A cross-job association rider continuous no-run single over-limit determination method

Abstract

The application discloses a rider continuous no-run single cross-work number association judgment method, personnel information, labor form conversion record and employment incentive configuration information of a rider to be judged are acquired, a statistical interval is determined according to a continuous no-run single statistical mode, a work number association chain is constructed by tracing a work number after each conversion along a conversion direction of a main work number, daily run single records of the main work number and each converted work number in the work number association chain in the continuous no-run single statistical interval are collected, daily run single sequences are generated by merging according to dates, valid daily run single sequences are obtained through in-service filtering and post filtering, then cross-work number continuous no-run single scanning is performed on the valid daily run single sequences, continuous no-run single states are accumulated at adjacent work number connection dates, the number of continuous no-run single days is counted and compared with a continuous no-run single upper limit, and finally a judgment result is output, thereby realizing accurate statistics and over-limit judgment of cross-work number continuous no-run single.

Description

Technical Field

[0001] This invention relates to the field of instant delivery data processing technology, and in particular to a method for determining the consecutive non-delivery exceeding limits of riders across employee IDs. Background Technology

[0002] As the on-demand delivery business continues to expand, the market has placed higher demands on the stability of delivery capacity and service quality. To encourage riders, especially newly hired riders, to maintain stable order attendance, the industry has generally established rider incentive management mechanisms. Common incentive schemes include peak-hour delivery bonuses, full attendance bonuses, and onboarding incentives. Among them, onboarding incentives are usually based on whether the rider has continuously participated in delivery within a specified statistical period after joining the company. The specific implementation method is usually as follows: the system reads the daily delivery records corresponding to the rider's ID number at fixed intervals, determines whether there are any deliveries on any given day within the statistical period, and continuously accumulates the days without deliveries. When the accumulated number of consecutive days without deliveries reaches a preset limit, the rider is determined to be ineligible for incentives.

[0003] However, the aforementioned method of accumulating data daily based on a single employee ID has significant limitations in real-world employment scenarios. Firstly, riders may change their employee IDs after joining the company due to changes in employment status. The order data before and after the change belongs to different employee IDs. If statistics are still compiled based on a single employee ID, the continuous period without orders will be interrupted at the employee ID change point, artificially truncating the statistical interval and failing to accurately reflect the rider's order continuity throughout the statistical period. Secondly, riders may change positions during the statistical period. If the days spent in non-delivery positions are included in the accumulation of continuous orders without orders, it will further cause the statistical results to be inconsistent with the actual on-duty situation. These shortcomings make the existing judgment method insufficiently applicable in scenarios with frequently changing employment status and job adjustments, easily leading to misjudgments. This affects the accuracy of incentive distribution and makes it difficult to effectively curb riders' behavior of circumventing assessments. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a method for determining the continuous no-order limit of riders by linking multiple employee IDs of the same rider as a whole for continuous no-order statistics, thereby accurately reflecting the rider's order continuity within a complete statistical period.

[0005] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is: a method for determining continuous non-performing orders exceeding limits by riders across work IDs, comprising: Obtain the rider's personnel information, employment form conversion records, and onboarding incentive configuration information. The personnel information should include at least the onboarding date, the offboarding date, and the job title. The onboarding incentive configuration information should include at least the method for calculating consecutive no-orders and the upper limit for consecutive no-orders. The statistical interval for consecutive no-orders is determined based on the statistical method for consecutive no-orders, which includes statistics based on the number of days since joining the company and statistics based on a specified date. Based on the employment form conversion records, the employee number of the rider to be judged is used as the main employee number. The employee numbers after each conversion are traced sequentially along the direction of employment form conversion to construct an employee number association chain. Collect daily order records of each converted employee ID in the main employee ID and employee ID association chain within the continuous no-order statistical interval, merge them by date to generate a daily order sequence, and the daily order sequence indicates whether there is an order on the day in the date order; Perform on-the-job filtering and job filtering on the daily delivery sequence to remove dates other than the departure date and dates whose job labels do not match the delivery job, thus obtaining the valid daily delivery sequence; Perform a cross-employee number continuous no-order scan on the valid day order sequence, and continuously accumulate the status of no orders on the connecting date of adjacent employment numbers to count the number of consecutive no-order days within the consecutive no-order statistical interval. Compare the number of consecutive days without orders with the upper limit for consecutive days without orders, generate a judgment result on whether the rider has exceeded the upper limit for consecutive days without orders, and output the result.

[0006] In some embodiments, the statistical interval for consecutive no-orders is determined according to the statistical method for consecutive no-orders. The statistical method for consecutive no-orders includes statistics based on the number of days since joining the company and statistics based on a specified date, including: The onboarding incentive configuration information also includes the number of days for the start offset, the number of days for the end offset, the specified start date, and the specified end date; When the continuous no-order statistics are based on the number of days since joining the company, the rider's joining date is used as the statistical anchor date. The statistical anchor date is shifted along the time axis in days to calculate the starting offset day. The date after the shift is determined as the starting date of the continuous no-order statistics. The statistical anchor date is also shifted along the time axis in days to calculate the ending offset day. The starting offset day and the ending offset day together define the span of the continuous no-order statistics interval, so that the continuous no-order statistics interval changes dynamically for each rider as their joining date changes. When the method for calculating consecutive no-run statistics is based on a specified date, the statistical anchor date is not used. The specified start date is determined as the start date of the consecutive no-run statistics, and the specified end date is determined as the end date of the consecutive no-run statistics. The starting date and ending date of the continuous no-run statistics are used as the two boundaries to define the interval for generating continuous no-run statistics.

[0007] In some embodiments, based on the employment form conversion record, using the rider's ID number as the primary ID number, the employee ID numbers are traced sequentially along the employment form conversion direction to construct an employee ID association chain, including: The employment form conversion record should include at least the employee number before conversion, the employee number after conversion, the conversion scenario, and the invalidation mark. Each employment form conversion record represents the correspondence between the employee number before conversion and the employee number after conversion in an employment form conversion. From the employment form conversion records, remove records marked as invalid and records where the conversion scenario is an internal transfer within part-time jobs to obtain valid employment form conversion records; When the rider's personnel category to be determined is part-time or elite rider, the main employee number is used as the current traceable employee number, and chain-style employee number tracing is performed. Chain-style employee number tracing includes: searching the effective employment form conversion records for records that match the employee number before conversion with the current traceable employee number. If a matching record is found, the converted employee number of the matching record is determined as the associated employee number and added to the employee number association chain. Then, the current traceable employee number is updated with the associated employee number, and chain-style employee number tracing continues. If no matching record is found, chain-style employee number tracing is terminated. The main employee ID is used as the head of the employee ID association chain, and the associated employee IDs are connected in sequence according to the tracing order to generate the employee ID association chain; when the rider's personnel category to be determined is not part-time or elite rider, the main employee ID is used as the employee ID association chain alone.

[0008] In some embodiments, the daily order records of each converted employee ID in the main employee ID and employee ID association chain within a continuous period without order records are collected, and merged by date to generate a daily order sequence, including: The main employee number and all associated employee numbers in the employee number association chain are used together as the employee number to collect data, forming a collection set of employee numbers, so that the collection set of employee numbers covers the head of the employee number association chain and all associated employee numbers. Collect the daily order records generated by each employee ID in the collection of employee IDs within a continuous period of no order execution. The daily order record must include at least the order execution date and the number of orders executed that day. The order execution date is the date on which the employee ID actually generated an order. A continuous date axis is constructed based on all dates within the consecutive period of no order cancellations. The order cancellation status corresponding to each date in the continuous date axis is initially set to no order cancellations, so that the dates without order cancellations are retained in the continuous date axis and are not missed. The collected daily order records are merged across work IDs. The daily order records of each work ID are merged into a continuous date axis according to the order date. For multiple daily order records with the same order date, the order volume of each record on that day is added up to the cumulative order volume for the order date. The order status of the order date is updated from no order to order, while the other dates remain in the no order status. Arrange the dates and their running status in the continuous dateline in chronological order to generate a daily running sequence.

