CN115829169A

CN115829169A - Service processing method and device based on mixed integer linear programming

Info

Publication number: CN115829169A
Application number: CN202310126641.0A
Authority: CN
Inventors: 王孟昌
Original assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Current assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Priority date: 2023-02-10
Filing date: 2023-02-10
Publication date: 2023-03-21
Anticipated expiration: 2043-02-10
Also published as: CN115829169B

Abstract

The embodiment of the specification provides a business processing method and a business processing device based on mixed integer linear programming, wherein the business processing method based on mixed integer linear programming comprises the following steps: receiving a service prediction request aiming at a target service, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the target service; decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information; and respectively solving the plurality of service solving sub-information to obtain target prediction sampling values corresponding to the target services. The method decomposes the mixed integer linear programming problem into a plurality of service solving sub-information according to the service constraint information, each service solving sub-information is independent, parallel calculation can accelerate the solving speed of the service solving sub-information, and the subsequent calculation efficiency is improved.

Description

Service processing method and device based on mixed integer linear programming

Technical Field

The embodiment of the specification relates to the technical field of computers, in particular to a business processing method based on mixed integer linear programming.

Background

In many business scenarios, the problem of solving the mixed integer linear programming model, such as energy power, electronic commerce, supply chain, cloud computing, chemical engineering, finance, education and scientific research, is encountered, taking energy power as an example, each level of power grid needs to determine the unit combination in a period of time in the future, a scheme is made for meeting the power consumption requirements of users, the time when each generator set is turned on and off and the output power at each time point in the future are specified, the physical requirements are met, the power generation cost is enabled to be as low as possible, and the problem is generally modeled as a mixed integer linear programming task.

With the increasing abundance of service scenes and the increasing of service scale of a single service scene, the conventional branch-and-bound method needs longer time to obtain a satisfactory solution, but in an actual service scene, the time for solving the mixed integer linear programming task model for each service is limited, and the conventional method is more and more difficult to adapt to a new situation, so that a faster method is needed to obtain the optimal solution of the target service.

Disclosure of Invention

In view of this, the embodiments of the present specification provide a business processing method based on mixed integer linear programming. One or more embodiments of the present disclosure also relate to a mixed integer linear programming-based service processing apparatus, a computing device, a computer-readable storage medium, and a computer program, so as to solve the technical deficiencies in the prior art.

According to a first aspect of the embodiments of the present specification, there is provided a business processing method based on mixed integer linear programming, including:

receiving a service prediction request aiming at a target service, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the target service;

decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information;

and respectively solving the plurality of service solving sub-information to obtain target prediction sampling values corresponding to the target services.

According to a second aspect of the embodiments of the present specification, there is provided a method for predicting a unit combination based on mixed integer linear programming, including:

receiving a service prediction request aiming at a unit combination prediction service, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the unit combination prediction service;

and respectively solving the plurality of service solving sub-information to obtain a prediction unit combination corresponding to the unit combination prediction service.

According to a third aspect of the embodiments of the present specification, there is provided a business processing system based on mixed integer linear programming, including

The terminal side equipment is used for generating a service prediction request aiming at a target service and sending the service prediction request to the cloud equipment, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the target service;

the cloud equipment is used for receiving the service prediction request, decomposing the service expected information into a plurality of service solving sub-information based on the service constraint information, respectively solving the plurality of service solving sub-information, obtaining a target prediction sampling value corresponding to the target service, and sending the target prediction sampling value to the end-side equipment.

According to a fourth aspect of the embodiments of the present specification, there is provided a service processing apparatus based on mixed integer linear programming, including:

the system comprises a receiving module, a service prediction module and a service management module, wherein the receiving module is configured to receive a service prediction request aiming at a target service, and the service prediction request comprises service expectation information and service constraint information corresponding to the target service;

a generation module configured to decompose the service expectation information into a plurality of service solving sub-information based on the service constraint information;

and the prediction module is configured to respectively solve the plurality of service solving sub-information to obtain a target prediction sampling value corresponding to the target service.

According to a fifth aspect of embodiments herein, there is provided a computing device comprising:

a memory and a processor;

the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions are executed by the processor to realize the steps of the business processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming.

According to a sixth aspect of the embodiments of the present specification, there is provided a computer-readable storage medium storing computer-executable instructions, which when executed by a processor, implement the steps of the mixed integer linear programming-based business processing method or the mixed integer linear programming-based crew combination prediction method described above.

According to a seventh aspect of the embodiments of the present specification, there is provided a computer program, wherein when the computer program is executed in a computer, the computer program causes the computer to execute the steps of the business processing method based on mixed integer linear programming or the unit combination prediction method based on mixed integer linear programming.

One embodiment of the present specification realizes that a mixed integer linear programming problem is decomposed into a plurality of service solving sub-information according to service constraint information, the plurality of service solving sub-information are processed in parallel, a solving result corresponding to each service solving sub-information is obtained, each service solving sub-information is independent of each other, parallel computation can accelerate the solving speed of the service solving sub-information, so as to accelerate the solving speed of the mixed integer linear programming problem, and finally, a target prediction sampling value of the mixed integer linear programming problem is obtained according to the result of each service solving sub-information, so that an optimal solution can be rapidly screened out in a limited solving result, thereby further accelerating the solving speed, and saving the computation time.

Drawings

Fig. 1 is a schematic diagram of a business processing method based on mixed integer linear programming according to an embodiment of the present specification;

fig. 2 is a flowchart of a business processing method based on mixed integer linear programming according to an embodiment of the present specification;

FIG. 3 is a schematic diagram of a sample feasible domain provided by one embodiment of the present description;

FIG. 4 is a schematic diagram of determining an initial sample value provided by one embodiment of the present description;

fig. 5 is a flowchart illustrating a processing procedure of a mixed integer linear programming-based service processing method applied to a dock allocation berthage scene according to an embodiment of the present specification;

fig. 6 is a flowchart of a method for predicting a unit combination based on mixed integer linear programming according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of a service processing apparatus based on mixed integer linear programming according to an embodiment of the present specification;

FIG. 8 is a schematic diagram of an apparatus for a crew assembly based on mixed integer linear programming according to an embodiment of the present disclosure;

FIG. 9 is a mixed integer linear programming based business processing system provided by an embodiment of the present description;

fig. 10 is a block diagram of a computing device according to an embodiment of the present disclosure.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will be able to make and use the present disclosure without departing from the spirit and scope of the present disclosure.

The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first can also be referred to as a second and, similarly, a second can also be referred to as a first without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at" \8230; "or" when 8230; \8230; "or" in response to a determination ", depending on the context.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in this specification are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the relevant data need to comply with relevant laws and regulations and standards of relevant countries and regions, and are provided with corresponding operation entrances for the user to choose to authorize or reject.

