CN110533186B - Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system - Google Patents
Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system Download PDFInfo
- Publication number
- CN110533186B CN110533186B CN201910831574.6A CN201910831574A CN110533186B CN 110533186 B CN110533186 B CN 110533186B CN 201910831574 A CN201910831574 A CN 201910831574A CN 110533186 B CN110533186 B CN 110533186B
- Authority
- CN
- China
- Prior art keywords
- crowdsourcing
- pricing
- pricing system
- parameters
- original
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
Abstract
The invention discloses an evaluation method, a device, equipment and a readable storage medium of a crowdsourcing pricing system, comprising the following steps: extracting original system parameters from a crowdsourcing pricing system to be evaluated, and establishing an initial group set according to the original system parameters; data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained; determining a corresponding fitness function value according to the new population set; and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value. By the method, the crowdsourcing price of the original user can be adjusted, the crowdsourcing price is given to the new user point, and the parameters of the crowdsourcing pricing system can be optimized according to the crowdsourcing data completed in real time, so that the task completion rate is greatly improved.
Description
Technical Field
The present invention relates to the field of evaluation of crowdsourcing pricing systems, and in particular, to a method, apparatus, device and readable storage medium for evaluating a crowdsourcing pricing system.
Background
Crowd sourcing refers to the practice of a company or organization to outsource work tasks performed by employees in the past to unspecified (and often large) mass volunteers in a free voluntary fashion. Crowd-sourced tasks are typically undertaken by individuals and, if related to tasks requiring multi-person collaboration, may also occur in the form of individual productions relying on open sources.
At present, a data analysis method based on a statistical idea is mostly adopted for the evaluation of a crowdsourcing pricing system, and the method can find out a crowdsourcing pricing rule and find out a pricing influence factor through price and implemented information analysis of a crowdsourcing process. The method is also suitable for various fields of current society economy, science and technology and culture. However, the data analysis method based on the statistical idea cannot provide an operable price system, the task completion rate is low, crowdsourcing prices cannot be provided for new user points, and the use is inconvenient.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide an evaluation method, an evaluation device and an evaluation device of a crowdsourcing pricing system and a readable storage medium, and aims to solve the technical problems of high cost and long period in the prior art.
To achieve the above object, the present invention provides a method for evaluating a crowdsourcing pricing system, the method comprising the steps of:
extracting original system parameters from a crowdsourcing pricing system to be evaluated, and establishing an initial group set according to the original system parameters;
data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained;
determining a corresponding fitness function value according to the new population set;
and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value.
Preferably, the method for evaluating the crowdsourcing pricing system further includes, before the data optimization is performed on the initial population set through a genetic algorithm to obtain a new population set:
acquiring a corresponding initial fitness function value according to the original system parameters;
acquiring selection probability according to the initial fitness function value;
the calculating the initial group set through a genetic algorithm and acquiring new parameters specifically comprises the following steps:
and carrying out data optimization on the initial population set through a genetic algorithm according to the preset crossover probability, the preset variation probability and the selection probability to obtain a new population set.
Preferably, the data optimization is performed on the initial population set according to a preset crossover probability, a preset mutation probability and the selection probability through a genetic algorithm, so as to obtain a new population set, which specifically includes:
according to the preset crossover probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain crossover parameters;
according to the preset variation probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain variation parameters;
according to the selection probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain new parameters;
combining the crossover parameter, the mutation parameter and the new parameter into a group set, and taking the group set as a new group set.
Preferably, the data optimization is performed on the initial population set through the genetic algorithm according to the preset crossover probability, so as to obtain crossover parameters, which specifically includes:
extracting the original system parameters from the initial population set according to the genetic algorithm, and performing cross operation on the original system parameters according to the preset cross probability to obtain cross parameters.
Preferably, according to the preset mutation probability, the data optimization is performed on the initial population set through the genetic algorithm, so as to obtain mutation parameters, which specifically includes:
extracting the original system parameters from the initial population set according to the genetic algorithm, and performing mutation operation on the original system parameters according to the preset mutation probability to obtain mutation parameters.
Preferably, the data optimization is performed on the initial population set through the genetic algorithm according to the selection probability, so as to obtain new parameters, which specifically includes:
and extracting the original system parameters from the initial population set through the genetic algorithm according to the selection probability, and taking the extracted original system parameters as new parameters.
