CN105786813A - Method and device for sending task information - Google Patents
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- CN105786813A CN105786813A CN201410790220.9A CN201410790220A CN105786813A CN 105786813 A CN105786813 A CN 105786813A CN 201410790220 A CN201410790220 A CN 201410790220A CN 105786813 A CN105786813 A CN 105786813A
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Abstract
The embodiment of the application discloses a method and device for sending task information. The method comprises the steps that the task information and candidate user information is received; according to the task information and the candidate user information, values of related parameters corresponding to candidate users are calculated; the candidate users are sequenced according to the values of the related parameters, and a target user is screened out according to a first preset relation; the task information is sent to the target user; and the related parameters are used to denote related relations between the candidate users and the task information. The method and device for sending the task information disclosed by the embodiment of the application can ensure that a receiver of the task information can satisfy task demands.
Description
Technical field
The application relates to Computer Applied Technology field, particularly to a kind of mission bit stream sending method and device.
Background technology
Team unity is a kind of means completing task in modern society.The formal group that team unity refers to realize a certain target and is made up of co-operating individuality.If team unity is that it will produce the strength that one is powerful and lasting for time voluntarily.Therefore, select appropriate personnel to form team, and to the personnel in described team push mission bit stream for task complete it is critical that.
Existing mission bit stream sending method generally includes: the incidence relation according to the occupation of candidate user, hobby and candidate user and task promoter, candidate user is divided, according to the similarity between task and the interest of candidate user that user promoter initiates, select mission bit stream recipient, and to described mission bit stream recipient's distributed tasks information.
In realizing the application process, inventor have found that in prior art, at least there are the following problems: according only to the similarity between task and the interest of candidate user that user promoter initiates to choose mission bit stream recipient, screening granularity is thicker, not can determine that whether selected task recipient meets the requirement having cooperated described task with other people, therefore, when adopting prior art to send mission bit stream, corresponding task recipient is likely to be unsatisfactory for mission requirements.
Summary of the invention
The purpose of the embodiment of the present application is to provide a kind of mission bit stream sending method and device, and the recipient to ensure mission bit stream meets mission requirements.
For solving above-mentioned technical problem, the embodiment of the present application provides a kind of mission bit stream sending method and device to be achieved in that
A kind of mission bit stream sending method, including: receive mission bit stream and candidate user information；According to described mission bit stream and candidate user information, calculate the value of relevant parameter corresponding to described candidate user；Candidate user is ranked up by the value according to described relevant parameter, according to the first preset relation, filters out targeted customer；Described mission bit stream is sent to described targeted customer；Described relevant parameter is for representing the incidence relation of described candidate user and described mission bit stream.
In preferred version, relevant parameter value corresponding to described candidate user obtains according to the first parameter and the second parameter.
In preferred version, relevant parameter value corresponding to described candidate user obtains according to the first parameter and the second parameter, specifically includes: relevant parameter value corresponding to described candidate user is multiplied by the second parameter equal to the first parameter.
In preferred version, described first parameter is used for representing whether described candidate user is ready to receive described mission bit stream；When described candidate user is ready to receive described mission bit stream, the value of the first parameter is 1；When described candidate user is unwilling to receive described mission bit stream, the value of the first parameter is 0.
In preferred version, described second parameter is equal to the weighted sum of property parameters, value parameter, behavioral parameters and cognitive parameter.
In preferred version, described property parameters is for representing the personal attribute of described user and the correlation degree of described mission bit stream, and sex in described property parameters user profile, age, educational background and specialty calculate acquisition, obtain especially by following formula calculating:
PersonAttr=a1 × Sex+a2 × age+a3 × degree+a4 × profession
In formula, PersonAttr represents property parameters；Sex represents that the number of the candidate user identical with active user's sex accounts for the ratio of candidate user total number of persons；Age represent with active user belonging to the number of the identical candidate user of age range account for the ratio of candidate user total number of persons；Degree represent with active user belonging to the number of the educational background identical candidate user of grade account for the ratio of candidate user total number of persons；Profession represent with active user belonging to the number of the identical candidate user of career field account for the ratio of candidate user total number of persons；The coefficient weights of a1, a2, a3, a4 respectively sex, age, degree, profession；Described coefficient weights a1, a2, a3, a4 meet formula a1+a2+a3+a4=1.
