WO2018103369A1 - 一种运力监测方法及装置 - Google Patents

一种运力监测方法及装置 Download PDF

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Publication number
WO2018103369A1
WO2018103369A1 PCT/CN2017/097459 CN2017097459W WO2018103369A1 WO 2018103369 A1 WO2018103369 A1 WO 2018103369A1 CN 2017097459 W CN2017097459 W CN 2017097459W WO 2018103369 A1 WO2018103369 A1 WO 2018103369A1
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Prior art keywords
capacity
order
location
completed
area
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PCT/CN2017/097459
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English (en)
French (fr)
Inventor
孔兵
郝井华
张涛
周易
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北京三快在线科技有限公司
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Application filed by 北京三快在线科技有限公司 filed Critical 北京三快在线科技有限公司
Priority to US16/467,858 priority Critical patent/US20200090296A1/en
Priority to EP17877790.0A priority patent/EP3553716A1/en
Priority to CA3053975A priority patent/CA3053975A1/en
Publication of WO2018103369A1 publication Critical patent/WO2018103369A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/0833—Tracking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06311—Scheduling, planning or task assignment for a person or group
    • G06Q10/063114—Status monitoring or status determination for a person or group
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data

Definitions

  • the present application relates to a capacity monitoring method and apparatus in the field of information technology.
  • the rational allocation of regional capacity is an important factor to ensure quick response to orders and improve user experience.
  • the number of orders that will be generated in the area can be estimated based on historical order information for an area.
  • the distribution of capacity in the area can be calculated based on the location of the performer, such as takeaway rider, courier, driver, and the like.
  • the purpose of the embodiments of the present application is to provide a method and device for monitoring the capacity.
  • an embodiment of the present application provides a method for monitoring a capacity, including:
  • the transportation capacity in the monitoring area is obtained.
  • obtaining the capacity in the monitoring area according to the location information of each performer and the progress information of the order processing to be completed includes:
  • the capacity of each location area is obtained.
  • the capacity of each of the location areas is obtained according to the location information of each performer and the progress information of the order processing to be completed, including:
  • the capacity of the corresponding location area in the monitoring area is increased according to the information of each order to be completed.
  • the capacity of the corresponding location area in the monitoring area is increased according to the information of each order to be completed, including:
  • the capacity of the location area to which the to-be-completed order is completed is increased by a preset first adjustment value
  • the capacity of the location area to which the response to the completed order belongs is increased by a preset second adjustment value.
  • the first adjustment value corresponds to a first threshold range to which the number of completed responses in the pending order held by the executor of the to-be-completed order belongs; the second adjustment value and the waiting The second threshold range to which the number of outstanding responses in the pending order held by the executor of the completed order belongs.
  • the capacity of the corresponding location area in the monitoring area is increased according to the information of the to-be-completed order, and the method further includes:
  • the capacity of each of the location areas is obtained according to the location information of each of the performers and the progress information of the order processing to be completed, and further includes:
  • the capacity of the location area is smoothed according to the capacity of the adjacent location area of the location area;
  • the value obtained by the smoothing process is taken as the final capacity of the location area.
  • the capacity monitoring method of the present application further includes:
  • the capacity shortage degree of each of the location areas is determined according to the order quantity of each of the location areas in the future set time period and the capacity of each of the location areas.
  • determining the number of orders of the location area in the future set period includes:
  • the order quantity of the location area in the future set time period is obtained according to the order quantity ratio and the order quantity of the monitoring area in the future set time period.
  • dividing the monitoring area into a plurality of location areas includes:
  • the plurality of location areas are obtained by dividing the monitoring area by a geohash algorithm.
  • dividing the monitoring area into a plurality of location areas includes:
  • the plurality of location areas are obtained by taking the location coordinate range of the order completion location in each order cluster as a location area.
  • an embodiment of the present application provides a capacity monitoring apparatus, including: a processor and a machine readable storage medium, the machine readable storage medium storing machine executable instructions executable by the processor, The processor is caused by the machine executable instructions:
  • the capacity in the monitoring area is obtained.
  • an embodiment of the present application provides a machine readable storage medium having stored thereon machine executable instructions that, when invoked and executed by a processor, cause the processor to execute The capacity monitoring method described in the first aspect of the present application.
  • the capacity monitoring method and apparatus provided by the embodiments of the present application subtly introduces the performer's pending order processing progress information to monitor the capacity of the area.
  • the executor's pending order processing progress information the executor's position change trajectory can be estimated, and the traffic distribution can be monitored more accurately and reliably according to the executor's position change trajectory.
  • FIG. 1 is a schematic diagram showing the hardware structure of a capacity monitoring device 100 according to an embodiment of the present application.
  • FIG. 2 is a flowchart of a method for monitoring a capacity according to an embodiment of the present application.
  • FIG. 3 is a schematic diagram of sub-steps included in step S230 of FIG. 2 in an embodiment of the present application.
  • FIG. 4 is a schematic diagram of sub-steps included in step S230 of FIG. 2 in another embodiment of the present application.
  • FIG. 5 is a schematic diagram of sub-steps included in step S236 of FIG. 4 in an embodiment of the present application.
  • FIG. 6 is a schematic diagram of sub-steps included in step S236 of FIG. 4 in another embodiment of the present application.
  • FIG. 7 is a flowchart of a method for monitoring a capacity according to another embodiment of the present application.
  • FIG. 8 is a flowchart of a method for monitoring a capacity according to still another embodiment of the present application.
  • FIG. 9 is a schematic diagram of sub-steps included in step S210 of FIG. 8 in an embodiment of the present application.
  • FIG. 10 is a flowchart of a location area division according to an embodiment of the present application.
  • FIG. 11 is a flowchart of a method for monitoring a capacity according to another embodiment of the present application.
  • FIG. 12 is a functional block diagram of a capacity monitoring logic shown in FIG. 1.
  • Icons 100-capacity monitoring device; 110-machine readable storage medium; 120-processor; 130-network module; 200-capacity monitoring logic; 220-information acquisition module; 230-capacity acquisition module.
  • a capacity monitoring method determines the capacity of each location area based on the current location of each performer.
  • this capacity monitoring method determines the capacity of each location area based on the current location of each performer.
  • the change of capacity in the future is not considered.
  • the accuracy of the capacity of the region is limited according to the current location information of each performer. Due to It is true that the monitoring of the capacity of each location area is the basis for rationally allocating the capacity of each location area. Therefore, the embodiment of the present application can process the progress information according to the pending order of each performer (for simplicity, the following may be referred to as the pending order information). It is estimated that the position of each performer will change in the future time period, so that the capacity of each location area can be accurately monitored, which can provide a basis for rational allocation of capacity in each location area.
  • FIG. 1 it is a hardware structure diagram of a capacity monitoring device 100 according to an embodiment of the present application.
  • the capacity monitoring device 100 in the embodiment of the present application may be a device having a data processing capability such as a server or a computer.
  • the capacity monitoring device 100 can include a machine readable storage medium 110, a processor 120, and a network module 130.
  • the machine readable storage medium 110, the processor 120, and the network module 130 are electrically connected directly or indirectly to each other to implement transmission or interaction of data.
  • the components can be electrically connected to one another via one or more communication buses or signal lines.
  • the machine readable storage medium 110 stores machine executable instructions corresponding to the capacity monitoring logic 200, and the capacity monitoring logic 200 can include at least one software or firmware stored in the machine readable storage medium 110.
  • the processor 120 executes various functional applications and data processing by running software programs and modules stored in the machine-readable storage medium 110, such as machine-executable instructions corresponding to the capacity monitoring logic 200 in the embodiment of the present application. For example, the capacity monitoring method in the embodiment of the present application is implemented.
  • the machine readable storage medium 110 may be, but not limited to, a random access memory (RAM), a read only memory (ROM), and a programmable read only memory (Programmable Read-Only). Memory, PROM, Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), flash memory, storage drive (such as hard disk drive) , solid state drive, any type of storage disk (such as CD, dvd, etc.), or similar storage media, or a combination thereof.
  • the machine readable storage medium 110 is configured to store a program, and the processor 120 executes the program after receiving an execution instruction.
  • the processor 120 may be an integrated circuit chip with signal processing capabilities.
  • the processor 120 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), and the like. It can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, and a discrete gate. Or transistor logic devices, discrete hardware components.
  • DSP Digital Signal Processor
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • the general purpose processor may be a microprocessor, or the processor may be any conventional processor or the like.
  • the network module 130 is configured to establish a communication connection between the capacity monitoring device 100 and the external communication terminal through the network, and implement the transmission and reception operations of the network signal and the data.