Preferably, the evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value specifically includes:
and comparing the fitness function value with the initial fitness function value, and evaluating the crowdsourcing pricing system to be evaluated according to a comparison result.
In addition, to achieve the above object, the present invention further proposes an evaluation device of a crowdsourcing pricing system, the device comprising:
the acquisition module is used for extracting original system parameters from the crowdsourcing pricing system to be evaluated and establishing an initial group set according to the original system parameters;
the optimization module is used for carrying out data optimization on the initial population set through a genetic algorithm to obtain a new population set;
the calculation module is used for determining a corresponding fitness function value according to the new group set;
and the evaluation module is used for evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value.
In addition, to achieve the above object, the present invention also proposes an evaluation device of a crowdsourcing pricing system, the device comprising: a memory, a processor, and an evaluation program of a crowdsourcing pricing system stored on the memory and executable on the processor, the evaluation program of the crowdsourcing pricing system configured to implement the steps of the evaluation method of the crowdsourcing pricing system as described above.
In addition, in order to achieve the above object, the present invention also proposes a readable storage medium, which is a computer readable storage medium, on which an evaluation program of a crowdsourcing pricing system is stored, the evaluation program of the crowdsourcing pricing system implementing the steps of the evaluation method of the crowdsourcing pricing system when being executed by a processor.
The invention discloses an evaluation method, a device, equipment and a readable storage medium of a crowdsourcing pricing system, comprising the following steps: extracting original system parameters from a crowdsourcing pricing system to be evaluated, and establishing an initial group set according to the original system parameters; data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained; determining a corresponding fitness function value according to the new population set; and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value. By the method, the crowdsourcing price of the original user can be adjusted, the crowdsourcing price is given to the new user point, and the parameters of the crowdsourcing pricing system can be optimized according to the crowdsourcing data completed in real time, so that the task completion rate is greatly improved.
Drawings
FIG. 1 is a schematic diagram of an evaluation device of a crowdsourcing pricing system of a hardware runtime environment, in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of an evaluation method of the crowdsourcing pricing system of the present invention;
FIG. 3 is a flow chart of a second embodiment of an evaluation method of the crowdsourcing pricing system of the present invention;
fig. 4 is a functional block diagram of a first embodiment of an evaluation method of the crowdsourcing pricing system of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an evaluation device structure of a crowdsourcing pricing system of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the evaluation device of the crowdsourcing pricing system may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 does not constitute a limitation of the evaluation device of the crowdsourcing pricing system, and may include more or fewer components than illustrated, or may combine certain components, or may be a different arrangement of components.
As shown in FIG. 1, an operating system, a data storage module, a network communication module, a user interface module, and an evaluation program of a crowdsourcing pricing hierarchy may be included in memory 1005 as one type of storage medium.
In the evaluation device of the crowdsourcing pricing system shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the evaluation device of the crowdsourcing pricing system of the present invention may be disposed in the evaluation device of the crowdsourcing pricing system, where the evaluation device of the crowdsourcing pricing system invokes the evaluation program of the crowdsourcing pricing system stored in the memory 1005 through the processor 1001, and executes the evaluation method of the crowdsourcing pricing system provided by the embodiment of the present invention.
The embodiment of the invention provides an evaluation method of a crowdsourcing pricing system, and referring to fig. 2, fig. 2 is a flow chart of a first embodiment of the evaluation method of the crowdsourcing pricing system.
In this embodiment, the method for evaluating the crowdsourcing pricing system includes the following steps:
step S10: extracting original system parameters from a crowdsourcing pricing system to be evaluated, and establishing an initial group set according to the original system parameters.
It is appreciated that the raw system parameters extracted from the under-evaluation crowdsourcing pricing system refer to multi-dimensional factors that have an impact on task pricing, including: price, number of participants, total price of area, etc., and the initial population set is established by randomly extracting the original system parameters.
Step S20: and carrying out data optimization on the initial group set through a genetic algorithm to obtain a new group set.
