A system and method for processing order data of online car-hailing platform

Abstract

The present invention discloses a system and method for processing order data of an online car-hailing platform, which belongs to the technical field of information design and management. The system and method are used to solve the technical problem that the overall effect of processing and mining and utilizing online car-hailing order data in existing solutions is not good. The system and method obtain the corresponding local monitoring data processing sequence by implementing real-time monitoring, statistics and processing of periodic online car-hailing platform order data in a target area, identify and analyze the data integrated and calculated in different dimensions in the early stage and combine them to obtain the corresponding monitoring data analysis identification sequence, further analyze and evaluate the operation status of online car-hailing orders on the integration results of different dimensions in the early stage of the local monitoring period, and dynamically control the marketing plans corresponding to the subsequent local monitoring periods according to the evaluation results, thereby realizing the flexible implementation of the corresponding marketing plans in different periods and further improving the overall effect of processing and utilizing online car-hailing order data.

Description

Technical Field

[0001] The present invention relates to the technical field of information design management, and in particular to a system and method for processing order data on an online car-hailing platform. Background Art

[0002] Processing online ride-hailing orders is a complex and highly automated process, involving multiple links such as passengers placing orders, drivers accepting orders, trip execution, and fee settlement.

[0003] When implementing the existing online ride-hailing platform order data processing solution, there is no data processing of different dimensions on the online ride-hailing platform order data, and no integration of the processing results of different dimensions to analyze and verify the overall operating status of the current online ride-hailing platform, and no dynamic management and control of the subsequent marketing of the online ride-hailing platform based on the verification results, resulting in poor overall effect of online ride-hailing order data processing and mining and utilization. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for processing order data of an online car-hailing platform, so as to solve the technical problem that the overall effect of online car-hailing order data processing and mining and utilization in existing solutions is poor.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An online car-hailing platform order data processing system, comprising:

[0007] The order data monitoring and statistical processing module is used to monitor, count and process all real-time online car-hailing platform order data in the target area within the local monitoring period, obtain local monitoring and processing data and upload it to the supervision platform in real time;

[0008] The order data processing calculation and analysis module is used to perform multi-dimensional data processing on the local monitoring and processing data and integrate and calculate to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the operation status of the online car-hailing order corresponding to the local monitoring and processing data;

[0009] The order operation status expansion control module is used to dynamically control the online car-hailing operation and marketing plan in the target area within the next local monitoring cycle based on the monitoring data analysis identification sequence obtained through integration and processing.

[0010] Preferably, the real-time online car-hailing order data appearing in the target area is monitored within the local monitoring period, and the real-time online car-hailing orders are sorted and combined according to the order numbers to obtain the online car-hailing order monitoring data;

[0011] And, obtaining the total number of local online ride-hailing orders and the total amount of local online ride-hailing orders within a local monitoring period based on the online ride-hailing order monitoring data;

[0012] The total number of local online car-hailing orders and the total amount of local online car-hailing orders are extracted and sorted to obtain the local monitoring data processing sequence [Dc, Jc];

[0013] The local monitoring data processing sequence is associated with the corresponding online car-hailing order monitoring data to obtain the local monitoring processing data.

[0014] Preferably, when the local monitoring processing data is subjected to data processing and integration calculation of the first dimension, the last local monitoring cycle of the local monitoring processing data and its corresponding first historical local monitoring processing data are obtained, and the first historical local monitoring data processing sequence [Dc′, Jc′] in the first historical local monitoring processing data and the local monitoring processing sequence [Dc, Jc] in the local monitoring processing data are calculated by the formula to obtain the corresponding first data contrast SD1 (i) ; The calculation formula of the first data contrast is: In the formula, a1 and a2 are different proportional coefficients, 0<a1≤a2<1, α is the local monitoring data correction factor; i=1, 2, 3, ..., n; n is a positive integer; i is the cycle number corresponding to the local monitoring cycle to which the local monitoring processing data belongs.