First, the noun terms to which one or more embodiments of the present specification relate are explained.

The unit combination: the scheme is established in a power grid for meeting the power demand of users, so as to specify when each generator set is turned on and off and output power at each time point in a future period, the scheme needs to meet a series of physical requirements, and the total power generation cost needs to be as small as possible, and the problem is generally modeled as mixed integer linear programming, especially mixed 0-1 linear programming.

Mixed integer linear programming: the method refers to that variables (whole or part) in the programming are limited to integers, if the variables are limited to the integers in the linear model, the linear model is mixed integer linear programming, and the method for solving the mixed integer programming is usually only suitable for the mixed integer linear programming.

Hybrid 0-1 linear programming: the method is one kind of mixed integer linear programming, the objective function and the constraint condition of the method are linear functions of decision variables, and a part of the decision variables can only take values of 0 or 1.

Feasible solution: in the linear planning problem, the solution of all constraint conditions can be satisfied.

Linear programming relaxation: the constraint that is required to be an integer in the mixed integer linear programming is cancelled, so that a linear programming model, i.e., linear Programming Relaxation (LPR), can be obtained.

In many business scenarios, a solution problem is typically encountered for a mixed integer linear programming model, which includes objective functions and constraints. Taking the unit combination of a power grid scene as an example, the unit combination of the next day needs to be determined every day for each level of power grids, the problem is usually converted into a mixed 0-1 linear programming model, and a branch-and-bound method is used for solving the corresponding mixed 0-1 linear programming model to obtain an optimal solution or a near-optimal solution.

With the increasing of power grid users and units and the increasing of corresponding model scales, the traditional branch-and-bound method needs longer time to obtain a satisfactory solution, but the time for model solution in the actual production of the power grid is limited, and the traditional method is more and more difficult to adapt to a new situation.

In a conventional branch-and-bound method, usually, an integer constraint of a model is ignored, corresponding variables are used as common continuous variables, so that a Linear Programming (LP) model, called as Linear Programming Relaxation (LPR) of an original problem, is obtained, a solution of the relaxation problem, called as a relaxation solution, can be obtained by using the LP model to solve, a corresponding objective function is recorded as a lower bound, an objective function value of the original problem cannot be smaller than the lower bound, and if values of the corresponding variables in the relaxation solution just meet integer requirements of the original problem, the relaxation solution is an optimal solution of the original model, so that the solution is finished.

If the corresponding variable in the relaxation solution does not meet the integer requirement of the original problem, branching is carried out, namely the variable is respectively fixed to two LP subproblems constructed by 0 and 1, the two LP subproblems are respectively solved, if the obtained solution meets the integer requirement, a feasible solution of the original problem is obtained, and a corresponding objective function value is recorded as an upper bound, namely the target value of the original problem cannot be larger than the upper bound; and if the obtained solution does not meet the integer requirement, continuously performing branch solution on the LP subproblem, updating the upper bound and the lower bound, and obtaining the optimal solution when the upper bound and the lower bound are combined.

The traditional branch-and-bound method is a tree search method, and needs to traverse each leaf node from a tree root node, wherein the upper and lower bounds are used for pruning poor branches to reduce the search space, and in the worst case, the method needs to traverse all the leaf nodes. Since the solution of the leaf node depends on the parent node, when the node where the feasible solution appears is deeper, a series of sub-problems need to be sequentially solved to obtain the feasible solution of the node, and the sequential process cannot be accelerated by utilizing parallel computation, so that in practical application, the problem solving speed of the method cannot be effectively improved by utilizing large-scale parallel computation.

There is also a research in academia that proposes a method for solving based on random sampling in a hypercube, and the method can use a parallel method for solving because the collected points are independent from each other and the corresponding subproblem solution has no precedence relationship, but the method can collect a large number of invalid points which cause infeasible solution, so the efficiency is very low and the method can not be practically applied.

Based on this, in the present specification, a mixed integer linear programming based service processing method is provided, and the present specification relates to a mixed integer linear programming based unit combination prediction method, a mixed integer linear programming based service processing apparatus, a mixed integer linear programming based service processing system, a computing device, and a computer readable storage medium, which are described in detail in the following embodiments one by one.

Referring to fig. 1, fig. 1 is a schematic diagram illustrating a service processing method based on mixed integer linear programming according to an embodiment of the present disclosure, and as shown in fig. 1, the service processing method based on mixed integer linear programming according to the embodiment of the present disclosure is applied to a terminal 100, where the terminal 100 may be a terminal device such as a notebook computer, an intelligent terminal, a server, and a cloud server.

In the terminal 100, the original mix of the target service corresponding to the target service is created according to the target serviceThe linear programming model usually includes service expectation information and service constraint information in the original mixed integer linear programming model of the target service, where the service expectation information specifically refers to an objective function corresponding to the target service, for example: min 3x ₁ +5x ₂ -2x ₃ . With the objective task expectation, the objective function is minimizedx ₁ 、x ₂ 、x ₃ . Wherein the content of the first and second substances,x ₁ 、x ₂ 、x ₃ in particular parameters in the target task.

Traffic constraint information refers in particular to the conditions that several parameters in the objective function need to satisfy, e.g. 5x ₁ +x ₃ ≥3，3x ₂ -3.5x ₃ ≤1，x ₁ ≥0，x ₂ ≥0，x ₃ E {0,1}. Specifically, the service constraint information generally includes first service constraint information and second service constraint information, where the first service constraint information includes an integer decision parameter, that is, the first service constraint information refers to limiting the type of the parameter, specifically, the parameter in the objective function is an integer, for examplex ₃ E {0,1} representsx ₃ Values can only be found in 0 or 1. The constraint condition is the first service constraint information, and the second service constraint information is specifically the linear constraint condition in the service constraint information, for example, 5x ₁ +x ₃ ≥3，3x ₂ -3.5x ₃ ≤1，x ₁ ≥0，x ₂ ≥0。

Performing linear programming relaxation on the first service constraint information in the original mixed integer linear programming model of the target service to generate a linear programming relaxation model of the target service, specifically, canceling the parameter constraint which is required to be an integer in the first service constraint information, and adjusting the parameter constraint to be a linear constraint, for example, adjusting the parameter constraint to be a linear constraintx ₃ Adjusting the epsilon {0,1} to be less than or equal to 0x ₃ ≤1。

After a target service linear programming relaxation model is obtained, generating a plurality of target sampling values based on constraint conditions of the target service linear programming relaxation model, wherein the target sampling values specifically refer to possible values meeting parameters of first service constraint information and second service constraint information, respectively transmitting the target sampling values into the target service linear programming relaxation model, respectively calculating initial solution information corresponding to each target sampling value, then selecting one target solution information from the plurality of initial solution information, and determining the target sampling value corresponding to the target solution information as a final target prediction sampling value, wherein a specific selection process is related to a target function, for example, if the target function is to find a minimum value, the minimum initial solution information is selected from the plurality of initial solution information as target solution information; and if the objective function is to find the maximum value, selecting the maximum initial solution information from the plurality of initial solution information as the objective solution information.