It can be understood that the genetic algorithm is a computational model of a biological evolution process simulating natural selection and genetic mechanism of the darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. Genetic algorithms start with a population representing a solution that may potentially be of a problem, where a population is composed of a number of individuals that are genetically encoded. Each individual is actually an entity whose chromosome carries a characteristic. The chromosome serves as the main vector of genetic material, i.e., a collection of genes whose internal appearance (i.e., genotype) is a combination of genes that determines the external appearance of the shape of the individual, as the characteristics of black hair are determined by a combination of genes that control this characteristic in the chromosome. Thus, it is necessary to achieve mapping from phenotype to genotype, i.e. coding work, at the beginning. Because the work of imitating gene coding is complex, we tend to simplify, such as binary coding, after the generation of the first generation population, the evolution from generation to generation generates better and better approximate solutions according to the principles of survival and superior and inferior of the fittest, in each generation, individuals are selected according to the fitness of individuals in the problem domain, and combination crossover and mutation are carried out by means of genetic operators of natural genetics, so as to generate the population representing the new solution set. This process will result in the offspring population of the population as if it had evolved naturally being more environmentally friendly than the previous generation, and the optimal individuals in the last population will be decoded and can be used as an approximate optimal solution to the problem.
In addition, it should be understood that in practical application, the genetic algorithm performs data optimization on the initial population set through operations such as crossover, mutation, replication and the like, so as to obtain a new population set. In this embodiment, the genetic algorithm performs data optimization on the initial population set through operations such as crossover, mutation, replication, and the like, so as to obtain a new population set.
Step S30: and determining a corresponding fitness function value according to the new population set.
In the genetic algorithm, the fitness function value is a main index for describing the performance of the individual, and the individual is subjected to the superior and inferior elimination according to the size of the fitness function value. The fitness function value is the motive force driving the genetic algorithm. From a biological perspective, fitness corresponds to the viability of "competing for survival, surviving the fittest" organism, and is of great importance in genetic processes. And establishing a mapping relation between the objective function of the optimization problem and the fitness of the individual, and optimizing the objective function of the optimization problem in the group evolution process. The fitness function, also called the evaluation function, is a criterion for distinguishing between the quality of an individual in a population, determined from an objective function, which is always non-negative, the larger its value is in any case desired to be the better.
Furthermore, it should be understood that, in this embodiment, the fitness function value is used to evaluate the crowdsourcing pricing system to be evaluated, where a higher fitness function value indicates that the better the crowdsourcing pricing system to be evaluated is evaluated, and a higher fitness function value indicates that, for a system parameter, the higher the fitness of the system parameter to the crowdsourcing pricing system to be evaluated is, the more advantageous the crowdsourcing pricing system to be evaluated is evaluated.
Step S40: and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value.
And comparing the fitness function value with the initial fitness function value, and evaluating the crowdsourcing pricing system to be evaluated according to a comparison result.
In order to better understand the evaluation method of the crowdsourcing pricing system provided by the invention, the following is specifically described:
step one, randomly establishing an initial population S= { x of n individuals 1 ,x 2 ,...,x n }；
Step two, prescribing a pricing system function
m is the size of the pricing scale in the original pricing system, x r ＝{a r ,b r ,...,d r }，a r ,b r ,...,d r Multidimensional factors influencing task pricing; />
Step three, definition
Pricing in an original pricing system, wherein k is an adjustable parameter; calculating fitness function value of each individual>
Wherein->
Step four, setting the crossover probability p c =50%, variation probability p m =0.001 and selection probability p r The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of
Step five, genetic probability is carried out to generate a new population:
1. crossing: selecting two individuals x by roulette selection operation i 、x j Individual x i And x j Respectively corresponding multidimensional factor { a i ,b i ,...,d i }、{a j ,b j ,...,d j Conversion into binary and coding according to the crossover probability p c The two individuals are placed into a new group S * In (a) and (b);
2. variation: roulette selection by roulette wheelSelecting operation method in new population S * Selecting a body
According to the mutation probability p m Performing mutation operation, if new individuals are generated, then adding the new individuals into a new population S * If not, carrying out the next step;
3. replication: copying elite individuals to New population S by roulette selection operation * If the population size is satisfied, the population s=s is updated * ；
Step six, if an individual with the fitness value larger than 0.9 appears, terminating the algorithm, and taking the algorithm as a multidimensional parameter of a pricing system; otherwise, go to step three.