[0015] Preferably, when the local monitoring and processing data are processed in the second dimension and integrated, the same historical local monitoring cycle of the local monitoring and processing data in the previous month and its corresponding second historical local monitoring and processing data are obtained, and the second historical local monitoring data processing sequence [Dc″, Jc″] in the second historical local monitoring and processing data is compared with the local monitoring and processing sequence [Dc, Jc] in the local monitoring and processing data by the formula to obtain the corresponding second data contrast SD2 (i) ; The calculation formula of the second data contrast is: In the formula, b1 and b2 are different proportional coefficients, 0<b1≤b2<1, and β is the correction factor of historical monitoring data;

[0016] The first data contrast and the second data contrast obtained by calculation are sorted and combined to obtain a monitoring data processing sequence.

[0017] Preferably, when integrating and analyzing the data contrast of different dimensions, the monitoring data processing sequence is input into a pre-built order data recognition model for data analysis and outputting a monitoring data analysis identification sequence;

[0018] The monitoring data analysis identification sequence includes [0, 0], [0, 1], [1, 0], [1, 1];

[0019] According to the monitoring data analysis identification sequence, the operation status of the online car-hailing order corresponding to the target area within the local monitoring period is determined and prompted; the operation status of the online car-hailing order includes normal operation status, fluctuating abnormal operation status and stable abnormal operation status.

[0020] Preferably, the expression of the order data identification model is: Wherein, k=1, 2; SDk′ includes SD1′ and SD2′, and SD1′ and SD2′ are the first data contrast threshold and the second data contrast threshold, respectively.

[0021] Preferably, the monitoring data analysis identification sequence is traversed to count the total number of elements N′ with a value of 0 and analyzed;

[0022] If the total number of elements N′ with a value of 0 is 1, a marketing verification instruction is generated and the monitoring data analysis identification sequence corresponding to the previous K local monitoring cycles is counted and marked as the target sequence. The target sequence is traversed and counted, and data calculation and analysis is performed, and the online car-hailing operation marketing plan is dynamically adjusted according to the analysis results.

[0023] Preferably, by the formula Calculate and obtain the historical operational impact η corresponding to the previous K local monitoring cycles; where N1 and N2 are the total number of target sequences with only one value of 1 and the total number of target sequences with two values ​​of 1, respectively, and B is the anomaly correction coefficient;

[0024] If the historical operation impact is not 0, a first marketing adjustment instruction is generated and the existing marketing plan is adjusted.

[0025] Preferably, if the total number N′ of elements with a value of 0 is 0, a second marketing adjustment instruction is generated and the existing marketing plan is adjusted.

[0026] A method for processing order data of an online car-hailing platform, comprising:

[0027] During the local monitoring period, all real-time online ride-hailing platform order data in the target area is monitored, counted and processed, and local monitoring and processing data is obtained and uploaded to the supervision platform in real time;

[0028] Perform multi-dimensional data processing and integrated calculation on the local monitoring and processing data to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the operation status of the online car-hailing order corresponding to the local monitoring and processing data;

[0029] According to the identification sequence of the integrated monitoring data analysis, the online car-hailing operation and marketing plan in the target area within the next local monitoring cycle is dynamically controlled.

[0030] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0031] The present invention implements real-time monitoring, statistics and processing of periodic online car-hailing platform order data in the target area to obtain the corresponding local monitoring data processing sequence, identifies and analyzes the data integrated and calculated in different dimensions in the early stage, and combines them to obtain the corresponding monitoring data analysis identification sequence. According to the monitoring data analysis identification sequence, the integration results of different dimensions can be digitally represented, and reliable data support can be provided for the dynamic adjustment of the online car-hailing operation and marketing plan in the subsequent local monitoring period, thereby improving the diversity of online car-hailing order data processing and mining and utilization.

