CN117272838A - Government affair big data platform data acquisition optimization method - Google Patents

Government affair big data platform data acquisition optimization method Download PDF

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CN117272838A
CN117272838A CN202311532359.9A CN202311532359A CN117272838A CN 117272838 A CN117272838 A CN 117272838A CN 202311532359 A CN202311532359 A CN 202311532359A CN 117272838 A CN117272838 A CN 117272838A
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朱光荣
清毕西勒图
张金弘
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Henghaiyun Technology Group Co ltd
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Abstract

The invention relates to the field of data acquisition, in particular to a government affair big data platform data acquisition optimization method. A government affair big data platform data acquisition optimization method comprises the following steps: acquisition node C i And acquisition node C i Corresponding CPU calculation force Q i The method comprises the steps of carrying out a first treatment on the surface of the Setting acquisition task T j And acquisition task T j Corresponding acquisition task data amount N j The method comprises the steps of carrying out a first treatment on the surface of the Based on acquisition node C i CPU computing power Q i Acquisition task T j And collecting task data volume N j Data acquisition by improved genetic algorithmAnd (5) carrying out optimization simulation on the collection task, and outputting a data acquisition scheme. According to the invention, the problem that the acquisition tasks are distributed to the acquisition nodes is simulated through the improved genetic algorithm, the data acquisition scheme with the highest acquisition efficiency is output, and the social network data acquisition is performed based on the data acquisition scheme with the highest acquisition efficiency, so that the data on the social network can be acquired in time.

Description

Government affair big data platform data acquisition optimization method
Technical Field
The invention relates to the field of data acquisition, in particular to a government affair big data platform data acquisition optimization method.
Background
With the continuous development of big data technology, accumulation and collection of government big data becomes increasingly critical. In this context, the collection of social network data is a particularly important aspect, including social media platforms such as microblogs, forums, and the like. However, because of the rapid information influx on social networks, the data volume is huge, and the traditional data acquisition method may not be attractive. Therefore, how to effectively cope with mass data on a social network, and ensure accurate and comprehensive acquisition in a short time becomes a problem to be solved in the field of government affair big data.
Disclosure of Invention
The invention provides a government affair big data platform data acquisition optimization method, which simulates the problem that acquisition tasks are distributed to acquisition nodes through an improved genetic algorithm, outputs a data acquisition scheme with highest acquisition efficiency, and performs social network data acquisition based on the data acquisition scheme with highest acquisition efficiency, so that data on a social network can be acquired in time.
A government affair big data platform data acquisition optimization method comprises the following steps:
acquisition node C i And acquisition node C i Corresponding CPU calculation force Q i I=1, 2,3 · the contents of which are (I), I is the total number of acquisition nodes, and the CPU calculates the force Q i From the collection node C i CPU performance determination of (2);
setting acquisition task T j And acquisition task T j Corresponding acquisition task data amount N j J=1, 2,3 · the contents of the components are as follows, J is the total number of acquisition tasks;
based on acquisition node C i CPU computing power Q i Acquisition task T j And collecting task data volume N j Performing optimization simulation on the data acquisition task through the improved genetic algorithm, and outputting a data acquisition scheme;
j acquisition tasks T according to a data acquisition scheme j And distributing the data to the I acquisition nodes for social network data acquisition.
In a preferred aspect of the invention, the data acquisition task is optimally simulated by an improved genetic algorithm, and the method specifically comprises the following steps:
s1: let g=1, G be used to record the iteration number, set the maximum iteration number G;
s2: initializing and generating M data acquisition schemes F to be simulated m ，m=1，2，3······M，F m Is in the form ofWill->Is marked as a gene segment, and one gene segment represents the acquisition task T j The corresponding allocation would be an I×J matrix, wherein +.>The value of (2) is 0 or 1, when +.>When representing the acquisition task T j Distributed to collection node C i On the basis of->When representing the acquisition task T j Not assigned to acquisition node C i And M data acquisition schemes F to be simulated m Forming a first population;
s3: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Calculating a data acquisition scheme F to be simulated m Corresponding fitness delta m ；
S4: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Executing updating operation of the data acquisition scheme to be simulated, and acquiring all the data to be simulatedAggregation scheme F m After updating, entering S5; otherwise, selecting the data acquisition scheme to be simulated to carry out F m Executing updating operation of a data acquisition scheme to be simulated;
s5: according to the data acquisition scheme F to be simulated m Corresponding fitness delta m Calculating each data acquisition scheme F to be simulated m Corresponding following probability，/>Represents the mth data acquisition scheme F to be simulated m Corresponding following probability, generating M random numbers epsilon between 0 and 1 through random function m Selecting the random number epsilon one by one m For each selected random number epsilon m Determination of random number epsilon by roulette selection algorithm m Corresponding data acquisition scheme F to be simulated m For a selected random number epsilon m Corresponding data acquisition scheme F to be simulated m Executing updating operation of a data acquisition scheme to be simulated;