It should be understood that the invention realizes the evaluation of the crowdsourcing pricing system by establishing an innovative pricing model, and the establishment process of the innovative pricing model is as follows:
establishing an objective function
The constraint conditions are as follows:
in the process of establishing the model, max F is a representative objective function, namely a value output after the crowdsourcing pricing system is evaluated,
representing pricing in the original pricing system, k is a regulatable parameter, (t 1 ,t 2 ,L,t m ) The parameters represent influence factors in the original pricing system, m is the size of the pricing scale in the original pricing system, x= (a, b, L, d) represents dimension factors influencing task pricing, and F is more than or equal to 0.9 represents the expected value to be reached by the objective function at the end of model establishment.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
Through the above description, it is easy to find that, in this embodiment, an original system parameter is extracted from a crowdsourcing pricing system to be evaluated, and an initial group set is established according to the original system parameter; data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained; determining a corresponding fitness function value according to the new population set; and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value. By the method, the crowdsourcing price of the original user can be adjusted, the crowdsourcing price is given to the new user point, and the parameters of the crowdsourcing pricing system can be optimized according to the crowdsourcing data completed in real time, so that the task completion rate is greatly improved.
Referring to fig. 3, fig. 3 is a flow chart illustrating a second embodiment of an evaluation method of the crowdsourcing pricing system of the present invention.
Based on the above first embodiment, the method for evaluating a crowdsourcing pricing system according to the present embodiment further includes, after the step S20:
step S301, performing data optimization on the initial population set by using the genetic algorithm according to the preset crossover probability, so as to obtain crossover parameters.
It will be appreciated that the preset crossover probability is a probability preset by the user or the system for crossover operation of the system parameters, and crossover operation is to exchange part of the chromosome between two individuals with a certain probability, and in actual operation, part of the code may be exchanged.
The operation process in practical application can be as follows:
firstly, randomly pairing groups;
secondly, randomly setting the positions of the crossing points;
part of genes between the mutually exchanged paired chromosomes after renting;
for example, 4 individuals numbered 1-4 need to perform crossing operations at present, "01|1101, 11|1001, 1010|11, 1110|01", wherein "|" represents the position of the crossing point, the pairing condition is that the number 1 individual is paired with the number 2 individual, the number 3 individual is paired with the number 4 individual, the last obtained crossing result is that the number 1 individual is 011001,2 individual 111101,3 individual 101001,4 individual 111011, if the number of "1" represents the high or low fitness, the more fitness is the number, the fitness of the number 2 individual and the number 4 individual generated after the crossing operation can be seen to be higher than the original individual.
Step S302, according to the preset mutation probability, data optimization is carried out on the initial population set through the genetic algorithm, and mutation parameters are obtained.
It will be appreciated that the mutation probability, as well as the crossover probability, is a probability predetermined by the user or the system for performing a mutation operation on a system parameter, where the mutation operation is to change the gene value at a certain locus or loci of an individual with a smaller probability, and is also an operation method for generating a new individual.
The operation process in practical application can be as follows:
firstly, determining the genetic variation position of each individual, wherein the positions of variation points generated randomly are shown in the following table, and the numbers indicate that the variation points are arranged at the positions of the variation loci;
then the original gene value of the variation point is inverted according to a certain probability.
| Individual numbering | Crossover results | Variation point | Mutation results | Progeny population p |
| 1 | 011001 | 4 | 011101 | 011101 |
| 2 | 111101 | 5 | 111111 | 111111 |
| 3 | 101001 | 2 | 111001 | 111001 |
| 4 | 111011 | 6 | 111010 | 111010 |
TABLE 1 Individual Gene variation tables
As can be seen from the table, after the population is subjected to the first generation mutation, if the number of the '1' is used for representing the fitness, the fitness is higher as the number is larger, and the fitness of individuals generated after the mutation operation is higher than that of the original individuals.
Step S303, according to the selection probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain new parameters.
It will be appreciated that the selection probability is calculated by the system based on the fitness of the individual in the current environment, and that the selection operation (or "duplication operation") inherits the higher fitness individuals in the current population to the next generation population according to some rule or model. Individuals who generally require higher fitness will have more opportunities to be passed on to the next generation population.