In preferred version, described value parameter is for representing the correlation degree of described candidate user interest information and described task promoter's interest information and described mission bit stream；Described value parameter value is more big, represents that described correlation degree is more high.
In preferred version, described behavioral parameters obtains according to the cooperation degree of described candidate user, cooperation actively degree and user's score calculation, calculates especially by following formula and obtains:
CoBehaviour=c1 × CoParticipation+c2 × CoPassion+c3 × ScoreAve
In formula, CoBehaviour represents the behavioral parameters of candidate user；CoParticipation represents the cooperation degree of candidate user；CoPassion represents that the cooperation of candidate user is actively spent；ScoreAve represents user's scoring of candidate user；C1, c2 and c3 respectively CoParticipation, CoPassion and ScoreAve weights coefficient.
In preferred version, described cognitive parameter calculates according to object cognition parameter and task cognition parameter and obtains, and specifically, is calculated by following formula and obtains:
CoCognition=d1 × PartnerCognition+ (1-d1) × ConceptCognition
In formula, PartnerCognition represents object cognition parameter；ConceptCognition represents task cognition parameter；The weights coefficient of d1 and (1-d1) respectively PartnerCognition and ConceptCognition.
A kind of mission bit stream dispensing device, including: information receiving module, relevant parameter module, targeted customer's module and sending module；Wherein, described information receiving module, it is used for receiving mission bit stream and candidate user information；Described relevant parameter module, for the described mission bit stream and the candidate user information that receive according to described information receiving module, calculates the value of relevant parameter corresponding to described candidate user；Described targeted customer's module, candidate user is ranked up by the value of the relevant parameter for obtaining according to described relevant parameter module, according to the first preset relation, filters out targeted customer；Described sending module, sends described mission bit stream for the targeted customer filtered out to described targeted customer's module.
The technical scheme provided from above the embodiment of the present application, mission bit stream sending method disclosed in the embodiment of the present application above-described embodiment and dress child, in the process calculating relevant parameter, taken into full account cooperated in task process required user task performance, user marks the factor such as incidence relation of situation, user and task promoter, utilize calculated relevant parameter that candidate user is screened, it is ensured that the targeted customer filtered out and the recipient of mission bit stream meet mission requirements.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present application or technical scheme of the prior art, the accompanying drawing used required in embodiment or description of the prior art will be briefly described below, apparently, the accompanying drawing that the following describes is only some embodiments recorded in the application, for those of ordinary skill in the art, under the premise not paying creative work, it is also possible to obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the flow chart of the application one embodiment of mission bit stream sending method；
Fig. 2 is the module map of the application one embodiment of mission bit stream dispensing device.
Detailed description of the invention
The embodiment of the present application provides a kind of mission bit stream sending method and device.
In order to make those skilled in the art be more fully understood that the technical scheme in the application, below in conjunction with the accompanying drawing in the embodiment of the present application, technical scheme in the embodiment of the present application is clearly and completely described, obviously, described embodiment is only some embodiments of the present application, rather than whole embodiments.Based on the embodiment in the application, the every other embodiment that those of ordinary skill in the art obtain under not making creative work premise, all should belong to the scope of the application protection.
Fig. 1 is the flow chart of the application one embodiment of mission bit stream sending method.As it is shown in figure 1, described mission bit stream sending method may include that
S101: receive mission bit stream and candidate user information.
Receive mission bit stream and candidate user information.Described mission bit stream may include that task theme and required by task number.
Described candidate user information may include that the information such as the incidence relation of user's sex, age, educational background, specialty, interest vector, accepted number of tasks, the general assignment number of propelling movement, completed task quantity, default deadline, actual finish time, user's scoring and task promoter.
S102: according to described mission bit stream and candidate user information, calculate the value of relevant parameter corresponding to described candidate user.
According to described mission bit stream and candidate user information, it is possible to calculate the value of relevant parameter corresponding to described candidate user.
Described relevant parameter can be used to indicate that the incidence relation of described candidate user and described mission bit stream.Each candidate user can a corresponding relevant parameter value.Relevant parameter value corresponding to described candidate user can obtain according to the first parameter and the second parameter, and the relevant parameter value that specifically described candidate user is corresponding can be equal to the first parameter be multiplied by the second parameter.