[0032] The present invention further analyzes and evaluates the operational status of online car-hailing orders by integrating the results of different dimensions in the early stage of a local monitoring cycle, and dynamically manages and controls the marketing plans corresponding to subsequent local monitoring cycles based on the evaluation results, thereby achieving flexible implementation of corresponding marketing plans in different periods and further improving the overall effect of online car-hailing order data processing and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 This is a module block diagram of an online car-hailing platform order data processing system of the present invention.

[0035] Figure 2 The present invention is a flowchart of a method for processing order data of an online car-hailing platform. DETAILED DESCRIPTION

[0036] 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 operation and maintenance personnel in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1: Figure 1 As shown, the present invention is an order data processing system for an online car-hailing platform, including an order data monitoring and statistical processing module, an order data processing calculation and analysis module, an order operation status expansion control module and a supervision platform;

[0038] The order data monitoring and statistical processing module is used to monitor, count and process all real-time online car-hailing platform order data in the target area within the local monitoring period, obtain local monitoring and processing data and upload it to the supervision platform in real time; including:

[0039] The real-time online car-hailing order data in the target area is monitored within the local monitoring period. The unit of the local monitoring period is day, which can be 7 days. The target area can be a city, which can be determined according to the actual application scenario. The real-time online car-hailing orders are sorted and combined according to the order numbers to obtain the online car-hailing order monitoring data;

[0040] And, obtaining the total number of local online ride-hailing orders and the total amount of local online ride-hailing orders within a local monitoring period based on the online ride-hailing order monitoring data;

[0041] The total number of local online car-hailing orders and the total amount of local online car-hailing orders are extracted and sorted to obtain the local monitoring data processing sequence [Dc, Jc];

[0042] Associating the local monitoring data processing sequence with the corresponding online car-hailing order monitoring data to obtain local monitoring processing data;

[0043] In the embodiment of the present invention, by implementing real-time monitoring and statistics of online car-hailing platform order data in the target area periodically and processing to obtain the corresponding local monitoring data processing sequence, reliable data support can be provided for the subsequent analysis of the local online car-hailing order operation status corresponding to the target area;

[0044] The order data processing calculation and analysis module is used to perform multi-dimensional data processing on the local monitoring and processing data and integrate and calculate to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the online car-hailing order operation status corresponding to the local monitoring and processing data; including:

[0045] When the local monitoring processing data is processed and integrated in the first dimension, the previous local monitoring cycle of the local monitoring processing data and its corresponding first historical local monitoring processing data are obtained, and the first historical local monitoring data processing sequence [Dc′, Jc′] in the first historical local monitoring processing data and the local monitoring processing sequence [Dc, Jc] in the local monitoring processing data are calculated by the formula to obtain the corresponding first data contrast SD1 (i) ; The calculation formula of the first data contrast is: Wherein, a1 and a2 are different proportional coefficients, 0<a1≤a2<1, α is the local monitoring data correction factor; i=1, 2, 3, ..., n; n is a positive integer; i is the cycle number corresponding to the local monitoring cycle to which the local monitoring processing data belongs;

[0046] Among them, the proportionality coefficient is used to differentiate the calculation importance of different data items in the calculation process. The specific value is not limited and can be customized according to the actual online car-hailing operation design requirements; the local monitoring data correction factor is calculated by the formula α=J1 / J2, J1 is the median of the total amount of local online car-hailing orders corresponding to all the same local monitoring periods in history, and J2 is the median of the total amount of all local online car-hailing orders corresponding to all the same previous local monitoring periods in history;

[0047] And, when performing data processing and integration calculation of the second dimension on the local monitoring and processing data, obtain the same historical local monitoring cycle of the previous month of the local monitoring cycle to which the local monitoring and processing data belongs and its corresponding second historical local monitoring and processing data, and calculate the corresponding second data contrast SD2 by the formula by comparing the second historical local monitoring data processing sequence [Dc″, Jc″] in the second historical local monitoring and processing data with the local monitoring and processing sequence [Dc, Jc] in the local monitoring and processing data (i) ; The calculation formula of the second data contrast is: Where b1 and b2 are different proportional coefficients, 0<b1≤b2<1, β is the historical monitoring data correction factor, which is calculated by the formula β=J1 / J3, and J3 is the median of the total amount of all local online car-hailing orders corresponding to the same local monitoring period in the previous month;