The operation process in practical application can be as follows:
firstly, calculating the fitness of all individuals in the population
Wherein the method comprises the steps of
In the above formula, define
Pricing in an original pricing system, wherein k is an adjustable parameter;
specifying pricing system functions
m is the size of the pricing scale in the original pricing system, x r ＝{a r ,b r ,...,d r }，a r ,b r ,...,d r Multidimensional factors influencing task pricing;
then calculate the selection probability
It is the probability that each individual is inherited into the next generation population;
each probability value forms an area, and the sum of all probability values is 1;
finally, a random number between 0 and 1 is generated, and the selected times of each individual are determined according to the probability area in which the random number appears, as shown in the table below.
Table 2 initial population selection table
As can be seen from the above table, the selection operation will select individuals with higher fitness values to be inherited into the next generation population.
It is easily found from the above description that, in this embodiment, the original system parameters are extracted from the crowdsourcing pricing system to be evaluated, and an initial group set is established according to the original system parameters; data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained; determining a corresponding fitness function value according to the new population set; and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value. By the method, the crowdsourcing price of the original user can be adjusted, the crowdsourcing price is given to the new user point, and the parameters of the crowdsourcing pricing system can be optimized according to the crowdsourcing data completed in real time, so that the task completion rate is greatly improved.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with an evaluation program of the crowdsourcing pricing system, and the evaluation program of the crowdsourcing pricing system realizes the steps of the evaluation method of the crowdsourcing pricing system when being executed by a processor.
Referring to fig. 4, fig. 4 is a block diagram illustrating a first embodiment of an evaluation device of the crowdsourcing pricing system of the present invention.
As shown in fig. 4, an evaluation device for a crowdsourcing pricing system according to an embodiment of the present invention includes: the system comprises an acquisition module 10, an optimization module 20, a calculation module 30 and an evaluation module 40.
The acquisition module 10 is configured to extract an original system parameter from a crowdsourcing pricing system to be evaluated, and establish an initial group set according to the original system parameter;
the optimizing module 20 is configured to perform data optimization on the initial population set through a genetic algorithm, so as to obtain a new population set;
the computing module 30 is configured to determine a corresponding fitness function value according to the new population set;
the evaluation module 40 is configured to evaluate the crowdsourcing pricing system to be evaluated according to the fitness function value.
In the embodiment, original system parameters are extracted from a crowdsourcing pricing system to be evaluated, and an initial group set is established according to the original system parameters; data optimization is carried out on the initial group set through a genetic algorithm, and a new group set is obtained; determining a corresponding fitness function value according to the new population set; and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value. By the method, the crowdsourcing price of the original user can be adjusted, the crowdsourcing price is given to the new user point, and the parameters of the crowdsourcing pricing system can be optimized according to the crowdsourcing data completed in real time, so that the task completion rate is greatly improved.
Other embodiments or specific implementations of the evaluation device of the crowdsourcing pricing system of the present invention may refer to the above method embodiments, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A method of evaluating a crowdsourcing pricing system, the method comprising:
extracting original system parameters from a crowdsourcing pricing system to be evaluated, and establishing an initial group set according to the original system parameters;
determining a selection probability according to the initial fitness function value corresponding to the original system parameter;
carrying out data optimization on the initial population set through a genetic algorithm according to the selection probability to obtain a new population set;
determining a corresponding fitness function value according to the new population set;
and evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value and an innovative pricing model, wherein the establishment process of the innovative pricing model is as follows:
establishing an objective function
The constraint conditions are as follows:
above modelDuring the establishment of (2), H h Representing the pricing system to be optimized, max F is a representative objective function, i.e. the value output after evaluating the crowdsourcing pricing system,
representing pricing in the original pricing system, k is a regulatable parameter, (t 1 ,t 2 ,L,t m ) The parameters represent influence factors in the original pricing system, m is the size of the pricing scale in the original pricing system, x= (a, b, L, d) represents dimension factors influencing task pricing, and F is more than or equal to 0.9 represents the expected value to be reached by the objective function at the end of model establishment.
2. The method of claim 1, wherein the optimizing the initial population set by genetic algorithm based on the selection probability, before obtaining a new population set, further comprises:
acquiring a corresponding initial fitness function value according to the original system parameters;
acquiring selection probability according to the initial fitness function value;
the data optimization is carried out on the initial group set through a genetic algorithm according to the selection probability, and a new group set is obtained, which comprises the following steps:
and carrying out data optimization on the initial population set through a genetic algorithm according to the preset crossover probability, the preset variation probability and the selection probability to obtain a new population set.