Described first parameter can be used to indicate that whether described candidate user is ready to receive described mission bit stream.First parameter can value be 0 or 1.When described candidate user is ready to receive described mission bit stream, the value of the first parameter can be 1；When described candidate user is unwilling to receive described mission bit stream, the value of the first parameter can be 0.
Described second parameter can obtain based on property parameters, value parameter, behavioral parameters and cognitive parameter.Described second parameter can be equal to the weighted sum of property parameters, value parameter, behavioral parameters and cognitive parameter.
Described property parameters can be used to indicate that the personal attribute of described user and the correlation degree of described mission bit stream.Described property parameters value is more big, it is possible to represent that described correlation degree is more high.
Described property parameters can calculate acquisition according to the personal attribute in user profile.Described personal attribute may include that sex, the age, academic and professional.Specifically, it is possible to calculated by following formula and obtain:
PersonAttr=a1 × Sex+a2 × age+a3 × degree+a4 × profession (1)
In formula (1), PersonAttr represents property parameters；Sex represents that the number of the candidate user identical with active user's sex accounts for the ratio of candidate user total number of persons；Age represent with active user belonging to the number of the identical candidate user of age range account for the ratio of candidate user total number of persons；Degree represent with active user belonging to the number of the educational background identical candidate user of grade account for the ratio of candidate user total number of persons；Profession represent with active user belonging to the number of the identical candidate user of career field account for the ratio of candidate user total number of persons.The coefficient weights of a1, a2, a3, a4 respectively sex, age, degree, profession；Described coefficient weights a1, a2, a3, a4 meet formula a1+a2+a3+a4=1.
Wherein, described age range, educational background grade, career field can be configured in advance.
Such as, described age range can be preset as 8 intervals, may include that respectively
Interval 1:20 year below；Interval 2:20-25 year；Interval 3:26-30 year；Interval 4:31-35 year；Interval 5:36-40 year；Interval 6:41-45 year；Interval 7:46-50 year；Interval 8:50 year more than.
Described educational background grade can be preset as 4, may include that respectively below senior middle school, junior college, undergraduate course and master and more than.
Described career field can be preset as 12 classes, and distribution may include that philosophy, economics, the science of law, pedagogy, literature, history, Neo-Confucianism, engineering, agronomy, medical science, management and Arts.
Described coefficient weights a1, a2, a3, a4 can be obtained by linear regression algorithm in machine learning algorithm.
Described linear regression algorithm can be expressed as:
Y=A X (2)
When asking for described coefficient weights a1, a2, a3, a4, it is possible to make X=< Sex, Age, Degree, Profession >T, Y=sigmoid (CompliTaskCount × 2)-0.5, X, Y are brought into respectively in formula (2), the A tried to achieve is the value of coefficient weights a1, a2, a3, a4.Wherein, CompliTaskCount represents the completed task quantity of active user；Sigmoid represents the value in bracket is normalized；Described Y ranges for :-0.5～0.5.
Linear regression algorithm in described machine learning algorithm is the conventional Calculation Method of this area, and concrete calculating in process the application repeats no more.
Linear regression algorithm in the application may be replaced by this area other machines learning algorithm, it is possible to reaches to obtain the effect of the value of accurate A, and this is not made restriction by the application.
Described value parameter can be used to indicate that the correlation degree of described candidate user interest information and described task promoter's interest information and described mission bit stream.Described value parameter value is more big, it is possible to represent that described correlation degree is more high.
Described value parameter can be passed through the calculating of following formula and obtain:
CoValues=b1 × SimPartner+ (1-b1) × SimItem (3)
In formula (3), CoValues represents value parameter；SimPartner represents the correlation degree of described candidate user interest information and task promoter's interest information, and span is 0～1；SimItem represents the correlation degree of described candidate user interest information and mission bit stream, and span is 0～1.
The value of described SimPartner can obtain based on the interest vector of the interest vector of described candidate user and described task promoter.The value of described SimItem can obtain based on the interest vector of the interest vector of described candidate user and described mission bit stream.The weights coefficient of b1 and (1-b1) respectively SimPartner and SimItem.