[0009] In some embodiments, the collected daily order records are merged across employee IDs. The daily order records of each employee ID are merged into a continuous date axis based on the order date. For multiple daily order records with the same order date, their respective daily order volumes are summed to obtain the cumulative order volume for that date. The order status for that date is updated from "no orders" to "orders received," while other dates remain in the "no orders received" state. The daily order records of each collection object employee number are grouped according to the order date. Daily order records with the same order date are grouped into the same group, so that the group corresponds one-to-one with the order date. Daily order records with the same order date but from different collection object employee numbers are grouped into the same group. For each merge group, cross-work number day order merging is performed. Following the order association chain from the head to each associated work number, the order volume of each data collection object work number in the merge group on the order date is added up sequentially to obtain the cumulative order volume corresponding to the order date. This allows the order volume generated by the main work number and each associated work number on the same order date to be merged into the same date, eliminating the impact of work number boundaries on the order volume merging. Write the cumulative order volume into the position corresponding to the order date in the continuous date axis, and determine whether there is an order on the order date based on whether the cumulative order volume is greater than zero. When the cumulative order volume is greater than zero, update the order status corresponding to the order date from no order to order. For dates in the continuous date axis that do not correspond to any merged group, keep the order status of the date as no orders, and obtain the merged continuous date axis, so that each date in the merged continuous date axis corresponds to a unique cumulative order volume.

[0010] In some embodiments, the daily delivery sequence is filtered by both the employee status filter and the job position filter to remove dates other than the employee's departure date and dates whose job identification does not match the delivery job, thus obtaining a valid daily delivery sequence, including: A dual-constraint filtering is applied to the daily running sequence, which includes filtering based on employment interval and job affiliation. The start date of employment is taken as the employment start date, and the end date of employment is taken as the employment end date; when it is determined that the rider has not resigned, the current date is taken as the employment end date; the employment period is defined by the employment start date and the employment end date, and the employment period is a continuous time range that includes the boundaries of the employment start date and the employment end date. Perform on-the-job interval filtering, and determine whether each date in the daily order sequence falls within the on-the-job interval. Dates that fall within the on-the-job interval are retained, and dates that do not fall within the on-the-job interval are removed to obtain the on-the-job daily order sequence. Obtain the daily job identifier for each date in the daily order-running sequence. The daily job identifier represents the job position of the rider to be judged on that date, and it changes with different dates. Perform job assignment filtering, and determine whether the job identifier of each day belongs to the delivery job set. The delivery job set includes at least delivery personnel and store delivery team leaders. Dates with job identifiers belonging to the delivery job set are retained, while dates with job identifiers not belonging to the delivery job set are removed, thus obtaining the effective daily order sequence.

[0011] In some embodiments, job affiliation filtering is performed, determining daily whether the job identifier for each date belongs to the delivery job set. The delivery job set includes at least delivery personnel and store delivery team leaders. Dates whose job identifiers belong to the delivery job set are retained, while dates whose job identifiers do not belong to the delivery job set are removed, resulting in a valid daily order sequence, including: Iterate through each day in the order of the day of work in chronological order, and perform job assignment filtering on each day so that the job assignment filtering is carried out day by day as the job of the rider to be judged changes. The delivery job category should include at least delivery personnel and store delivery team leaders; For each date, the job identifier of that day is matched and compared with each delivery job in the delivery job set one by one to determine whether the job identifier of that day belongs to the delivery job set. The job classification filtering is based on the actual job of the rider to be judged on each date, rather than based on the fixed job of the rider to be judged. When the job identifier for a given day matches any delivery job in the delivery job set, the date is determined to belong to a delivery job and is retained in the order sequence for working days; when the job identifier for a given day does not match any delivery job in the delivery job set, the date is determined not to belong to a delivery job and is removed from the order sequence for working days. The dates that are retained after daily evaluation are arranged in chronological order to generate a valid day delivery sequence. Each date in the valid day delivery sequence is a date when the job identifier belongs to the delivery job set for that day.

[0012] In some embodiments, a cross-employee number consecutive no-order scan is performed on the valid day order sequence. At the connecting dates of adjacent employee numbers, the status of no orders on that day is continuously accumulated, and the number of consecutive no-order days within the consecutive no-order statistical interval is counted, including: For the valid daily order sequence, group by employee number and sort by date order, and assign date sequence number to the date corresponding to each employee number in sequence; Using the maximum number of consecutive days without orders as the length of the sliding window, a sliding window scan is performed on the sorted sequence of each employee number. Starting from each date, consecutive dates along the date sequence are extracted to form the sliding window. It is determined whether the order status of each date within the sliding window is all without orders. If all dates are without orders, the starting date of the sliding window is marked as the start date of the limit exceeding the limit. The start date of the limit exceeding the limit indicates that the number of consecutive days without orders has reached the maximum number of consecutive days without orders since that date. For adjacent employee numbers in the employee number association chain, the last order-running date of the previous employee number and the first order-running date of the subsequent employee number are determined as the connection boundary. The last order-running date is the latest date when the previous employee number generates an order, and the first order-running date is the earliest date when the subsequent employee number generates an order. At the boundary of the connection, perform continuous accumulation of no orders across work numbers. The number of days without orders after the last order date of the previous work number is continuously added to the number of days without orders before the start order date of the subsequent work number to obtain the number of consecutive days without orders across work numbers. The maximum value between the number of consecutive days without orders obtained by scanning the no-order-run area within each work number through a sliding window and the number of consecutive days without orders across work numbers is determined as the number of consecutive days without orders within the consecutive no-order-run statistical interval.

[0013] In some embodiments, a continuous no-order accumulation is performed across work orders at the connection boundary. The number of no-order days after the last order date of the preceding work order is continuously accumulated with the number of no-order days before the first order date of the following work order to obtain the number of consecutive no-order days across work orders, including: For each pair of adjacent preceding and following employee numbers in the employee number association chain, perform continuous non-running order accumulation across employee numbers at the connection boundary between the preceding and following employee numbers. For the previous employee number, the dates in the previous employee number sequence that are later than the last order-running date are determined as the last order-running dates of the previous employee number. The order-running status of the last order-running dates of the previous employee number is all no order-running. The last order-running dates of the previous employee number constitute the last order-running date segment of the previous employee number. The number of last order-running dates of the previous employee number is counted to obtain the number of last order-running days of the previous employee number. For subsequent employee numbers, the dates in the subsequent employee number sequence that are earlier than the starting order-running date are determined as the starting no-order-running dates of the subsequent employee number. The order-running status of the starting no-order-running dates of the subsequent employee number is all no-order-running. The starting no-order-running dates of the subsequent employee number constitute the starting no-order-running date segment of the subsequent employee number. The number of starting no-order-running dates of the subsequent employee number is counted to obtain the starting no-order-running days of the subsequent employee number. The number of days with no orders at the end of the previous work number and the number of days with no orders at the beginning of the subsequent work number are continuously added together to obtain the number of consecutive days with no orders across work numbers. The continuous addition ensures that the date segment with no orders at the end of the previous work number and the date segment with no orders at the beginning of the subsequent work number are connected at the boundary, forming a consecutive date segment with no orders that crosses the work number boundary, so that the statistics of consecutive days with no orders are not interrupted when the work number is changed.

[0014] In some embodiments, the number of consecutive days without orders is compared with the upper limit for consecutive days without orders to generate and output a judgment result on whether the rider has exceeded the upper limit for consecutive days without orders, including: A threshold comparison is performed between the number of consecutive days without orders and the upper limit of consecutive days without orders. The upper limit of consecutive days without orders is used as the upper limit comparison threshold, and the numerical values ​​of the number of consecutive days without orders and the upper limit of consecutive days without orders are compared. When the number of consecutive days without orders reaches or exceeds the limit for consecutive days without orders, the rider is judged to have exceeded the limit for consecutive days without orders, including situations where the number of consecutive days without orders is equal to the limit for consecutive days without orders; when the number of consecutive days without orders is less than the limit for consecutive days without orders, the rider is judged to have not exceeded the limit for consecutive days without orders. When there are no dates in the valid day order sequence, that is, when the rider to be judged has no valid order dates in the consecutive no-order statistics period, the number of consecutive no-order days of the rider to be judged is regarded as reaching the consecutive no-order limit, and the rider to be judged is directly judged as exceeding the consecutive no-order limit. The system generates a judgment result on whether the rider to be judged has exceeded the limit of consecutive no-orders. The judgment result is expressed as yes or no, with yes indicating that the rider has exceeded the limit of consecutive no-orders and no indicating that the rider has not exceeded the limit of consecutive no-orders. The judgment result is then output as the basis for issuing onboarding incentives to the rider to be judged.