3. The method for evaluating a crowdsourcing pricing system according to claim 2, wherein the data optimization is performed on the initial population set according to a preset crossover probability, a preset mutation probability and the selection probability by a genetic algorithm to obtain a new population set, and the method specifically comprises:
according to the preset crossover probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain crossover parameters;
according to the preset variation probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain variation parameters;
according to the selection probability, carrying out data optimization on the initial population set through the genetic algorithm to obtain new parameters;
combining the crossover parameter, the mutation parameter and the new parameter into a group set, and taking the group set as a new group set.
4. The method for evaluating a crowdsourcing pricing system according to claim 3, wherein the performing data optimization on the initial population set by the genetic algorithm according to the preset crossover probability to obtain crossover parameters specifically comprises:
extracting the original system parameters from the initial population set according to the genetic algorithm, and performing cross operation on the original system parameters according to the preset cross probability to obtain cross parameters.
5. The method for evaluating a crowdsourcing pricing system according to claim 3, wherein the optimizing the data of the initial population set by the genetic algorithm according to the preset mutation probability, to obtain mutation parameters, specifically comprises:
extracting the original system parameters from the initial population set according to the genetic algorithm, and performing mutation operation on the original system parameters according to the preset mutation probability to obtain mutation parameters.
6. The method for evaluating a crowdsourcing pricing system according to claim 3, wherein the data optimization is performed on the initial population set by the genetic algorithm according to the selection probability to obtain new parameters, specifically comprising:
and extracting the original system parameters from the initial population set through the genetic algorithm according to the selection probability, and taking the extracted original system parameters as new parameters.
7. The method for evaluating a crowdsourcing pricing system according to any of claims 1-6, wherein the evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value comprises:
and comparing the fitness function value with the initial fitness function value, and evaluating the crowdsourcing pricing system to be evaluated according to a comparison result.
8. An apparatus for evaluating a crowdsourcing pricing system, the apparatus comprising:
the acquisition module is used for extracting original system parameters from the crowdsourcing pricing system to be evaluated and establishing an initial group set according to the original system parameters;
the acquisition module is further used for determining selection probability according to the initial fitness function value corresponding to the original system parameter;
the optimization module is used for carrying out data optimization on the initial population set through a genetic algorithm according to the selection probability to obtain a new population set;
the calculation module is used for determining a corresponding fitness function value according to the new group set;
the evaluation module is used for evaluating the crowdsourcing pricing system to be evaluated according to the fitness function value and an innovative pricing model, and the establishment process of the innovative pricing model is as follows:
establishing an objective function
The constraint conditions are as follows:
in the process of establishing the model, H h Representing the pricing system to be optimized, max F is the representative objective function, i.e. the targetThe crowd-sourced pricing system evaluates the output values,
representing pricing in the original pricing system, k is a regulatable parameter, (t 1 ,t 2 ,L,t m ) The parameters represent influence factors in the original pricing system, m is the size of the pricing scale in the original pricing system, x= (a, b, L, d) represents dimension factors influencing task pricing, and F is more than or equal to 0.9 represents the expected value to be reached by the objective function at the end of model establishment.
9. An apparatus for evaluating a crowdsourcing pricing system, the apparatus comprising: a memory, a processor, and an evaluation program of a crowdsourcing pricing system stored on the memory and executable on the processor, the evaluation program of the crowdsourcing pricing system configured to implement the steps of the evaluation method of the crowdsourcing pricing system of any of claims 1 to 7.