  • the above network signal may include a wireless signal or a wired signal.
  • FIG. 1 is merely illustrative, and the capacity monitoring device 100 may further include more or less components than those shown in FIG. 1, or have a different configuration than that shown in FIG.
  • the components shown in Figure 1 can be implemented in hardware, software, or a combination thereof.
  • FIG. 2 is a flowchart of a method for monitoring a capacity according to an embodiment of the present application.
  • the method steps defined by the process related to the method may be implemented by the processor 120.
  • the specific flow shown in FIG. 2 will be described in detail below.
  • Step S220 Acquire location information and pending order information of each performer in the monitoring area.
  • each performer can carry a handheld terminal or a wearable device.
  • each wearer carries a wearable device, and the wearable device may include a positioning module, a processing module, a communication module, and the like.
  • the positioning module can record the location information of the performer.
  • the processing module can record and adjust the performer's pending order information.
  • the communication module can transmit the location information of each performer and the order information to be completed to the processor 120. In this step, the location information of each performer sent by the communication module and the order information to be completed may be acquired.
  • the order information to be completed may include information such as the responsiveness and completion of the order to be completed in the executor's hand, whether the order to be completed has completed the response, and the like.
  • the response to the order to be completed may refer to the order place where the order is to be completed.
  • the order to be completed is a take-out order
  • the response place of the take-out order is the location of the ordering merchant.
  • the completion location of the take-out order can be the location of the order customer, and the completed response means that the take-away rider has received the customer's take-out from the order merchant.
  • the order to be completed is an approximate car order
  • the response place of the approximate car order is about the car user's boarding place.
  • the completion date of the approximate car order is the destination of the approximate car user, and the completed response means that the driver has received the approximate car user on the vehicle of the approximate car user.
  • Step S230 Obtain the capacity in the monitoring area according to the location information of each performer and the order information to be completed.
  • the location information of each executor can reflect the current location of each executor, and according to the information of the executor to be completed, such as the response location of the order to be completed, the completion location, etc., each can be estimated
  • the executor changes the trajectory in the future.
  • the capacity of the monitoring area will also change. For example, if the responsiveness and completion of the executor's pending order is outside the monitoring area, then it can be estimated that the executor will leave the monitoring area in order to complete the pending order, thereby reducing the capacity of the monitoring area. Similarly, if the responsiveness and completion of the pending order of the executor outside the monitoring area are within the monitoring area, it can be estimated that the executor will enter the monitoring area in order to complete the pending order, thereby making the monitoring area The capacity has increased.
  • the monitoring area may include a plurality of location areas, and each performer may move within a plurality of location areas of the monitoring area.
  • step S230 may include: dividing the monitoring area into a plurality of location areas; and obtaining the capacity of each location area according to the location information of each performer and the order information to be completed.
  • the executor's pending order information it is possible to estimate the change of the location area of each executor in the future. As the position of each performer changes, the capacity of each location area also changes.
  • FIG. 3 is a schematic diagram of sub-steps included in step S230 of FIG. 2 in an embodiment of the present application. Referring to FIG. 3 together, step S230 includes three sub-steps of step S231, step S232 and step S233.
  • Step S231 Acquire the number of orders to be completed by each performer.
  • Step S232 It is judged whether there is an executor whose number of orders to be completed is 0.
  • Step S233 If there is an executor whose number of pending orders is 0, the capacity of the location area to which the executor's location information belongs is increased.
  • the capacity of the location area to which the performer's location information belongs may be increased by a set value, such as 5.
  • FIG. 4 is a schematic diagram of sub-steps included in step S230 of FIG. 2 in another embodiment of the present application. Referring to FIG. 4 together, step S230 may further include three sub-steps of step S234, step S235, and step S236.
  • Step S234 Acquire the number of orders to be completed for each performer.
  • Step S235 It is judged whether there is an executor whose number of orders to be completed is at least 1.
  • Step S236 If there is an executor whose number of orders to be completed is at least 1, the capacity of the location area of each pending order or the location area of the completed location is increased according to each pending order information.
  • FIG. 5 is a schematic diagram of sub-steps included in step S236 of FIG. 4 according to an embodiment of the present application.
  • step S236 may include three sub-steps of step S2361, step S2362, and step S2363.
  • Step S2361 For each pending order, determine whether the to-be-completed order is an order that has completed the response, if If the response has been completed, step S2362 is performed, otherwise step S2363 is performed.
  • Step S2362 Increase the capacity of the location area to which the completion of the to-be-completed order belongs.
  • Step S2363 Increase the capacity of the location area to which the response to the completed order belongs.
  • Step S236 may also include other embodiments depending on actual needs. For example, if the order to be completed is an order that has not yet completed the response, the capacity of the location area to which the response to the completed order belongs and the capacity of the location area to which the completion is located may be separately increased. For another example, different capacity may be added according to the number of orders to be completed in the executor. In general, the fewer orders to be completed in the executor's hand, the greater the capacity, and the more orders the executor has to complete. The smaller the capacity is.
  • FIG. 6 is a schematic diagram of sub-steps included in step S236 of FIG. 4 in another embodiment of the present application. Referring to FIG. 6, step S236 may further include five sub-steps of step S2364 to step S2368.
  • Step S2364 Determine a threshold range to which the number of orders to be completed by each performer belongs.
  • Step S2365 Find an adjustment value corresponding to the threshold range.
  • Step S2366 For each pending order, it is determined whether the to-be-completed order is an order that has completed the response. If the order has been completed, step S2367 is performed, otherwise step S2368 is performed.
  • Step S2367 The capacity of the location area to which the completion of the order to be completed is added is increased by the adjustment value.
  • Step S2368 increasing the capacity of the location area to which the response to the completed order belongs is increased by the adjustment value.
  • the threshold range can be flexibly set according to the actual demand according to the gradient. For example, 1 to 3 are set to a threshold range, 4 to 6 are a threshold range, and 7 to 9 are a threshold range. And setting the correspondence between different threshold ranges and different capacity values. For example, among the three threshold ranges listed, the adjustment values corresponding to the set threshold ranges 1 to 3 are the largest, and the adjustment values corresponding to the threshold ranges 4 to 6 are next, and the threshold is The adjustment values corresponding to the range 7 to 9 are the smallest. In this way, by analyzing the threshold range of the number of orders to be completed by the executor, it is possible to obtain a corresponding increase in the capacity of the responsiveness of each of the executors' orders or the location area to which the executor belongs.
  • step S2364 to step S2368 may be various.
  • step S2366 of determining whether the order to be completed is an order that has completed the response may be performed first, and then performing a step S2364 of determining a threshold range to which the number of orders to be completed in the executor's hand belongs to find an adjustment value corresponding to the threshold range.
  • step S2365 For another example, the step S2364, S2365 for determining the threshold range to which the number of orders to be completed in the executor's hand belongs to find the adjustment value corresponding to the threshold range, and the step S2366 for determining whether the order to be completed is the order that has completed the response may be Execute in parallel.
  • step 236 can also include determining that the response has been completed in each of the performer's pending orders. The number and the number of outstanding responses; determining a first threshold range to which the number of completed responses in the performer's pending order belongs and a second threshold range to which the number of outstanding responses belongs; finding out the first threshold range Corresponding first adjustment value and a second adjustment value corresponding to the second threshold range; increasing the capacity of the location area of the completion point of each of the performer's pending orders by the first adjustment value; and executing the performer The capacity of the location area to which the respective orders to be completed are responsive is increased by the second adjustment value.
  • FIG. 7 is a flowchart of a capacity monitoring method according to another embodiment of the present application. As shown in FIG. 7, the capacity monitoring method may further include step S260, step S270, and step S280.
  • Step S260 For each pending order, in combination with the pre-stored map data, obtain a running path from the response of the to-be-completed order to the completed place.
  • Step S270 Find out a location area through which the running path passes.
  • Step S280 Increase the capacity of the found location area.
  • the capacity of the found location area can be flexibly increased.
  • the capacity of each of the found location areas can be increased by a fixed value.
  • a capacity calculation model may be established, and the capacity of each of the found location areas is increased by a different value according to the difference between the number of performers in the location area and the number of orders to be completed.
  • the capacity of the location area is increased by a larger value; if the ratio of the number of performers to the number of orders to be completed in a location area is smaller, Then, the capacity of the location area is increased to a smaller value.
  • information of the executor who will pass the location area can be transmitted to the terminal of the responsive area of the location area.
  • the to-be-completed order information of the location area to be passed may be sent to the wearer of the corresponding performer. It is also possible to automatically assign the to-be-completed order of the passing location area to the executor of the route.