[0015] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: The present invention provides a method for determining the consecutive no-order limit for riders across employee IDs. After obtaining the personnel information, employment form conversion records, and onboarding incentive configuration information of the rider to be determined, the statistical interval is determined according to the consecutive no-order statistical method. The employee ID is used to trace back the employee IDs after each conversion along the employment form conversion direction to construct an employee ID association chain. The daily order records of each converted employee ID in the main employee ID and employee ID association chain are collected within the consecutive no-order statistical interval. The daily order sequence is generated by merging by date. After in-service filtering and job filtering, the effective daily order sequence is obtained. Then, a cross-employee ID consecutive no-order scan is performed on the effective daily order sequence. The consecutive no-order status is accumulated at the connecting dates of adjacent employee IDs. The number of consecutive no-order days is counted and compared with the upper limit of consecutive no-order days before the judgment result is output. This realizes accurate statistics and over-limit determination of consecutive no-order across employee IDs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1This is a schematic diagram of steps S101 to S107 of the determination method described in the specific implementation embodiment; Figure 2 This is a schematic diagram of steps S201 to S205 of the determination method described in the specific implementation. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 This embodiment provides a method for determining the consecutive non-performing orders exceeding limits for riders across different work IDs, including: S101. Obtain the rider's personnel information, employment form conversion record, and onboarding incentive configuration information. The personnel information shall at least include the onboarding date, the departure date, and the job identifier. The onboarding incentive configuration information shall at least include the method for calculating consecutive no-orders and the upper limit for consecutive no-orders. S102. Determine the statistical interval for consecutive no-running orders based on the statistical method for consecutive no-running orders. The statistical method for consecutive no-running orders includes statistics based on the number of days since joining the company and statistics based on a specified date. S103. Based on the employment form conversion record, take the rider's ID number as the main ID number and trace the ID numbers after each conversion in the direction of employment form conversion to construct an ID number association chain. S104. Collect the daily running records of each converted employee number in the main employee number and employee number association chain within the continuous no-running-order statistical interval, merge them by date to generate a daily running-order sequence, and the daily running-order sequence indicates whether there is a running order on the day by date. S105. Perform on-the-job filtering and job filtering on the daily delivery sequence, and remove dates other than the departure date and dates whose job labels do not match the delivery job to obtain the valid daily delivery sequence. S106. Perform a cross-work number continuous no-run scan on the valid day running order sequence. At the connection date of adjacent work numbers, continuously accumulate the status of no running orders on that day, and count the number of consecutive no-run days within the consecutive no-run order statistical interval. S107. Compare the number of consecutive days without orders with the upper limit for consecutive days without orders, generate a judgment result on whether the rider has exceeded the upper limit for consecutive days without orders, and output the result.

[0020] In step S101, the personnel information may include the rider's identity identifier, start date, end date, job identifier, department, and personnel category, which can be retrieved from the human resources management system by identity identifier; the employment form conversion record may include the employee number before conversion, the employee number after conversion, and the conversion date, which can be obtained from the employment form change management module; the onboarding incentive configuration information may include the continuous no-order statistics method, the continuous no-order limit, and configuration parameters related to the statistical interval, which are pre-maintained by business personnel in the configuration storage object and read according to the rider's city, onboarding store, personnel category, and salary month and year during the determination.

[0021] In step S102, when statistics are calculated based on the number of days since joining the company, the statistical interval is generated by extending the timeline based on the date of joining the company. Different riders will have different statistical intervals due to their different dates of joining the company. When statistics are calculated based on a specified date, the statistical interval is directly defined by the start and end dates specified in the configuration and does not change with the individual rider's situation. The two methods correspond to two business scenarios: personalized incentives and unified management, respectively.

[0022] In step S103, the main employee number is the employee number currently used by the rider to be judged, as recorded in the personnel information, and this employee number is used as the starting point for tracing. The tracing is based on the correspondence between the employee number before and after the employment form conversion record. During tracing, the tracing is performed by searching for the corresponding converted employee number in the employment form conversion record. Once found, the converted employee number is added to the employee number association chain and used as the new current employee number to continue the search until no corresponding record is found, at which point the search terminates.

[0023] In step S104, during data collection, the time range is defined as the period between consecutive periods with no order processing, and the daily order processing records for each employee number within this range are obtained. During merging, the date is used as the alignment benchmark, and daily order processing records from different employee numbers on the same date are merged, so that the date corresponds to only one order processing status in the daily order processing sequence.

[0024] In step S105, the "on-the-job" filter is based on the employee's start and end dates in the employee information, filtering out employees by comparing whether each date falls within the on-the-job range defined by the start and end dates; the "job" filter is based on the job identifier corresponding to each date, filtering out employees by comparing whether the job identifier belongs to a delivery job. Both filters are performed daily on the daily delivery sequence.

[0025] In step S106, the cross-work number continuous no-order scan takes the valid day order sequence as the object and judges the order status day by day in date order. When the date crosses the junction of adjacent work numbers, the accumulated count of no-order status is not cleared due to the change of work number, but continues to accumulate from the previous work number.

[0026] In step S107, the comparison is made between the number of consecutive days without orders and the upper limit for consecutive days without orders. The result is output as a conclusion indicating whether the limit has been exceeded, which can be used as the basis for issuing onboarding incentives. Preferably, when there are no valid orders within the statistical interval of consecutive days without orders, it can be directly determined that the upper limit for consecutive days without orders has been exceeded.

[0027] This embodiment determines the statistical interval for consecutive no-order periods by acquiring personnel information, employment form change records, and onboarding incentive configuration information. It constructs an employee ID association chain and collects daily order records for each employee ID, merging them into a daily order sequence. After filtering by employment status and job position, a valid daily order sequence is obtained. Then, a cross-employee ID consecutive no-order scan is performed to count the number of consecutive no-order days, and finally, a comparison is made to output the judgment result. This scheme associates multiple employee IDs of the same rider as a whole for statistical analysis, effectively solving the problem of interruption in consecutive no-order statistics caused by employee ID changes due to employment form changes. This ensures that the judgment result truly reflects the rider's order continuity within the complete statistical period.

[0028] In some embodiments, the statistical interval for consecutive no-orders is determined according to the statistical method for consecutive no-orders. The statistical method for consecutive no-orders includes statistics based on the number of days since joining the company and statistics based on a specified date, including: The onboarding incentive configuration information also includes the number of days for the start offset, the number of days for the end offset, the specified start date, and the specified end date; When the continuous no-order statistics are based on the number of days since joining the company, the rider's joining date is used as the statistical anchor date. The statistical anchor date is shifted along the time axis in days to calculate the starting offset day. The date after the shift is determined as the starting date of the continuous no-order statistics. The statistical anchor date is also shifted along the time axis in days to calculate the ending offset day. The starting offset day and the ending offset day together define the span of the continuous no-order statistics interval, so that the continuous no-order statistics interval changes dynamically for each rider as their joining date changes. When the method for calculating consecutive no-run statistics is based on a specified date, the statistical anchor date is not used. The specified start date is determined as the start date of the consecutive no-run statistics, and the specified end date is determined as the end date of the consecutive no-run statistics. The starting date and ending date of the continuous no-run statistics are used as the two boundaries to define the interval for generating continuous no-run statistics.

[0029] In this embodiment, the start offset days, end offset days, specified start date, and specified end date are all pre-stored as configuration items in the onboarding incentive configuration information. These are set by business personnel according to the incentive plan and saved to the configuration storage object during the incentive rule configuration phase. During the judgment process, the corresponding configuration items are matched and read from the configuration storage object based on conditions such as the rider's city, the store where they joined, their personnel category, and their salary year. For statistics based on the number of days since joining, the start offset days and end offset days are read; for statistics based on a specified date, the specified start date and specified end date are read.