10. A readable storage medium, characterized in that the readable storage medium is a computer readable storage medium having stored thereon an evaluation program of a crowdsourcing pricing system, which when executed by a processor, implements the steps of the method of evaluating a crowdsourcing pricing system according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910831574.6A CN110533186B (en) | 2019-09-04 | 2019-09-04 | Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910831574.6A CN110533186B (en) | 2019-09-04 | 2019-09-04 | Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110533186A CN110533186A (en) | 2019-12-03 |
| CN110533186B true CN110533186B (en) | 2023-05-12 |
Family
ID=68666714
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910831574.6A Active CN110533186B (en) | 2019-09-04 | 2019-09-04 | Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110533186B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926923A (en) * | 2021-03-24 | 2021-06-08 | 拉扎斯网络科技（上海）有限公司 | Method and device for acquiring test data set and electronic equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106708600A (en) * | 2016-12-12 | 2017-05-24 | 大连理工大学 | Multi-agent modeling and expert system-based device for generating optimal release policy of crowd-sourcing platform |
| CN107301519A (en) * | 2017-06-16 | 2017-10-27 | 佛山科学技术学院 | A kind of task weight pricing method in mass-rent express system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9911088B2 (en) * | 2014-05-01 | 2018-03-06 | Microsoft Technology Licensing, Llc | Optimizing task recommendations in context-aware mobile crowdsourcing |
| US20160071048A1 (en) * | 2014-09-08 | 2016-03-10 | Xerox Corporation | Methods and systems for crowdsourcing of tasks |
| CN106204117A (en) * | 2016-06-30 | 2016-12-07 | 河南蓝海通信技术有限公司 | Mass-rent platform pricing method under multitask environment |
| CN106203893A (en) * | 2016-09-09 | 2016-12-07 | 扬州大学 | A kind of method for allocating tasks of based on genetic algorithm under mass-rent environment |
| CN107239929A (en) * | 2017-04-26 | 2017-10-10 | 深圳市华傲数据技术有限公司 | The price appraisal procedure and system of mass-rent task |
| CN110020098A (en) * | 2017-08-17 | 2019-07-16 | 南京东方网信网络科技有限公司 | Inhibit the fine granularity recommendation mechanisms of waterborne troops's problem in crowdsourcing system |
-
2019
- 2019-09-04 CN CN201910831574.6A patent/CN110533186B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106708600A (en) * | 2016-12-12 | 2017-05-24 | 大连理工大学 | Multi-agent modeling and expert system-based device for generating optimal release policy of crowd-sourcing platform |
| CN107301519A (en) * | 2017-06-16 | 2017-10-27 | 佛山科学技术学院 | A kind of task weight pricing method in mass-rent express system |
Non-Patent Citations (1)
| Title |
|---|
| 胡若妍 ; 刘美含 ; .基于单目标规划的众包任务定价的研究.电脑知识与技术.2018,(14),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110533186A (en) | 2019-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shang et al. | A survey on the hypervolume indicator in evolutionary multiobjective optimization | |
| CN106647262B (en) | Differential evolution method for agile satellite multi-target task planning | |
| Rasmussen et al. | A Bayesian approach for fast and accurate gene tree reconstruction | |
| CN106327240A (en) | Recommendation method and recommendation system based on GRU neural network | |
| CN111967971B (en) | Bank customer data processing method and device | |
| Liu et al. | A modified decision tree algorithm based on genetic algorithm for mobile user classification problem | |
| CN111832949A (en) | Construction method of equipment combat test identification index system | |
| CN110533186B (en) | Method, device, equipment and readable storage medium for evaluating crowdsourcing pricing system | |
| CN113326900A (en) | Data processing method and device of federal learning model and storage medium | |
| KR20090073937A (en) | Method for short term electric load prediction system with the genetic algorithm and the fuzzy system | |
| CN112215278B (en) | Multi-dimensional data feature selection method combining genetic algorithm and dragonfly algorithm | |
| CN112765367B (en) | Method and device for constructing topic knowledge graph | |
| CN113362920B (en) | Feature selection method and device based on clinical data | |
| CN113141272B (en) | Network security situation analysis method based on iteration optimization RBF neural network | |
| CN110990353B (en) | Log extraction method, log extraction device and storage medium | |
| Gower et al. | Inference of population genetics parameters using discriminator neural networks: an adversarial Monte Carlo approach | |
| Xu | QTL analysis in plants | |
| Galinier et al. | Genetic algorithm to improve diversity in MDE | |
| Ünal et al. | Genetic algorithm | |
| CN113779877B (en) | Automatic feature construction method based on genetic algorithm | |
| Zamuda et al. | Tree model reconstruction innovization using multi-objective differential evolution | |
| Limón et al. | Class-specific feature generation for 1NN through genetic programming | |
| Rathore et al. | Genetic algorithms | |
| Toomajian | On computing relative effective population size estimates and parameters from an equilibrium cycle of hermaphrodite frequency fluctuation due to mixed reproductive modes in filamentous fungi | |
| CN114202667A (en) | Image matching method, device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |