CN109102105B

CN109102105B - Productivity prediction method, system and equipment for service executors in service system

Info

Publication number: CN109102105B
Application number: CN201810719345.0A
Authority: CN
Inventors: 杨君星; 张旭东
Original assignee: NIO Anhui Holding Co Ltd
Current assignee: NIO Holding Co Ltd
Priority date: 2018-07-03
Filing date: 2018-07-03
Publication date: 2022-02-08
Anticipated expiration: 2038-07-03
Also published as: CN109102105A

Abstract

The invention relates to the field of electric vehicle charging and battery replacement, in particular to a method, a system and equipment for predicting the productivity of a service executive in a service system, and aims to provide a decision basis for receiving an appointment order and distributing the service executive. The capacity prediction method comprises the following steps: acquiring request information of a service order; acquiring available service executives according to the order execution positions, and calculating the probability that each available service executor moves to the order execution position; predicting the initial position of each available service executive and the capacity on the order execution position in a future period of time according to the order execution period and the calculated probability; and adjusting the prediction result in real time according to the current execution state of the service order, such as order distribution, order cancellation, order early completion, order delay and the like. The prediction method of the invention can effectively reflect the position distribution and the service capability change of the service executive body in a future period of time in time, and provides reliable decision basis for order reservation and distribution.

Description

Productivity prediction method, system and equipment for service executors in service system

Technical Field

The invention relates to the field of electric vehicle charging and battery replacement, in particular to a method, a system and equipment for predicting productivity of a service executive in a service system.

Background

In a traditional electric vehicle charging and battery replacing mode, a user needs to drive an electric vehicle to a charging pile, a charging station or a battery replacing station and other charging and battery replacing resources to complete charging and battery replacing, and much time is needed for the user in the process.

In order to improve user experience, a 'customer-replacement power-on' charging mode is provided in the industry in recent two years, namely, a charging order request is initiated by a user, and a charging process is completed by special service personnel, so that the time of the user is effectively saved. However, due to the user-initiated order request in this mode, the location of the service personnel and the service capabilities will change over a future period of time. The prediction of changes to the service personnel will be the basis for order reservation decisions and service personnel allocation management and there is currently no solution to the problem in this new model.

Disclosure of Invention

In order to solve the above problems in the prior art, the present invention provides a method, a system and a device for predicting productivity of a service executor in a service system, which provide a reliable decision basis for receiving a reservation order and allocating the service executor.

In one aspect of the present invention, a method for forecasting productivity of a service executor in a service system is provided, where the method includes:

acquiring request information of a service order, wherein the request information of the service order comprises an order execution time interval and an order execution position;

acquiring available service executives according to the order execution positions, and calculating the probability that each available service executor moves to the order execution position;

predicting the initial position of each available service executive and the capacity on the order execution position in a future period of time according to the order execution period and the calculated probability;

adjusting a prediction result according to the current execution state of the service order;

the capacity is the number estimated value of the service executors at the corresponding position; the initial position of the available service executive body is the position reached by the last movement before the available service executive body moves to the order execution position.

Preferably, "predicting an initial location of each of the available service executives and capacity at the order fulfillment location in a future period of time based on the order fulfillment period and the calculated probability" comprises:

judging whether the order execution position is the same as the initial position of the available service executors;

under the condition that the order execution position is the same as the initial position of one available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) -1 and t_begin<t<t_end(ii) a Restoring the capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end；

Wherein k is the serial number of the order execution position; e₁(k,t_begin) Capacity at the order execution location for the service order to begin execution; t is t_begin、t_endRespectively being the starting time and the ending time of the order execution time interval;

under the condition that the order execution position is different from the initial position of each available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) And t is_begin<t<t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment time period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And,

the capacity at the initial position of each available service execution body after the order starts to be executed is set as:

E₂(j,t)＝E₂(j,t_begin)×(1-1/S_F)

wherein E is₂(j, t) is the capacity at the jth location at time t, t>t_begin，E₂(j,t_begin) Capacity at the jth location at which the service order began executing; j belongs to F, and F is the set of initial positions of the available service executors; s_FIs the sum of capacities at each location in the collection; 1/S_FA probability of moving to the order fulfillment location for each of the available service executives.

Preferably, in a case where an available service executor has been allocated to the service order and the allocated available service executor has not executed a corresponding order operation, the "adjusting the prediction result according to the current execution state of the service order" includes:

acquiring the latest execution time t' when the distributed available service executors execute corresponding order operations;

from t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin)-¹And t'<t<t_end；

Restoring the capacity of the original position of each unassigned available service executive to E after the order begins to execute₂(n,t)＝E₂(n,t_begin)；

Wherein m is the sequence number of the initial position of the allocated available service executive; n is the sequence number of the initial position of each unallocated available service executive in the set F, n belongs to F, and n is not equal to m; e₂(m,t_begin)、E₂(n,t_begin) Respectively, the capacity at the mth location and the capacity at the nth location when the service order starts to be executed.

Preferably, "obtaining the latest execution time t' of the allocated available service executors to execute the corresponding order operation" includes:

the capacity of the allocated available service executors at the initial position before the order is executed satisfies E₂(m, t) ≧ 1, and the obtained latest time is taken as the latest execution time t'.

Preferably, in a case where the service order is cancelled, "adjusting the prediction result according to the current execution state of the service order" includes:

if the available service executives have not been allocated for the cancelled service order, the capacity forecast values at the positions in the set F after the order starts to be executed are recovered to be E₂(j,t)＝E₂(j,t_begin) (ii) a Restoring the capacity of the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin)；

Wherein j is the serial number of the initial position of the available service executive body, j belongs to F, k is the serial number of the order execution position, t>t_begin；E₂(j,t_begin)、E₁(k,t_begin) Capacity at the jth location and at the kth location, respectively, at which the service order begins execution;

if the available service execution body is allocated to the cancelled service order, restoring the capacity predicted value of the available service execution body allocated after the latest execution time on the initial position to be E₂(m,t)＝E₂(m,t_begin) And t is>t'; restoring the capacity on the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin) And t is>t_begin；

Wherein m is the sequence number of the initial position of the allocated available service executive; e₂(m,t_begin) Capacity at the mth location for the service order to begin execution.

Preferably, in a case where the service order is completed in advance, "adjusting the prediction result according to the current execution state of the service order" includes:

to actual end time t'_endTo the end of the order execution period t_endAdjusting the capacity at the order execution position, specifically:

if the order execution position is the same as the initial position of the allocated available service executors, adjusting the capacity of the order execution position to be E₁(k,t)＝E₁(k,t_begin) (ii) a Otherwise, adjusting the capacity of the order execution position to be E₁(k,t)＝E₁(k,t_begin)+1；

Wherein, t'_end≤t<t_end。

Preferably, when the service order is delayed, "adjusting the prediction result according to the current execution state of the service order" includes:

adjusting the capacity at the order execution position within the delay time period, specifically:

if the order execution position is the same as the initial position of the allocated available service executors, adjusting the capacity on the order execution position to be E₁(k,t)＝E₁(k,t_begin) -1; otherwise, adjusting the capacity on the order execution position to be E₁(k,t)＝E₁(k,t_begin)；

Wherein, t_end≤t<(t_end+t_delay)，t_endAnd t_delayRespectively the ending time of the order execution time interval and the length of the delay time interval; e₁(k,t_begin) And k is the serial number of the order execution position.

Preferably, after the step of "adjusting the prediction result according to the current execution state of the service order", the method further comprises:

predicting the capacity of other service orders at relevant positions in a future period of time;

the relevant positions include: an initial location of an available service execution for the other service order, and an order execution location for the other service order.

Preferably, the step of acquiring available service executives according to the order execution positions comprises:

taking the order execution position as a center, taking a preset minimum radius threshold value as a search radius, and acquiring an idle service execution body in the search range within the order execution time period;

calculating the sum of the capacities of all searched idle service executors at the positions;

if the sum of the capacities is less than 1, increasing the search radius, so as to obtain an idle service execution body again and calculate the sum of the capacities until the sum of the capacities is more than or equal to 1 or the search radius is more than or equal to a preset maximum radius threshold;

if the sum of the productivity is more than or equal to 1, all the idle service executors are searched to be used as the available service executors; otherwise, the order request is denied.

In the capacity prediction method for the service executive in the service system, the service system is a power-on service system, and the service executive is a passenger-substitute power-on person for executing the power-on service or a mobile charging car or a mobile battery replacement car for providing the power-on service.

In a second aspect of the present invention, a system for predicting productivity of a service executor in a service system is provided, comprising: the system comprises an order request acquisition module, an available service executive acquisition module, a probability calculation module, a capacity prediction module and a capacity adjustment module;

the order request acquisition module is configured to: acquiring request information of a service order; the request information of the service order comprises an order execution time interval and an order execution position;

the available service executor obtaining module is configured to: acquiring an available service executive according to the order execution position;

the probability calculation module is configured to: calculating the probability that each available service execution body moves to the order execution position;

the capacity forecasting module is configured to: predicting the initial position of each available service executive and the capacity on the order execution position in a future period of time according to the order execution period and the calculated probability; the capacity is the number estimated value of the service executors at the corresponding position; the initial position of the available service executive body is the position reached by the last movement before the available service executive body moves to the order execution position;

the capacity adjustment module is configured to: and adjusting the prediction result according to the current execution state of the service order.

Preferably, the capacity forecasting module comprises: the system comprises a first judging unit, a first capacity forecasting unit and a second capacity forecasting unit;

the first judgment unit is configured to: judging whether the order execution position is the same as the initial position of the available service executors;

the first capacity forecasting unit is configured to: under the condition that the order execution position is the same as the initial position of one available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) -1 and t_begin<t<t_end(ii) a Restoring the capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end；

the second capacity prediction unit is configured to: under the condition that the order execution position is different from the initial position of each available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) And t is_begin<t<t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment time period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And,

E₂(j,t)＝E₂(j,t_begin)×(1-1/S_F)

Preferably, the capacity adjustment module includes: a yield adjustment submodule after order distribution;

the post-order allocation yield adjustment submodule comprises: a latest execution time acquisition unit and an after-order-distribution productivity adjustment unit;

the latest execution time acquisition unit is configured to: acquiring the latest execution time t' when the distributed available service executors execute corresponding order operations;

the post-order allocation capacity adjustment unit is configured to: from t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t'<t<t_end(ii) a Restoring the capacity of the original position of each unassigned available service executive to E after the order begins to execute₂(n,t)＝E₂(n,t_begin)；

Preferably, the latest execution time obtaining unit is specifically configured to:

Preferably, the capacity adjustment module further comprises: a yield energy adjusting submodule after the order is cancelled;

the yield adjustment submodule after the order is cancelled is configured to: under the condition that the service order is cancelled, adjusting a prediction result according to the current execution state of the service order;

the yield adjustment submodule after the order is cancelled comprises: a second judging unit, a first adjusting unit and a second adjusting unit;

the second determination unit is configured to: determining whether an available service executant has been allocated for the cancelled service order;

the first adjusting unit is configured to: restoring the capacity forecast values at the locations in the set F to E after the order begins execution in the event that an available service executant has not been allocated for the cancelled service order₂(j,t)＝E₂(j,t_begin) (ii) a Restoring the capacity of the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin)；

the second adjusting unit is configured to: restoring the predicted capacity value at the initial position of the available service executive distributed after the latest execution time to E when the available service executive is distributed to the cancelled service order₂(m,t)＝E₂(m,t_begin) And t is>t'; restoring the capacity on the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin) And t is>t_begin；

Preferably, the capacity adjustment module further comprises: a production energy adjusting submodule after the order is finished in advance;

the yield adjustment submodule is configured to: when the service order is completed in advance, the actual end time t 'is compared'_endTo the end of the order execution period t_endAdjusting the capacity at the order execution position, specifically:

if the order execution position is the same as the initial position of the allocated available service executors, adjusting the capacity of the order execution position to be E₁(k,t)＝E₁(k,t_begin) (ii) a Otherwise, adjusting the capacity of the order execution position to be E₁(k,t)＝E₁(k,t_begin) + 1; wherein,

t'_end≤t<t_end。

preferably, the capacity adjustment module further comprises: a yield adjustment submodule after the order is delayed;

the yield adjustment submodule after the order is delayed is configured as follows: when the service order is delayed, adjusting the capacity at the order execution position in the delay time period, specifically:

Preferably, the system further comprises: a capacity re-prediction module;

the capacity re-forecast module is configured to: according to the adjustment result of the productivity adjustment module, the productivity of other service orders at relevant positions in a future period of time is predicted again;

Preferably, the available service executor acquiring module is specifically configured to:

The service system is a power-on service system, and the service execution body is a passenger-substitute power-on person for executing the power-on service or a mobile charging car or a mobile battery replacement car for providing the power-on service.