[0030] When the continuous no-order statistics are based on the number of days since joining the company, a statistical anchor point shifting method is used. This means that the joining date is used as the shifting benchmark, and date addition and subtraction operations are performed along the positive time axis based on the statistical start and end offset days. The resulting two dates correspond to the start and end dates of the continuous no-order statistics, respectively. This allows the continuous no-order statistical interval to dynamically change for each rider depending on their joining date. When the continuous no-order statistics are based on a specified date, the start and end dates are directly taken as the specified start and end dates, respectively. The joining date is not involved in determining the endpoints of the statistical interval. All dates between the start and end dates of the continuous no-order statistics, including both the start and end dates, belong to the continuous no-order statistical interval.

[0031] This embodiment configures the endpoints of the statistical interval, enabling the statistical interval to dynamically change with the rider by calculating the onboarding date and offset days under the onboarding-days statistical method, and enabling the unified and fixed statistical interval to be directly determined by the configured date under the specified date statistical method, thereby meeting the differentiated needs of the statistical interval determination method under the two scenarios of personalized incentives and unified management.

[0032] In some embodiments, based on the employment form conversion record, using the rider's ID number as the primary ID number, the employee ID numbers are traced sequentially along the employment form conversion direction to construct an employee ID association chain, including: The employment form conversion record should include at least the employee number before conversion, the employee number after conversion, the conversion scenario, and the invalidation mark. Each employment form conversion record represents the correspondence between the employee number before conversion and the employee number after conversion in an employment form conversion. From the employment form conversion records, remove records marked as invalid and records where the conversion scenario is an internal transfer within part-time jobs to obtain valid employment form conversion records; When the rider's personnel category to be determined is part-time or elite rider, the main employee number is used as the current traceable employee number, and chain-style employee number tracing is performed. Chain-style employee number tracing includes: searching the effective employment form conversion records for records that match the employee number before conversion with the current traceable employee number. If a matching record is found, the converted employee number of the matching record is determined as the associated employee number and added to the employee number association chain. Then, the current traceable employee number is updated with the associated employee number, and chain-style employee number tracing continues. If no matching record is found, chain-style employee number tracing is terminated. The main employee ID is used as the head of the employee ID association chain, and the associated employee IDs are connected in sequence according to the tracing order to generate the employee ID association chain; when the rider's personnel category to be determined is not part-time or elite rider, the main employee ID is used as the employee ID association chain alone.

[0033] In this embodiment, the conversion scenario in the employment form conversion record represents the business type of the employment form conversion, and the invalidation flag indicates whether the record has become invalid due to an input error or business cancellation. The employee number before conversion, the employee number after conversion, the conversion scenario, and the invalidation flag can be written and saved by the employment form change management module each time an employment form conversion is completed. The values ​​of the conversion scenario and the invalidation flag are selected by the business personnel according to the actual situation during the processing. When deleting records with the invalidation flag set to invalid or records whose conversion scenario pertains to internal transfers within part-time positions, the deletion is performed on a record-by-removal basis, and the deleted records no longer participate in subsequent chain-based employee number tracing.

[0034] In chained employee ID tracing, matching the employee ID before conversion with the currently traced employee ID can be achieved through equivalence comparison of the employee ID field. The comparison scope is valid employment form conversion records. The personnel category determination data can be read from the personnel information. Chained employee ID tracing is performed when the personnel category is part-time or elite rider. When the personnel category is not part-time or elite rider, the main employee ID is used as a separate employee ID association chain, that is, the employee ID association chain only contains the main employee ID.

[0035] This embodiment filters out invalid and untraceable employment form conversion records by conversion scenario and invalidation mark, and then searches and updates the current traceable employment number level by level along the employment form conversion direction starting from the main employment number. Multiple employment numbers generated by the same rider due to employment form conversion are linked together into an employment number association chain according to the conversion order, providing an employment number range for subsequent cross-employee order record collection.

[0036] Please see Figure 2 In some embodiments, the daily order records of each converted employee ID in the main employee ID and employee ID association chain within a continuous period without order records are collected and merged by date to generate a daily order sequence, including: S201. The main employee number and all associated employee numbers in the employee number association chain are used together as the collection object employee number to form a collection object employee number set, so that the collection object employee number set covers the head of the employee number association chain and all associated employee numbers. S202. Collect the daily order records generated by each employee number in the collection object employee number set during the continuous no-order statistics interval. The daily order record shall include at least the order date and the order quantity on that day. The order date shall be the date on which the collection object employee number actually generates an order. S203. Construct a continuous date axis based on all dates within the continuous no-run statistics interval. Initially set the run status of each date in the continuous date axis to no run, so that dates without run are retained in the continuous date axis without being missed. S204. Perform cross-employee daily order merging on the collected daily order records. Merge the daily order records of each collected employee number into a continuous date axis according to the order date. For multiple daily order records with the same order date, add up the order volume of each day to the cumulative order volume of the order date, and update the order status of the order date from no order to order. Keep the no order status of other dates. S205. Arrange the dates and their running status in the continuous date axis in chronological order to generate a daily running sequence.

[0037] In step S202, daily order records can be retrieved from the rider order statistics database by employee ID and date. The storage granularity is one record per employee ID for each order date; dates without orders are not recorded in the daily order records, and records are only written when an order occurs. During collection, the employee ID identifier in the set of employee IDs and the consecutive no-order statistical interval are used as search criteria to extract daily order records falling within the consecutive no-order statistical interval for each employee ID.

[0038] In step S203, the continuous date axis can be generated one by one according to the date order of all dates in the continuous no-run statistics interval, that is, it increases day by day from the start date of the continuous no-run statistics to the end date of the continuous no-run statistics, to obtain a continuous date set covering the continuous no-run statistics interval, and the running status corresponding to each date is initially set to no running.

[0039] In step S204, cross-work number daily order merging is based on the order date. Daily order records with the same order date are identified as the same merging object and merged into the corresponding date position in the continuous date axis. Preferably, during merging, the daily order volume of each collection object work number under the same order date can be added to obtain the cumulative order volume, and the order status of the order date is updated to have orders.

[0040] This embodiment obtains the set of employee IDs for collection by expanding the employee ID association chain, and collects daily order records based on this. A continuous date axis is constructed using all dates within the consecutive no-order statistical interval, and cross-employee daily order records are merged. This merges the order records of the main employee ID and each associated employee ID on the same order date into the same date, thereby generating a daily order sequence that is date-ordered and represents the existence of orders day by day. This provides a complete and continuous date sequence foundation for subsequent on-the-job filtering and job filtering.

[0041] In some embodiments, the collected daily order records are merged across employee IDs. The daily order records of each employee ID are merged into a continuous date axis based on the order date. For multiple daily order records with the same order date, their respective daily order volumes are summed to obtain the cumulative order volume for that date. The order status for that date is updated from "no orders" to "orders received," while other dates remain in the "no orders received" state. The daily order records of each collection object employee number are grouped according to the order date. Daily order records with the same order date are grouped into the same group, so that the group corresponds one-to-one with the order date. Daily order records with the same order date but from different collection object employee numbers are grouped into the same group. For each merge group, cross-work number day order merging is performed. Following the order association chain from the head to each associated work number, the order volume of each data collection object work number in the merge group on the order date is added up sequentially to obtain the cumulative order volume corresponding to the order date. This allows the order volume generated by the main work number and each associated work number on the same order date to be merged into the same date, eliminating the impact of work number boundaries on the order volume merging. Write the cumulative order volume into the position corresponding to the order date in the continuous date axis, and determine whether there is an order on the order date based on whether the cumulative order volume is greater than zero. When the cumulative order volume is greater than zero, update the order status corresponding to the order date from no order to order. For dates in the continuous date axis that do not correspond to any merged group, keep the order status of the date as no orders, and obtain the merged continuous date axis, so that each date in the merged continuous date axis corresponds to a unique cumulative order volume.

[0042] In this embodiment, a key-value correspondence structure is preferably constructed using the order date as the key and the merge group as the value. Daily order records for the same order date are written into the same merge group, and daily order records for different order dates are written into different merge groups, thereby ensuring a one-to-one correspondence between the merge group and the order date. Each daily order record within the merge group retains its respective data collection object employee number and the number of orders executed that day.

[0043] When calculating the cumulative order volume corresponding to the order execution date, the numbers are added sequentially along the employee ID association chain to ensure that the merging process maintains the same order as the employee ID association chain. The cumulative order volume is independent of the order of addition. If an employee ID in a merging group has no daily order execution records for a given order execution date, the daily order volume for that employee ID on that date is set to zero or is not included in the addition, without affecting the cumulative order volume result.