[0048] The first data contrast and the second data contrast obtained by calculation are sorted and combined to obtain a monitoring data processing sequence;

[0049] It should be noted that, in the embodiment of the present invention, the gap between the current local monitoring cycle and the corresponding dimension is digitally represented by integrating and calculating the monitoring data of different dimensions with the current monitoring data. This can not only realize the digital representation of the gap between the current local monitoring cycle and different dimensions, but also provide data support of different dimensions for the overall online car-hailing order operation status analysis of the subsequent local monitoring cycle;

[0050] When integrating and analyzing the data contrasts of different dimensions, the monitoring data processing sequence is input into a pre-built order data recognition model for data analysis and outputting a monitoring data analysis identification sequence, wherein the order data recognition model is established based on an isolation forest algorithm combined with a first data contrast threshold and a second data contrast threshold;

[0051] The expression of the order data recognition model is: Wherein, k=1, 2; SDk′ includes SD1′ and SD2′, SD1′ and SD2′ are respectively the first data contrast threshold and the second data contrast threshold, and the first data contrast threshold and the second data contrast threshold are determined according to the median values ​​of all first data contrasts and all second data contrasts in the history corresponding to the online car-hailing platform;

[0052] The monitoring data analysis identification sequence includes [0, 0], [0, 1], [1, 0], [1, 1];

[0053] According to the monitoring data analysis identification sequence, the operation status of online car-hailing orders corresponding to the target area within the local monitoring period is determined and prompted;

[0054] The online car-hailing order operation states corresponding to [0, 0], [0, 1] and [1, 0], [1, 1] are normal operation state, fluctuating abnormal operation state, and stable abnormal operation state, respectively;

[0055] In the embodiment of the present invention, by identifying and analyzing the data integrated and calculated in different dimensions in the early stage and combining them to obtain the corresponding monitoring data analysis identification sequence, the integration results of different dimensions can be digitally represented according to the monitoring data analysis identification sequence, and reliable data support can be provided for the dynamic adjustment of the online car-hailing operation and marketing plan in the subsequent local monitoring cycle, thereby improving the diversity of online car-hailing order data processing and mining and utilization;

[0056] The order operation status expansion control module is used to dynamically control the online car-hailing operation and marketing plan of the target area in the next local monitoring cycle according to the monitoring data analysis identification sequence obtained through integration and processing; including:

[0057] Traverse the monitoring data analysis identification sequence to count the total number of elements N′ with a value of 0 and analyze;

[0058] If the total number of elements N′ with a value of 0 is 2, a marketing maintenance instruction is generated and the existing marketing plan is maintained;

[0059] If the total number of elements N′ with a value of 0 is 1, a marketing verification instruction is generated and the monitoring data analysis identification sequence corresponding to the previous K local monitoring cycles is counted and marked as the target sequence. K is a positive integer. The specific value is set according to the actual application scenario requirements. The target sequence is traversed and counted, and the formula is used. Calculate and obtain the historical operational impact η corresponding to the previous K local monitoring cycles; where N1 and N2 are the total number of target sequences with only one value of 1 and the total number of target sequences with two values ​​of 1, respectively, and B is the anomaly correction coefficient. The anomaly correction coefficient can be determined according to the operational design requirement parameters corresponding to the online car-hailing platform, or it can be obtained by performing data simulation based on all historical operational impacts through simulation software to obtain the error in the calculation process. The anomaly correction coefficient reduces the error in the calculation process to improve the accuracy and reliability of the calculation;

[0060] If the historical operation impact is 0, a marketing observation instruction is generated and the existing marketing plan is maintained;

[0061] If the historical operation impact is not 0, a first marketing adjustment instruction is generated and the existing marketing plan is adjusted;