Referring to fig. 2, fig. 2 is a flowchart illustrating a business processing method based on mixed integer linear programming according to an embodiment of the present disclosure, which specifically includes the following steps.

Step 202: receiving a service prediction request aiming at a target service, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the target service.

The target service specifically refers to a specific service application scenario in actual application. For example, the unit combination problem, the transformer combination problem in an energy power scenario; dock allocation problems with vessel berthing; a routing line distribution problem in a network distribution scenario; channel allocation problems for satellite communication scenarios; a warehouse scenario cargo distribution problem, etc. It should be noted that the target service provided by the embodiments of the present specification is a mixed integer linear programming task, and preferably, the target service is a mixed 0-1 linear programming task.

The service prediction request specifically refers to a request for predicting a target service, and in practical application, the service prediction request includes service expectation information and service constraint information corresponding to the target service, the service expectation information specifically refers to a mathematical model generated after the target service is abstracted, and the service constraint information specifically refers to a constraint condition for the service expectation information. In the embodiments provided in this specification, the service constraint information specifically includes first service constraint information and second service constraint information, where the first service constraint information includes an integer decision parameter, and the integer decision parameter specifically indicates that at least one parameter in the service expectation information is an integer.

In a specific embodiment provided in this specification, taking an example of a problem of allocating a satellite communication channel in a scenario in which a target service is satellite communication, the problem may be abstracted to a corresponding mixed integer linear programming task model, which is specifically referred to as the following target service original linear programming model 1:

minc ^T x+d ^T y

s.t.A ₁ x+A ₂ y=b

C ₁ x+C ₂ y≤d

x _i ∈{L _i ,U _i }，i= 1，……p

y _j ∈{0，1}，j= 1，……q

wherein, minc ^T x+d ^T yInformation desired for a service, i.e. an objective function, in particular an objective function intended to be made in the problem of allocation of satellite communication channels by taking the values x and y "c ^T x+d ^T y"min, x and y are traffic parameters,c ^T andd ^T the method comprises the steps that a constant parameter in an objective function is represented by s.t., constraint conditions are represented, 4 pieces of service constraint information are included in a target service original linear planning model 1, wherein the first three pieces of service constraint information are second service constraint information, and the fourth piece of service constraint information is first service constraint information. WhereinA ₁ 、A ₂ 、b、C ₁ 、 C ₂ 、d、L _i 、U _i 、p、qThe isoparametric parameters are specifically constant parameters in the mixed integer linear programming task model. In the fourth traffic constraint information, guarantees are requiredy _j Take values in 0 or 1.

Step 204: and decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information.

The embodiments provided in this specification are applied to a mixed integer linear programming problem, where the mixed integer linear programming problem includes an integer decision parameter, that is, at least one parameter in service desired information must be an integer.

Based on this, in order to improve the solving efficiency of the service expectation information, a plurality of parameters can be generated for the service expectation information according to the service constraint information, the parameters are assigned to the service expectation information, a plurality of service solving sub-information are generated, each service solving sub-information is solved, and an optimal solution is selected from the solving result of each service solving sub-information.

Specifically, as described in the foregoing step, the service constraint information includes first service constraint information and second service constraint information, where the first service constraint information includes an integer decision parameter, and correspondingly, the service expectation information is decomposed into a plurality of service solution sub-information based on the service constraint information, which includes S2042-S2046:

s2042: slack constraint information is generated based on the first traffic constraint information.

When the problem of the mixed integer linear programming task model is processed, a linear programming relaxation mode is usually adopted to perform linear relaxation on the first service constraint information, obtain a linear programming relaxation model corresponding to the target service, and adjust the mixed integer linear programming problem into a linear programming problem.

For example, as described abovey _j E {0,1} is an example, and is subjected to linear programming relaxation, denoted as yj E [0,1 ]]. The target business original linear programming model 1 can be written as the following target business linear programming relaxation model 2:

minc ^T x+d ^T y

s.t.A ₁ x+A ₂ y=b

C ₁ x+C ₂ y≤d

x _i ∈{L _i ,U _i }，i= 1，……p

y _j ∈[0，1]，j= 1，……q

and linearly relaxing the first service constraint information to obtain relaxed constraint information, so that the subsequent solution task can be rapidly solved.

S2044: and determining at least one target sampling value according to the relaxation constraint information and the second service constraint information.

After the relaxation constraint information is determined, at least one target sampling value is determined according to the relaxation constraint information and the second service constraint information, where the target sampling value specifically refers to a group of sampling values conforming to the first service constraint information and the second service constraint information, for example, if there are 3 parameters in an objective function, there will be values of the 3 parameters in one target sampling value, and each target sampling value is obtained on the basis of the relaxation constraint information and the second constraint information and further satisfies the value of the first service constraint information.

Specifically, determining at least one target sample value according to the relaxation constraint information and the second traffic constraint information includes S20442 to S20446:

s20442, determining a sampling feasible region according to the relaxation constraint information and the second traffic constraint information.

The sampling feasible region is specifically an area which is determined based on the relaxation constraint information and the second service constraint information and can be sampled, and the relaxation constraint information and the second service constraint information are linear constraint information and are continuous, so that the corresponding sampling feasible region can be determined based on the relaxation constraint information and the second service constraint information.

Referring to fig. 3, fig. 3 is a schematic diagram illustrating a sampling feasible domain provided by an embodiment of the present specification. As shown in fig. 3, there are 3 business constraints, and the 3 business constraints form a closed region in the coordinate system, and the closed region is the sampling feasible region. The points in the sampling feasible region are all the points which satisfy the relaxation constraint information and the second service constraint information.

And S20444, determining a sampling value to be processed in the sampling feasible domain.

Specifically, the to-be-processed sampling value refers to a point that is selected from the sampling feasible region and satisfies the relaxation constraint information and the second service constraint information.

Still taking the above target business linear programming relaxation model 2 as an example, in order to solve the solution of the target business linear programming relaxation model 2, it may be deformed:

the above target business linear programming relaxation model 2 can be rewritten as:

at this point, the problem is converted to solve a system of linear equationsA’z=bGeneral solution of (1). If the linear equation set has no solution, the original linear planning model 1 of the target service has no solution; if the system of linear equations has a solution, a particular solution can be obtainedz* And a matrixBTo make an arbitrary vectorvAll satisfyz=z*+BvIs thatA’z=bThe solution of (1). Will be provided withz=z*+BvReturning to the target business linear programming relaxation model 2, the target business linear programming relaxation model 2 may be transformed into the following linear programming model 3:

min (c’) ^T Bv+ (c’) ^T z*

s.t.C’Bv≤d-C’z*

L-z*≤Bv≤U-z*

solving the linear programming model 3, from the resulting solutionv* One inner point (to-be-processed sampling value) can be constructed:z ⁰ =z*+Bv*。

a specific solution for solving the linear programming model 3 is provided below, and in practical applications, there may be other solutions, which are not limited in this specification.

