodiments;

[0039] Figure 3 This is a schematic diagram of the components of an electronic device. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0041] In some embodiments, a privacy calculation system for the total driving time of ride-hailing drivers is provided, including a privacy calculation center and several distributed calculation terminals.

[0042] The computing end includes a privacy computing access point and a time-series database.

[0043] The privacy computing access terminal receives a privacy computing request for driver working hours within a specific time period sent by the privacy computing center, forms relevant computing tasks related to time series data and sends them to the time series database, reads the computing results from the time series database, determines intermediate data based on the computing results, and sends the intermediate data to the privacy computing center.

[0044] In one embodiment, to avoid leaking data needed by the privacy computing access point during time-series database computation, or to prevent the leakage of data required for time-series database computation, the privacy computing access point obtains the computation results from the time-series database using privacy computing methods, such as privacy information retrieval (PIR) or privacy set intersection (PSI). This avoids obtaining data involved in privacy computing and prevents the acquisition of the intent behind privacy computing.

[0045] Furthermore, the privacy-preserving computation access point receives computation results from the time-series database and encrypts and transforms the results to form intermediate data, which is then fed back to the privacy-preserving computation center. Methods such as isomorphic encryption and data transformation can be used to process the computation results and form intermediate data. The privacy-preserving computation center can perform computations on the intermediate data or restore the intermediate data before performing computations.

[0046] Specific time periods, such as 24 hours, 12 hours, or 8 hours, are determined based on demand.

[0047] The time-series database synchronizes in real time the driver dispatch data and driver driving data of the corresponding ride-hailing platform; it reads the corresponding time-series data and performs calculations based on the calculation task to form the calculation result.

[0048] Further, the time-series database receives the computation task i initiated by the privacy computing access terminal according to the computation task; extracts the data required to compute the computation task i, calls the corresponding function, completes the computation of the computation task i, and forms the computation result. The time-series database is used to store the user's (driver's) time-related data (various time data during operation, such as start time of order acceptance, order start time, order end time, end time of order acceptance, etc.). In an optional embodiment, the time-series database in this invention can periodically compute the user's (driver's) time-series data to form a pre-computation result. For example, the time-series database can statistically analyze the user's duration information within a specific time period every minute as a pre-computation result; the time-series database can extract the pre-computation result according to the computation task to determine the computation result of the computation task; the time-series database can also periodically (e.g., once per minute) send the pre-computation result to the privacy computing access terminal, and the privacy computing access terminal can determine the user's working hours based on the pre-computation result sent by the time-series database.

[0049] In an optional embodiment, the privacy computing access terminal can receive a working time query request sent by the privacy computing center to determine the data extraction rules and data computing algorithm information related to the time series data, and form a corresponding processing task. The processing task may include at least one computing task. The computing task can be processed by the time series database or by the privacy computing access terminal. It can be configured according to needs. For example, the computing task related to the time series data can be sent to the time series database for computing processing, and the privacy computing access terminal can process other computing tasks.

[0050] Alternatively, in an optional embodiment, the process of generating computing tasks can also be completed at the privacy computing center. The privacy computing center can generate computing tasks based on working time query requests and send the computing tasks to the privacy computing access terminal. The privacy computing access terminal then sends the tasks to the time-series database for computation, thereby determining the computation result.

[0051] Data extraction rules include user information, information intervals, order start time, and order end time, in order to extract the corresponding data. An information interval can be understood as a time period, such as different time periods like morning, afternoon, and evening of the same day, to obtain data within those specific time periods. Data calculation algorithm information may include the algorithm name to be invoked. The time-series database invokes the algorithm based on its name, for example, invoking an algorithm that subtracts the order end time from the order start time.

[0052] In one specific embodiment, time series data is extracted according to data extraction rules, and the time series data is calculated according to data calculation algorithm information to form a calculation result, including:

[0053] According to the first data extraction rule, time-series data related to operational status is extracted, and the time-series data is calculated according to the data calculation algorithm to determine the first calculation result. The time-series data related to operational status is the data from when the driver accepts the order to when the order ends. The first data extraction rule can be, for example, to extract data based on the time from when the driver accepts the order to when the order ends.

[0054] According to the second data extraction rule, time series data related to low-speed operation under no-load conditions are extracted, and the time series data are calculated according to the data calculation algorithm information to determine the second calculation result.