[0062] If the total number N′ of elements with a value of 0 is 0, a second marketing adjustment instruction is generated and the existing marketing plan is adjusted;

[0063] And, shortening the duration of the local monitoring cycle according to the first marketing adjustment instruction or the second marketing adjustment instruction to obtain an abnormal monitoring cycle, and using the abnormal monitoring cycle to implement targeted supervision on the operation status of online car-hailing orders within the corresponding cycle of the adjusted marketing plan;

[0064] Among them, adjusting the existing marketing plan includes but is not limited to increasing the target total number of marketing objects based on the existing marketing plan, and increasing the marketing discount amount, such as increasing the coupon amount for taxi promotions.

[0065] Different from the existing technical solutions that only stay at the statistics and intuitive display of online car-hailing order data, without implementing multi-dimensional and in-depth data mining and analysis and utilization of online car-hailing order data, in the implementation of the present invention, the online car-hailing order operation status is further analyzed and evaluated by integrating the results of different dimensions in the early stage of the local monitoring cycle, and the marketing plans corresponding to the subsequent local monitoring cycles are dynamically managed and controlled according to the evaluation results, thereby realizing the flexible implementation of corresponding marketing plans in different periods and further improving the overall effect of online car-hailing order data processing and utilization.

[0066] Example 2: Figure 2 As shown, the present invention is a method for processing order data of an online car-hailing platform, comprising:

[0067] During the local monitoring period, all real-time online ride-hailing platform order data in the target area is monitored, counted and processed, and local monitoring and processing data is obtained and uploaded to the supervision platform in real time;

[0068] Perform multi-dimensional data processing and integrated calculation on the local monitoring and processing data to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the operation status of the online car-hailing order corresponding to the local monitoring and processing data;

[0069] According to the identification sequence of the integrated monitoring data analysis, the online car-hailing operation and marketing plan in the target area within the next local monitoring cycle is dynamically controlled.

[0070] In addition, the formulas involved in the above are all dimensionless and numerical calculations. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it with simulation software.