In a third aspect of the present invention, a storage device is provided, in which a program is stored, the program being adapted to be loaded and executed by a processor to implement the method for forecasting the productivity of a service executor in a service system as described above.

In a fourth aspect of the present invention, a control apparatus is provided comprising: a processor and a memory;

the processor is adapted to execute a program; the memory is adapted to store the program;

the program is suitable for being loaded and executed by the processor to realize the capacity forecasting method of the service executive in the service system.

Compared with the closest prior art, the invention has the following beneficial effects:

the productivity prediction method of the service executive in the service system is based on a service mode that the service executive needs to move to an order execution position to perform service, and when an order request is received, firstly, performing pre-deduction operation on the productivity in the service system in a period of time in the future based on a probability model; then tracking the execution state of the order, respectively adjusting the capacity in the service system in real time under the conditions of order allocation, order cancellation, order early ending, order delay and the like, and effectively reflecting the position distribution and the service capacity change of the service executors in a future period of time in time. By the method, the following problems are effectively solved:

(1) when the service order is generated, the service executive body to be used is not specified, and how to express the influence of the service order on the production performance of the service executive body in a future period of time is shown;

(2) after the order execution status changes, such as specifying a specific service executor, canceling the order, ending the order ahead, and delaying the order, how to adjust the capacity of the affected service executor.

Therefore, the invention provides reliable decision basis for receiving the reservation order and how the service execution body is distributed.

The scheme 1 is a capacity forecasting method for a service executive in a service system, and is characterized in that the capacity forecasting method comprises the following steps:

The method for predicting the capacity of the service executives in the service system according to the claim 2 and the claim 1, wherein the step of predicting the initial position and the capacity at the order execution position of each available service executant in a future period of time according to the order execution period and the calculated probability comprises:

E₂(j,t)＝E2(j,t_begin)×(1-1/S_F)

wherein E is₂(j, t) is the capacity at the jth location at time t, t>t_begin，

E₂(j,t_begin) Capacity at the jth location at which the service order began executing; j belongs to F, and F is the set of initial positions of the available service executors; s_FIs the sum of capacities at each location in the collection; 1/S_FA probability of moving to the order fulfillment location for each of the available service executives.

The method for predicting the capacity of the service executant in the service system according to the claim 3 and the claim 2, wherein when the available service executant is allocated to the service order and the allocated available service executant does not execute the corresponding order operation, the step of adjusting the prediction result according to the current execution state of the service order comprises:

from t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t'<t<t_end；

The method for predicting the capacity of the service executant in the service system according to the scheme 4 and the scheme 3, wherein the step of obtaining the latest execution time t' of the allocated available service executant for executing the corresponding order operation comprises the following steps:

obtaining available service executions allocated before order executionThe productivity at the initial position of the body satisfies E₂(m, t) ≧ 1, and the obtained latest time is taken as the latest execution time t'.

The method for predicting the capacity of the service executant in the service system according to the claim 5 and the claim 3, wherein the step of "adjusting the prediction result according to the current execution state of the service order" comprises the following steps:

The method for predicting the capacity of the service executor in the service system according to the scheme 6 and the scheme 3 is characterized in that when the service order is completed in advance, "adjusting the prediction result according to the current execution state of the service order" comprises the following steps:

Wherein, t'_end≤t<t_end。

The method for predicting the capacity of the service executor in the service system according to the claim 7 and the claim 3 is characterized in that when the service order is delayed, "adjusting the prediction result according to the current execution state of the service order" comprises:

Scheme 8, the method for forecasting the capacity of a service executor in a service system according to any of the schemes 1 to 7, wherein after the step of "adjusting the forecast result according to the current execution status of the service order", the method further comprises:

the relevant positions include: an initial location of an available service execution for the other service order and an order execution location for the other service order.

The method for forecasting the productivity of a service executor in a service system according to any one of the schemes 1 to 7 and the scheme 9, wherein the step of acquiring an available service executor according to the order execution position includes:

The method for predicting the productivity of a service executor in a service system according to any one of the schemes 1 to 7 and scheme 10 is characterized in that the service system is a power-on service system, and the service executor is a passenger-substitute power-on person for executing power-on service or a mobile charging vehicle or a mobile battery replacement vehicle for providing power-on service.

The project 11, a productivity prediction system for a service executor in a service system, is characterized by comprising: the system comprises an order request acquisition module, an available service executive acquisition module, a probability calculation module, a capacity prediction module and a capacity adjustment module;

the order request acquisition module is configured to: acquiring request information of a service order, wherein the request information of the service order comprises an order execution time interval and an order execution position;

The system according to claim 12 and 11, wherein the capacity forecasting module comprises: the system comprises a first judging unit, a first capacity forecasting unit and a second capacity forecasting unit;

Wherein k is the serial number of the order execution position; e₁(k,t_begin) Capacity at the order execution location for the service order to begin execution; t is t_begin、t_endAre respectively the orderA start time and an end time of the execution period;

E₂(j,t)＝E2(j,t_begin)×(1-1/S_F)

The system according to claim 13 and 12, wherein the capacity forecasting module comprises: a yield adjustment submodule after order distribution;

The project 14 and the productivity prediction system for the service executors in the service system according to the project 13 are characterized in that the latest execution time obtaining unit is specifically configured to:

The system according to claim 15 and 13, wherein the capacity forecasting module further comprises: a yield energy adjusting submodule after the order is cancelled;

The system according to claim 16 and 13, wherein the capacity forecasting module further comprises: a production energy adjusting submodule after the order is finished in advance;

Wherein, t'_end≤t<t_end。

The system according to claim 17 and 13, wherein the capacity forecasting module further comprises: a yield adjustment submodule after the order is delayed;

The capacity forecast system for the service executant in the service system according to any of the claims 11-17, according to scheme 18, wherein the system further comprises a capacity re-forecast module;

The system according to any of the claims 11 to 17, and claim 19, wherein the available service executant acquiring module is specifically configured to:

The system according to claim 20 or any one of the systems 11 to 17, wherein the service system is a power-on service system, and the service executant is a customer power-on person for performing power-on service or a mobile charging vehicle or a mobile battery replacement vehicle for providing power-on service.

The method according to claim 21, wherein the program is adapted to be loaded and executed by a processor to perform the method for forecasting the capacity of the service executant in the service system according to any one of the embodiments 1 to 10.

Scheme 22, a control device comprising a processor and a memory;

the processor is adapted to execute a program;

the memory is adapted to store the program;

wherein the program is adapted to be loaded and executed by the processor to implement the method for forecasting the productivity of the service executant in the service system according to any one of the embodiments 1 to 10.

Drawings

FIG. 1 is a schematic diagram illustrating the main steps of a method for forecasting the productivity of a service executor in a service system according to an embodiment of the present invention;

FIG. 2 is a schematic diagram illustrating the main steps of another method for forecasting the productivity of a service executor in a service system according to an embodiment of the present invention;

FIG. 3 is a schematic diagram illustrating the main steps of the idle service executor searching method according to an embodiment of the present invention;

FIG. 4 is a schematic diagram illustrating a main structure of a capacity forecasting system for a service executor in a service system according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a main structure of a capacity forecasting module according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a main structure of a capacity adjustment module according to an embodiment of the present invention;

FIG. 7 is a schematic diagram of a main structure of a capacity forecasting system for a service executor in another service system according to an embodiment of the present invention.

Detailed Description

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and are not intended to limit the scope of the present invention.

First, the method for predicting the productivity of the service executor in the service system according to the present invention is directed to the case where the service executor needs to leave the original location and go to the destination for service. The service execution body is maintained at the position of the order just completed during the period from the completion of a certain order to the start of execution of the next order. If the position of the service execution body is moved in the meantime, the movement can be realized through a virtual order. The present invention solves the following problems:

The invention can provide reliable decision basis for receiving the reservation order and distributing the service execution body by predicting the capacity change of the service execution body in a period of time in the future.

The 'productivity' in the invention is the estimated value of the number of the service executors at the corresponding position; the "initial position" of the available service executables is the position reached by the last move before the available service executables were moved to the order execution position.

In the following embodiments, the present invention is described by taking a "power-on-behalf" service in the field of electric vehicle charging and battery replacing as an example, where the "service system" is a power-on service system, and the "service execution body" may be a power-on-behalf person performing power-on service or a mobile charging vehicle or a mobile battery replacing vehicle providing power-on service. The practical application range of the invention is not limited to the field of charging and battery replacement of electric automobiles, and can also be used in the fields of door-to-door maintenance, small worker service and the like in after-sale service of products, which require that service personnel move to a specific position to perform corresponding operation. If the work to be completed is completed by a movable intelligent robot or other equipment, the service execution body can also be robot or other equipment.

We set the number of service executors needed for each service order to be 1, and bind the service executors with their locations, if there are N service executors in a certain location, the capacity in that location is N. If one of the N MES is executing an order, i.e., the MES is not idle, the capacity at this location is N-1.

Referring to FIG. 1, FIG. 1 schematically illustrates the main steps of a capacity forecasting method for a service executor in a service system according to the present embodiment. As shown in FIG. 1, the capacity forecasting method of the present embodiment may include the following steps S11-S14:

in step S11, request information of the service order is acquired.

The request information for servicing the order may include, among other things, an order fulfillment time period and an order fulfillment location.

In step S12, the available service executors are acquired according to the order execution position, and the probability that each available service executors moves to the order execution position is calculated. The steps specifically include steps S121-125:

in step S121, taking the order execution position as the center, and taking the preset minimum radius threshold r as the search radius, the idle service executors in the search range within the order execution time period are obtained, and form a set F.

In step S122, the sum S of the capacities of all the searched idle service executors at the positions is calculated_F。

In step S123, if the sum of the capacities is less than 1, the search radius r is increased to retrieve the idle service executors and calculate the sum of the capacities S_FUp to the sum of productivity S_FAnd the radius R is more than or equal to 1 or the search radius R is more than or equal to R (R is a preset maximum radius threshold).

In step S124, according to the final search result, the sum of the production capacities S_FIf the service is more than or equal to 1, all the idle service executors are searched and used as available service executors; otherwise, the order request is rejected, which indicates that no available service executors meeting the requirements can be found within the preset maximum radius threshold range.

In step S125, the probability of each available service executant moving to the order execution position is calculated.

In step S13, the initial position and the capacity at the order fulfillment position of each available service executant in a future period of time are predicted according to the order fulfillment period and the calculated probability.

The step specifically comprises steps S131-S133:

in step S131, it is determined whether the order execution position is the same as the initial position of a certain available service execution body. If yes, go to step S132; otherwise, go to step S133.

In step S132, the capacity at the order execution position during the order execution period is set to E₁(k,t)＝E₁(k,t_begin) -1 and t_begin<t<t_end(at t)_beginThe service executors move from the initial positions only at the time of the moment, and therefore t is not included here_beginThe time of day. ) (ii) a Restoring capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end(t_endThe time may or may not be included in the order execution period, and in this embodiment, we generally classify it after the execution is finished).

Wherein k is the serial number of the order execution position; e₁(k,t_begin) Capacity at the order execution location for the service order when execution begins; t is t_begin、t_endThe start time and the end time of the order execution period are respectively.

In step S133, the capacity at the order execution position during the order execution period is set to E₁(k,t)＝E₁(k,t_begin) And t is_begin<t<t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And,

the capacity at the initial location of each available service execution block after the order begins to execute is prebuckled, as shown in equation (1):

E₂(j,t)＝E₂(j,t_begin)×(1-1/S_F) (1)

wherein E is₂(j, t) is the capacity at the jth location at time t, t>t_begin，E₂(j,t_begin) Capacity at the jth location for the service order to begin execution; j belongs to F, and F is a set of initial positions of available service executors; s_FIs the sum of the capacities at each location in the collection; in this embodiment, when an order is requested, all the available service executables within a certain range centered on the order position have equal probability of being allocated to the order, and therefore the probability of each available service executables moving to the order execution position is 1/S_F。

In step S14, the prediction result is adjusted according to the current execution state of the service order. In this step, different adjustment methods are adopted for four different order execution states, namely, order distribution, order cancellation, order early completion and order delay, and for the case of normal completion, attention is not needed here because the processing is already performed in the previous step.

(1) In the case that an available service executor has been allocated for a service order and the allocated available service executor has not performed the corresponding order operation, the following adjustment method is adopted:

a) obtaining the latest execution time t' at which the allocated available service executors execute the corresponding order operation, specifically:

the allocated capacity at the initial position of the available service executors satisfies E before the order is executed₂(m, t) the latest moment which is more than or equal to 1, and taking the obtained latest moment as the latest execution moment t';

b) from the time t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors in the period is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t'<t<t_end；

c) Restoring the capacity of the original position of each unassigned available service executive to E after the order begins to execute₂(n,t)＝E₂(n,t_begin)。

Wherein, m is the sequence number of the initial position of the distributed available service executive; n is the sequence number of the initial position of each unallocated available service executive in the set F, n belongs to F, and n is not equal to m; e₂(m,t_begin)、E₂(n,t_begin) Capacity at the mth location and capacity at the nth location, respectively, at which the service order begins execution.

(2) In the case that the service order is cancelled, the following adjustment method is adopted:

a) if no available service executives have been allocated for the cancelled service order, the capacity forecast values at the positions in the set F after the order begins to be executed are restored to be E₂(j,t)＝E₂(j,t_begin) (ii) a Restoring the capacity of the order execution position to E after the order starts to execute₁(k,t)＝E₁(k,t_begin)；

Wherein j is the sequence number of the initial position of the available service executive, j belongs to F,k is the order number of the order execution position, t>t_begin；E₂(j,t_begin)、E₁(k,t_begin) Capacity at the jth location and at the kth location, respectively, at which the service order begins execution;

b) if the available service execution body is allocated to the cancelled service order, the capacity predicted value of the available service execution body allocated after the latest execution time at the initial position is recovered to be E₂(m,t)＝E₂(m,t_begin) And t is>t'; restoring the capacity to E at the order execution position after the order starts to execute₁(k,t)＝E₁(k,t_begin) And t is>t_begin；

Wherein, m is the sequence number of the initial position of the distributed available service executive; e₂(m,t_begin) Capacity at the mth location for the service order to begin execution.

(3) When the service order is completed earlier, the actual end time t 'is set'_endTo the end of the order execution period t_endThe capacity at the order execution position is adjusted, specifically:

if the order execution position is the same as the initial position of the allocated available service executors, the capacity of the order execution position is adjusted to be E₁(k,t)＝E₁(k,t_begin) (ii) a Otherwise, adjusting the capacity of the order execution position to be E₁(k,t)＝E₁(k,t_begin) + 1; wherein, t'_end≤t<t_end。

(4) Under the condition that the service order is delayed, the capacity at the order execution position in the delay time period is adjusted, and the method specifically comprises the following steps:

if the order execution position is the same as the initial position of the allocated available service executors, the capacity on the order execution position is adjusted to E₁(k,t)＝E₁(k,t_begin) -1; otherwise, adjusting the capacity at the order execution position to E₁(k,t)＝E₁(k,t_begin)；

Wherein, t_end≤t<(t_end+t_delay)，t_endAnd t_delayRespectively the ending time of the order execution time interval and the length of the delay time interval; e₁(k,t_begin) Capacity at the order execution location for the service order when execution begins, and k is the order execution location serial number.

With reference to FIG. 2, FIG. 2 is a schematic diagram illustrating the main steps of the capacity forecasting method for the service executant in another service system according to the present embodiment. As shown in FIG. 2, the capacity forecasting method of the present embodiment may include the following steps S21-S25:

in step S21, request information of the service order is acquired. Specifically, the method for obtaining the request information of the service order in this embodiment is the same as the corresponding method in the capacity forecasting method shown in fig. 1, and for brevity of description, no further description is given here.

In step S22, the available service executors are acquired according to the order execution position, and the probability that each available service executors moves to the order execution position is calculated. Specifically, the method for acquiring the available service executors and the method for calculating the probability that each available service executor moves to the order execution position in the embodiment are respectively the same as the corresponding methods in the capacity prediction method shown in fig. 1, and for brevity of description, no further description is given here.

In step S23, the initial position and the capacity at the order fulfillment position of each available service executant in a future period of time are predicted according to the order fulfillment period and the calculated probability. Specifically, the method for forecasting the capacity at the initial position and the order execution position of each available service executor in the embodiment is the same as the corresponding method in the method for forecasting the capacity shown in fig. 1, and for brevity of description, no further description is given here.

In step S24, the prediction result is adjusted according to the current execution state of the service order. Specifically, the adjustment method of the prediction result in the present embodiment is the same as the corresponding method in the capacity prediction method shown in fig. 1, and for brevity of description, no further description is provided herein.

In step S25, capacity at locations associated with other service orders is forecasted again in a future time period.

The "relative position" here includes: the initial location of available service executives for other service orders, and the order execution location for other service orders.

To more clearly illustrate the design concept of the present invention, we can further illustrate in conjunction with the dynamic diagram:

assuming that there is an idle service executor at each of the

positions

1, 2, 3, and 4 at the current time, the corresponding capacity table is shown in table 1:

TABLE 1 dynamic resource Productivity Table I

T in Table 1₀、t₁、t₂、t₃Indicating different times of day (t)₀,t₁)、(t₁,t₂)、(t₂,t₃) Representing different time periods, which are evenly divided in this embodiment, for example, every 15 minutes, one or more of such time periods may be included for the "order execution period" of a certain service order.

With continuing reference to fig. 3, fig. 3 illustrates the main steps of the idle service executor searching method in the present embodiment. As shown in FIG. 3, it is assumed that the first service order request is received, the corresponding order execution location number is 5, and the expected occupied time, i.e. the order execution time period, is [ t ]₀,t₂) Positions 1, 2 and 3 are within a search range with R (a preset maximum radius threshold) as a radius, and the sum of the capacities at the three positions is 3. The probability that 3 service executors in the three positions move to the position 5 to execute the order is 1/3, i.e. the remaining probability is 2/3, and according to the calculated probability values, we deduct the capacity in table 1 and update it to table 2:

TABLE 2 dynamic resources Productivity table 2

To clearly show the change process of the service execution body position, [ t ] is shown in Table 2₀,t₂) The capacity for a location in the time slot is marked as-1, indicating that there is a service execution at the location but the order is being executed, in fact [ t ]₀,t₂) The capacity at that location over the time period is 0. When the service executors complete the order, it will remain in position 5, so t₂After that time, the capacity at

locations

1, 2, 3 is still 2/3, and the capacity at location 5 becomes 1.

Assuming that a second service order request is received next, the corresponding order execution position number is 6, and the expected occupation time, i.e. the order execution time period, is t₁,t₃) If the

positions

3 and 4 are in the search range with the radius of R, the sum of the capacities at the two positions is 2/3+ 1-5/3. The probability of 2 service executors in both locations moving to location 6 to execute the order is 3/5, and [ t ] can be calculated according to equation (1)₁,t₃) The capacity at position 3 becomes (2/3) × (1-3/5) ═ 4/15, and the capacity at position 4 becomes 1 × (1-3/5) ═ 2/5 in the time period. Therefore, it is necessary to perform a capacity pre-deduction again on the base number in table 2, and the updated capacity is shown in table 3:

TABLE 3 dynamic resources Productivity Table III

t₀ Time position 6 has no service executors yet, so the time capacity is set to 0 in table 3; at [ t ]₁,t₃) A service agent moves to location 6 to execute an order during the time period, so that there is a service agent at location 6 that is working during the time period, which is marked as-1 in Table 3, and the capacity at location 6 is actually 0 during the time period. After the order is complete, the service execution remains in position 6, so t₃After that time the capacity at position 6 becomes 1.

Assuming that the status of the first order execution has changed, the service executives at location 3 are assigned to the orderAt this time, we find out that the latest execution time that the service execution body can be allocated is t₀To prevent subsequent orders from tying up the service executors, we will take t₀The capacity at the time starting position 3 is set to 0. Since we have also prebored the capacity at

locations

1 and 2 when they receive the order request, having now determined that the first order will not use the service executives at both locations, it is necessary to restore the capacity at both locations to 1. The capacity at each location is updated as shown in table 4:

TABLE 4 dynamic resources Productivity Table IV

Since the service executant at location 3 is assigned to the first order, it can only use the service executant at location 4 for the second order, and therefore the capacity prebuckling needs to be performed again for the second order, so [ t ] will be shown in Table 4₁,t₃) The capacity at position 4 during the time period is set to 0.

Similarly, after a certain order is cancelled, completed in advance or delayed, the capacity table needs to be updated, and if the subsequent order of the unallocated specific executive is affected, the capacity adjustment at the relevant position needs to be performed on the subsequent order.

Although the foregoing embodiments describe the steps in the above sequential order, those skilled in the art will understand that, in order to achieve the effect of the present embodiments, the steps may not be executed in such an order, and may be executed simultaneously (in parallel) or in an inverse order, and these simple variations are within the scope of the present invention.

Based on the same technical concept as the method embodiment, the invention also provides a productivity prediction system of the service executors in the service system. The capacity forecasting system will be described in detail below.

Referring to FIG. 4, FIG. 4 schematically illustrates a main structure of a capacity forecasting system for a service executor in a service system according to the present embodiment. As shown in fig. 4, the capacity forecasting system 100 of the present embodiment may include: an order request obtaining module 110, an available service executor obtaining module 120, a probability calculating module 130, a capacity forecasting module 140, and a capacity adjusting module 150.

The order request obtaining module 110 is configured to: acquiring request information of a service order; the request information of the service order comprises an order execution time interval and an order execution position; the available service executant acquisition module 120 is configured to: acquiring an available service executive according to the order execution position; the probability calculation module 130 is configured to: calculating the probability that each available service executive moves to the order execution position; the capacity forecast module 140 is configured to: predicting the initial position of each available service executive and the capacity on the order execution position in a future period of time according to the order execution period and the calculated probability; a capacity adjustment module 150 configured to: and adjusting the prediction result according to the current execution state of the service order.

In this embodiment, the available service executor acquiring module 120 is specifically configured to:

taking an order execution position as a center, taking a preset minimum radius threshold value as a search radius, and acquiring an idle service execution body in the search range within an order execution time period;

if the sum of the productivity is less than 1, increasing the search radius, so as to obtain the idle service execution body again and calculate the sum of the productivity until the sum of the productivity is more than or equal to 1 or the search radius is more than or equal to a preset maximum radius threshold;

if the sum of the productivity is more than or equal to 1, all the idle service executors are searched to be used as available service executors; otherwise, the order request is denied.

With continuing reference to FIG. 5, FIG. 5 is a schematic diagram illustrating the main structure of the capacity forecasting module in this embodiment. As shown in fig. 5, the capacity forecast module 140 in this embodiment may include: a first determining unit 141, a first capacity forecasting unit 142, and a second capacity forecasting unit 143.

The first judgment unit 141 is configured to: and judging whether the order execution position is the same as the initial position of the available service executors.

The first capacity prediction unit 142 is configured to: in the case that the order execution position is the same as the initial position of an available service execution body, the capacity at the order execution position in the order execution time period is set to be E₁(k,t)＝E₁(k,t_begin) -1 and t_begin<t<t_end(ii) a Restoring capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end. Wherein k is the serial number of the order execution position; e₁(k,t_begin) Capacity at the order execution location for the service order when execution begins; t is t_begin、t_endThe start time and the end time of the order execution period are respectively.

The second capacity prediction unit 143 is configured to: in the case that the order execution position is different from the initial position of each available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) And t is_begin<t<t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And, pre-deducting the capacity at the initial position of each available service execution body after the order is started according to the formula (1).

Wherein E is₂(j, t) is the capacity at the jth location at time t, t>t_begin，E₂(j,t_begin) Capacity at the jth location for the service order to begin execution; j belongs to F, and F is a set of initial positions of available service executors; s_FIs the sum of the capacities at each location in the collection; 1/S_FProbability of moving to order execution location for each available service execution.

With continuing reference to FIG. 6, FIG. 6 is a schematic diagram illustrating a main structure of the capacity adjustment module according to the present embodiment. As shown in fig. 6, the capacity adjustment module 150 in this embodiment may include: an order post-allocation performance adjustment sub-module 151, an order cancellation post-performance adjustment sub-module 152, an order early-end post-performance adjustment sub-module 153, and an order late-delay post-performance adjustment sub-module 154.

In this embodiment, the post-order allocation yield adjustment submodule 151 includes: a latest execution time acquisition unit 1511, and a post-order assignment capacity adjustment unit 1512.

The latest execution time acquisition unit 1511 is configured to: the allocated capacity at the initial position of the available service executors satisfies E before the order is executed₂(m, t) ≧ 1, and the obtained latest time is taken as the latest execution time t'.

The post-order allocation capacity adjustment unit 1512 is configured to: from the time t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t'<t<t_end(ii) a Restoring the capacity of the original position of each unassigned available service executive to E after the order begins to execute₂(n,t)＝E₂(n,t_begin) (ii) a Wherein, m is the sequence number of the initial position of the distributed available service executive; n is the sequence number of the initial position of each unallocated available service executive in the set F, n belongs to F, and n is not equal to m; e₂(m,t_begin)、E₂(n,t_begin) Capacity at the mth location and capacity at the nth location, respectively, at which the service order begins execution.

In this embodiment, the yield adjustment submodule 152 after the order is cancelled is configured to: and under the condition that the service order is cancelled, adjusting the prediction result according to the current execution state of the service order.

The post order cancellation performance adjustment submodule 152 includes: a second determining unit 1521, a first adjusting unit 1522, and a second adjusting unit 1523.

The second determination unit 1521 is configured to: a determination is made as to whether an available service executant has been allocated for the cancelled service order.

The first adjusting unit 1522 is configured to:restoring the capacity forecast values at each location in the set F to E after the order begins execution in the event that an available service executant has not been allocated for the cancelled service order₂(j,t)＝E₂(j,t_begin) (ii) a Restoring the capacity of the order execution position to E after the order starts to execute₁(k,t)＝E₁(k,t_begin) (ii) a Wherein j is the serial number of the initial position of the available service executive body, j belongs to F, k is the serial number of the order execution position, t>t_begin；E₂(j,t_begin)、E₁(k,t_begin) Capacity at the jth location and at the kth location, respectively, at which the service order begins execution;

the second adjusting unit 1523 is configured to: in the case that the available service execution body is allocated to the cancelled service order, the capacity predicted value of the available service execution body allocated after the latest execution time at the initial position is recovered to be E₂(m,t)＝E₂(m,t_begin) And t is>t'; restoring the capacity to E at the order execution position after the order starts to execute₁(k,t)＝E₁(k,t_begin) And t is>t_begin(ii) a Wherein, m is the sequence number of the initial position of the distributed available service executive; e₂(m,t_begin) Capacity at the mth location for the service order to begin execution.

In this embodiment, the production capacity adjustment submodule 153 after the order is finished in advance is configured to: when the service order is completed earlier, the actual end time t 'is set'_endTo the end of the order execution period t_endThe capacity at the order execution position is adjusted, specifically:

In this embodiment, the post-order-delay production performance adjustment submodule 154 is configured to: under the condition that the service order is delayed, the capacity at the order execution position in the delay time period is adjusted, and the method specifically comprises the following steps:

if the order execution position is the same as the initial position of the allocated available service executors, the capacity on the order execution position is adjusted to E₁(k,t)＝E₁(k,t_begin) -1; otherwise, adjusting the capacity at the order execution position to E₁(k,t)＝E₁(k,t_begin) (ii) a Wherein, t_end≤t<(t_end+t_delay)，t_endAnd t_delayRespectively the ending time of the order execution time interval and the length of the delay time interval; e₁(k,t_begin) Capacity at the order execution location for the service order when execution begins, and k is the order execution location serial number.

Referring to fig. 7, fig. 7 is a schematic diagram illustrating a main structure of a capacity forecasting system of a service executor in another service system according to the embodiment. As shown in fig. 7, the capacity forecasting system 200 of the present embodiment includes: an order request acquisition module 210, an available service executant acquisition module 220, a probability calculation module 230, a capacity forecast module 240, a capacity adjustment module 250, and a capacity forecast module 260.

The functional configurations and constitutions of the order request obtaining module 210, the available service executor obtaining module 220, the probability calculating module 230, the capacity predicting module 240, and the capacity adjusting module 250 are the same as those of the corresponding modules in fig. 6, and are not described herein again.

The capacity re-forecast module 260 is configured to: based on the adjustment result of the capacity adjustment module 250, the capacity at the relevant location of other service orders in the future is re-predicted.

The relevant positions include: the initial location of available service executives for other service orders, and the order execution location for other service orders.

Based on the above embodiments of the capacity forecasting method, the present invention further provides an embodiment of a storage device, which stores a program, the program is suitable for being loaded and executed by a processor, so as to implement the above method for forecasting the capacity of the service executant in the service system.

Further, based on the above embodiment of the capacity forecasting method, the present invention further provides a control device, where the control device may include: a processor and a memory;

wherein the processor is adapted to execute a program and the memory is adapted to store the program; the program is suitable for being loaded and executed by a processor to realize the capacity forecasting method of the service executive in the service system.

The division of the modules, sub-modules and units in this application is only for better understanding of the functions related to the technical solutions of the present invention, and in practice, the functions corresponding to these modules, sub-modules and units may be loaded and executed by a single hardware.

Those of skill in the art will appreciate that the method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described above generally in terms of their functionality in order to clearly illustrate the interchangeability of electronic hardware and software. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent changes or substitutions of related technical features can be made by those skilled in the art without departing from the principle of the invention, and the technical scheme after the changes or substitutions can fall into the protection scope of the invention.

Claims

1. A capacity forecasting method for a service executive in a service system is characterized by comprising the following steps:

the capacity is the number estimated value of the service executors at the corresponding position; the initial position of the available service executive body is the position reached by the last movement before the available service executive body moves to the order execution position;

the step of predicting the initial position of each available service executor and the capacity at the order execution position in a future period of time according to the order execution period and the calculated probability includes:

under the condition that the order execution position is the same as the initial position of one available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) -1 and t_begin＜t＜t_end(ii) a Restoring the capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end；

at the order execution location andsetting the capacity on the order execution position in the order execution time period to be E under the condition that the initial position of each available service execution body is different₁(k,t)＝E₁(k,t_begin) And t is_begin＜t＜t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment time period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And,

E₂(j,t)＝E₂(j,t_begin)×(1-1/S_F)

wherein E is₂(j, t) is the capacity at the jth location at time t, t > t_begin，E₂(j,t_begin) Capacity at the jth location at which the service order began executing; j belongs to F, and F is the set of initial positions of the available service executors; s_FIs the sum of capacities at each location in the collection; 1/S_FA probability of moving to the order fulfillment location for each of the available service executives.

2. The method as claimed in claim 1, wherein in the case that the available service executors are allocated to the service order and the allocated available service executors have not executed the corresponding order operation, the adjusting the forecast result according to the current execution status of the service order comprises:

from t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t' < t_end；

3. The method for forecasting the capacity of a service executor in a service system according to claim 2, wherein the step of obtaining the latest execution time t' of the allocated available service executor to execute the corresponding order operation comprises:

4. The method as claimed in claim 2, wherein the step of "adjusting the forecast result according to the current execution status of the service order" comprises:

Wherein j is the serial number of the initial position of the available service executive body, j belongs to F, k is the serial number of the order execution position, and t is more than t_begin；E₂(j,t_begin)、E₁(k,t_begin) Capacity at the jth location and at the kth location, respectively, at which the service order begins execution;

if the service order is cancelledIf the available service execution bodies are singly distributed, the predicted capacity value of the initial position of the available service execution body distributed after the latest execution time is recovered to be E₂(m,t)＝E₂(m,t_begin) And t > t'; restoring the capacity on the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin) And t > t_begin；

5. The method as claimed in claim 2, wherein the step of "adjusting the forecast result according to the current execution status of the service order" comprises:

Wherein, t'_end≤t＜t_end。

6. The method as claimed in claim 2, wherein the step of "adjusting the forecast result according to the current execution status of the service order" comprises:

if the order execution position and the allocated available serviceIf the initial positions of the business executives are the same, the capacity on the order execution position is adjusted to be E₁(k,t)＝E₁(k,t_begin) -1; otherwise, adjusting the capacity on the order execution position to be E₁(k,t)＝E₁(k,t_begin)；

Wherein, t_end≤t＜(t_end+t_delay)，t_endAnd t_delayRespectively the ending time of the order execution time interval and the length of the delay time interval; e₁(k,t_begin) And k is the serial number of the order execution position.

7. The method according to any one of claims 1 to 6, wherein after the step of "adjusting the forecast result according to the current execution status of the service order", the method further comprises:

8. The method according to any one of claims 1 to 6, wherein the step of obtaining available service executives based on the order execution location comprises:

determining a search range by taking the order execution position as a center and a preset minimum radius threshold as a search radius, and acquiring an idle service execution body in the search range within the order execution time period;

if the sum of the productivity is more than or equal to 1, all the idle service executors are searched to be used as the available service executors;

and if the sum of the capacities in the search range is less than 1 when the search radius is greater than or equal to a preset maximum radius threshold value, rejecting the order request.

9. The method according to any one of claims 1 to 6, wherein the service system is a power-on service system, and the service executor is a customer-assistant power-on person for performing power-on service or a mobile charging cart or a mobile battery-replacement cart for providing power-on service.

10. A capacity forecast system for a service executor in a service system, comprising: the system comprises an order request acquisition module, an available service executive acquisition module, a probability calculation module, a capacity prediction module and a capacity adjustment module;

the capacity adjustment module is configured to: adjusting the prediction result according to the current execution state of the service order;

wherein the capacity forecasting module comprises: the system comprises a first judging unit, a first capacity forecasting unit and a second capacity forecasting unit;

the first capacity forecasting unit is configured to: under the condition that the order execution position is the same as the initial position of one available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) -1 and t_begin＜t＜t_end(ii) a Restoring the capacity to E at the location after the order execution period₁(k,t)＝E₁(k,t_begin) And t is more than or equal to t_end；

the second capacity prediction unit is configured to: under the condition that the order execution position is different from the initial position of each available service execution body, the capacity on the order execution position in the order execution time period is set as E₁(k,t)＝E₁(k,t_begin) And t is_begin＜t＜t_endAdjusting the capacity at the order fulfillment location to E after the order fulfillment time period₁(k,t)＝E₁(k,t_begin) +1 and t is not less than t_end(ii) a And,

E₂(j,t)＝E₂(j,t_begin)×(1-1/S_F)

wherein E is₂(j, t) is time tCapacity at j positions, t > t_begin，E₂(j,t_begin) Capacity at the jth location at which the service order began executing; j belongs to F, and F is the set of initial positions of the available service executors; s_FIs the sum of capacities at each location in the collection; 1/S_FA probability of moving to the order fulfillment location for each of the available service executives.

11. The system of claim 10, wherein the capacity forecast module comprises: a yield adjustment submodule after order distribution;

the latest execution time acquisition unit is configured to: acquiring the latest execution time t' when the allocated available service executors execute corresponding order operations;

the post-order allocation capacity adjustment unit is configured to: from t' to the end time t of the order execution period_endThe capacity at the initial position of the allocated available service executors is adjusted to E₂(m,t)＝E₂(m,t_begin) -1 and t' < t_end(ii) a Restoring the capacity of the original position of each unassigned available service executive to E after the order begins to execute₂(n,t)＝E₂(n,t_begin)；

12. The system of claim 11, wherein the latest execution time obtaining unit is specifically configured to:

13. The system of claim 11, wherein the capacity forecast module further comprises: a yield energy adjusting submodule after the order is cancelled;

the second adjusting unit is configured to: restoring the predicted capacity value at the initial position of the available service executive distributed after the latest execution time to E when the available service executive is distributed to the cancelled service order₂(m,t)＝E₂(m,t_begin) And t > t'; restoring the capacity on the order execution position to E after the order starts to be executed₁(k,t)＝E₁(k,t_begin) And t > t_begin；

14. The system of claim 11, wherein the capacity forecast module further comprises: a production energy adjusting submodule after the order is finished in advance;

Wherein, t'_end≤t＜t_end。

15. The system of claim 11, wherein the capacity forecast module further comprises: a yield adjustment submodule after the order is delayed;

if the order execution position is the same as the initial position of the distributed available service executors, executing the orderThe throughput on the row position is adjusted to E₁(k,t)＝E₁(k,t_begin) -1; otherwise, adjusting the capacity on the order execution position to be E₁(k,t)＝E₁(k,t_begin)；

16. The system according to any one of claims 10 to 15, further comprising a capacity forecast module;

17. The system according to any of claims 10-15, wherein the available service executant acquiring module is specifically configured to:

18. The system of any one of claims 10 to 15, wherein the service system is a power-on service system, and the service implementation is a customer-assistant power-on person for performing power-on service or a mobile charging cart or a mobile battery-replacement cart for providing power-on service.

19. A storage device having a program stored therein, wherein the program is adapted to be loaded and executed by a processor to implement the method for forecasting productivity of a service execution unit in a service system according to any one of claims 1 to 9.

20. A control device comprising a processor and a memory;

the processor is adapted to execute a program;

the memory is adapted to store the program;

wherein the program is adapted to be loaded and executed by the processor to implement the method for forecasting the productivity of the service executant in the service system according to any one of claims 1 to 9.