  • the capacity monitoring method in this embodiment may further include: obtaining, for each location area, the capacity of the location area adjacent to the location area, and performing the capacity of the location area and the location area adjacent to the location area.
  • Comprehensive processing such as smoothing, yields the final capacity of the location area. For example, for each location area, the capacity of the location area is smoothed according to the capacity of the adjacent location area of the location area, and the value obtained by the smoothing process is used as the final capacity of the location area.
  • FIG. 8 is a flowchart of a capacity monitoring method according to another embodiment of the present application. As shown in FIG. 8 , the embodiment of the present application further provides a capacity monitoring method capable of analyzing a capacity shortage. Including step S210 and steps S240.
  • Step S210 Determine the number of orders of each location area in the monitoring area in a future set time period.
  • step S210 there are various implementations of step S210, as long as the number of orders in each location area can be estimated.
  • the average order quantity of the previous time period of each location area such as the previous quarter, the previous month, the previous week, etc.
  • the average order quantity is used as the order quantity of each location area in the future set time period.
  • big data analysis can be performed on historical order information of each location area, and the number of orders in each location area at different time periods, such as morning, afternoon, night, certain hours, and several minutes, such as 15 minutes, can be obtained. .
  • the order quantity of each location area in the historical order information in a specific time period can be used as the order quantity of each location area in the specific time period.
  • FIG. 9 is a schematic diagram of sub-steps included in step S210 of FIG. 8 in an embodiment of the present application.
  • an embodiment of the present application enumerates one implementation of step S210.
  • the step S210 may include four sub-steps of step S211, step S212, step S213, and step S214.
  • Step 211 Train a single quantity estimation model according to the historical order information.
  • the historical order information may include information such as the number of orders in the history of the monitoring area, the time of placing the order, the current date, and the real-time weather.
  • a single estimate model can have different estimation rules. For example, moving average estimation, exponential smoothing estimation, etc. can be used. This embodiment does not limit this.
  • the single-quantity prediction model can be obtained through offline training without online training to meet real-time computational requirements.
  • Step S212 Determine the order quantity of the monitoring area in the future setting period according to the current date, the real-time weather, and the single quantity estimation model.
  • the number of orders for the future set time period of the monitoring area can be obtained according to whether the current date is a working day, whether it is a holiday, whether the real-time weather is raining, or the like. For example, if the current date is a working day and there is rain in the real-time weather, the future setting period is 11:30 ⁇ 14:00. Generally speaking, the calculated order quantity will increase compared with other time periods.
  • Step S213 Determine, according to the historical order information, an order quantity ratio of each location area of the monitoring area in the future set time period.
  • the percentage value of the average order quantity of each location area and the average order quantity of the monitoring area can be used as the proportion of the order quantity in each location area in the future set time period. It can also perform big data analysis on historical order information, and obtain the change of orders in different time zones, such as morning, afternoon, night, and several hours in each location area and monitoring area, and record the historical order information in specific time periods. The percentage value of the order quantity of the location area and the monitoring area as the proportion of the order quantity of each location area in the specific time period.
  • Step S214 According to the order quantity of each order and the order quantity of the monitoring area in the future set time period, the order quantity of each location area in the future set time period is obtained.
  • each location area is obtained in the future setting.
  • the order quantity (total amount) of the monitoring area in the future set time period is first calculated, and the order quantity of each position area in the future set time period is calculated according to the proportion of the order quantity of each position area in the future set time period.
  • the efficiency is higher and the calculation results are more accurate.
  • the number of orders in each location area the number of orders in each location area can be obtained through offline training, without online training to meet real-time calculation requirements.
  • Step S240 Determine the capacity shortage degree of each location area according to the order quantity of each location area in the future setting period and the capacity of each location area obtained in the above steps S220 and S230.
  • the capacity monitoring method may further include step S250.
  • Step S250 Sending the capacity shortage degree of each location area to each performer in the monitoring area.
  • transmitting the capacity shortage to each performer within the monitoring area includes transmitting the capacity shortage to a terminal device of each performer within the monitoring area, such as a wearable device.
  • the wearable device carried by each performer may also include a display module or/and a voice module. After obtaining the capacity shortage of each location area, it is sent to the wearer's wearable device in the monitoring area for display and/or voice reminder to guide the performer, in particular, the number of orders to be completed is small, such as 0 execution. Going to a location where the capacity is relatively tight, so that the response efficiency of the order can be improved, thereby improving the user experience.
  • the monitoring area is divided into multiple manners.
  • the monitoring area may be divided into multiple location areas, for example, the monitoring area is divided by the geohash algorithm to obtain multiple location areas.
  • the monitoring area may be divided into a plurality of location areas by steps S310, S320, and S330.
  • Step S310 Acquire a position coordinate point of the completion point of the historical order.
  • Step S320 Clustering a plurality of order clusters according to the density-based clustering algorithm.
  • Step S330 A plurality of location areas are obtained by using the location coordinate range of the order completion location in each order cluster as a location area.
  • the present embodiment is a take-out rider
  • the monitoring area is a delivery area
  • the delivery area is divided into a plurality of position areas of a certain side length according to the geohash coding algorithm, for example, the solution of the present application Be explained. Perform the following steps 1101-1103 when performing capacity monitoring.
  • Step 1101 Estimate the order quantity of each location area at a certain time in the future according to historical order information and real-time information of the delivery area.
  • a single estimate model can be trained offline based on historical order information.
  • the historical order information mainly includes the total order information in the history of the delivery area, the time of placing the order, whether it is a working day, and the weather of the day.
  • Step 1102 Determine the capacity value of each location area in the delivery area and the distribution of the capacity of the delivery area according to the order information and current location information of each takeaway rider in the delivery area.
  • the order amount of the takeaway rider is not 0, ⁇ >0, then all the pending orders in the takeaway rider are traversed. For the pending order that has not yet responded, the capacity of the location area to which the pending order is responded is increased. For the pending order that has been responded but not yet delivered, the capacity of the location area where the pending order is completed is increased.
  • the embodiment provides a capacity calculation method, as shown in the following formula (1):
  • R b represents the capacity of the b-position region
  • a i represents the adjacent position region of the b-position region
  • ⁇ represents the smoothing factor
  • Step 1103 Determine the capacity shortage degree of each location area according to the obtained order quantity and capacity value of each location area, and send the capacity shortage degree of the location area to each takeaway rider in the distribution area.
  • the single quantity of a certain location area in the future t time is s geohash-t and the capacity is r geohash-t .
  • This embodiment provides a method for calculating a capacity shortage value, which is shown in the following formula (2).
  • n is the capacity shortage value and f is the logarithmic function for adjusting the confidence. The more the quantity, the more reliable the result.
  • the capacity shortage and the single quantity value of each location area in the distribution area can be sent to the takeaway rider in the form of interaction diagrams such as heat map and voice.
  • FIG. 12 is a functional block diagram of a capacity monitoring logic according to an embodiment of the present application.
  • the capacity monitoring logic 200 includes an information acquisition module 220 and a capacity acquisition module 230.
  • the information obtaining module 220 is configured to acquire location information of each performer in the monitoring area and pending order information of each performer.
  • step S220 in FIG. 2 Since the information acquisition module 220 and the implementation principle of step S220 in FIG. 2 are similar, no further explanation is provided herein.
  • the capacity acquisition module 230 is configured to obtain the capacity in the monitoring area according to the location information of each performer and the order information to be completed.
  • the monitoring area may include a plurality of location areas, in which case the capacity acquisition module 230 may be configured to divide the monitoring area into a plurality of location areas; according to location information of each performer and each The executor's pending order information obtains the capacity of each location area.
  • the capacity acquisition module 230 may include a first acquisition submodule, a first addition submodule, and a second addition submodule.
  • the first obtaining submodule may be configured to acquire the number of orders to be completed by each performer in the monitoring area.
  • the first adding submodule may be configured to increase the capacity of the location area to which the location information belongs according to the location information of the performer whose number of orders to be completed is zero.
  • the second add sub-module can be configured to increase the capacity of the corresponding location area within the monitored area based on the information of each order to be completed.
  • the capacity of the corresponding location area in the monitoring area is increased according to the information of the to-be-completed order, including: if the to-be-completed order is an order that has completed the response, the order to be completed is Completion of the place
  • the capacity of the set area is increased by a preset adjustment value; if the order to be completed is an order that has not completed the response, the capacity of the location area to which the response of the pending order belongs is increased by the adjustment value.
  • the adjustment value corresponds to a threshold range to which the number of orders to be completed held by the executor of the order to be completed belongs.
  • the second adding submodule further includes a second obtaining submodule, a searching submodule and a third adding submodule.
  • the second obtaining submodule may be configured to combine the pre-stored map data to obtain a running path from the response of the to-be-completed order to the completed location.
  • the lookup submodule can be configured to find a location area through which the run path passes.
  • the capacity increase sub-module can be configured to increase the capacity of the located location area.
  • the second adding submodule further includes a processing submodule and a first determining submodule.
  • the processing sub-module may be configured to smooth the capacity of the location area according to the capacity of the adjacent location area of the location area for each of the location areas.
  • the first determining sub-module may be configured to use the value obtained by the smoothing process as the final capacity of the location area.
  • the capacity monitoring logic further includes a second determining sub-module and a third determining sub-module.
  • the second determining sub-module may be configured to determine an order quantity of each location area in the monitoring area in a future set time period.
  • the third determining sub-module may be configured to determine a capacity shortage degree of each location area according to the order quantity of each location area in the future set time period and the capacity of each location area.
  • the determining the quantity of the order of each location area in the monitoring area in the future setting period comprises: obtaining a single quantity estimation model according to the historical order information; and according to the current date, the real-time weather, and the single quantity pre- Estimating a model to determine an order quantity of the monitoring area in the future set time period; determining, according to the historical order information, an order quantity ratio of the location area in the future set time period; The product quantity ratio in the future set time period is the product of the number of orders of the monitoring area in the future set time period, and the order quantity of the location area in the future set time period is obtained.
  • the capacity acquisition module 230 may further include a first location division sub-module.
  • the first dividing submodule may divide the monitoring area by a geohash algorithm to obtain the plurality of location areas.
  • the capacity acquisition module 230 may further include a third acquisition submodule, an order cluster acquisition submodule, and a second location division submodule.
  • the third acquisition sub-module may be configured to acquire a location coordinate point of a completion location of a historical order in the monitoring area
  • the order cluster acquisition sub-module may be configured to be aggregated according to a density-based clustering algorithm.
  • the plurality of order clusters are classed, and the second location dividing submodule can be configured to obtain the plurality of location regions by using a location coordinate range of the order completion in each order cluster as a location area.
  • a machine readable storage medium comprising machine executable instructions, such as the machine readable storage medium 110 of FIG. 1, which may be processed by the capacity monitoring device 100
  • the device 120 is executed to implement the capacity monitoring method described above.
  • the capacity monitoring method and the capacity monitoring device 100 in the embodiment of the present application subtly introduce the executor's pending processing progress order information, thereby more accurately describing the capacity distribution of each location area.
  • Sending the capacity distribution information to each performer can guide the performers who have a small number of orders to be completed to the area where the capacity is tight, thereby ensuring a reasonable distribution of the capacity of each location area and improving the user experience.
  • the single-quantity estimation, data statistics and other work are completed offline, and the efficiency is high, which can meet the real-time calculation requirements. It has a wide range of applications and can be applied to scenes such as takeaway, carpooling, and real-time logistics.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may also occur in a different order than those illustrated in the drawings.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or function. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • each functional module in each embodiment of the present application may be integrated to form a separate part, or each module may exist separately, or two or more modules may be integrated to form a separate part.
  • the functions, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium.
  • the technical solution of the present application which is essential or contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium, including A number of instructions are used to cause a computer device (which may be a personal computer, capacity monitoring device 100, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like.
  • ROM read-only memory
  • RAM random access memory
  • magnetic disk or an optical disk, and the like.

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Abstract

一种运力监测方法和装置，所述运力监测方法包括获取监测区域内各执行者的位置信息和待完成订单信息（S220）；根据各所述执行者的位置信息和待完成订单信息，得到所述监测区域内的运力（S230）。

Description

一种运力监测方法及装置
相关申请的交叉引用
本专利申请要求于2016年12月9日提交的、申请号为201611136523.4、发明名称为“一种运力监测方法、装置及电子设备”的中国专利申请的优先权，该申请的全文以引用的方式并入本文中。
技术领域
本申请涉及信息技术领域的一种运力监测方法及装置。
背景技术
在外卖、拼车、快递等领域，合理分配各区域运力是确保快速响应订单，提高用户体验的重要因素。在一个例子中，可以根据某区域的历史订单信息预估该区域将产生的订单数量。根据执行者，如外卖骑手、快递员、司机等的位置可计算该区域的运力分布。
发明内容
有鉴于此，本申请实施例的目的在于提供一种运力监测方法及装置。
第一方面，本申请实施例提供了一种运力监测方法，包括：
获取监测区域内各执行者的位置信息和待完成订单处理进展信息；及
根据各执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力。
在一实施例中，根据各执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力包括：
将所述监测区域划分为多个位置区域；
根据各执行者的位置信息和待完成订单处理进展信息，得到各位置区域的运力。
在一实施例中，根据所述各执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力，包括：
获取每个所述执行者的待完成订单的数量；
当存在待完成订单的数量为0的执行者，增加该执行者的位置信息所属位置区域的运力； 及
当存在待完成订单的数量至少为1的执行者，根据每个待完成订单的信息，增加所述监测区域内相应的位置区域的运力。
在一实施例中，根据每个待完成订单的信息，增加所述监测区域内相应的位置区域的运力，包括：
若所述待完成订单为已完成了响应的订单，则将该待完成订单的完成地所属位置区域的运力增加预设的第一调整值；
若所述待完成订单为还未完成响应的订单，则将该待完成订单的响应地所属位置区域的运力增加预设的第二调整值。
在一实施例中，所述第一调整值与该待完成订单的执行者所持有的待完成订单中已完成响应的数量所属的第一阈值范围对应；所述第二调整值与该待完成订单的执行者所持有的待完成订单中未完成响应的数量所属的第二阈值范围对应。
在一实施例中，根据所述待完成订单的信息，增加所述监测区域内相应的位置区域的运力，还包括：
结合预存的地图数据，获取从所述待完成订单的响应地到达完成地的运行路径；
查找出所述运行路径经过的位置区域；及
增加查找出的位置区域的运力。
在一实施例中，根据各所述执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力，还包括：
针对每个所述位置区域，根据该位置区域的相邻位置区域的运力对该位置区域的运力进行平滑处理；以及
将所述平滑处理得到的值作为该位置区域的最终运力。
在一实施例中，本申请的运力监测方法还包括：
确定各所述位置区域在未来设定时段的订单数量；
根据各所述位置区域在所述未来设定时段的订单数量和各所述位置区域的运力确定各所述位置区域的运力紧缺度。
在一实施例中，确定所述位置区域在所述未来设定时段的订单数量，包括：
根据历史订单信息训练得到单量预估模型；
根据当前日期、实时天气和所述单量预估模型确定所述监测区域在所述未来设定时段的订单数量；
根据所述历史订单信息确定所述位置区域在所述未来设定时段内的订单数量占比；和
根据所述订单数量占比与所述监测区域在所述未来设定时段的订单数量，得到所述位置区域在所述未来设定时段的订单数量。
在一实施例中，将所述监测区域划分为多个位置区域，包括：
通过geohash算法对所述监测区域进行划分得到所述多个位置区域。
在另一实施例中，将所述监测区域划分为多个位置区域，包括：
获取所述监测区域内历史订单的完成地的位置坐标点；
根据基于密度的聚类算法聚类出多个订单簇；及
通过将每个订单簇中的订单完成地的位置坐标范围作为一位置区域，得到所述多个位置区域。
第二方面，本申请一实施例提供了一种运力监测装置，包括：处理器和机器可读存储介质，所述机器可读存储介质存储有能够被所述处理器执行的机器可执行指令，所述处理器被所述机器可执行指令促使：
获取监测区域内各执行者的位置信息和待完成订单处理进展信息；及
根据各所述执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力。
第三方面，本申请一实施例提供了一种机器可读存储介质，其上存储有机器可执行指令，在被处理器调用和执行时，所述机器可执行指令促使所述处理器执行如本申请第一方面所述的运力监测方法。
本申请实施例提供的运力监测方法及装置，巧妙地引入执行者的待完成订单处理进展信息以监测区域的运力。根据执行者的待完成订单处理进展信息可以预估执行者的位置变化轨迹，进而能够根据执行者的位置变化轨迹更加准确可靠地监测运力分布。
为使本申请的上述目的、特征和优点能更明显易懂，下文特举较佳实施例，并配合所附附图，作详细说明如下。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请一实施例提供的一种运力监测装置100的硬件结构示意图。
图2为本申请一实施例提供的一种运力监测方法的流程图。
图3为本申请一实施例中图2所示步骤S230包括的子步骤的示意图。
图4为本申请另一实施例中图2所示步骤S230包括的子步骤的示意图。
图5为本申请一实施例中图4所示步骤S236包括的子步骤的示意图。
图6为本申请另一实施例中图4所示步骤S236包括的子步骤的示意图。
图7为本申请另一实施例提供的一种运力监测方法的流程图。
图8为本申请再一实施例提供的一种运力监测方法的流程图。
图9为本申请一实施例中图8所示步骤S210包括的子步骤的示意图。
图10为本申请一实施例提供的一种位置区域划分的流程图。
图11为本申请又一实施例提供的一种运力监测方法的流程图。
图12为图1所示的一种运力监测逻辑的功能模块框图。
图标：100-运力监测装置；110-机器可读存储介质；120-处理器；130-网络模块；200-运力监测逻辑；220-信息获取模块；230-运力获取模块。
具体实施方式
在外卖、拼车、快递等领域，合理分配各位置区域运力是确保快速响应订单，提高用户体验的重要因素。外卖骑手、车辆司机、快递员等执行者的位置大都处于变化状态。随着各执行者从一个位置区域移动到另一个位置区域，各位置区域的运力将会发生变化。
在一个例子中，一种运力监测方法是根据各执行者当前的位置，确定各位置区域的运力。但在该运力监测方法中，没有考虑运力在未来一段时间的变化情况。在执行者位置变化频繁的应用场景中，单纯根据各执行者目前的位置信息描述区域的运力准确度较为有限。由于准 确监测各位置区域的运力是合理分配各位置区域运力的基础，因而，本申请实施例可根据各执行者的待完成订单处理进展信息(为了简单，后文可被称为待完成订单信息)预估各执行者在未来时间段的位置变化，从而可准确监测各位置区域的运力，进而可为合理分配各位置区域运力提供基础。
下面将结合本申请实施例中附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本申请实施例的组件可以以各种不同的配置来布置和设计。因此，以下对在附图中提供的本申请的实施例的详细描述并非旨在限制要求保护的本申请的范围，而是仅仅表示本申请的选定实施例。基于本申请的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本申请保护的范围。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。同时，在本申请的描述中，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
如图1所示，是本申请一实施例提供的运力监测装置100的硬件结构示意图。本申请实施例中的运力监测装置100可以为服务器、计算机等具备数据处理能力的装置。如图1所示，运力监测装置100可包括：机器可读存储介质110、处理器120及网络模块130。
所述机器可读存储介质110、处理器120以及网络模块130相互之间直接或间接地电性连接，以实现数据的传输或交互。例如，这些元件相互之间可通过一条或多条通讯总线或信号线实现电性连接。机器可读存储介质110中存储有运力监测逻辑200对应的机器可执行指令，所述运力监测逻辑200可包括至少一个可以软件或固件(firmware)的形式存储于所述机器可读存储介质110中的软件功能模块。所述处理器120通过运行存储在机器可读存储介质110内的软件程序以及模块，如本申请实施例中的运力监测逻辑200对应的机器可执行指令，从而执行各种功能应用以及数据处理，例如，实现本申请实施例中的运力监测方法。
其中，所述机器可读存储介质110可以是，但不限于，随机存取存储器(Random Access Memory，RAM)，只读存储器(Read Only Memory，ROM)，可编程只读存储器(Programmable Read-Only Memory，PROM)，可擦除只读存储器(Erasable Programmable Read-Only Memory，EPROM)，电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory，EEPROM)，闪存，存储驱动器(如硬盘驱动器)，固态硬盘，任何类型的存储盘(如光盘、dvd等)，或者类似的存储介质，或者它们的组合等。其中，机器可读存储介质110用于存储程序，所述处理器120在接收到执行指令后，执行所述程序。
所述处理器120可能是一种集成电路芯片，具有信号的处理能力。上述的处理器120可以是通用处理器，包括中央处理器(Central Processing Unit，CPU)、网络处理器(Network Processor，NP)等。还可以是数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit,ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器，或者该处理器也可以是任何常规的处理器等。
网络模块130用于通过网络建立运力监测装置100与外部通信终端之间的通信连接，实现网络信号及数据的收发操作。上述网络信号可包括无线信号或者有线信号。
可以理解，图1所示的结构仅为示意，运力监测装置100还可包括比图1中所示更多或者更少的组件，或者具有与图1所示不同的配置。图1中所示的各组件可以采用硬件、软件或其组合实现。
请参阅图2，是本申请一实施例提供的一种运力监测方法的流程图。所述方法有关的流程所定义的方法步骤可以由所述处理器120实现。下面将对图2所示的具体流程进行详细阐述。
步骤S220：获取监测区域内各执行者的位置信息和待完成订单信息。
在一个例子中，各执行者可携带一手持终端或一穿戴设备。以各执行者携带一穿戴设备为例，穿戴设备可包括定位模块、处理模块和通信模块等。其中，定位模块可以记录执行者的位置信息。处理模块可以记录及调整执行者的待完成订单信息。通信模块可以将各执行者的位置信息和待完成订单信息发送给处理器120。在本步骤中，可以获取通信模块发送的各执行者的位置信息和待完成订单信息。
待完成订单信息可以包括执行者手中待完成订单的响应地和完成地，待完成订单是否已完成响应等信息。
应理解，本申请实施例中，待完成订单的响应地可以指待完成订单的接单地。例如，若待完成订单为外卖订单，那么外卖订单的响应地为接单商家所在地。对应地，外卖订单的完成地可为下单客户所在地，已完成响应是指外卖骑手已从接单商家领取客户所需外卖。
又例如，若待完成订单为约车订单，那么约车订单的响应地为约车用户上车地。对应地，约车订单的完成地为约车用户的目的地，已完成响应是指司机已在约车用户上车地接到约车用户。
步骤S230：根据各执行者的位置信息和待完成订单信息，得到所述监测区域内的运力。
其中，各执行者的位置信息所能体现的是各执行者当前所处位置，而根据各执行者的待完成订单信息，如待完成订单的响应地、完成地等信息，则可以估算出各执行者在未来的位置变化轨迹。随着各执行者位置的变化，监测区域的运力亦会有变化。例如，若执行者的待完成订单的响应地和完成地在监测区域外，那么，可以预估到执行者为了完成该待完成订单将离开监测区域，从而会使得监测区域的运力减少。同理，若监测区域外的执行者的待完成订单的响应地和完成地在监测区域内，那么，可以预估到执行者为了完成该待完成订单将进入监测区域内，从而会使得监测区域的运力增加。
对应地，监测区域可包括多个位置区域，各执行者可以在监测区域的多个位置区域内移动。这样，步骤S230可包括：将所述监测区域划分为多个位置区域；以及根据各执行者的位置信息和待完成订单信息，得到各位置区域的运力。
根据各执行者的待完成订单信息，可以估算出各执行者在未来一段时间的位置区域变化情况。随着各执行者位置区域的变化，各位置区域的运力亦会有变化。
图3为本申请一实施例中图2所示步骤S230包括的子步骤的示意图。请结合参阅图3，步骤S230包括步骤S231、步骤S232和步骤S233三个子步骤。
步骤S231：获取每个执行者待完成订单的数量。
步骤S232：判断是否存在待完成订单的数量为0的执行者。
步骤S233：若存在待完成订单的数量为0的执行者，则增加该执行者的位置信息所属位置区域的运力。
其中，可以将执行者的位置信息所属位置区域的运力增加一设定值，比如5。
图4为本申请另一实施例中图2所示步骤S230包括的子步骤的示意图。请结合参阅图4，步骤S230还可以包括步骤S234、步骤S235和步骤S236三个子步骤。
步骤S234：获取每个执行者待完成订单的数量。
步骤S235：判断是否存在待完成订单的数量至少为1的执行者。
步骤S236：若存在待完成订单的数量至少为1的执行者，则根据每个待完成订单信息增加每个待完成订单的响应地或完成地所属位置区域的运力。
图5为本申请一实施例中图4所示步骤S236包括的子步骤的示意图，请参阅图5，步骤S236可以包括步骤S2361、步骤S2362和步骤S2363三个子步骤。
步骤S2361：针对每个待完成订单，判断该待完成订单是否为已完成了响应的订单，若为 已完成了响应的订单，则执行步骤S2362，否则执行步骤S2363。
步骤S2362：增加该待完成订单的完成地所属位置区域的运力。
步骤S2363：增加该待完成订单的响应地所属位置区域的运力。
根据实际需求，步骤S236还可以包括其他实施方式。例如，若待完成订单为还未完成响应的订单，那么，可以分别增加该待完成订单的响应地所属位置区域的运力和完成地所属位置区域的运力。又例如，可以根据执行者手中待完成订单数量的不同，增加不同的运力，一般情况下，执行者手中待完成订单数量越少，增加的运力越大，执行者手中待完成订单数量越多，增加的运力越小。
图6为本申请另一实施例中图4所示步骤S236包括的子步骤的示意图。请参阅图6，步骤S236还可以包括步骤S2364～步骤S2368五个子步骤。
步骤S2364：确定每个执行者待完成订单的数量所属的阈值范围。
步骤S2365：查找出与所述阈值范围对应的调整值。
步骤S2366：针对每个待完成订单，判断该待完成订单是否为已完成了响应的订单，若为已完成了响应的订单，则执行步骤S2367，否则执行步骤S2368。
步骤S2367：将该待完成订单的完成地所属位置区域的运力增加所述调整值。
步骤S2368：将该待完成订单的响应地所属位置区域的运力增加所述调整值。
在一实施例中，可以根据实际需求按梯度灵活设定阈值范围。例如，设定1～3为一阈值范围，4～6为一阈值范围，7～9为一阈值范围等。并设定不同阈值范围与不同运力值的对应关系，例如，列举的三个阈值范围中，设定阈值范围1～3对应的调整值最大，阈值范围4～6对应的调整值次之，阈值范围7～9对应的调整值最小。这样，通过对执行者待完成订单的数量所属阈值范围的分析，可得到执行者的每个待完成订单的响应地或完成地所属位置区域相应增加的运力。
本申请实施例中，步骤S2364～步骤S2368的执行顺序可有多种。例如，可以先执行判断待完成订单是否为已完成了响应的订单的步骤S2366，然后执行确定执行者手中待完成订单的数量所属的阈值范围以查找出与该阈值范围对应的调整值的步骤S2364、S2365。又例如，确定执行者手中待完成订单的数量所属的阈值范围以查找出与该阈值范围对应的调整值的步骤S2364、S2365，与判断待完成订单是否为已完成了响应的订单的步骤S2366可以并行执行。
在又一实施例中，步骤236还可以包括：确定每个执行者的待完成订单中已完成响应的 数量和未完成响应的数量；确定该执行者的待完成订单中已完成响应的数量所属的第一阈值范围和未完成响应的数量所属的第二阈值范围；查找出与所述第一阈值范围对应的第一调整值和所述第二阈值范围对应的第二调整值；将该执行者的各待完成订单的完成地所属位置区域的运力增加所述第一调整值；以及将该执行者的各待完成订单的响应地所属位置区域的运力增加所述第二调整值。
考虑到除了待完成订单响应地和完成地之外，各执行者亦可以增加途经的位置区域的运力。因而，图7为本申请另一实施例提供的一种运力监测方法的流程图，如图7所示，所述运力监测方法还可以包括步骤S260、步骤S270和步骤S280。
步骤S260：针对每个待完成订单，结合预存的地图数据，获取从该待完成订单的响应地到达完成地的运行路径。
步骤S270：查找出所述运行路径经过的位置区域。
步骤S280：增加查找出的位置区域的运力。
其中，可以灵活增加查找出的位置区域的运力。例如，可以将查找出的每个位置区域的运力增加一固定值。又例如，可以建立一运力计算模型，根据位置区域内的执行者数量和待完成订单数量比值的不同，将查找出的每个位置区域的运力增加不同值。一般情况下，若一位置区域内执行者数量和待完成订单数量比值越大，则将该位置区域的运力增加越大值；若一位置区域内执行者数量和待完成订单数量比值越小，则将该位置区域的运力增加越小值。
为了确保各位置区域能够充分利用途经的执行者增加运力，针对每个位置区域，可以将即将途经该位置区域的执行者的信息发送至该位置区域的响应地的终端。例如，可将即将途经的位置区域的待完成订单信息发送至对应的执行者的穿戴设备。还可以将途经的位置区域的待完成订单自动分配给途经的执行者。
由于各执行者，如外卖骑手、快递员、司机等的移动有随机性，且可以较快地从一个位置区域到达相邻位置区域。因而，本实施例中的运力监测方法还可以包括：针对每个位置区域，获取与该位置区域相邻的位置区域的运力，对该位置区域和与该位置区域相邻的位置区域的运力做综合处理，如平滑处理，得到该位置区域的最终运力。例如，针对每个位置区域，根据该位置区域的相邻位置区域的运力对该位置区域的运力进行平滑处理，将所述平滑处理得到的值作为该位置区域的最终运力。
在上述基础上，图8为本申请再一实施例提供的一种运力监测方法的流程图，如图8所示，本申请实施例还提供一种可以分析运力紧缺度的运力监测方法，还包括步骤S210和步骤 S240。
步骤S210：确定监测区域内各位置区域在未来设定时段的订单数量。
其中，步骤S210的实现方式有多种，只要能够估算到各位置区域的订单数量即可。例如：可以计算各位置区域前一时间段，如前一季度、前一个月、前一周等的平均订单数量，将平均订单数量作为各位置区域在未来设定时段的订单数量。又例如：可以对各位置区域的历史订单信息做大数据分析，获得各位置区域在不同时间段，如上午、下午、夜晚、某几个小时、某几分钟如15分钟内的订单数量变化情况。这样，可将历史订单信息中各位置区域在特定时间段的订单数量，作为各位置区域在该特定时间段的订单数量。
图9为本申请一实施例中图8所示步骤S210包括的子步骤的示意图。请结合参阅图9，本申请实施例列举了步骤S210的其中一种实现方案，所述步骤S210可包括步骤S211、步骤S212、步骤S213和步骤S214四个子步骤。
步骤211：根据历史订单信息训练得到单量预估模型。
其中，历史订单信息可以包括监测区域历史上的订单数量、下单时间、当前日期、实时天气等信息。应当理解，单量预估模型可以有不同的预估规则。例如，可以采用移动平均法预估、指数平滑法预估等。本实施例中对此不作限制。为了确保计算效率，在一实施例中，单量预估模型可通过离线训练得到，无需在线训练，以满足实时计算要求。
步骤S212：根据当前日期、实时天气和所述单量预估模型确定所述监测区域在未来设定时段的订单数量。
例如，可根据当前日期是否为工作日、是否为节假日、实时天气是否下雨等得出监测区域未来设定时段的订单数量。例如：若当前日期为工作日、实时天气存在下雨状况，未来设定时段为11:30～14:00，一般来讲，计算得到的订单数量会较其他时间段有增加。
步骤S213：根据所述历史订单信息确定所述监测区域的每个位置区域在所述未来设定时段内的订单数量占比。
其中，可以计算各位置区域和监测区域前一时间段，如前一季度、前一个月、前一周等的平均订单数量。这样，可将各位置区域的平均订单数量与监测区域的平均订单数量的百分比值，作为各位置区域在未来设定时段内的订单数量占比。还可以对历史订单信息做大数据分析，获得各位置区域和监测区域不同时间段，如上午、下午、夜晚、某几个小时内的订单数量变化情况，将历史订单信息中特定时间段内各位置区域和监测区域的订单数量的百分比值，作为各位置区域在该特定时间段内的订单数量占比。
步骤S214：根据各订单数量占比与所述监测区域在未来设定时段的订单数量，得到各位置区域在所述未来设定时段的订单数量。
在该步骤214中，可通过计算所述各位置区域在未来设定时段内的订单数量占比与所述监测区域在未来设定时段的订单数量的乘积，得到各位置区域在所述未来设定时段的订单数量。
通过上述方式，首先计算出监测区域在未来设定时段的订单数量(总量)，再根据各位置区域在未来设定时段的订单数量占比计算出各位置区域在未来设定时段的订单数量，效率较高，计算结果亦较为准确。为了确保计算效率，在一个例子中，各位置区域的订单数量占比、各位置区域的订单数量可通过离线训练得到，无需在线训练，以满足实时计算要求。
步骤S240：根据各位置区域在未来设定时段的订单数量以及上述步骤S220和步骤S230得到的各位置区域的运力确定各位置区域的运力紧缺度。
考虑到实际需求，如图8所示，该运力监测方法还可以包括步骤S250。
步骤S250：将各位置区域的将运力紧缺度发送给监测区域内的各执行者。
在一个例子中，将运力紧缺度发送至监测区域内的各执行者包括，将运力紧缺度发送至监测区域内的各执行者的终端设备，如穿戴设备。各执行者携带的穿戴设备还可以包括显示模组或/和语音模组。得到各位置区域的运力紧缺度之后，将其发送至监测区域内的各执行者的穿戴设备进行显示和/或语音提醒，以指引执行者特别是待完成订单数量较少，如为0的执行者前往运力相对紧张的位置区域，从而可提高订单的响应效率，进而可提升用户体验。
本申请实施例中，监测区域的划分方式有多种，例如，可以将监测区域划分为多个位置区域，如通过geohash算法对所述监测区域进行划分得到多个位置区域。又例如，如图10所示，可以通过步骤S310、步骤S320和步骤S330将所述监测区域划分为多个位置区域。
步骤S310：获取历史订单的完成地的位置坐标点。
步骤S320：根据基于密度的聚类算法聚类出多个订单簇。
步骤S330：通过将每个订单簇中的订单完成地的位置坐标范围作为一位置区域，得到多个位置区域。
为了使本申请实施例的方案更为明了，现以执行者为外卖骑手，监测区域为配送区域，配送区域根据geohash编码算法被划分为多个一定边长的位置区域为例，对本申请的方案进行说明。在进行运力监测时，需执行以下步骤1101-1103。
步骤1101：根据配送区域的历史订单信息、实时信息预估各位置区域在未来某段时间的订单数量。
在一个例子中，可根据历史订单信息离线训练单量预估模型。历史订单信息主要包括配送区域历史上的订单总量信息、下单时间、是否为工作日、当日天气等。获取当前进单量、天气等实时信息，结合单量预估模型预估配送区域未来一段时间的总单量。根据历史订单信息获取每一个位置区域的单量占比。根据该占比与配送区域总单量的乘积，得到每一个位置区域未来一段时间的单量。
步骤1102：根据配送区域内各外卖骑手待完成订单信息、当前位置信息确定配送区域内每一个位置区域的运力值和配送区域的运力分布情况。
获取配送区域内所有外卖骑手的位置信息以及每一个外卖骑手手中的待完成订单信息。可对每一个外卖骑手做如下计算。
获取外卖骑手当前位置和待完成订单列表。统计外卖骑手手中的待完成订单量为κ。
如果外卖骑手手中待完成订单量为0，κ＝0，则将外卖骑手所在位置所处的位置区域的运力增加ε。
如果外卖骑手手中待完成订单量不为0，κ＞0，则遍历外卖骑手手中所有待完成订单。对于还未响应的待完成订单，该待完成订单响应地所属位置区域的运力增加
Figure PCTCN2017097459-appb-000001
对于已响应但还未配送的待完成订单，该待完成订单完成地所属位置区域的运力增加
Figure PCTCN2017097459-appb-000002
其中，ε、α、β、γ是系统设定的固定参数值，例如ε＝1，α＝0.6，β＝0.7，γ＝0.8。
由于外卖骑手运动有随机性，且外卖骑手能够比较快到达相邻的位置区域，因此对每一个位置区域的运力做平滑。使得一个位置区域的运力由本身及周边位置区域综合计算得到。当位置区域b存在8个相邻位置区域时，本实施例提供一种运力计算方法，如下公式(1)所示：
Figure PCTCN2017097459-appb-000003
其中，Rb代表b位置区域的运力，Ai代表b位置区域的相邻位置区域，μ代表平滑因子。
步骤1103：根据获得的每个位置区域的订单数量和运力值确定每个位置区域的运力紧缺程度，并将位置区域的运力紧缺程度发送给配送区域内的各外卖骑手。
假设得到某一个位置区域在未来t时间的单量为sgeohash-t，运力为rgeohash-t。
本实施例提供一种运力紧缺值计算方法，如下公式(2)所示。
Figure PCTCN2017097459-appb-000004
其中，n为运力紧缺值，f为用于调节置信度的对数函数，单量越多，结果越可信。
为了使得配送区域的运力紧缺度能够直观地展示给外卖骑手，可以将配送区域内各位置区域的运力紧缺度和单量值以热力图、语音等交互形式发送给外卖骑手。
图12是本申请一实施例提供的运力监测逻辑的功能模块图。从功能上划分，该运力监测逻辑200包括信息获取模块220和运力获取模块230。
其中，所述信息获取模块220用于获取监测区域内各执行者的位置信息和各执行者的待完成订单信息。
由于信息获取模块220和图2中步骤S220的实现原理类似，因而在此不作更多说明。
所述运力获取模块230用于根据各执行者的位置信息和待完成订单信息，得到所述监测区域内的运力。
由于运力获取模块230和图2中步骤S230的实现原理类似，因而在此不作更多说明。
其中，所述监测区域可包括多个位置区域，在这种情况下，所述运力获取模块230可被配置为将所述监测区域划分为多个位置区域；根据各执行者的位置信息和各执行者的待完成订单信息，得到各位置区域的运力。
在一个实施例中，所述运力获取模块230可包括第一获取子模块，第一增加子模块和第二增加子模块。
其中，第一获取子模块可被配置为获取监测区域内每个执行者待完成订单的数量。第一增加子模块可被配置为根据待完成订单的数量为0的执行者的位置信息，增加该位置信息所属位置区域的运力。第二增加子模块可被配置为根据每个待完成订单的信息，增加所述监测区域内相应的位置区域的运力。
在一实施例中，根据所述待完成订单的信息，增加所述监测区域内相应的位置区域的运力，包括：若所述待完成订单为已完成了响应的订单，则将该待完成订单的完成地所属位 置区域的运力增加预设的调整值；若所述待完成订单为还未完成响应的订单，则将该待完成订单的响应地所属位置区域的运力增加所述调整值。
在一实施例中，所述调整值与该待完成订单的执行者所持有的待完成订单的数量所属的阈值范围对应。
在一个实施例中，所述第二增加子模块还包括第二获取子模块，查找子模块和第三增加子模块。
其中，所述第二获取子模块可被配置为结合预存的地图数据，获取从该待完成订单的响应地到达完成地的运行路径。所述查找子模块可被配置为查找出所述运行路径经过的位置区域。所述运力增加子模块可被配置为增加查找出的位置区域的运力。
在一实施例中，所述第二增加子模块还包括处理子模块和第一确定子模块。
其中，所述处理子模块可被配置为针对每个所述位置区域，根据该位置区域的相邻位置区域的运力对该位置区域的运力进行平滑处理。所述第一确定子模块可被配置为将所述平滑处理得到的值作为该位置区域的最终运力。
在一实施例中，所述运力监测逻辑还包括第二确定子模块和第三确定子模块。
其中，所述第二确定子模块可被配置为确定监测区域内各位置区域在未来设定时段的订单数量。所述第三确定子模块可被配置为根据各位置区域在所述未来设定时段的订单数量和各位置区域的运力确定各位置区域的运力紧缺度。
在一实施例中，所述确定监测区域内各位置区域在未来设定时段的订单数量，包括：根据历史订单信息训练得到单量预估模型；根据当前日期、实时天气和所述单量预估模型确定所述监测区域在所述未来设定时段的订单数量；根据所述历史订单信息确定所述位置区域在所述未来设定时段内的订单数量占比；通过计算所述位置区域在所述未来设定时段内的订单数量占比与所述监测区域在所述未来设定时段的订单数量的乘积，得到所述位置区域在所述未来设定时段的订单数量。
在一实施例中，所述运力获取模块230还可包括第一位置划分子模块。所述第一划分子模块可通过geohash算法对所述监测区域进行划分得到所述多个位置区域。
在另一实施例中，所述运力获取模块230还可包括第三获取子模块，订单簇获取子模块和第二位置划分子模块。其中，所述第三获取子模块可被配置为获取所述监测区域内历史订单的完成地的位置坐标点，所述订单簇获取子模块可被配置为根据基于密度的聚类算法聚 类出多个订单簇，所述第二位置划分子模块可被配置为通过将每个订单簇中的订单完成地的位置坐标范围作为一位置区域，得到所述多个位置区域。
根据本申请的实施例，还提供了一种包括机器可执行指令的机器可读存储介质，例如图1中的机器可读存储介质110，所述机器可执行指令可由运力监测装置100中的处理器120执行以实现以上描述的运力监测方法。
本申请实施例中的运力监测方法及运力监测装置100，巧妙地引入执行者的待完成处理进展订单信息，从而更加准确地描述各位置区域的运力分布。将运力分布信息发送至各执行者，能够指引待完成订单数量较少如为0的执行者前往运力紧张的区域，进而确保各位置区域运力的合理分配，提升用户体验。单量预估、数据统计等工作离线完成，效率较高，能够满足实时计算要求。适用范围较广，可以适用于外卖、拼车、实时物流等场景。
在本申请实施例所提供的几个实施例中，应该理解到，所揭露的装置和方法，也可以通过其它的方式实现。以上所描述的装置和方法实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本申请的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本申请各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，运力监测装置100，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性 的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅为本申请的优选实施例而已，并不用于限制本申请，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (15)

  1. 一种运力监测方法，包括:
    获取监测区域内各执行者的位置信息和待完成订单处理进展信息；及
    根据各所述执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力。
  2. 根据权利要求1所述的运力监测方法，其中，根据各所述执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力包括：
    将所述监测区域划分为多个位置区域；及
    根据各所述执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力。
  3. 根据权利要求2所述的运力监测方法，其中，根据所述各执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力包括：
    获取每个所述执行者的待完成订单的数量；
    当存在待完成订单的数量为0的执行者，增加该执行者的位置信息所属位置区域的运力；及
    当存在待完成订单的数量至少为1的执行者，根据每个待完成订单的信息，增加所述监测区域内相应的位置区域的运力。
  4. 根据权利要求3所述的运力监测方法，其中，根据所述待完成订单的信息，增加所述监测区域内相应的位置区域的运力，包括：
    若所述待完成订单为已完成了响应的订单，则将该待完成订单的完成地所属位置区域的运力增加预设的第一调整值；
    若所述待完成订单为还未完成响应的订单，则将该待完成订单的响应地所属位置区域的运力增加预设的第二调整值。
  5. 根据权利要求4所述的运力监测方法，其中，
    所述第一调整值与该待完成订单的执行者所持有的待完成订单中已完成响应的数量所属的第一阈值范围对应；
    所述第二调整值与该待完成订单的执行者所持有的待完成订单中未完成响应的数量所属的第二阈值范围对应。
  6. 根据权利要求3所述的运力监测方法，其中，根据所述待完成订单的信息，增加所述监测区域内相应的位置区域的运力，还包括：
    结合预存的地图数据，获取从所述待完成订单的响应地到达完成地的运行路径；
    查找出所述运行路径经过的位置区域；及
    增加查找出的位置区域的运力。
  7. 根据权利要求2所述的运力监测方法，根据各所述执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力，还包括：
    针对每个所述位置区域，根据该位置区域的相邻位置区域的运力对该位置区域的运力进行平滑处理；以及
    将所述平滑处理得到的值作为该位置区域的最终运力。
  8. 根据权利要求2所述的运力监测方法，还包括：
    确定各所述位置区域在未来设定时段的订单数量；
    根据各所述位置区域在所述未来设定时段的订单数量和各所述位置区域的运力确定各所述位置区域的运力紧缺度。
  9. 根据权利要求8所述的运力监测方法，其中，确定所述位置区域在所述未来设定时段的订单数量，包括：
    根据历史订单信息训练得到单量预估模型；
    根据当前日期、实时天气和所述单量预估模型确定所述监测区域在所述未来设定时段的订单数量；
    根据所述历史订单信息确定所述位置区域在所述未来设定时段内的订单数量占比；
    根据所述订单数量占比与所述监测区域在所述未来设定时段的订单数量，得到所述位置区域在所述未来设定时段的订单数量。
  10. 根据权利要求2所述的运力监测方法，其中，将所述监测区域划分为多个位置区域，包括：
    通过geohash算法对所述监测区域进行划分得到所述多个位置区域。
  11. 根据权利要求2所述的运力监测方法，其中，将所述监测区域划分为多个位置区域， 包括：
    获取所述监测区域内历史订单的完成地的位置坐标点；
    根据基于密度的聚类算法聚类出多个订单簇；及
    通过将每个订单簇中的订单完成地的位置坐标范围作为一位置区域，得到所述多个位置区域。
  12. 一种运力监测装置，包括：
    处理器；和
    机器可读存储介质；
    所述机器可读存储介质存储有能够被所述处理器执行的机器可执行指令，所述处理器被所述机器可执行指令促使：
    获取监测区域内各执行者的位置信息和待完成订单处理进展信息；及
    根据各所述执行者的位置信息和待完成订单处理进展信息，得到所述监测区域内的运力。
  13. 根据权利要求12所述的运力监测装置，其中，当根据各所述执行者的位置信息和待完成订单处理进展信息，得到所述监测区域的运力时，所述处理器被所述机器可执行指令促使：
    将所述监测区域划分为多个位置区域；和
    根据各所述执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力。
  14. 根据权利要求13所述的运力监测装置，其中，当根据所述各执行者的位置信息和待完成订单处理进展信息，得到各所述位置区域的运力，所述处理器被所述机器可执行指令促使：
    获取每个所述执行者的待完成订单的数量；
    当存在待完成订单的数量为0的执行者，增加该执行者的位置信息所属位置区域的运力；及
    当存在待完成订单的数量至少为1的执行者，根据每个待完成订单的信息，增加所述监测区域内相应的位置区域的运力。
  15. 一种机器可读存储介质，其上存储有机器可执行指令，在被处理器调用和执行时，所述机器可执行指令促使所述处理器执行如权利要求1所述的运力监测方法。
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109636166A (zh) * 2018-12-04 2019-04-16 首约科技(北京)有限公司 对预定地区的网约车进行调度的方法，系统以及存储介质
CN112232605A (zh) * 2020-12-14 2021-01-15 北京顺达同行科技有限公司 派送资源的处理方法、装置、设备及计算机可读存储介质

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110728432A (zh) * 2019-09-19 2020-01-24 杭州飞步科技有限公司 一种运力调度方法和装置、电子设备、存储介质
CN113139137B (zh) * 2020-01-19 2022-05-03 北京三快在线科技有限公司 确定poi坐标的方法、装置、存储介质及电子设备
CN111815229B (zh) * 2020-06-12 2024-01-23 北京顺达同行科技有限公司 订单信息的处理方法、装置、电子设备及存储介质
CN111985697A (zh) * 2020-07-30 2020-11-24 中国外运股份有限公司 一种送装车辆拼载方法、装置、电子设备及存储介质
US11720850B1 (en) * 2020-08-10 2023-08-08 Amazon Technologies, Inc. Dynamic package selection algorithm for delivery
CN114897480B (zh) * 2022-07-11 2022-11-18 中化现代农业有限公司 配送状态确定方法、装置、电子设备和存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011014076A1 (en) * 2009-07-31 2011-02-03 Trond Paulsen Method and system for ordering a vehicle
CN104751271A (zh) * 2015-03-04 2015-07-01 径圆（上海）信息技术有限公司 智能订单调度方法、服务器、电动车、移动终端及系统
CN105373840A (zh) * 2015-10-14 2016-03-02 深圳市天行家科技有限公司 代驾订单预测方法和代驾运力调度方法
CN105719110A (zh) * 2015-05-22 2016-06-29 北京小度信息科技有限公司 订单处理方法和装置
CN105844349A (zh) * 2016-03-21 2016-08-10 上海壹佰米网络科技有限公司 一种订单自动分配方法及系统

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103985247B (zh) * 2014-04-24 2016-08-24 北京嘀嘀无限科技发展有限公司 基于城市叫车需求分布密度的出租车运力调度系统
CN104537831B (zh) * 2015-01-23 2018-12-11 北京嘀嘀无限科技发展有限公司 车辆调度的方法及设备
CN104599088A (zh) * 2015-02-13 2015-05-06 北京嘀嘀无限科技发展有限公司 基于订单的调度方法和调度系统
US9805605B2 (en) * 2015-08-12 2017-10-31 Madhusoodhan Ramanujam Using autonomous vehicles in a taxi service

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011014076A1 (en) * 2009-07-31 2011-02-03 Trond Paulsen Method and system for ordering a vehicle
CN104751271A (zh) * 2015-03-04 2015-07-01 径圆（上海）信息技术有限公司 智能订单调度方法、服务器、电动车、移动终端及系统
CN105719110A (zh) * 2015-05-22 2016-06-29 北京小度信息科技有限公司 订单处理方法和装置
CN105373840A (zh) * 2015-10-14 2016-03-02 深圳市天行家科技有限公司 代驾订单预测方法和代驾运力调度方法
CN105844349A (zh) * 2016-03-21 2016-08-10 上海壹佰米网络科技有限公司 一种订单自动分配方法及系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3553716A4 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109636166A (zh) * 2018-12-04 2019-04-16 首约科技(北京)有限公司 对预定地区的网约车进行调度的方法，系统以及存储介质
CN112232605A (zh) * 2020-12-14 2021-01-15 北京顺达同行科技有限公司 派送资源的处理方法、装置、设备及计算机可读存储介质
CN112232605B (zh) * 2020-12-14 2021-05-14 北京顺达同行科技有限公司 派送资源的处理方法、装置、设备及计算机可读存储介质

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