In advance interest can be divided into 15 classifications, including: military politics, star's Eight Diagrams, film game, sports lottery ticket, Technology Digital, education job hunting, culture and arts, fashion tourism, society governed by law, finance and economics stock, healthy food, emotion life, house and family property, history archaeology and public good religion.
Described interest vector can be used to indicate that the interest weight to above-mentioned category of interest.For example, it is possible to vectorial Represent a certain user or interest information corresponding to mission bit stream.Wherein, each vector element can be used to indicate that the weighted value of a category of interest, and span is 0～1.Such as, vector element interest1 can be used to indicate that a certain user or the mission bit stream interest weighted value in military this classification of politics.
The defining method of the interest vector that described mission bit stream is corresponding may include that sets up characteristic vector to described mission bit stream, calculates the described characteristic vector probit at above-mentioned default interest vector element.
For example, it is possible to mission bit stream to be carried out Chinese word segmentation, and extract key word therein, characteristic vector T=<W1, W2, W3 can be set up according to the key word of described extraction ... Wn>；Naive Bayesian file classification method is adopted to calculate the interest vector P=<P1, P2, P3 that described characteristic vector is corresponding ..., P15>.Specifically, it is possible to adopt following formula to calculate and obtain:
P (Cj | T)=P (Cj) * ∏ P (Wi | Cj) (4)
Wherein,
I ranges for 1～n；J ranges for 1～15；N (Wi ∈ Cj) represents the quantity comprising mission bit stream characteristic key words in category of interest Cj；N (T ∈ Cj) represents the quantity of all key words corresponding for category of interest Cj.
For a candidate user or mission bit stream promoter, it is possible to calculate the interest vector value that each task received corresponding to described user is corresponding, the interest vector value that described task is corresponding is overlapped, it is possible to obtain the interest vector value that described user is corresponding.
In another embodiment, it is also possible to the interest vector value that described user is corresponding is normalized computing, the value after normalization can facilitate the data that user carries out next step to process.
After trying to achieve the interest vector of described candidate user, the interest vector of task promoter and the interest vector of mission bit stream, it is possible to try to achieve the value of described SimPartner and the value of described SimItem.The value of described SimPartner can be equal to the cosine similarity of the interest vector of described candidate user and the interest vector of described task promoter, as shown in formula (5).The value of described SimItem can be equal to the cosine similarity of the interest vector of described candidate user and the interest vector of described mission bit stream, as shown in formula (6).
In formula (5) and (6),The interest vector of expression task promoter；Represent the interest vector of candidate user；PItem represents the interest vector of mission bit stream.
Described coefficient weights b1 can utilize the linear regression algorithm described in formula (2) to calculate and obtain.Specifically, it is possible to make X=< SimPartner, SimItem >T, Y=ScoreAve, X, Y are brought into respectively in formula (2), the A tried to achieve is the value of coefficient weights b1 and (1-b1).Wherein, ScoreAve represents the user's scoring in user profile.
Described behavioral parameters can be used to indicate that described candidate user completes the qualitative factor of task.The behavioral parameters of described candidate user can obtain according to the cooperation degree of described candidate user, cooperation actively degree and user's score calculation.Described behavioral parameters value is more big, it is possible to represent that described candidate user completes the quality of task more high.
Described behavioral parameters can pass through following formula and calculate acquisition:
CoBehaviour=c1 × CoParticipation+c2 × CoPassion+c3 × ScoreAve (7)
In formula (7), CoBehaviour represents the behavioral parameters of candidate user；CoParticipation represents the cooperation degree of candidate user；CoPassion represents that the cooperation of candidate user is actively spent；ScoreAve represents user's scoring of candidate user.C1, c2 and c3 respectively CoParticipation, CoPassion and ScoreAve weights coefficient.
The cooperation degree of described candidate user can account for the ratio of the total task number being pushed to described user equal to the number of tasks that user has received.
The cooperation of described candidate user is actively spent and can have been completed, equal to described candidate user, the meansigma methods that the task of task is actively spent.Specifically, it is possible to represent with following formula:
In formula (8), n represents the completed task quantity of candidate user.Passion (f) represents that the task of f the task that user completes actively is spent.It should be noted that when the result of calculation of formula (8) is less than 0, it is possible to make the value that the cooperation of described candidate user is actively spent equal to 0.
In formula (8), Passion (f)=(SetTimeForSingle-RealTimeForSingle)/SetTimeForSingle；
Wherein, SetTimeForSingle represents the deadline that the f task is preset；RealTimeForSingle represents the deadline that the f task is actual.
User's scoring of described candidate user can obtain according to the scoring of other candidate user existing.For example, it is possible to the scoring of other candidate user to be averaging the value that the user as described candidate user marks.
In another embodiment, it is also possible to the value that described user is marked is normalized.
Described weights coefficient c1, c2, c3 can utilize the linear regression algorithm described in formula (2) to calculate and obtain.Specifically, it is possible to make X=< CoParticipation, CoPassion, ScoreAve >T, Y=ScoreAve, X, Y are brought into respectively in formula (2), the A tried to achieve is the value of coefficient weights c1, c2 and c3.Wherein, ScoreAve represents the user's scoring in user profile.
Described cognitive parameter can be used to indicate that the social incidence relation of described candidate user and described task promoter.
Described cognitive parameter can include object cognition parameter and task cognition parameter.Wherein said object cognition parameter can be used to indicate that the intimate degree between described candidate user and described task promoter.Described task cognition parameter can be used to indicate that the performance of user's current task quantity.Specifically, described cognitive parameter can pass through the calculating acquisition of following formula:
CoCognition=d1 × PartnerCognition+ (1-d1) × ConceptCognition (9)
In formula (9), PartnerCognition represents object cognition parameter；ConceptCognition represents task cognition parameter.The weights coefficient of d1 and (1-d1) respectively PartnerCognition and ConceptCognition.
Described object cognition parameter can calculate with following formula and obtain:
In formula (10), PartnerCognition represents object cognition parameter；IsFri is Boolean, if described task promoter and described candidate user exist one-level social activity incidence relation, then IsFri=1, otherwise Isfri=0；DescribedThe Jaccard likeness coefficient of expression task promoter and candidate user, described ComFriCount is task promoter friend quantity identical with candidate user, and AllFriCount is friend's quantity of task promoter；ShortPath represents the shortest friend path when task promoter and candidate user do not have a common friends.Described one-level social activity incidence relation can represent that 2 users are for friends.If A and B is friend, B and C is friend, and C and D is friend, and other do not have any relation between the two, then the shortest friend path of A and D is 3.
Described task cognition parameter can be equal to the completed number of tasks of described candidate user and account for the ratio of the number of tasks that described candidate user has received.
Described coefficient weights d1 can utilize the linear regression algorithm described in formula (2) to calculate and obtain.Specifically, it is possible to make X=< PartnerCognition, ConceptCognition >T, Y=ScoreAve, X, Y are brought into respectively in formula (2), the A tried to achieve is the value of coefficient weights d1 and (1-d1).Wherein, ScoreAve represents the user's scoring in user profile.
Calculated by said method after obtaining property parameters corresponding to each user, value parameter, behavioral parameters and cognitive parameter, it is possible to calculate and obtain the second parameter.Specifically, it is possible to calculated by formula (11) and obtain:
Second parameter=PersonAttr × α+CoValue × β+CoBehaviour × γ+CoCognition × δ (11)
Coefficient weights α in formula (11), β, γ and δ can utilize the linear regression algorithm described in formula (2) to calculate and obtain.Specifically, it is possible to make X=< PersonAttr, CoValue, CoBehaviour, CoCognition >T, Y=ScoreAve, X, Y are brought into respectively in formula (2), the A tried to achieve is the value of coefficient weights α, β, γ and δ.Wherein, ScoreAve represents the user's scoring in user profile.
Value according to the first parameter can calculate, with the value of the second parameter, the value obtaining relevant parameter corresponding to described candidate user.
Taken into full account by the calculated relevant parameter of this step cooperated in task process required user task performance, user marks the factor such as incidence relation of situation, user and task promoter, when making to screen according to calculated relevant parameter, the selection result more meets mission requirements.
S103: candidate user is ranked up according to the value of described relevant parameter, according to the first preset relation, filters out targeted customer.
Described candidate user is ranked up including by the described value according to relevant parameter: according to the value of described relevant parameter order from big to small, described candidate user is ranked up；Or, according to the value of described relevant parameter order from small to large, described candidate user is ranked up.
Described filter out targeted customer according to the first preset relation, including filtering out the targeted customer meeting following at least one situation in described candidate user:
Candidate user after sequence is selected the top n targeted customer that described relevant parameter value is bigger；Described N is less than or equal to the total number of persons of described candidate user, and described N is more than or equal to required by task number；
Candidate user after sequence is selected p% the targeted customer that described relevant parameter value is bigger；Described p is more than or equal to the ratio of required by task number Yu described number of candidates, and described p is less than or equal to 100.
S104: send described mission bit stream to described targeted customer.
Described mission bit stream can be sent to described targeted customer.The mode sent may include that the mode such as mail, information, and this is not made restriction by the application.
Mission bit stream sending method disclosed in above-described embodiment, in the process of middle calculating relevant parameter, taken into full account cooperated in task process required user task performance, user marks the factor such as incidence relation of situation, user and task promoter, utilize calculated relevant parameter that candidate user is screened, it is ensured that the targeted customer filtered out and the recipient of mission bit stream meet mission requirements.
The application one mission bit stream dispensing device embodiment is described below.Fig. 2 is the module map of the application one embodiment of mission bit stream dispensing device.As in figure 2 it is shown, described mission bit stream dispensing device may include that information receiving module 201, relevant parameter module 202, targeted customer's module 203 and sending module 204.Wherein,
Described information receiving module 201, it is possible to be used for receiving mission bit stream candidate user information.
Described relevant parameter module 202, it is possible to for the described mission bit stream and the candidate user information that receive according to described information receiving module 201, calculate the value of relevant parameter corresponding to described candidate user.
Described targeted customer's module 203, it is possible to candidate user is ranked up by the value of the relevant parameter for obtaining according to described relevant parameter module 202, according to the first preset relation, filters out targeted customer.
Described sending module 204, it is possible to the targeted customer for filtering out to described targeted customer's module 203 sends described mission bit stream.
A kind of mission bit stream dispensing device disclosed in above-described embodiment is corresponding with the mission bit stream sending method embodiment of the application, it is possible to achieve the present processes embodiment, reaches the technique effect of the application mission bit stream sending method embodiment.
In the nineties in 20th century, can clearly distinguish for the improvement of a technology is improvement (such as, the improvement to circuit structures such as diode, transistor, switches) on hardware or the improvement (improvement for method flow) on software.But, along with the development of technology, the improvement of current a lot of method flows can be considered as directly improving of hardware circuit.Designer is nearly all by being programmed in hardware circuit by the method flow of improvement and obtaining corresponding hardware circuit.Therefore, it cannot be said that the improvement of a method flow cannot realize by hardware entities module.Such as, PLD (ProgrammableLogicDevice, PLD) (such as field programmable gate array (FieldProgrammableGateArray, FPGA)) thus a kind of integrated circuit, device programming is determined by its logic function by user.Programmed voluntarily a digital display circuit " integrated " on a piece of PLD by designer, without chip maker designing and make special IC chip 2.nullAnd，Nowadays，Replace and manually make IC chip，This programming is also mostly used " logic compiler (logiccompiler) " software instead and is realized，Its software compiler used when writing with program development is similar，And the also handy specific programming language of the source code before compiling is write，This is referred to as hardware description language (HardwareDescriptionLanguage，HDL)，And HDL also not only has one，But have many kinds，Such as ABEL (AdvancedBooleanExpressionLanguage)、AHDL(AlteraHardwareDescriptionLanguage)、Confluence、CUPL(CornellUniversityProgrammingLanguage)、HDCal、JHDL(JavaHardwareDescriptionLanguage)、Lava、Lola、MyHDL、PALASM、RHDL (RubyHardwareDescriptionLanguage) etc.，That commonly use most at present is VHDL (Very-High-SpeedIntegratedCircuitHardwareDescriptionLangu age) and Verilog2.Those skilled in the art also it should also be apparent that, it is only necessary to method flow slightly made programming in logic with above-mentioned several hardware description languages and be programmed in integrated circuit, it is possible to being readily available the hardware circuit realizing this logical method flow process.
nullController can be implemented in any suitable manner，Such as，Controller can be taked such as microprocessor or processor and store the computer-readable medium of the computer readable program code (such as software or firmware) that can be performed by this (micro-) processor、Gate、Switch、Special IC (ApplicationSpecificIntegratedCircuit，ASIC)、The form of programmable logic controller (PLC) and embedding microcontroller，The example of controller includes but not limited to following microcontroller: ARC625D、AtmelAT91SAM、MicrochipPIC18F26K20 and SiliconeLabsC8051F320，Memory Controller is also implemented as a part for the control logic of memorizer.
Those skilled in the art it is also known that, except realizing controller in pure computer readable program code mode, controller can be made to realize identical function with the form of gate, switch, special IC, programmable logic controller (PLC) and embedding microcontroller etc. by method step carries out programming in logic completely.Therefore this controller is considered a kind of hardware component, and the device for realizing various function included in it can also be considered as the structure in hardware component.Or even, it is possible to be considered as not only can being realize the software module of method but also can be the structure in hardware component by the device being used for realizing various function.
System, device, module or the unit that above-described embodiment illustrates, specifically can be realized by computer chip or entity, or be realized by the product with certain function.
For convenience of description, it is divided into various unit to be respectively described with function when describing apparatus above.Certainly, the function of each unit can be realized in same or multiple softwares and/or hardware when implementing the application.
As seen through the above description of the embodiments, those skilled in the art is it can be understood that can add the mode of required general hardware platform by software to the application and realize.Based on such understanding, the part that prior art is contributed by the technical scheme of the application substantially in other words can embody with the form of software product, in a typical configuration, computing equipment includes one or more processor (CPU), input/output interface, network interface and internal memory.This computer software product can include some instructions with so that computer equipment (can be personal computer, server, or the network equipment etc.) performs the method described in some part of each embodiment of the application or embodiment.This computer software product can be stored in internal memory, internal memory potentially includes the volatile memory in computer-readable medium, the forms such as random access memory (RAM) and/or Nonvolatile memory, such as read only memory (ROM) or flash memory (flashRAM).Internal memory is the example of computer-readable medium.Computer-readable medium includes permanent and impermanency, removable and non-removable media can by any method or technology to realize information storage.Information can be computer-readable instruction, data structure, the module of program or other data.The example of the storage medium of computer includes, but it is not limited to phase transition internal memory (PRAM), static RAM (SRAM), dynamic random access memory (DRAM), other kinds of random access memory (RAM), read only memory (ROM), Electrically Erasable Read Only Memory (EEPROM), fast flash memory bank or other memory techniques, read-only optical disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tape, the storage of tape magnetic rigid disk or other magnetic storage apparatus or any other non-transmission medium, can be used for the information that storage can be accessed by a computing device.According to defining herein, computer-readable medium does not include of short duration computer readable media (transitorymedia), such as data signal and the carrier wave of modulation.
Each embodiment in this specification all adopts the mode gone forward one by one to describe, between each embodiment identical similar part mutually referring to, what each embodiment stressed is the difference with other embodiments.Especially for system embodiment, owing to it is substantially similar to embodiment of the method, so what describe is fairly simple, relevant part illustrates referring to the part of embodiment of the method.
The application can be used in numerous general or special purpose computing system environments or configuration.Such as: personal computer, server computer, handheld device or portable set, laptop device, multicomputer system, based on the system of microprocessor, set top box, programmable consumer-elcetronics devices, network PC, minicomputer, mainframe computer, the distributed computing environment including any of the above system or equipment etc..
The application can described in the general context of computer executable instructions, for instance program module.Usually, program module includes performing particular task or realizing the routine of particular abstract data type, program, object, assembly, data structure etc..The application can also be put into practice in a distributed computing environment, in these distributed computing environment, the remote processing devices connected by communication network perform task.In a distributed computing environment, program module may be located in the local and remote computer-readable storage medium including storage device.
Although depicting the application by embodiment, it will be appreciated by the skilled addressee that the application has many deformation and is varied without departing from spirit herein, it is desirable to appended claim includes these deformation and is varied without departing from spirit herein.
Claims (10)
1. a mission bit stream sending method, it is characterised in that including:
Receive mission bit stream and candidate user information；
According to described mission bit stream and candidate user information, calculate the value of relevant parameter corresponding to described candidate user；
Candidate user is ranked up by the value according to described relevant parameter, according to the first preset relation, filters out targeted customer；
Described mission bit stream is sent to described targeted customer；
Described relevant parameter is for representing the incidence relation of described candidate user and described mission bit stream.
2. mission bit stream sending method as claimed in claim 1 a kind of, it is characterised in that relevant parameter value corresponding to described candidate user obtains according to the first parameter and the second parameter.
3. a kind of mission bit stream sending method as claimed in claim 1, it is characterized in that, relevant parameter value corresponding to described candidate user obtains according to the first parameter and the second parameter, specifically includes: relevant parameter value corresponding to described candidate user is multiplied by the second parameter equal to the first parameter.
4. a kind of mission bit stream sending method as claimed in claim 2, it is characterised in that described first parameter is used for representing whether described candidate user is ready to receive described mission bit stream；
When described candidate user is ready to receive described mission bit stream, the value of the first parameter is 1；When described candidate user is unwilling to receive described mission bit stream, the value of the first parameter is 0.
5. a kind of mission bit stream sending method as claimed in claim 2, it is characterised in that described second parameter is equal to the weighted sum of property parameters, value parameter, behavioral parameters and cognitive parameter.
6. a kind of mission bit stream sending method as claimed in claim 5, it is characterized in that, described property parameters is for representing the personal attribute of described user and the correlation degree of described mission bit stream, sex in described property parameters user profile, age, educational background and specialty calculate and obtain, and calculate especially by following formula and obtain:
PersonAttr=a1 × Sex+a2 × age+a3 × degree+a4 × profession
In formula, PersonAttr represents property parameters；Sex represents that the number of the candidate user identical with active user's sex accounts for the ratio of candidate user total number of persons；Age represent with active user belonging to the number of the identical candidate user of age range account for the ratio of candidate user total number of persons；Degree represent with active user belonging to the number of the educational background identical candidate user of grade account for the ratio of candidate user total number of persons；Profession represent with active user belonging to the number of the identical candidate user of career field account for the ratio of candidate user total number of persons；The coefficient weights of a1, a2, a3, a4 respectively sex, age, degree, profession；Described coefficient weights a1, a2, a3, a4 meet formula a1+a2+a3+a4=1.
7. a kind of mission bit stream sending method as claimed in claim 5, it is characterised in that described value parameter is for representing the correlation degree of described candidate user interest information and described task promoter's interest information and described mission bit stream；Described value parameter value is more big, represents that described correlation degree is more high.
8. a kind of mission bit stream sending method as claimed in claim 5, it is characterised in that described behavioral parameters obtains according to the cooperation degree of described candidate user, cooperation actively degree and user's score calculation, calculates especially by following formula and obtains:
CoBehaviour=c1 × CoParticipation+c2 × CoPassion+c3 × ScoreAve
In formula, CoBehaviour represents the behavioral parameters of candidate user；CoParticipation represents the cooperation degree of candidate user；CoPassion represents that the cooperation of candidate user is actively spent；ScoreAve represents user's scoring of candidate user；C1, c2 and c3 respectively CoParticipation, CoPassion and ScoreAve weights coefficient.
9. a kind of mission bit stream sending method as claimed in claim 5, it is characterised in that described cognitive parameter calculates according to object cognition parameter and task cognition parameter and obtains, specifically, is calculated by following formula and obtains:
CoCognition=d1 × PartnerCognition+ (1-d1) × ConceptCognition
In formula, PartnerCognition represents object cognition parameter；ConceptCognition represents task cognition parameter；The weights coefficient of d1 and (1-d1) respectively PartnerCognition and ConceptCognition.
10. a mission bit stream dispensing device, it is characterised in that including: information receiving module, relevant parameter module, targeted customer's module and sending module；Wherein,
Described information receiving module, is used for receiving mission bit stream and candidate user information；
Described relevant parameter module, for the described mission bit stream and the candidate user information that receive according to described information receiving module, calculates the value of relevant parameter corresponding to described candidate user；
Described targeted customer's module, candidate user is ranked up by the value of the relevant parameter for obtaining according to described relevant parameter module, according to the first preset relation, filters out targeted customer；
Described sending module, sends described mission bit stream for the targeted customer filtered out to described targeted customer's module.
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