[0071] In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described embodiments of the invention are only illustrative, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0072] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0074] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic features of the present invention.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary operation and maintenance personnel in the field should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A system for processing order data of an online car-hailing platform, characterized in that: include: The order data monitoring and statistical processing module is used to monitor, count and process all real-time online car-hailing platform order data in the target area within the local monitoring period, obtain local monitoring and processing data and upload it to the supervision platform in real time; The order data processing calculation and analysis module is used to perform multi-dimensional data processing on the local monitoring and processing data and integrate and calculate to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the operation status of the online car-hailing order corresponding to the local monitoring and processing data; When the local monitoring and processing data are processed in the first dimension and integrated, the last local monitoring cycle of the local monitoring and processing data and its corresponding first historical local monitoring and processing data are obtained, and the first historical local monitoring data processing sequence [Dc', Jc'] in the first historical local monitoring and processing data is compared with the local monitoring and processing sequence [Dc, Jc] in the local monitoring and processing data by the formula to obtain the corresponding first data contrast SD1 (i) ; The calculation formula of the first data contrast is: ; In the formula, a1 and a2 are different proportional coefficients, 0<a1≤a2<1, α is the local monitoring data correction factor; i=1, 2, 3, ..., n; n is a positive integer; i is the cycle number corresponding to the local monitoring cycle to which the local monitoring processing data belongs; When performing data processing and integration calculation of the second dimension on the local monitoring and processing data, obtain the same historical local monitoring cycle of the previous month of the local monitoring cycle to which the local monitoring and processing data belongs and its corresponding second historical local monitoring and processing data, and calculate the corresponding second data contrast SD2 by the formula by comparing the second historical local monitoring data processing sequence [Dc´´, Jc´´] in the second historical local monitoring and processing data with the local monitoring and processing sequence [Dc, Jc] in the local monitoring and processing data (i) ; The calculation formula of the second data contrast is: ; In the formula, b1 and b2 are different proportional coefficients, 0<b1≤b2<1, and β is the correction factor of historical monitoring data; The first data contrast and the second data contrast obtained by calculation are sorted and combined to obtain a monitoring data processing sequence; When integrating and analyzing the data contrast of different dimensions, the monitoring data processing sequence is input into the pre-built order data recognition model for data analysis and outputs the monitoring data analysis identification sequence; The monitoring data analysis identification sequence includes [0, 0], [0, 1], [1, 0], [1, 1]; According to the monitoring data analysis identification sequence, the online car-hailing order operation status corresponding to the target area within the local monitoring period is determined and prompted; the online car-hailing order operation status includes normal operation status, fluctuating abnormal operation status and stable abnormal operation status; The order operation status expansion control module is used to dynamically control the online car-hailing operation and marketing plan of the target area in the next local monitoring cycle based on the integrated monitoring data analysis identification sequence; Among them, the total number of elements N' with a value of 0 is traversed and analyzed in the monitoring data analysis identification sequence; If the total number of elements N´ with a value of 0 is 2, a marketing maintenance instruction is generated and the existing marketing plan is maintained; If the total number of elements N' with a value of 0 is 1, a marketing verification instruction is generated and the monitoring data analysis identification sequence corresponding to the previous K local monitoring cycles is counted and marked as the target sequence. K is a positive integer. The target sequence is traversed and counted, and the formula is used Calculate and obtain the historical operational impact η corresponding to the previous K local monitoring cycles; where N1 and N2 are the total number of target sequences with only one value of 1 and the total number of target sequences with two values ​​of 1, respectively, and B is the anomaly correction coefficient; If the historical operation impact is 0, a marketing observation instruction is generated and the existing marketing plan is maintained; If the historical operation impact is not 0, a first marketing adjustment instruction is generated and the existing marketing plan is adjusted; If the total number N' of elements with a value of 0 is 0, a second marketing adjustment instruction is generated and the existing marketing plan is adjusted; Furthermore, according to the first marketing adjustment instruction or the second marketing adjustment instruction, the duration of the local monitoring cycle is shortened to obtain an abnormal monitoring cycle, and the abnormal monitoring cycle is used to implement targeted supervision on the operation status of the online car-hailing orders within the corresponding period of the adjusted marketing plan.

2. The online car-hailing platform order data processing system according to claim 1, characterized in that: Monitor the real-time online ride-hailing order data in the target area during the local monitoring period, sort and combine the real-time online ride-hailing orders according to the order numbers, and obtain the online ride-hailing order monitoring data; And, obtaining the total number of local online ride-hailing orders and the total amount of local online ride-hailing orders within a local monitoring period based on the online ride-hailing order monitoring data; The total number of local online car-hailing orders and the total amount of local online car-hailing orders are extracted and sorted to obtain the local monitoring data processing sequence [Dc, Jc]; The local monitoring data processing sequence is associated with the corresponding online car-hailing order monitoring data to obtain the local monitoring processing data.

3. The online car-hailing platform order data processing system according to claim 1, characterized in that: The expression of the order data recognition model is: ; Wherein, k=1, 2; SDk´ includes SD1´ and SD2´, SD1´ and SD2´ are the first data contrast threshold and the second data contrast threshold respectively.

4. A method for processing order data of an online car-hailing platform, using an online car-hailing platform order data processing system as described in any one of claims 1 to 3, characterized in that: include: During the local monitoring period, all real-time online ride-hailing platform order data in the target area is monitored, counted and processed, and local monitoring and processing data is obtained and uploaded to the supervision platform in real time; Perform multi-dimensional data processing and integrated calculation on the local monitoring and processing data to obtain data contrast of different dimensions, and integrate and analyze the data contrast of different dimensions to determine the operation status of the online car-hailing order corresponding to the local monitoring and processing data; According to the identification sequence of the integrated monitoring data analysis, the online car-hailing operation and marketing plan in the target area within the next local monitoring cycle is dynamically controlled.

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