[0055] The second data extraction rule includes, for example, setting a speed threshold to distinguish between high-speed and low-speed operation. If the operating speed exceeds the set threshold during non-working hours, the threshold is, for example, selected as 10 km / h to 15 km / h, and the driver is considered to be in a low-speed state. If the speed is higher than the speed threshold, it is considered high-speed operation; otherwise, it is considered low-speed operation. The speed threshold can be set, for example, to 30 km / h to 40 km / h. The second calculation result, for example, indicates that the operating speed is low-speed operation.

[0056] According to the third data extraction rule, time-series data related to high-speed operation in an empty state are extracted, and the time-series data are calculated according to the data calculation algorithm to determine the third calculation result. The third data extraction rule, for example, involves extracting high-speed operation data and dividing it into time periods. The third calculation result, for example, represents operation at low speed. Different scaling factors can be set for different speeds. The privacy computing access point can perform weighted calculations and return the weighted calculation result, or the privacy computing access point can perform the weighted calculations independently, with the privacy computing center performing the weighted calculations.

[0057] In one embodiment, the time-series database sets different proportional coefficients for different time periods. The weighted calculation result can be returned by the privacy computing access terminal, or the privacy computing access terminal can perform the weighted calculation without weighting, and the weighted calculation can be performed by the privacy computing center. For example, if a driver works for 1 hour from 11 PM to 12 AM, the coefficient for this time period is 1.2; if they work for 1 hour from 9 AM to 10 AM, the coefficient for this time period is 0.5. Then, the time-series database calculates the total working time as: 0.5*1 + 1.2*1.

[0058] The calculation result is determined based on the first, second, and third calculation results. The calculation task of the time-series database includes the calculations actually required to calculate the driver's total driving time; therefore, the acquired data includes at least the calculation data for calculating the driver's total working time on the platform within a specific time period.

[0059] The time-series database receives computing tasks from the privacy-preserving computing access point, extracts time-series data according to data extraction rules, and performs calculations on the time-series data according to the data computing algorithm information to generate calculation results, which are then fed back to the privacy-preserving computing access point. Further, the time-series database calls function calls based on the data computing algorithm information to perform calculations on the time-series data according to the function, generating calculation results.

[0060] In one embodiment, the calculation of the time-series database includes at least the following: obtaining the driver's corresponding ID from the calculation request; reading the start and end times of each dispatch within a specific time period corresponding to that ID; calculating the difference between the start and end times using the sub function to obtain the working time of each dispatch; summing the working times of different dispatches using the sum function; obtaining the driver's cumulative working time within the specific time period on the platform after accumulation; and sending the cumulative working time to the privacy computing center.

[0061] Traditional privacy computing tasks are performed by the privacy computing access point, which needs to constantly retrieve its stored data from the database, resulting in significant time consumption. However, after loading certain functionalities, a time-series database can directly and quickly perform time-series data calculations on the stored data. The privacy computing center does not need to retrieve start and end times for each dispatch from the database, thus improving computational efficiency.

[0062] The privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for a specific time period to several privacy computing access terminals; based on the intermediate data sent by each privacy computing access terminal, it determines the total working time of the driver within the specific time period.

[0063] In one embodiment, the privacy computing center obtains intermediate data from at least one privacy computing access point, performs calculations on the intermediate data from at least one privacy access point, determines the total working time, and determines whether the total working time exceeds the fatigue threshold to determine the fatigue analysis result.

[0064] The privacy computing center accumulates the drivers' operating hours within a specific time period reported by each privacy computing access terminal to obtain the total operating hours for the drivers within that specific time period. The union of the drivers' operating hours within a specific time period reported by each privacy computing access terminal is the total operating hours. Periods outside of operating hours are considered non-operating periods.

[0065] The intersection of the drivers' empty driving time within a specific time period reported by each privacy computing access terminal is taken as the total empty driving time.

[0066] The total empty running time is calculated separately for the low-speed running segment and the high-speed running segment.

[0067] The total working time of a driver within a specific time period is calculated as: t = α(t1 + βt2 + λt3), where t1 is the total operating time; t2 is the low-speed operating time; t3 is the high-speed operating time; α is a weather coefficient, which is greater than 1 when the specific time period includes rain or snow, and α is 1 when there is no rain or snow; β is the low-speed operating time coefficient, which is less than 1, for example, 0.9; λ is the high-speed operating time coefficient, which is greater than 1, for example, 1.1. By setting these coefficients, the total operating time, low-speed operating time, and high-speed operating time are weighted and calculated to obtain the total working time. In one embodiment, to protect privacy data, the privacy computing center determines whether the driver's total working time exceeds a fatigue threshold. If it does, it reports a violation of driving regulations; otherwise, it reports no violation of driving regulations. Only the presence of a violation of driving regulations by the data requester is provided, not the specific working time. In one embodiment, the privacy computing access terminal and the time-series database of each computing terminal use the same container (Docker). In another embodiment, the time-series database is provided by the ride-hailing platform.