[0044] The cumulative order volume is written into the continuous date axis. The order date is used as the positioning basis. The cumulative order volume replaces the value corresponding to the original order status of that date in the continuous date axis, rather than superimposing the original value, so that each date in the merged continuous date axis corresponds to a unique cumulative order volume.

[0045] This embodiment groups daily order records by order date and adds the daily order volume from different data collection object IDs on the same order date sequentially along the ID association chain. This merges the order volume generated by the main ID and each associated ID on the same order date into the same date in the continuous date axis, eliminating the influence of ID boundaries on the merging of order volume. This results in a continuous date axis with a unique cumulative order volume for each date.

[0046] In some embodiments, the daily delivery sequence is filtered by both the employee status filter and the job position filter to remove dates other than the employee's departure date and dates whose job identification does not match the delivery job, thus obtaining a valid daily delivery sequence, including: A dual-constraint filtering is applied to the daily running sequence, which includes filtering based on employment interval and job affiliation. The start date of employment is taken as the employment start date, and the end date of employment is taken as the employment end date; when it is determined that the rider has not resigned, the current date is taken as the employment end date; the employment period is defined by the employment start date and the employment end date, and the employment period is a continuous time range that includes the boundaries of the employment start date and the employment end date. Perform on-the-job interval filtering, and determine whether each date in the daily order sequence falls within the on-the-job interval. Dates that fall within the on-the-job interval are retained, and dates that do not fall within the on-the-job interval are removed to obtain the on-the-job daily order sequence. Obtain the daily job identifier for each date in the daily order-running sequence. The daily job identifier represents the job position of the rider to be judged on that date, and it changes with different dates. Perform job assignment filtering, and determine whether the job identifier of each day belongs to the delivery job set. The delivery job set includes at least delivery personnel and store delivery team leaders. Dates with job identifiers belonging to the delivery job set are retained, while dates with job identifiers not belonging to the delivery job set are removed, thus obtaining the effective daily order sequence.

[0047] In this embodiment, whether a rider has resigned can be determined based on the value of the resignation date. If the resignation date is empty or represents a value indicating employment status, the rider is considered not to have resigned, and the current date is used as the employment end date. If the resignation date is a specific date, the rider is considered to have resigned, and the resignation date is used as the employment end date. The employment period can be represented in data format as a date pair consisting of the employment start date and the employment end date.

[0048] When performing the on-the-job interval filtering, it is determined whether each date in the daily order sequence falls within the on-the-job interval. This can be determined based on the relationship between the date and the on-the-job start date and the on-the-job end date. That is, if the date is not earlier than the on-the-job start date and not later than the on-the-job end date, it is determined to fall within the on-the-job interval.

[0049] In the daily delivery sequence, the corresponding job identifier for each date can be retrieved from the attendance record by date. The attendance record records the job position of the rider on each day, and the job identifier changes with the date, rather than being taken from the job identifier in the personnel information.

[0050] Delivery job sets can be pre-configured in a configuration storage object and maintained by business personnel according to the jobs involved in delivery operations. The delivery job set must include at least delivery personnel and store delivery team leaders. When performing job affiliation filtering, the job identifier for the day is compared with each delivery job in the delivery job set. If the comparison is consistent, the job identifier for the day is determined to belong to the delivery job set.

[0051] This embodiment filters out dates when the rider to be judged is not employed by filtering the employment period and filters out dates when the rider to be judged is not in the delivery position by filtering the job position. After the double constraint filtering, each date in the valid daily order sequence is a date when the rider to be judged is in the delivery position, which provides a filtered date sequence for subsequent cross-employee number continuous no-order scanning.

[0052] In some embodiments, job affiliation filtering is performed, determining daily whether the job identifier for each date belongs to the delivery job set. The delivery job set includes at least delivery personnel and store delivery team leaders. Dates whose job identifiers belong to the delivery job set are retained, while dates whose job identifiers do not belong to the delivery job set are removed, resulting in a valid daily order sequence, including: Iterate through each day in the order of the day of work in chronological order, and perform job assignment filtering on each day so that the job assignment filtering is carried out day by day as the job of the rider to be judged changes. The delivery job category should include at least delivery personnel and store delivery team leaders; For each date, the job identifier of that day is matched and compared with each delivery job in the delivery job set one by one to determine whether the job identifier of that day belongs to the delivery job set. The job classification filtering is based on the actual job of the rider to be judged on each date, rather than based on the fixed job of the rider to be judged. When the job identifier for a given day matches any delivery job in the delivery job set, the date is determined to belong to a delivery job and is retained in the order sequence for working days; when the job identifier for a given day does not match any delivery job in the delivery job set, the date is determined not to belong to a delivery job and is removed from the order sequence for working days. The dates that are retained after daily evaluation are arranged in chronological order to generate a valid day delivery sequence. Each date in the valid day delivery sequence is a date when the job identifier belongs to the delivery job set for that day.

[0053] In this embodiment, the daily job identifier for each date is obtained by retrieving the corresponding job field from the attendance record. The traversal process is synchronized with the reading of the daily job identifier; that is, when processing a certain date, the daily job identifier corresponding to that date is read. Job affiliation filtering is performed daily as the job of the rider to be judged changes. This can be understood as follows: during the daily retrieval of the daily job identifier, when the rider to be judged changes their job on a certain day, the daily job identifier for that day and thereafter changes accordingly. The judgment result of job affiliation filtering naturally reflects the job change as the date progresses, without the need to set up a separate job change detection step.

[0054] When comparing the daily job identifier with each delivery job in the delivery job set, the basis is a field that uniquely identifies the job, such as job name or job code. The value format of this field is consistent with the value format of the delivery jobs in the delivery job set. A successful match is determined when the daily job identifier matches any delivery job in the delivery job set, and the date is considered a delivery job. After daily comparison, the resulting valid day delivery sequence no longer maintains continuity on the timeline, and excluded dates do not occupy a position in the valid day delivery sequence.

[0055] This embodiment iterates through the order sequence of working days in chronological order and matches the job identifier of the day with the set of delivery jobs one by one. The job classification filtering is carried out day by day according to the actual job of the rider to be judged on each date. The dates that belong to the delivery job are retained and the dates that do not belong to the delivery job are removed. In this way, a valid order sequence of days is obtained in which the job identifier of the day belongs to the set of delivery jobs.

[0056] In some embodiments, a cross-employee number consecutive no-order scan is performed on the valid day order sequence. At the connecting dates of adjacent employee numbers, the status of no orders on that day is continuously accumulated, and the number of consecutive no-order days within the consecutive no-order statistical interval is counted, including: For the valid daily order sequence, group by employee number and sort by date order, and assign date sequence number to the date corresponding to each employee number in sequence; Using the maximum number of consecutive days without orders as the length of the sliding window, a sliding window scan is performed on the sorted sequence of each employee number. Starting from each date, consecutive dates along the date sequence are extracted to form the sliding window. It is determined whether the order status of each date within the sliding window is all without orders. If all dates are without orders, the starting date of the sliding window is marked as the start date of the limit exceeding the limit. The start date of the limit exceeding the limit indicates that the number of consecutive days without orders has reached the maximum number of consecutive days without orders since that date. For adjacent employee numbers in the employee number association chain, the last order-running date of the previous employee number and the first order-running date of the subsequent employee number are determined as the connection boundary. The last order-running date is the latest date when the previous employee number generates an order, and the first order-running date is the earliest date when the subsequent employee number generates an order. At the boundary of the connection, perform continuous accumulation of no orders across work numbers. The number of days without orders after the last order date of the previous work number is continuously added to the number of days without orders before the start order date of the subsequent work number to obtain the number of consecutive days without orders across work numbers. The maximum value between the number of consecutive days without orders obtained by scanning the no-order-run area within each work number through a sliding window and the number of consecutive days without orders across work numbers is determined as the number of consecutive days without orders within the consecutive no-order-run statistical interval.

[0057] In this embodiment, when grouping by work number, the work number field value corresponding to each date in the valid day running sequence is used as the grouping basis, and dates corresponding to the same work number are grouped into the same group. When assigning date sequence numbers to the dates corresponding to each work number, the date sequence numbers within each work number are independently numbered. The sequence number value reflects the position of the date after being arranged in chronological order within that work number. Since some dates have been removed from the valid day running sequence, the dates within each work number are not continuous on the time axis, and the actual number of days between the date sequence number and the date does not correspond.

[0058] The sliding window performing the no-run scan extracts consecutive dates along the date sequence direction, with the starting position of the sliding window shifting forward one date number at a time. The run status of each date within the sliding window is based on the run status of that date in the valid day run sequence. Preferably, the sliding window length is directly taken as the upper limit of consecutive no-run days; that is, the number of days represented by the upper limit of consecutive no-run days is the number of consecutive dates extracted by the sliding window, without the need for separate setting or conversion. Using the upper limit of consecutive no-run days as the sliding window length ensures that the run status of each date within the sliding window is no-run days. The starting date of this sliding window corresponds to the situation where the number of consecutive no-run days reaches the upper limit of consecutive no-run days. The extraction range of the sliding window is numerically consistent with the determination criterion of the upper limit of consecutive no-run days.

[0059] When determining the connection boundary, the last order-running date of the preceding employee number is taken as the latest date among the dates in the valid order-running sequence where the order-running status is "order-running," and the first order-running date of the following employee number is taken as the earliest date among the dates in the valid order-running sequence where the order-running status is "order-running." Preferably, if there are no dates in the valid order-running sequence where the current employee number has an order-running status, the endpoints of its date range in the valid order-running sequence can be used as an alternative basis for determining the connection boundary.

[0060] This embodiment sorts employees by work number and assigns them date numbers. Using the upper limit for consecutive no-order days as the sliding window length, it performs a sliding window scan within each work number to determine the start date of exceeding the limit. At the same time, it performs cross-work number consecutive no-order accumulation at the boundary between adjacent previous and subsequent work numbers in the work number association chain. The maximum value between the consecutive no-order days within each work number and the consecutive no-order days across work numbers is determined as the consecutive no-order days within the consecutive no-order statistical interval, so that the statistics of consecutive no-order days are not limited by work number boundaries.

[0061] In some embodiments, a continuous no-order accumulation is performed across work orders at the connection boundary. The number of no-order days after the last order date of the preceding work order is continuously accumulated with the number of no-order days before the first order date of the following work order to obtain the number of consecutive no-order days across work orders, including: For each pair of adjacent preceding and following employee numbers in the employee number association chain, perform continuous non-running order accumulation across employee numbers at the connection boundary between the preceding and following employee numbers. For the previous employee number, the dates in the previous employee number sequence that are later than the last order-running date are determined as the last order-running dates of the previous employee number. The order-running status of the last order-running dates of the previous employee number is all no order-running. The last order-running dates of the previous employee number constitute the last order-running date segment of the previous employee number. The number of last order-running dates of the previous employee number is counted to obtain the number of last order-running days of the previous employee number. For subsequent employee numbers, the dates in the subsequent employee number sequence that are earlier than the starting order-running date are determined as the starting no-order-running dates of the subsequent employee number. The order-running status of the starting no-order-running dates of the subsequent employee number is all no-order-running. The starting no-order-running dates of the subsequent employee number constitute the starting no-order-running date segment of the subsequent employee number. The number of starting no-order-running dates of the subsequent employee number is counted to obtain the starting no-order-running days of the subsequent employee number. The number of days with no orders at the end of the previous work number and the number of days with no orders at the beginning of the subsequent work number are continuously added together to obtain the number of consecutive days with no orders across work numbers. The continuous addition ensures that the date segment with no orders at the end of the previous work number and the date segment with no orders at the beginning of the subsequent work number are connected at the boundary, forming a consecutive date segment with no orders that crosses the work number boundary, so that the statistics of consecutive days with no orders are not interrupted when the work number is changed.

[0062] In this embodiment, the number of combinations of adjacent preceding and following employee numbers in the employee number association chain is determined by the chain length. An employee number association chain with a chain length of n has n minus one pair of adjacent employee number pairs. Preferably, when the employee number association chain contains only the main employee number and not associated employee numbers, there are no pairs of adjacent employee numbers. In this case, the accumulation of consecutive no-order days across employee numbers is not executed, and the number of consecutive no-order days is determined only by the number of consecutive no-order days obtained by scanning the no-order days within each employee number through a sliding window.

[0063] For previous employee IDs, the last order-running date is used as the dividing line. Dates in the valid order-running sequence that belong to the previous employee ID and are later than the last order-running date are considered "no-order-running days" at the end of the previous employee ID's timeline. The last order-running date itself is considered to have orders running and is not included in the "no-order-running days" segment at the end of the previous employee ID's timeline. The number of "no-order-running days" at the end of the previous employee ID's timeline can be calculated by counting the total number of "no-order-running days" at the end of the previous employee ID's timeline.

[0064] For subsequent employee IDs, the starting order-running date serves as the dividing line. Dates in the valid order-running sequence that belong to the subsequent employee ID and are earlier than the starting order-running date are considered the initial no-order-running dates for that subsequent employee ID. The starting order-running date itself is considered to have order-running activity and is not included in the initial no-order-running date period for the subsequent employee ID. The number of initial no-order-running days for a subsequent employee ID can be counted by counting the number of initial no-order-running days for that subsequent employee ID.

[0065] When continuously adding the number of days without orders at the end of the previous work number and the number of days without orders at the beginning of the subsequent work number, the date segment with no orders at the end of the previous work number and the date segment with no orders at the beginning of the subsequent work number are connected end to end at the boundary. The point of connection is the date boundary between the last date of the date segment with no orders at the end of the previous work number and the first date of the date segment with no orders at the beginning of the subsequent work number. Whether the two periods of no orders are consecutive in date does not affect the accumulation result. The accumulated number of consecutive days without orders across work numbers is the sum of the number of days without orders in the two periods.

[0066] This embodiment calculates the number of days without orders at the end of the previous employee ID and the number of days without orders at the beginning of the subsequent employee ID, and then continuously adds the two together. This results in the date segment with no orders at the end of the previous employee ID and the date segment with no orders at the beginning of the subsequent employee ID being connected at the boundary to form a continuous date segment with no orders that crosses the employee ID boundary. This ensures that the statistics of continuous no orders are not interrupted when the employee ID changes, and the number of continuous no orders across employee IDs can reflect the rider's actual continuous no-order situation before and after the employee ID change.

[0067] In some embodiments, the number of consecutive days without orders is compared with the upper limit for consecutive days without orders to generate and output a judgment result on whether the rider has exceeded the upper limit for consecutive days without orders, including: A threshold comparison is performed between the number of consecutive days without orders and the upper limit of consecutive days without orders. The upper limit of consecutive days without orders is used as the upper limit comparison threshold, and the numerical values ​​of the number of consecutive days without orders and the upper limit of consecutive days without orders are compared. When the number of consecutive days without orders reaches or exceeds the limit for consecutive days without orders, the rider is judged to have exceeded the limit for consecutive days without orders, including situations where the number of consecutive days without orders is equal to the limit for consecutive days without orders; when the number of consecutive days without orders is less than the limit for consecutive days without orders, the rider is judged to have not exceeded the limit for consecutive days without orders. When there are no dates in the valid day order sequence, that is, when the rider to be judged has no valid order dates in the consecutive no-order statistics period, the number of consecutive no-order days of the rider to be judged is regarded as reaching the consecutive no-order limit, and the rider to be judged is directly judged as exceeding the consecutive no-order limit. The system generates a judgment result on whether the rider has exceeded the limit for consecutive orders. The judgment result is expressed as yes or no, where yes indicates that the rider has exceeded the limit for consecutive orders and no indicates that the rider has not exceeded the limit for consecutive orders. The judgment result is then output, and the output can be written to a result table or returned to the caller as the basis for issuing the rider's onboarding incentive.

[0068] In this embodiment, threshold comparison is achieved through numerical comparison, that is, the number of consecutive days without orders and the upper limit of consecutive days without orders are used as comparison objects. The comparison result is divided into two types: the number of consecutive days without orders is not less than the upper limit of consecutive days without orders, and the number of consecutive days without orders is less than the upper limit of consecutive days without orders. The upper limit of consecutive days without orders and the number of consecutive days without orders are both integers, so numerical comparison can be performed directly.

[0069] In the judgment rules, the situation where the number of consecutive days without orders reaches or exceeds the limit for consecutive days without orders includes the situation where the number of consecutive days without orders is equal to the limit for consecutive days without orders. When making a judgment, the condition for exceeding the limit for consecutive days without orders is that the number of consecutive days without orders is not less than the limit for consecutive days without orders, and the condition for not exceeding the limit for consecutive days without orders is that the number of consecutive days without orders is less than the limit for consecutive days without orders.

[0070] When there are no dates in the valid day order sequence, no threshold comparison is performed. The rider's consecutive days without orders are directly considered to have reached the consecutive days without orders limit and are judged as exceeding the consecutive days without orders limit. The trigger condition for this situation is that there are no dates in the valid day order sequence, but there are no days in the non-valid day order sequence with an order status of "orders completed". The two should not be confused.

[0071] This embodiment compares the number of consecutive days without orders with the upper limit of consecutive days without orders. If the number of consecutive days without orders reaches or exceeds the upper limit, it is determined to be above the upper limit of consecutive days without orders. If there are no dates in the valid daily order sequence, it is directly determined to be above the upper limit of consecutive days without orders. The result is then generated as either yes or no and output, providing a basis for the issuance of onboarding incentives.

[0072] By adopting the above technical solution, this invention differs from existing technologies and has the following beneficial effects: It constructs an employee ID association chain by using the rider's ID number as the main ID and tracing back through each conversion along the employment form conversion direction. It collects daily order records for each converted employee ID within a continuous period without order runs, merging them by date to generate a daily order run sequence. After filtering by employment status and job position, it removes dates other than the departure date and dates where the job identification does not match the delivery job, obtaining a valid daily order run sequence. Finally, it executes the process on the valid daily order run sequence. The cross-employee number consecutive no-order scanning method continuously accumulates the number of days with no orders on the connecting dates of adjacent employee numbers. It counts the number of consecutive no-order days within the statistical interval and compares it with the upper limit of consecutive no-order days to generate a judgment result on whether the rider exceeds the upper limit of consecutive no-order days. This allows multiple employee numbers of the same rider due to changes in employment form to be associated as a whole for statistical purposes. The number of consecutive no-order days can be accumulated across employee number boundaries. The judgment result is close to the rider's actual on-the-job performance in the delivery position, providing an accurate basis for the issuance of onboarding incentives.

[0073] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0074] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0075] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for determining the consecutive non-performing orders exceeding limits for riders across employee ID associations, characterized in that, include: Obtain the rider's personnel information, employment form conversion records, and onboarding incentive configuration information. The personnel information includes at least the onboarding date, the offboarding date, and the job identifier. The onboarding incentive configuration information includes at least the method for calculating consecutive no-orders and the upper limit for consecutive no-orders. The consecutive no-order statistics interval is determined according to the aforementioned consecutive no-order statistics method, which includes statistics based on the number of days since joining the company and statistics based on a specified date. Based on the employment form conversion record, the employee number of the rider to be judged is used as the main employee number, and the employee number after each conversion is traced sequentially along the employment form conversion direction to construct an employee number association chain. Collect the daily order records of each converted employee number in the main employee number and the employee number association chain within the continuous no-order statistical interval, and merge them by date to generate a daily order sequence. The daily order sequence is used to indicate whether there is an order on the current day. Perform on-the-job filtering and job filtering on the daily delivery sequence to remove dates other than the departure date and dates whose job labels do not match the delivery job, thus obtaining the valid daily delivery sequence; Perform a cross-employee number continuous no-run scan on the valid daily run sequence, and continuously accumulate the status of no run on the connecting date of adjacent employee numbers, and count the number of consecutive no-run days within the consecutive no-run statistical interval. The system compares the number of consecutive days without orders with the upper limit for consecutive days without orders, generates and outputs a determination result on whether the rider to be determined has exceeded the upper limit for consecutive days without orders.

2. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 1, characterized in that, The consecutive no-order statistical interval is determined according to the aforementioned consecutive no-order statistical method, which includes statistics based on the number of days since joining the company and statistics based on a specified date, including: The onboarding incentive configuration information also includes the number of days for statistical start offset, the number of days for statistical end offset, the specified start date, and the specified end date; When the continuous no-order statistics are based on the number of days since joining the company, the joining date of the rider to be judged is used as the statistical anchor date. The statistical anchor date is shifted by the number of days from the start of the statistics along the time axis in the positive direction, and the date after the shift is determined as the start date of the continuous no-order statistics. The statistical anchor date is also shifted by the number of days from the end of the statistics along the time axis in the positive direction, and the date after the shift is determined as the end date of the continuous no-order statistics. The number of days from the start of the statistics and the number of days from the end of the statistics together define the span of the continuous no-order statistics interval, so that the continuous no-order statistics interval changes dynamically for each rider as the joining date changes. When the continuous no-run statistics method is to count according to a specified date, the statistical anchor date is not used. Instead, the specified start date is determined as the start date of the continuous no-run statistics, and the specified end date is determined as the end date of the continuous no-run statistics. The continuous no-run statistics interval is defined by using the start date and end date of the continuous no-run statistics as the two boundaries.

3. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 1, characterized in that, Based on the employment form conversion record, using the rider's employee number as the primary employee number, the employee numbers after each conversion are traced sequentially along the employment form conversion direction to construct an employee number association chain, including: The employment form conversion record includes at least the employee number before conversion, the employee number after conversion, the conversion scenario, and the invalidation mark. Each employment form conversion record represents the correspondence between the employee number before conversion and the employee number after conversion in an employment form conversion. From the employment form conversion records, records marked as invalid and records where the conversion scenario is an internal transfer within part-time work are removed to obtain valid employment form conversion records; When the rider to be determined is classified as part-time or elite rider, the main employee number is used as the current traceable employee number, and chain-style employee number tracing is performed. The chain-style employee number tracing includes: searching the effective employment form conversion records for records that match the employee number before conversion with the current traceable employee number; if a matching record is found, the converted employee number of the matching record is determined as the associated employee number and included in the employee number association chain; the current traceable employee number is then updated with the associated employee number, and the chain-style employee number tracing continues; if no matching record is found, the chain-style employee number tracing is terminated. The main employee ID is used as the head of the employee ID association chain, and the associated employee IDs are connected sequentially according to the tracing order to generate the employee ID association chain; when the rider to be determined does not belong to part-time or elite rider, the main employee ID is used as the employee ID association chain alone.

4. The method for determining continuous non-performing orders exceeding limits for riders across work IDs as described in claim 1, characterized in that, Collect the daily order records of each converted employee ID in the main employee ID and the employee ID association chain within the consecutive no-order statistical interval, and merge them by date to generate a daily order sequence, including: The main employee number and each associated employee number in the employee number association chain are used together as the employee number to be collected, forming a collection object employee number set, so that the collection object employee number set covers the head of the employee number association chain and all associated employee numbers; Collect daily order records generated by each employee ID in the collection of employee IDs within the continuous no-order statistical interval. The daily order record includes at least the order date and the number of orders executed on that day. The order date is the date on which the employee ID actually generated an order. A continuous date axis is constructed based on all dates within the continuous no-run statistics interval. The run status corresponding to each date in the continuous date axis is initially set to no run, so that the dates without run are retained in the continuous date axis without being omitted. The collected daily order records are merged across employee IDs. The daily order records of each collected employee ID are merged into the continuous date axis according to the order date. For multiple daily order records with the same order date, the order volume of each of them on that day is added up to the cumulative order volume of the order date. The order status of the order date is updated from no order to order, and the other dates remain in the no order status. Arrange the dates and their running status in the continuous date axis in chronological order to generate the daily running sequence.

5. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 4, characterized in that, The collected daily order records are merged across employee IDs. The daily order records for each employee ID are merged into the continuous date axis based on the order date. For multiple daily order records with the same order date, their respective daily order volumes are summed to obtain the cumulative order volume for that order date. The order status for that order date is updated from "no orders" to "orders received." Other dates remain in the "no orders" state, including: The daily running records of each collection object employee number are grouped according to the running date. Daily running records with the same running date are grouped into the same group, so that the group corresponds one-to-one with the running date. Daily running records with the same running date but from different collection object employee numbers are grouped into the same group. For each merge group, cross-work number day order merging is performed. Following the order from the head of the work number association chain to each associated work number, the daily order volume of each collection object work number in the merge group under the order date is added up sequentially to obtain the cumulative order volume corresponding to the order date. This allows the order volume generated by the main work number and each associated work number on the same order date to be merged and counted into the same date, eliminating the impact of work number boundaries on the order volume merging. Write the cumulative order volume into the position corresponding to the order date in the continuous date axis, and determine whether there is an order on the order date based on whether the cumulative order volume is greater than zero. When the cumulative order volume is greater than zero, update the order status corresponding to the order date from no order to order. For dates in the continuous date axis that do not correspond to any merged group, keep the order status of the date as no orders, and obtain the merged continuous date axis, so that each date in the merged continuous date axis corresponds to a unique cumulative order volume.

6. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 1, characterized in that, The daily delivery sequence is filtered by both current employee status and job position, removing dates other than the employee's departure date and dates whose job identification does not match the delivery job position, to obtain the valid daily delivery sequence, including: The daily running sequence is subjected to dual-constraint filtering, which includes job interval filtering and job affiliation filtering. The date of joining the company is taken as the start date of employment, and the date of leaving the company is taken as the end date of employment. When the rider to be determined has not left the company, the current date is taken as the end date of employment. The employment period is defined by the start date of employment and the end date of employment, and the employment period is a continuous time range that includes the two boundaries of the start date of employment and the end date of employment. Perform the on-the-job interval filtering, and determine whether each date in the daily order sequence falls within the on-the-job interval. Dates that fall within the on-the-job interval are retained, and dates that do not fall within the on-the-job interval are removed to obtain the on-the-job daily order sequence. Obtain the daily job identifier corresponding to each date in the order-running sequence of working days. The daily job identifier is the job position of the rider to be judged on that date, which changes with different dates. The job affiliation filtering is performed daily to determine whether the job identifier of each day belongs to the delivery job set. The delivery job set includes at least delivery personnel and store delivery team leaders. Dates whose job identifiers belong to the delivery job set are retained, while dates whose job identifiers do not belong to the delivery job set are removed, thus obtaining the effective daily order sequence.

7. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 6, characterized in that, The job affiliation filtering is performed daily to determine whether the job identifier for each day belongs to the delivery job set, which includes at least delivery personnel and store delivery team leaders. Dates whose job identifiers belong to the delivery job set are retained, while dates whose job identifiers do not belong to the delivery job set are removed, resulting in the effective daily order sequence, including: The system iterates through each day in the order of the day of work in chronological order, and performs job assignment filtering on each day, so that the job assignment filtering is carried out day by day as the job of the rider to be judged changes. The set of delivery positions includes at least delivery personnel and store delivery team leaders; For each date, the job identifier of the date is matched and compared with each delivery job in the delivery job set one by one to determine whether the job identifier of the date belongs to the delivery job set. The job affiliation filtering is based on the actual job of the rider to be determined on each date, rather than on the fixed job of the rider to be determined. When the job identifier for the day matches any delivery job in the set of delivery jobs, the date is determined to belong to a delivery job and is retained in the order sequence for working days; when the job identifier for the day does not match any delivery job in the set of delivery jobs, the date is determined not to belong to a delivery job and is removed from the order sequence for working days. The dates that are retained after daily evaluation are arranged in chronological order to generate the valid day delivery sequence. Each date in the valid day delivery sequence is a date when the job identifier belongs to the delivery job set on that day.

8. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 1, characterized in that, Perform a cross-employee number consecutive no-order scan on the valid daily order sequence. At the connecting dates of adjacent employee numbers, continuously accumulate the status of no orders on that day, and count the number of consecutive no-order days within the consecutive no-order statistical interval, including: For the valid daily running sequence, group by employee number and sort by date order, and assign date number to the date corresponding to each employee number in sequence; Using the maximum consecutive no-order limit as the sliding window length, a sliding window no-order scan is performed on the sorted sequence of each employee number. Starting from each date, consecutive dates along the date sequence direction are extracted to form a sliding window. It is determined whether the order status of each date in the sliding window is no order. If all dates are no order, the starting date of the sliding window is marked as the over-limit start date. The over-limit start date indicates that the number of consecutive no-order days since the date has reached the maximum consecutive no-order limit. For adjacent preceding and following employee numbers in the employee number association chain, the last order-running date of the preceding employee number and the first order-running date of the following employee number are determined as the connection boundary. The last order-running date is the latest date when the preceding employee number generates an order, and the first order-running date is the earliest date when the following employee number generates an order. At the connection boundary, perform continuous no-order accumulation across work numbers, continuously adding the number of no-order days after the last order date of the preceding work number to the number of no-order days before the starting order date of the following work number to obtain the number of consecutive no-order days across work numbers. The maximum value between the number of consecutive days without orders obtained by scanning the no-order-running data within each work number through the sliding window and the number of consecutive days without orders across work numbers is determined as the number of consecutive days without orders within the consecutive no-order-running statistical interval.

9. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 8, characterized in that, At the connection boundary, a continuous no-order accumulation across work orders is performed. The number of no-order days after the last order date of the preceding work order is continuously added to the number of no-order days before the first order date of the following work order to obtain the number of consecutive no-order days across work orders, including: For each pair of adjacent preceding and following employee numbers in the employee number association chain, at the connection boundary between the preceding and following employee numbers, perform continuous non-running order accumulation across employee numbers. For the previous employee number, the dates in the previous employee number sequence that are later than the last order-running date are determined as the last order-running dates of the previous employee number. The order-running status of the last order-running dates of the previous employee number is all no order-running. The last order-running dates of the previous employee number constitute the last order-running date segment of the previous employee number. The number of the last order-running dates of the previous employee number is counted to obtain the number of days with no order-running at the end of the previous employee number. For the subsequent employee number, the dates in the subsequent employee number sequence that are earlier than the initial order-running date are determined as the initial no-order-running dates of the subsequent employee number. The order-running status of the initial no-order-running dates of the subsequent employee number is all no-order-running. The initial no-order-running dates of the subsequent employee number constitute the initial no-order-running date segment of the subsequent employee number. The number of the initial no-order-running dates of the subsequent employee number is counted to obtain the initial no-order-running days of the subsequent employee number. The number of days without orders at the end of the previous employee number and the number of days without orders at the beginning of the subsequent employee number are continuously added together to obtain the number of consecutive days without orders across the employee number. The continuous addition makes the date segment with no orders at the end of the previous employee number and the date segment with no orders at the beginning of the subsequent employee number connected end to end at the connection boundary, forming a consecutive date segment with no orders across the employee number boundary, so that the statistics of consecutive days without orders are not interrupted when the employee number is changed.

10. The method for determining continuous non-performing orders exceeding limits by riders across work IDs as described in claim 1, characterized in that, Compare the number of consecutive days without orders with the upper limit for consecutive days without orders, generate and output a determination result on whether the rider to be judged has exceeded the upper limit for consecutive days without orders, including: A threshold comparison is performed between the number of consecutive days without a single order and the upper limit of consecutive days without a single order. The upper limit of consecutive days without a single order is used as the upper limit comparison threshold, and the numerical values ​​of the number of consecutive days without a single order and the upper limit of consecutive days without a single order are compared. When the number of consecutive days without orders reaches or exceeds the upper limit of consecutive days without orders, the rider to be judged is determined to have exceeded the upper limit of consecutive days without orders. Reaching or exceeding includes the case where the number of consecutive days without orders is equal to the upper limit of consecutive days without orders. When the number of consecutive days without orders is less than the upper limit of consecutive days without orders, the rider to be judged is determined to have not exceeded the upper limit of consecutive days without orders. When there are no dates in the valid daily order sequence, that is, when the rider to be judged has no valid order dates in the consecutive no-order statistical interval, the consecutive no-order days of the rider to be judged are regarded as reaching the consecutive no-order limit, and the rider to be judged is directly judged as exceeding the consecutive no-order limit. Based on the determination, a determination result is generated to determine whether the rider to be determined has exceeded the limit of consecutive no-orders. The determination result is expressed as yes or no, with yes indicating that the rider has exceeded the limit of consecutive no-orders and no indicating that the rider has not exceeded the limit of consecutive no-orders. The determination result is then output as the basis for issuing the rider's onboarding incentive.

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