Constructing a linear programming model 4 based on the linear programming model 3 as follows:

solving the linear programming model 4 can obtain the solution of the linear programming model 4v* Based onv* One sample value to be processed (inner point) can be determined:z ⁰ =z*+Bv*。

s20446, determining at least one target sampling value based on the to-be-processed sampling value.

After a to-be-processed sampling value is determined, at least one target sampling value can be determined based on the to-be-processed sampling value, and the selected target sampling value is determined based on the sampling feasible domain, so that a large number of invalid sampling values can be avoided, and the calculation workload is reduced.

Specifically, determining at least one target sampling value based on the to-be-processed sampling value includes:

determining at least one sampling direction based on the to-be-processed sampling value;

determining at least one initial sampling value in each sampling direction;

and determining at least one target sampling value according to the integer decision parameter and at least one initial sampling value.

After the to-be-processed sampling value is determined, a sampling direction may be randomly determined in the sampling feasible region based on the to-be-processed sampling value, and at least one initial sampling value may be determined in the interview direction, see fig. 4, where fig. 4 illustrates a schematic diagram of determining an initial sampling value provided in an embodiment of the present specification. As shown in fig. 4, the determination is made in the sample feasible regionA to-be-processed sampling valuez ⁰ And is based onz ⁰ Randomly determines a sampling directionu ⁰ It is noted that the sampling direction has a positive and negative difference, which can be based on the sampling directionu ⁰ Randomly selecting an initial sampling value in the directionz ¹ . In practical applications, a plurality of sampling directions may be determined, and a plurality of initial sampling values are collected in each sampling direction, and it should be noted that the determined initial sampling values are all determined in the sampling feasible region determined in the above steps.

After a plurality of initial sample values have been determined, the respective initial sample value can then be determined as the target sample value. In the above step, the first service constraint information is adjusted to be relaxed constraint information, so that the value of the initial sampling value may not satisfy the integer decision parameter in the first service constraint information. Based on this, the initial sampling value needs to be adjusted to the corresponding target sampling value according to the integer decision parameter in the first service constraint information, for example, the integer decision parameter in the initial sampling value needs to be adjusted to the corresponding integer.

Specifically, determining at least one target sampling value according to the integer decision parameter and at least one initial sampling value includes:

determining an initial sampling value to be processed;

determining a target sampling value to be processed based on the integer decision parameter and the initial sampling value to be processed;

and under the condition that the target sampling value to be processed meets the first service constraint information and the second service constraint information, determining the target sampling value to be processed as a target sampling value.

The to-be-processed initial sampling value specifically refers to an initial sampling value that needs to be rounded, and correspondingly, the to-be-processed target sampling value specifically refers to a value determined after the to-be-processed initial sampling value is rounded.

In practical application, each initial sampling value needs to be adjusted to an integer according to a certain rule, and taking a method of rounding nearby as an example, see the following formula 1:

equation 1

Referring to the above-mentioned formula 1,

meaning that the rounding is done down,

indicating rounding up, i.e. when

The difference from its rounding-down is smaller than

Rounded up by the difference thereof, will

Rounding down; in addition to that, will

And rounding upwards to obtain a target sampling value to be processed corresponding to the initial sampling value to be processed.

In practical application, after rounding the initial sampling value to be processed, the corresponding target sampling value to be processed may be determined, and at this time, the obtained target sampling value to be processed may exceed the constraint conditions of the first service constraint information and the second service constraint information.

S2046: and generating a plurality of service solving sub-information according to the at least one target sampling value and the service expectation information.

After the target sampling values are determined, each target sampling value is brought back to the original linear planning model of the target service, and the target sampling values already meet the requirements of the first service constraint information and the second service constraint information, so that the target sampling values can be brought into service expectation information, and the sub-information is solved by a plurality of services.

Specifically, generating a plurality of service solution sub-information according to the at least one target sampling value and the service expectation information includes:

determining a target sampling value to be processed;

and determining target service solving sub-information corresponding to the target sampling value to be processed based on the service expectation information and the target sampling value to be processed.

The target sampling value to be processed specifically refers to a target sampling value that needs to be calculated, for example, 10 target sampling values are available, and when a first target sampling value needs to be processed, the first target sampling value is the target sampling value to be processed; and when the fifth target sampling value is processed, the fifth target sampling value is the target sampling value to be processed.

And at this time, the target sampling value to be processed can be brought into the service expectation information, and target service solving sub-information corresponding to the target sampling value to be processed is generated. Further, the service expectation information includes its corresponding objective function, for example, the service expectation information "minc ^T x+d ^T yIn, the "c ^T x+d ^T yIs an objective function, wherein "y"is a parameter of the service expectation information. In order "yFor example, =1", the target sampling value to be processed is brought in"y"in can obtain the target business solves the sub information minc ^T x+d ^T I.e. solve forc ^T x+d ^T The value of (c). If so "yFor example, =3", the target sampling value to be processed is brought in"y"in can obtain the target business solves the sub information minc ^T x+3d ^T I.e. solve forc ^T x+3d ^T The value of (c).

And respectively bringing each target sampling value to be processed into the service expectation information, and constructing and generating target service solving sub-information corresponding to each target sampling value to be processed, so that a plurality of service solving sub-information can be generated.

In another specific embodiment provided in this specification, decomposing the service expectation information into a plurality of service solution sub-information based on the service constraint information includes:

receiving decomposition configuration information of a user, wherein the decomposition configuration information comprises the number of service solving sub-information;

and decomposing the service expectation information into a plurality of service solving sub-information based on the number of the service solving sub-information and the service constraint information.

Specifically, in the process of generating a plurality of service solution sub-information, the number of the service solution sub-information may be controlled by a user, and further, the user may send decomposition configuration information to the terminal, where the decomposition configuration information includes the number of the service solution sub-information, for example, the number of the service solution sub-information is 1000.

When the service solving sub-information is generated, the number of the target sampling values can be determined according to the number of the service solving sub-information carried in the decomposition configuration information, so that the number of the service solving sub-information is determined.

Step 206: and respectively solving the plurality of service solving sub-information to obtain target prediction sampling values corresponding to the target services.

After the plurality of service solving sub-information are obtained, each service solving sub-information is independent, so that each service solving sub-information can be solved respectively, and the service solving sub-information is independent and does not influence each other in the solving process. Solving is carried out on each service solving sub-information, namely solving information corresponding to each service solving sub-information can be obtained, then target solving information which accords with service expectation information is selected according to a plurality of solving information, and a target sampling value corresponding to the target solving information is used as a final target prediction sampling value.

For example, taking 3 total service solving sub-information as an example, if the solving information 1 of the service solving sub-information 1 is 4, the solving information 2 of the service solving sub-information 2 is 7, the solving information 3 of the service solving sub-information 3 is 15, and the service expectation information is a selected minimum value, it may be determined that the solving information 1 is the target solving information, and at the same time, it may be determined that the target sampling value 1 corresponding to the solving information 1 is the target prediction sampling value.

Specifically, the method for obtaining the target prediction sampling value corresponding to the target service by respectively solving the multiple service solving sub-information includes steps S2062 to S2064:

s2062, calculating each service solving sub-information, and obtaining initial solving information corresponding to each target sampling value.

In practical application, the data volume of the target sampling value may be large, so that the number of the generated service solution sub-information is large, and the calculation of the plurality of target sampling values is independent and does not affect each other, so that the plurality of service solution sub-information are independent and do not affect each other in the calculation solution process, and the overall calculation speed can be improved through the parallel calculation processing among the plurality of service solution sub-information.

Specifically, calculating each service solving sub-information to obtain initial solving information corresponding to each target sampling value includes:

generating a sub-model to be processed based on the target service solving sub-information;

and solving the submodel to be processed to obtain initial solving information corresponding to the target sampling value to be processed.

In order to facilitate mutual independent calculation between each service solving sub-information, a corresponding to-be-processed sub-model can be generated according to the service solving sub-information, wherein the to-be-processed sub-model refers to a mathematical processing model corresponding to each service solving sub-information and related to a target service, and a model result obtained by solving the to-be-processed sub-model can be used as initial solving information corresponding to a target sampling value to be processed.

In practical application, because each sub-model to be processed is processed independently and in parallel, in order to improve the processing efficiency of the model, the computation time can be shortened through a distributed processing idea.

Specifically, solving the to-be-processed submodel to obtain initial solving information corresponding to the to-be-processed target sampling value includes:

determining a target computing child node;

and sending the submodel to be processed to the target computing sub-node, and receiving initial solving information returned by the target computing sub-node.

In a specific embodiment provided in this specification, each to-be-processed submodel may be distributed to parallel-processed computing nodes for processing, where a target computing node specifically refers to a node having sufficient computing power resources and capable of computing a result of the to-be-processed submodel, the target computing node includes, but is not limited to, a computer, a virtual machine, a CPU core, a process, a thread, and the like, and the computing node has the capability of receiving model data of the to-be-processed submodel, executing a linear programming solver to obtain a solution of the submodel, and sending initial solution information of the to-be-processed submodel back to an execution subject through channels such as storage and a network.

The submodels to be processed are distributed to a plurality of target calculation sub-nodes for processing, so that the calculation resources can be fully utilized, and the total waiting time of model calculation is reduced.

And S2064, determining a target prediction sampling value corresponding to the target service based on each piece of initial solution information.

After each initial solution is determined, a final target predicted sample value for the target service may be further determined. Specifically, determining a target prediction sampling value corresponding to the target service based on each piece of initial solution information includes:

determining an expected value of the service expectation information;

determining target solution information in each initial solution information based on the expected values;

and determining a target sampling value corresponding to the target solving information as a target prediction sampling value.

In practical applications, since the service expectation information includes its corresponding objective function, for example, the service expectation information "minc ^T x+d ^T yIn, the "c ^T x+d ^T yThe ' is the target function, ' min ' is the expected value of the service expectation information, namely the minimum value of the expected target function, and the standard for determining the target prediction sampling value can be obtained through the expected value of the service expectation information. For example, when the expected value is "take a small value", the minimum value in the initial solution information is used as the target solution information, and the target sampling value corresponding to the target solution information is used as the target prediction sampling value of the target service.

In one embodiment provided in this specification, the service expectation information "min" is usedc ^T x+d ^T y"for example, at target sample value"yIn the case of a value of =1",c ^T x+d ^T is m, at the target sample value "yIn the case of =3",c ^T x+3d ^T n, and m is smaller than n by comparison, so that the initial solving information m is the target solving information, and simultaneously, the target sampling value corresponding to the target solving information m "yAnd =1", namely, the target prediction sampling value is obtained.

In a specific embodiment provided in the present specification, the method further includes:

and in the process of solving the submodel to be processed, sending solving information of the submodel to be processed to a front end, wherein the solving information comprises at least one of solving efficiency, solving progress and solving remaining time.

In practical application, the calculation between the submodels to be processed is independent and not influenced, and in the solving process of each submodel to be processed, the solving information generated in the solving process can be sent to the front end of a user for displaying, so that the user can know the processing conditions among a plurality of submodels to be processed, and the solving information includes but is not limited to solving efficiency, solving progress, solving remaining time and the like.

In another embodiment provided in this specification, there may be at least two target predicted sample values generated, and based on this, the method provided in this specification further includes:

under the condition that the number of the target prediction sampling values is at least two, the at least two target prediction sampling values are sent to a front end;

and receiving a selection instruction sent by a user at the front end, and determining a final target prediction sampling value according to the selection instruction.

In the mixed integer linear programming problem corresponding to the target service, an optimal solution needs to be found, and usually, only one optimal solution is needed. However, there may also be situations where there are at least two optimal solutions, i.e. there are at least two target predicted sample values. At this time, at least two target predicted sample values may be sent to the front end for selection by the user, and the user may select one of the at least two target predicted sample values as a final target predicted sample value.

In another embodiment provided by this specification, one of the at least two target predicted sample values may be selected as the final target predicted sample value.

Further, the service prediction request for the target service may be multiple times, and accordingly, the method provided by the present specification further includes:

and taking the target prediction sampling value as a candidate prediction value of the target service, wherein the candidate prediction value is used for providing selection for a user when a service prediction request is received again.

In practical application, service prediction may need to be periodically performed on a target service, for example, in a unit combination prediction service, prediction needs to be performed on a unit combination on the next day every day; in the scene of the ship berthing prediction service, prediction is provided for wharf allocation at preset time intervals.

In this case, the current target prediction sample value may be used as a candidate prediction value of a subsequent service prediction task, and the user may continue to use the target prediction sample value of the current prediction task in the next service prediction task, or may use the target prediction sample value of the current prediction task as the candidate prediction value of the next service prediction task.

One embodiment of the present specification realizes that a mixed integer linear programming problem is decomposed into a plurality of service solving sub-information according to service constraint information, the plurality of service solving sub-information are processed in parallel, a solving result corresponding to each service solving sub-information is obtained, each service solving sub-information is independent of each other, parallel computation can accelerate the solving efficiency of the service solving sub-information, thereby improving the solving speed of the mixed integer linear programming problem, and finally, a target prediction sampling value of the mixed integer linear programming problem is obtained according to the result of each service solving sub-information, so that an optimal solution can be rapidly screened out in a limited solving result, thereby further accelerating the solving speed and saving the computation time.

Furthermore, in the processing process, the mixed integer linear programming problem is firstly converted into a linear programming problem, a target sampling value is determined in the relaxation constraint information and the second service constraint information, the effectiveness of the target sampling value is ensured, the resource waste caused by invalid sampling points is greatly reduced, the subsequent computing efficiency is improved, each target sampling point is mutually independent, and a plurality of service solving sub-information are generated according to the target sampling value, so that the plurality of service solving sub-information support large-scale parallel computing, the solving speed can be accelerated, and the computing time is saved.

The mixed integer linear programming-based service processing method provided in this specification is further described below with reference to fig. 5, by taking an example of an application of the mixed integer linear programming-based service processing method in a dock distribution berth scene. Fig. 5 shows a processing flow chart of a business processing method based on mixed integer linear programming according to an embodiment of the present specification, which specifically includes the following steps.

Step 502: receiving a berthage prediction request for allocating berthages to a ship, wherein the berthage prediction request is a mixed integer linear programming problem.

Step 504: and analyzing the berth prediction request to obtain berth expectation information, first constraint information and second constraint information carried in the request, wherein the first constraint information requires that the number of berths is an integer.

Step 506: and performing linear relaxation on the first constraint information to generate relaxed constraint information.

Step 508: and determining a sampling feasible domain according to the relaxation constraint information and the second constraint information, and determining a sampling value to be processed in the sampling feasible domain.

Step 510: at least one sampling direction is determined based on the sample values to be processed, and at least one initial sample value is determined in each sampling direction.

Step 512: and determining at least one target sampling value according to the requirement that the number of the berths is an integer and at least one initial sampling value.

Step 514: and generating a to-be-processed sub model corresponding to each target sampling value according to the berth expectation information and each target sampling value.

Step 516: and determining a target computing node corresponding to each to-be-processed submodel, and sending each to-be-processed submodel to the corresponding target computing node.

Step 518: and receiving initial solving information returned by each target computing node.

Step 520: and selecting the minimum value in each initial solving message as the target solving message.

Step 522: and determining a target sampling value corresponding to the target solving information as predicted berth distribution information corresponding to the berth prediction request.

According to the method, the berthage distribution problem of mixed integer linear programming is converted into the linear programming problem, the target sampling value is determined in the relaxation constraint information and the second service constraint information, the effectiveness of the target sampling value is guaranteed, resource waste caused by invalid sampling points is greatly reduced, subsequent computing efficiency is improved, each target sampling point is mutually independent, and large-scale parallel computing is supported, so that the solving speed can be accelerated, and the computing time is saved.

Fig. 6, with reference to fig. 6, illustrates a flowchart of a method for predicting a unit combination based on mixed integer linear programming according to an embodiment of the present disclosure, which specifically includes the following steps:

step 602: receiving a service prediction request aiming at a unit combination prediction service, wherein the service prediction request comprises service expectation information and service constraint information corresponding to the unit combination prediction service.

Step 604: and decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information.

Step 606: and respectively solving the plurality of service solving sub-information to obtain a prediction unit combination corresponding to the unit combination prediction service.

In a specific embodiment provided in this specification, the service constraint information includes first service constraint information and second service constraint information, where the first service constraint information includes an integer decision parameter;

decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information, including:

generating relaxed constraint information based on the first traffic constraint information;

determining at least one target sampling unit combination according to the relaxation constraint information and the second service constraint information;

and generating a plurality of service solving sub-information according to the at least one target sampling unit combination and the service expectation information.

In a specific embodiment provided in this specification, solving the sub-information of the plurality of service solutions to obtain a prediction unit combination corresponding to the unit combination prediction service includes:

calculating each service solving sub-information to obtain initial solving information corresponding to each target sampling unit combination;

and determining a prediction unit combination corresponding to the unit combination prediction service based on each piece of initial solution information.

In a specific embodiment provided in this specification, determining at least one target sampling unit combination according to the relaxed constraint information and the second traffic constraint information includes:

determining a sampling feasible region according to the relaxation constraint information and the second service constraint information;

determining a sampling value to be processed in the sampling feasible domain;

and determining at least one target sampling unit combination based on the sampling values to be processed.

In a specific embodiment provided in this specification, determining at least one target sampler group combination based on the to-be-processed sample values includes:

determining at least one initial sampling value in each sampling direction;

and determining at least one target sampling unit combination according to the integer decision parameter and at least one initial sampling value.

In a specific embodiment provided in this specification, determining at least one target sampling unit combination according to the integer decision parameter and at least one initial sampling value includes:

determining an initial sampling value to be processed;

determining a target sampling unit combination to be processed based on the integer decision parameter and the initial sampling value to be processed;

and under the condition that the combination of the target sampling unit to be processed meets the first service constraint information and the second service constraint information, determining the combination of the target sampling unit to be processed as a combination of the target sampling unit.

In a specific embodiment provided in this specification, generating a plurality of service solution sub-information according to the at least one target sampling unit combination and the service expectation information includes:

determining a target sampling unit combination to be processed;

and determining unit combination prediction service solving sub-information corresponding to the target sampling unit combination to be processed based on the service expectation information and the target sampling unit combination to be processed.

Calculating each service solving sub-information to obtain initial solving information corresponding to each target sampling unit combination, wherein the initial solving information comprises the following steps:

generating a sub-model to be processed based on the unit combination prediction service solution sub-information;

and solving the submodel to be processed to obtain initial solving information corresponding to the target sampling unit combination to be processed.

and in the process of solving the submodel to be processed, sending the solving information of the submodel to be processed to a front end, wherein the solving information comprises at least one of solving efficiency, solving progress and solving remaining time.

In a specific embodiment provided in this specification, decomposing the service expectation information into a plurality of service solving sub-information based on the service constraint information includes:

under the condition that the number of the prediction unit combinations is at least two, the prediction unit combinations are sent to a front end;

and receiving a selection instruction sent by a user at the front end, and determining a final prediction unit combination according to the selection instruction.

and taking the predicted unit combination as a candidate predicted value of the unit combination predicted service, wherein the candidate predicted value is used for providing selection for a user when a service prediction request is received again.

According to the method, the unit combination prediction problem of mixed integer linear programming is firstly converted into the linear programming problem, the target sampling unit combination is determined in the relaxation constraint information and the second business constraint information, the effectiveness of the target sampling unit combination is guaranteed, resource waste caused by invalid sampling points is greatly reduced, subsequent computing efficiency is improved, each target sampling point is mutually independent, and large-scale parallel computing is supported, so that the solving speed can be accelerated, and the computing time is saved.

Corresponding to the embodiment of the service processing method based on the mixed integer linear programming, the present specification further provides an embodiment of a service processing apparatus based on the mixed integer linear programming, and fig. 7 shows a schematic structural diagram of the service processing apparatus based on the mixed integer linear programming provided in an embodiment of the present specification. As shown in fig. 7, the apparatus includes:

a receiving module 702 configured to receive a service prediction request for a target service, where the service prediction request includes service expectation information and service constraint information corresponding to the target service;

a generating module 704 configured to decompose the service expectation information into a plurality of service solving sub-information based on the service constraint information;

and the predicting module 706 is configured to separately solve the plurality of service solving sub-information to obtain target prediction sampling values corresponding to the target service.

Optionally, the service constraint information includes first service constraint information and second service constraint information, where the first service constraint information includes an integer decision parameter;

the generating module 704, further configured to:

determining at least one target sampling value according to the relaxation constraint information and the second service constraint information;

and generating a plurality of service solving sub-information according to the at least one target sampling value and the service expectation information.

Optionally, the prediction module 706 is further configured to:

calculating each service solving sub-information to obtain initial solving information corresponding to each target sampling value;

and determining a target prediction sampling value corresponding to the target service based on each piece of initial solution information.

Optionally, the generating module 704 is further configured to:

determining a sampling value to be processed in the sampling feasible domain;

at least one target sample value is determined based on the to-be-processed sample value.

Optionally, the generating module 704 is further configured to:

determining at least one initial sampling value in each sampling direction;

Optionally, the generating module 704 is further configured to:

determining an initial sampling value to be processed;

The generating module 704, further configured to:

determining a target sampling value to be processed;

Optionally, the prediction module 706 is further configured to:

Optionally, the apparatus further comprises:

the feedback module is configured to send solving information of the to-be-processed submodel to a front end in the process of solving the to-be-processed submodel, wherein the solving information comprises at least one of solving efficiency, solving progress and solving remaining time.

Optionally, the generating module 704 is further configured to:

Optionally, the apparatus further comprises:

an information sending module configured to send the at least two target predicted sample values to a front end if the target predicted sample values are at least two;

and the selection module is configured to receive a selection instruction sent by a user at the front end and determine a final target prediction sampling value according to the selection instruction.

Optionally, the apparatus further comprises:

and the candidate module is configured to take the target prediction sampling value as a candidate prediction value of the target service, wherein the candidate prediction value is used for providing a choice for a user when a service prediction request is received again.

The service processing apparatus based on mixed integer linear programming provided in the embodiment of the present specification includes receiving a service prediction request for a target service, where the service prediction request includes service expectation information, first service constraint information, and second service constraint information corresponding to the target service, and the first service constraint information includes an integer decision parameter; generating relaxed constraint information based on the first traffic constraint information; determining at least one target sampling value according to the relaxation constraint information and the second service constraint information; and calculating an initial service predicted value corresponding to each target sampling value according to the service expectation information, and determining a target predicted sampling value corresponding to the target service based on each initial service predicted value. According to the method, the mixed integer linear programming problem is converted into the linear programming problem, the target sampling value is determined in the relaxation constraint information and the second service constraint information, the effectiveness of the target sampling value is guaranteed, resource waste caused by invalid sampling points is greatly reduced, subsequent computing efficiency is improved, each target sampling point is mutually independent, large-scale parallel computing is supported, therefore, the solving speed can be accelerated, and the computing time is saved.

The foregoing is a schematic solution of a service processing apparatus based on mixed integer linear programming according to this embodiment. It should be noted that the technical solution of the service processing apparatus based on the mixed integer linear programming and the technical solution of the service processing method based on the mixed integer linear programming belong to the same concept, and details of the technical solution of the service processing apparatus based on the mixed integer linear programming, which are not described in detail, can be referred to the description of the technical solution of the service processing method based on the mixed integer linear programming.

Referring to fig. 8, fig. 8 shows an apparatus schematic diagram of a crew assembly based on mixed integer linear programming according to an embodiment of the present disclosure. As shown in fig. 8, the prediction problem of the unit combination is abstracted into unit combination model data, the unit combination model data includes service expectation information of the organic group combination prediction service, first service constraint information and second service constraint information, and the first service constraint information includes an integer decision parameter.

Inputting the data of the unit combination model into the interior point calculator 802, performing linear relaxation on the first service constraint information in the unit combination model by the interior point calculator 802 to obtain relaxation constraint information, generating a sampling feasible domain according to the relaxation constraint information and the second service constraint information, determining a feasible solution of the linear programming relaxation model in the sampling feasible domain, and constructing an interior point (to-be-processed sampling value) based on the feasible solution.

After the interior points are constructed, the interior points are input into a random sampler 804, at least one sampling direction is determined in the random sampler 804 based on the interior points, a line segment with two ends touching the boundary of a sampling feasible region can be constructed in the positive and negative directions of the sampling direction, at least one initial sampling value is determined on the line segment, the step of determining the sampling direction is repeated, and the step of determining the initial sampling values is repeated until a preset number of initial sampling values are obtained.

Inputting each initial sampling value into the sub-problem constructor 806, taking the integer decision parameter to the integer value of each initial sampling value in the sub-problem constructor 806 according to a preset rule, obtaining a target sampling value to be processed corresponding to each initial sampling value, and keeping the target sampling value to be processed meeting the first service constraint information and the second service constraint information as a final target sampling value. And the subproblem constructor 806 brings each target sampling value back to the unit combination model to generate a to-be-processed submodel corresponding to each target sampling value. The problem to be solved by the crew composition model is divided into a plurality of optimal solving sub-problems by the sub-problem constructor 806.

After the creation of each to-be-processed submodel is completed, the subproblem distributor transmits each to-be-processed submodel to a computing node provided with a linear programming solver through channels such as storage and network.

The compute nodes are any resource with sufficient computing power, including but not limited to a computer, a virtual machine, a CPU core, a process, a thread, and the like, and have the capability of receiving the submodel to be processed, executing a linear programming solver to solve a solution of the submodel to be processed, and transmitting the solution of each submodel to be processed to the summary verifier 810 through channels such as a storage channel, a network channel, and the like.

The summary verifier 810 is responsible for receiving solutions generated by a plurality of computing nodes, recording and updating the currently collected optimal solution, and judging whether a preset termination condition is achieved, if not, continuing to execute the random sampler 804 and subsequent links; if yes, outputting the currently recorded solution as a feasible solution of the unit combination model.

It should be noted that the preset termination condition refers to a termination condition that is set manually, and for example, the calculation turn can be set to 3 turns; it may also be arranged to solve for a feasible solution, etc. In the embodiments provided in the present specification, specific contents of the preset termination condition are not limited.

Referring to fig. 9, fig. 9 illustrates a mixed integer linear programming-based traffic processing system provided in an embodiment of the present specification, which may include an end-side device 901 and a cloud-side device 902, where the end-side device 901 is configured to send a traffic prediction request to the cloud-side device 902 and receive a target prediction sample value sent by the cloud-side device 902; the cloud test device 902 is configured to perform an operation of solving a target prediction sampling value on a received service prediction request for a target service.

The end-side device 901 is configured to generate a service prediction request for a target service, and send the service prediction request to a cloud-based device, where the service prediction request includes service expectation information and service constraint information corresponding to the target service;

the cloud-side equipment 902 is configured to receive the service prediction request, decompose the service expectation information into a plurality of service solving sub-information based on the service constraint information, respectively solve the plurality of service solving sub-information, obtain a target prediction sampling value corresponding to the target service, and send the target prediction sampling value to the end-side equipment.

The cloud-side device 902 may be a central cloud device of a distributed cloud architecture, the end-side device 901 may be an edge cloud device of the distributed cloud architecture, and the cloud-side device 902 and the end-side device 901 may be server-side devices such as a conventional server, a cloud server, or a server array, or may be terminal devices, which is not limited in this description embodiment. Moreover, the cloud-side device 902 provides super-strong computing and storage capabilities, and is far from the user; and the end-side device 901 is deployed in a large range and is close to the user. The end-side device 901 is an extension of the cloud-side device 902, and can sink the computing capability of the cloud-side device 902 towards the end-side device 901, and the service requirement which cannot be met in a centralized cloud computing mode is solved through integration and cooperative management of end clouds.

FIG. 10 illustrates a block diagram of a computing device 1000 provided in accordance with one embodiment of the present description. The components of the computing device 1000 include, but are not limited to, memory 1010 and a processor 1020. The processor 1020 is coupled to the memory 1010 via a bus 1030 and the database 1050 is used to store data.

Computing device 1000 also includes access device 1040, access device 1040 enabling computing device 1000 to communicate via one or more networks 1060. Examples of such networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The Access device 1040 may include one or more of any type of Network interface (e.g., a Network interface controller) that may be wired or Wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) Wireless interface, a worldwide interoperability for Microwave Access (Wi-MAX) interface, an ethernet interface, a Universal Serial Bus (USB) interface, a cellular Network interface, a bluetooth interface, a Near Field Communication (NFC), or a Near field communication (Near field communication).

In one embodiment of the present description, the above-described components of computing device 1000 and other components not shown in FIG. 10 may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 10 is for purposes of example only and is not limiting as to the scope of the present description. Other components may be added or replaced as desired by those skilled in the art.

Computing device 1000 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), a mobile phone (e.g., smartphone), a wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or Personal Computer (PC). Computing device 1000 may also be a mobile or stationary server.

Wherein the processor 1020 is configured to execute computer-executable instructions that, when executed by the processor, implement the steps of the data processing method described above. The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the above-mentioned technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming belong to the same concept, and details of the technical solution of the computing device, which are not described in detail, can be referred to the description of the above-mentioned technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming.

An embodiment of the present specification further provides a computer-readable storage medium, which stores computer-executable instructions, and when executed by a processor, the computer-executable instructions implement the steps of the service processing method based on mixed integer linear programming or the unit combination prediction method based on mixed integer linear programming.

The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium and the above-mentioned technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming belong to the same concept, and details of the technical solution of the storage medium, which are not described in detail, can be referred to the above description of the technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming.

An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the service processing method based on mixed integer linear programming or the unit combination prediction method based on mixed integer linear programming.

The above is an illustrative scheme of a computer program of the present embodiment. It should be noted that the technical solution of the computer program and the above-mentioned technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming belong to the same concept, and details of the technical solution of the computer program, which are not described in detail, can be referred to the above description of the technical solution of the service processing method based on the mixed integer linear programming or the unit combination prediction method based on the mixed integer linear programming.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.

It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts, but those skilled in the art should understand that the present embodiment is not limited by the described acts, because some steps may be performed in other sequences or simultaneously according to the present embodiment. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that acts and modules referred to are not necessarily required for an embodiment of the specification.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are intended only to aid in the description of the specification. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the teaching of the embodiments of the present disclosure. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the embodiments. The specification is limited only by the claims and their full scope and equivalents.

Claims

1. A business processing method based on mixed integer linear programming comprises the following steps:

and respectively solving the plurality of service solving sub-information to obtain a target prediction sampling value corresponding to the target service.

2. The method of claim 1, the traffic constraint information comprising first traffic constraint information and second traffic constraint information, the first traffic constraint information comprising an integer decision parameter;

3. The method of claim 1, wherein the step of respectively solving the sub-information of the plurality of services to obtain the target predicted sample value corresponding to the target service comprises:

4. The method of claim 2, determining at least one target sample value based on the relaxation constraint information and the second traffic constraint information, comprising:

determining a sampling value to be processed in the sampling feasible domain;

5. The method of claim 4, determining at least one target sample value based on the to-be-processed sample value, comprising:

determining at least one initial sampling value in each sampling direction;

6. The method of claim 5, determining at least one target sample value based on the integer decision parameter and at least one initial sample value, comprising:

determining an initial sampling value to be processed;

7. The method of claim 2, generating a plurality of traffic solving sub-information based on the at least one target sample value and the traffic expectation information, comprising:

determining a target sampling value to be processed;

8. The method of claim 7, wherein calculating each traffic solution sub-information to obtain initial solution information corresponding to each target sample value comprises:

9. The method of claim 8, further comprising:

10. The method of claim 1, decomposing the traffic expectation information into a plurality of traffic solving sub-information based on the traffic constraint information, comprising:

11. The method of claim 1, further comprising:

12. The method of claim 11, further comprising:

13. A unit combination prediction method based on mixed integer linear programming comprises the following steps:

14. The method of claim 13, further comprising:

and in the process of solving the business solving sub-information, sending solving information of the business solving sub-information to a front end, wherein the solving information comprises at least one of solving efficiency, solving progress and solving remaining time.

15. The method of claim 13, further comprising:

16. A mixed integer linear programming based business processing system comprising:

the cloud equipment is used for receiving the service prediction request, decomposing the service expected information into a plurality of service solving sub-information based on the service constraint information, respectively solving the plurality of service solving sub-information to obtain a target prediction sampling value corresponding to the target service, and sending the target prediction sampling value to the end-side equipment.

17. A computing device, comprising:

a memory and a processor;

the memory is for storing computer-executable instructions, and the processor is for executing the computer-executable instructions, which when executed by the processor, implement the steps of the method of any one of claims 1-12 or 13-15.

18. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, perform the steps of the method of any one of claims 1-12 or 13-15.