[0068] In some embodiments, a method for calculating the privacy of a ride-hailing driver's total driving time is provided, combined with Figure 2 It includes the following steps:

[0069] The S100 time-series database synchronizes driver dispatch data and driver driving data with the corresponding ride-hailing platform in real time.

[0070] This step is executed in real time and has no sequential order with other steps. Figure 2 At least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. Their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0071] The S200 privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for specific time periods to several privacy computing access terminals.

[0072] Several privacy computing access terminals of S300 receive the privacy computing request sent by the privacy computing center, form computing tasks related to time series data, and send them to the time series database.

[0073] In an optional embodiment, the privacy computing access terminal can receive a working time query request sent by the privacy computing center to determine the data extraction rules and data computing algorithm information related to the time series data, and form a corresponding processing task. The processing task may include at least one computing task. The computing task can be processed by the time series database or by the privacy computing access terminal. It can be configured according to needs. For example, the computing task related to the time series data can be sent to the time series database for computing processing, and the privacy computing access terminal can process other computing tasks.

[0074] The privacy computing access point generates a computing task that includes the steps required for a privacy computing request.

[0075] The time-series database described in S400 reads corresponding data and performs calculations based on the computing task.

[0076] In some embodiments, reading corresponding data and performing calculations based on the computation task includes:

[0077] According to the computing task, the system receives computing task i initiated by the privacy computing access terminal; extracts the data required to compute computing task i, calls the corresponding function, completes the computation of computing task i, and forms the computation result.

[0078] Data extraction rules include user information, information intervals, order start time, and order end time. Information intervals can be divided by time period, such as morning, afternoon, and evening of the same day. Data calculation algorithm information can include the algorithm name to be invoked. The time-series database invokes the algorithm based on its name, for example, invoking an algorithm that subtracts the order end time from the order start time.

[0079] In one specific embodiment, time series data is extracted according to data extraction rules, and the time series data is calculated according to data calculation algorithm information to form a calculation result, including:

[0080] According to the first data extraction rule, time-series data related to operational status is extracted, and the time-series data is calculated according to the data calculation algorithm to determine the first calculation result. The time-series data related to operational status is the data from when the driver accepts the order to when the order ends. The first data extraction rule can be, for example, to extract data based on the time from when the driver accepts the order to when the order ends.

[0081] According to the second data extraction rule, time-series data related to low-speed operation under no-load conditions are extracted, and the time-series data are calculated according to the data calculation algorithm to determine the second calculation result. People are more prone to fatigue in the morning, afternoon, and evening, while they are less prone to fatigue in the morning; therefore, statistics can be collected separately for different time periods. The second data extraction rule, for example, is to extract low-speed operation data and divide it into time periods, and the second calculation result, for example, represents the low-speed operation information for each time period.

[0082] The second data extraction rule includes, for example, setting a speed threshold to distinguish between high-speed and low-speed operation. If the operating speed exceeds the set threshold during non-working hours, the threshold is, for example, selected as 10 km / h to 15 km / h, and the driver is considered to be in a low-speed state. If the speed is higher than the speed threshold, it is considered high-speed operation; otherwise, it is considered low-speed operation. The speed threshold can be set, for example, to 30 km / h to 40 km / h. The second calculation result, for example, indicates that the operating speed is low-speed operation.

[0083] According to the third data extraction rule, time-series data related to high-speed operation in an empty state are extracted, and the time-series data is calculated according to the data calculation algorithm to determine the third calculation result. The third data extraction rule, for example, involves extracting high-speed operation data and dividing it into time periods. The third calculation result, for example, represents operation at low speed. Different scaling factors can be set for different speeds. The privacy computing access point can perform weighted calculations and return the weighted calculation result, or the privacy computing access point can perform the weighted calculations independently, with the privacy computing center performing the weighted calculations.

[0084] The empty driving time is obtained based on the dispatch and driving data. The low-speed driving period and the high-speed driving period are determined from the driving data. The speed is higher than the speed threshold and it is high-speed driving. Otherwise, it is low-speed driving.

[0085] Furthermore, people are more prone to fatigue in the morning, afternoon, and evening, but less so in the morning. Therefore, statistics can be compiled according to preset proportional coefficients for different time periods. In one embodiment, the time-series database sets different proportional coefficients for different time periods. The working hours of different dispatches are multiplied by the corresponding proportional coefficient and then summed to obtain the driver's cumulative working hours within a specific time period on the platform. This cumulative working hours are then sent to the privacy computing center.

[0086] The privacy computing access terminal in S500 reads the calculation results from the time-series database, determines intermediate data based on the calculation results, and sends the intermediate data to the privacy computing center.

[0087] Based on the intermediate data sent by each privacy computing access terminal, the privacy computing center terminal in S600 determines the driver's total working time within a specific time period.

[0088] The privacy computing center determines the calculation result based on the first, second, and third calculation results. The calculation task of the time-series database includes the calculations actually required to calculate the driver's total driving time; therefore, the acquired data includes at least the calculation data for calculating the driver's total working time on the platform within a specific time period.

[0089] The privacy computing center accumulates the drivers' operating time within a specific time period reported by each privacy computing access terminal to obtain the total operating time of the drivers within that specific time period.

[0090] The intersection of the empty driving time of the driver within a specific time period reported by each privacy computing access terminal is taken as the total empty driving time, and the low-speed running time and high-speed running time are calculated separately.

[0091] In one embodiment, the total working time of the driver within a specific period is calculated as: t=α(t1+βt2+λt3), where t1 is the total operating time; t2 is the low-speed operating time; t3 is the high-speed operating time; α is the weather coefficient, and the value of α is greater than 1 when the specific period includes rain or snow; β is the low-speed operating time coefficient, which is less than 1; and λ is the high-speed operating time coefficient, which is greater than 1.

[0092] The total operating time t1, the fast-running time t2, and the high-speed running time t3 can be the weighted calculation results of the time series database based on the operating time. Alternatively, they can be the unweighted calculation results of the time series database, which are then weighted by the privacy computing center to form the weighted duration.

[0093] In one embodiment, the privacy computing center obtains intermediate data from at least one privacy computing access point, performs calculations on the intermediate data from at least one privacy access point, determines the total working time, and determines whether the total working time exceeds the fatigue threshold to determine the fatigue analysis result.

[0094] In some embodiments, a ride-hailing driver dispatching method is provided. After a driver goes online on a ride-hailing platform and / or before dispatching a driver, the privacy calculation system for the total driving time of the ride-hailing driver is used to obtain the driver's total working time within a specific time period. When the total working time exceeds a fatigue threshold, dispatching a driver is prohibited.

[0095] Provide an electronic device, combined with Figure 3 It includes a processor and a memory, the memory being used to store program instructions, and the processor being used to invoke the program instructions to execute the privacy calculation method for the total driving time of ride-hailing drivers as described above.

[0096] The memory can be read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory can store programs, and when the program stored in the memory is executed by the processor, the processor executes the various steps of the image processing method of this application embodiment. For example, it can execute... Figure 1 The illustrated embodiment is a method for distributed privacy computing.

[0097] The processor may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, used to execute relevant programs to implement the image processing method of the embodiments of this application.

[0098] The processor can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the image processing method in this application embodiment can be completed by integrated logic circuits in the processor's hardware or by software instructions.

[0099] The aforementioned processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0100] The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the functions required by the units included in the image processing apparatus in the embodiments of this application, or, the image processing method of the method embodiments of this application can be executed... Figure 1 The illustrated embodiment describes a method for calculating the privacy of a ride-hailing driver's total driving time.

[0101] Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the privacy calculation method for the total driving time of ride-hailing drivers.

[0102] Computer-readable storage media may include, for example, a memory card for a smartphone, a storage component for a tablet computer, a hard disk for a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB storage device, or any combination of the above storage media. A computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0103] In summary, this invention provides a privacy-preserving calculation system, method, and application for the total driving time of ride-hailing drivers. It utilizes a time-series database to synchronize driver dispatch data with the corresponding ride-hailing platform in real time. A privacy-preserving calculation center receives driver working time query requests. Several privacy-preserving calculation access terminals receive these requests from the center, forming calculation tasks that are sent to the time-series database. The time-series database reads and calculates the corresponding data based on the calculation tasks. The privacy-preserving calculation access terminals read the calculation results from the time-series database and send the results to the privacy-preserving calculation center. The privacy-preserving calculation center accumulates the calculation results from each access terminal to obtain the total driving time of the driver within a specific time period. This invention enables cross-platform calculation of the total driving time of ride-hailing drivers and protects the security of privacy data through privacy calculation.

[0104] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. A privacy calculation system for the total driving time of ride-hailing drivers, characterized in that, This includes a privacy-preserving computing center and several distributed computing endpoints; The computing terminal includes a privacy computing access terminal and a time-series database; The privacy computing access terminal receives a privacy computing request for driver working time within a specific time period sent by the privacy computing center, forms a computing task related to time series data and sends it to the time series database, reads the computing results from the time series database, determines intermediate data based on the computing results, and sends the intermediate data to the privacy computing center. The time-series database synchronizes in real time the driver dispatch data and driver driving data of the corresponding ride-hailing platform. Based on the computation task, the corresponding time series data is read and calculated to form the computation result; The time series database periodically performs calculations on the time series data to generate pre-calculation results; According to the calculation task, extract the pre-calculation results to determine the calculation result of the calculation task; The privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for a specific time period to several privacy computing access terminals; based on the intermediate data sent by each of the privacy computing access terminals, it determines the total working time of the driver within the specific time period; wherein... The privacy computing access terminal receives privacy computing requests to determine data extraction rules and data computing algorithm information related to time series data, forms computing tasks, and sends the computing tasks to the time series database. The time-series database receives computing tasks from the privacy computing access terminal to extract time-series data according to data extraction rules, and performs calculations on the time-series data according to data computing algorithm information to form calculation results, which are then fed back to the privacy computing access terminal. The privacy computing access terminal receives the computing results from the time-series database, encrypts and transforms the computing results to form intermediate data, and then feeds the intermediate data back to the privacy computing center.

2. The privacy calculation system for the total driving time of ride-hailing drivers according to claim 1, characterized in that, The step of performing calculations on time series data according to data calculation algorithm information to form calculation results includes: The algorithm calls a function based on the data calculation information to perform calculations on the time series data and generate the calculation results.

3. The privacy calculation system for the total driving time of ride-hailing drivers according to claim 1, characterized in that, The process of extracting time-series data according to data extraction rules and performing calculations on the time-series data according to data calculation algorithm information to form calculation results includes: According to the first data extraction rule, extract time series data related to the operational status, and perform calculations on the time series data according to the data calculation algorithm information to determine the first calculation result; According to the second data extraction rule, extract the time series data related to low-speed operation in the empty state, and calculate the time series data according to the data calculation algorithm information to determine the second calculation result; According to the third data extraction rule, extract the time series data related to high-speed operation in the empty state, and calculate the time series data according to the data calculation algorithm information to determine the third calculation result; The calculation result is determined based on the first calculation result, the second calculation result, and the third calculation result.

4. The privacy calculation system for the total driving time of ride-hailing drivers according to claim 3, characterized in that, The privacy computing center end, Obtain intermediate data from at least one privacy computing access point, perform calculations on the intermediate data from at least one privacy access point, and determine the total working time; Determine whether the total working time exceeds the fatigue threshold to confirm the fatigue analysis results.

5. The privacy calculation system for the total driving time of ride-hailing drivers according to claim 1, characterized in that, Each of the aforementioned computing endpoints uses the same container (Docker) for its privacy computing access and time-series database; or the time-series database is provided by a ride-hailing platform.

6. A method for calculating the privacy of a ride-hailing driver's total driving time based on the privacy computing system described in any one of claims 1-5, characterized in that, include: The time-series database synchronizes driver dispatch data and driver driving data with the corresponding ride-hailing platform in real time; The privacy computing center receives driver work time query requests and sends driver work time privacy computing requests for a specific time period to several privacy computing access terminals. Several privacy computing access terminals receive the privacy computing request sent by the privacy computing center, form computing tasks related to time series data, and send them to the time series database; The time-series database reads and calculates corresponding data based on the computing task; The privacy computing access terminal reads the calculation results from the time-series database, determines intermediate data based on the calculation results, and sends the intermediate data to the privacy computing center. The privacy computing center determines the driver's total working time within a specific time period based on the intermediate data sent by each privacy computing access terminal.

7. A method for dispatching ride-hailing drivers, characterized in that, After a driver goes online on a ride-hailing platform and / or before a ride-hailing order is assigned to a driver, the privacy calculation method for the total driving time of a ride-hailing driver as described in claim 6 is used to obtain the driver's total working time within a specific time period. When the total working time exceeds the fatigue threshold, the driver is prohibited from being assigned a ride.

8. An electronic device, comprising a processor and a memory, the memory for storing program instructions, the processor for invoking the program instructions to execute the privacy calculation method for the total driving time of a ride-hailing driver as described in claim 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the privacy calculation method for the total driving time of ride-hailing drivers as described in claim 6.

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