s6: all data to be simulated are acquired according to the scheme F m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the selected collection of the population;
s7: acquiring all update times w m (g) And will update all times w m (g) Comparing with the update threshold W one by one, if it is all the update times W m (g) All are smaller than the update threshold W, and S8 is entered; otherwise, the updating times w are output one by one m (g) M value corresponding to the update threshold W is larger than the update threshold W, and the data acquisition scheme F to be simulated corresponding to the m value is carried out m Storing the data collection into a group candidate collection, and initializing to generate a data collection scheme F to be simulated, wherein the data collection scheme F to be simulated corresponds to the m value of the data collection scheme to be simulated m S8, entering;
s8: judging whether 'g < M' is established, if 'g < M' is established, assigning g+1 to g, and returning to S4; if "g < M" is not satisfied, entering S9;
s9: obtaining a group candidate set, and selecting the group candidate set according to the group candidate setM data acquisition schemes F to be simulated are selected by a machine m Forming a second population, and marking the data acquisition scheme to be simulated in the second population as H m ，H m Is in the form ofWill->Is marked as a gene fragment, and the gene fragment,the value of (2) and->Consistent;
s10: acquiring a data acquisition scheme H to be simulated in a second population m Corresponding fitness ζ m Fitness ζ m Is calculated and adapted delta m Consistent, calculate and wait for analog data acquisition scheme H m Corresponding selection probability，/>Refers to an mth data acquisition scheme H to be simulated m Corresponding selection probability based on selection probability +.>Selecting M/2 data acquisition schemes H to be simulated from a second population by adopting a roulette selection algorithm m Forming a father set of a data acquisition scheme to be simulated; s11: to-be-simulated data acquisition scheme H in to-be-simulated data acquisition scheme male parent m Performing cross recombination operation, and acquiring a data acquisition scheme H to be simulated, which is output through the cross recombination operation m Forming a sub-set of data acquisition schemes to be simulated;
s12: judging whether 'g < D' is established, wherein D is a variation threshold, if 'g < D' is established, entering S13; if 'g < D' is not satisfied, the data acquisition scheme H to be simulated in the sub-set of the data acquisition scheme to be simulated m Performing mutation operation, updating the sub-set of the analog data acquisition scheme, and entering S13;
s13: judging whether 'G < G' is established, if 'G < G' is established, forming a father set of the data acquisition scheme to be simulated and a child set of the data acquisition scheme to be simulated into a second population, judging whether an optimal simulated data acquisition scheme exists or not, if so, selecting all the data acquisition schemes F to be simulated in the second population m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the data as an optimal simulation data acquisition scheme; if the optimal simulated data acquisition scheme exists, selecting all the data acquisition schemes F to be simulated in the second population m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Replacing the optimal simulation data acquisition scheme for storage, assigning g+1 to g, and returning to S10; if "G < G" is not satisfied, the process proceeds to S14;
s14: and selecting an optimal simulation data acquisition scheme as a data acquisition scheme for output.
As a preferred aspect of the invention, the generation of the data acquisition scheme F to be simulated is initialized m The method specifically comprises the following steps: traversing all acquisition tasks T j For each acquisition task T j Order-making，/>Wherein k is a random integer between 1 and I, x satisfies x ε {1,2,3 }. I } and x+.k; when all acquisition tasks T j After traversing, generating a corresponding data acquisition scheme F to be simulated m 。
As a preferred aspect of the invention, a data acquisition scheme F to be simulated is calculated m Corresponding fitness delta m The method specifically comprises the following steps: calculating a data acquisition scheme F to be simulated m Corresponding node delayThe method comprises the steps of carrying out a first treatment on the surface of the Wherein node delay->To adopt the data acquisition scheme F to be simulated m Post-acquisition node C i Processing all assignments to acquisition node C i Time spent for acquisition tasks of (1) selecting all nodes delay +.>Maximum node delay ∈>As a fitness delta m 。
As a preferred aspect of the present invention, the updating operation of the data acquisition scheme to be simulated specifically includes the following: selecting the data acquisition schemes F to be simulated one by one n N.epsilon {1,2, 3.M } and n.noteq.m, for each selected data acquisition scheme F to be simulated n Generating a random integer gamma between 1 and J by a random function, and acquiring a data acquisition scheme F to be simulated m In (a) and (b)Alternative to data acquisition scheme F to be simulated n Middle->Calculating a replaced data acquisition scheme to be simulatedIs->Fitness +.>Degree of adaptation delta from the original m Comparing the sizes, if the fitness is +.>Is greater than the original fitness delta m Fitness +.>As a fitness delta m Storing and replacing the data to be simulated acquisition schemeAs a data acquisition scheme F to be simulated m Storing; if it is fitness->Not greater than the original fitness delta m No operation is performed; at the same time record the update times w m (g) Number of updates w m (g) Initial 0, each time data acquisition scheme F to be simulated m When the updating operation of the data acquisition scheme to be simulated is executed, if the replaced data acquisition scheme to be simulated is executed +.>As a data acquisition scheme F to be simulated m Store w m (g) Assigning +1 to w m (g) Otherwise, no operation is performed.
As a preferred aspect of the present invention, the cross-reorganization operation specifically includes the following: randomly selecting two data acquisition schemes H to be simulated from male parents of data acquisition schemes H to be simulated m Generating two random integers between 1 and J through a random function, wherein the larger random integer is recorded as eta 1 The smaller random integer is noted as eta 2 Two data acquisition schemes H to be simulated m The value of j in the middle is eta 1 And eta 2 All gene fragments in between are replaced.
In a preferred aspect of the present invention, the mutation operation specifically includes the following: selecting the data acquisition schemes H to be simulated in the sub-set of the simulation data acquisition schemes one by one m For the selected data acquisition scheme H to be simulated m Generating a random number lambda between 0 and 1 by a random function, and judging that lambda > P c Whether or not it is true, P c For the variation probability, if "lambda > P c "true", two random integers between 1 and J are generated by a random function, denoted μ respectively 1 Sum mu 2 Data acquisition scheme H to be simulated m Wherein j takes the value mu 1 Sum mu 2 Is replaced by a gene fragment of (a).
A government affair big data platform data acquisition optimizing system comprises:
the acquisition node acquisition module is used for acquiring acquisition nodes and numbering;
the CPU computing force acquisition module is used for acquiring the CPU computing force corresponding to the acquisition node;
the acquisition task acquisition module is used for acquiring acquisition nodes and numbering;
the acquisition task data volume acquisition module is used for acquiring acquisition task data volume corresponding to an acquisition task;
the data acquisition scheme simulation module is used for carrying out optimization simulation on the data acquisition task through the improved genetic algorithm based on the acquisition node, the CPU computing power, the acquisition task and the data quantity of the acquisition task and outputting a data acquisition scheme;
and the data acquisition module is used for distributing the acquisition tasks to the acquisition nodes according to the data acquisition scheme to acquire social network data.
The invention has the following advantages:
1. according to the invention, the problem that the acquisition tasks are distributed to the acquisition nodes is simulated through the improved genetic algorithm, the data acquisition scheme with the highest acquisition efficiency is output, and the social network data acquisition is performed based on the data acquisition scheme with the highest acquisition efficiency, so that the data on the social network can be acquired in time.
2. According to the invention, the improved bee colony algorithm is used for simulating the data acquisition scheme to be simulated, and the data acquisition scheme to be simulated with high fitness and high updating times is recorded, so that the data acquisition scheme to be simulated can be more similar to the optimal solution of the data acquisition scheme, can be used as the first generation population of a good genetic algorithm, and saves the simulation calculation time of the genetic algorithm.
Drawings
Fig. 1 is a flow chart of a government affair big data platform data acquisition optimization method adopted in an embodiment of the invention.
Fig. 2 is a schematic structural diagram of a government affair big data platform data acquisition optimization system adopted in the embodiment of the invention.
Detailed Description
In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Embodiment 1, a government affair big data platform data acquisition optimization method, see fig. 1, includes:
acquisition node C i And acquisition node C i Corresponding CPU calculation force Q i I=1, 2,3 · the contents of which are (I), I is the total number of acquisition nodes, and the CPU calculates the force Q i From the collection node C i CPU performance decisions of (2) for characterizing acquisition node C i The amount of data that can be processed per unit time; because the data volume in the social network is very huge, the acquisition of the social network data is realized by arranging a plurality of acquisition nodes;
setting acquisition task T j And acquisition task T j Corresponding acquisition task data amount N j J=1, 2, 3.j, J being the total number of acquisition tasks submitted by the user, specifically, the method can be used for collecting the data content related to a certain keyword, such as 'food discussion' with more recent discussion;
based on acquisition node C i CPU computing power Q i Acquisition task T j And collecting task data volume N j The improved genetic algorithm is used for carrying out optimization simulation on the data acquisition task, a data acquisition scheme is output, and as the acquisition task and the acquisition nodes are more in number, the improved genetic algorithm is used for carrying out target optimization, and a scheme with highest acquisition efficiency is searched;
j acquisition tasks T according to a data acquisition scheme j And distributing the data to the I acquisition nodes for social network data acquisition.
The application uses the improved genetic algorithm to collect J acquisition tasks T j The problem of distribution to I acquisition nodes is simulated and inputAnd outputting the data acquisition scheme with the highest acquisition efficiency, and carrying out social network data acquisition based on the data acquisition scheme with the highest acquisition efficiency, so that the data on the social network can be acquired timely.
The optimized simulation of the data acquisition task is carried out through the improved genetic algorithm, and the method specifically comprises the following steps:
s1: let g=1, G be used to record the iteration number, set the maximum iteration number G;
s2: initializing and generating M data acquisition schemes F to be simulated m ，m=1，2，3······M，F m Is in the form ofWill->Is marked as a gene segment, and one gene segment represents the acquisition task T j The corresponding allocation would be an I×J matrix, wherein +.>The value of (2) is 0 or 1, when +.>When representing the acquisition task T j Distributed to collection node C i On the basis of->When representing the acquisition task T j Not assigned to acquisition node C i And M data acquisition schemes F to be simulated m Forming a first population;
initializing and generating a data acquisition scheme F to be simulated m The method specifically comprises the following steps: traversing all acquisition tasks T j For each acquisition task T j Order-making，/>Where k is a random integer between 1 and I, x satisfies x ε {1,2, 3I and x is not equal to k; when all acquisition tasks T j After traversing, generating a corresponding data acquisition scheme F to be simulated m The method comprises the steps of carrying out a first treatment on the surface of the It should be noted that, when the data acquisition scheme to be simulated is initialized, the user may set a priori conditions according to the characteristics of the acquisition task and the acquisition node; repeating the steps for M times to generate M data acquisition schemes F to be simulated m ；
S3: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Calculating a data acquisition scheme F to be simulated m Corresponding fitness delta m ；
Calculating a data acquisition scheme F to be simulated m Corresponding fitness delta m The method specifically comprises the following steps: calculating a data acquisition scheme F to be simulated m Corresponding node delayThe method comprises the steps of carrying out a first treatment on the surface of the Wherein node delay->To adopt the data acquisition scheme F to be simulated m Post-acquisition node C i Processing all assignments to acquisition node C i Time spent for acquisition tasks of (1) selecting all nodes delay +.>Maximum node delay ∈>As a fitness delta m 。
S4: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Executing the updating operation of the data acquisition scheme to be simulated, and when all the data acquisition schemes to be simulated F m After updating, entering S5; otherwise, selecting the data acquisition scheme to be simulated to carry out F m Executing updating operation of a data acquisition scheme to be simulated;
updating operation of data acquisition scheme to be simulatedThe method specifically comprises the following steps: selecting the data acquisition schemes F to be simulated one by one n N.epsilon {1,2, 3.M } and n.noteq.m, for each selected data acquisition scheme F to be simulated n Generating a random integer gamma between 1 and J by a random function, and acquiring a data acquisition scheme F to be simulated m In (a) and (b)Alternative to data acquisition scheme F to be simulated n Middle->In the replacement process, I is increased from 1 to I, so that a data acquisition scheme F to be simulated is realized m And a data acquisition scheme F to be simulated n The exchange of the gene fragments is also combined with the data acquisition scheme F to be simulated n Scheme F for acquiring data to be simulated m Is modified by, e.g., assigning the original to C 3 Acquisition task assignment to C 7 Because the traditional swarm algorithm is generally calculated aiming at continuous data, and the application is calculated aiming at discrete data, the searching strategy of the traditional swarm algorithm is not suitable for the application, the searching strategy of the traditional swarm algorithm is improved, and the searching is realized by adopting a gene segment exchange mode; calculating the replaced data acquisition scheme to be simulated +.>Is->Fitness +.>Degree of adaptation delta from the original m Comparing the sizes, if the fitness is +.>Is greater than the original fitness delta m Explaining the replaced data acquisition scheme to be simulated +.>Has higher acquisition efficiency, and is suitable for the degree of +.>As a fitness delta m Storing and replacing the data acquisition scheme to be simulated +.>As a data acquisition scheme F to be simulated m Storing to realize the data acquisition scheme F to be simulated m Is updated according to the update of (a); if it is fitness->Not greater than the original fitness delta m Description of the alternative data acquisition scheme to be simulatedThe device has no higher acquisition efficiency and no operation; at the same time record the update times w m (g) Number of updates w m (g) Initial 0, each time data acquisition scheme F to be simulated m When the updating operation of the data acquisition scheme to be simulated is executed, if the replaced data acquisition scheme to be simulated is executed +.>As a data acquisition scheme F to be simulated m Store w m (g) Assigning +1 to w m (g) Otherwise, no operation is performed.
S5: according to the data acquisition scheme F to be simulated m Corresponding fitness delta m Calculating each data acquisition scheme F to be simulated m Corresponding following probability，/>Represents the mth data acquisition scheme F to be simulated m Corresponding following probability, generating M random numbers epsilon between 0 and 1 through random function m The M random numbers ε m Can be regarded as following bees in the bee colony algorithm based onFollow probability->To select the data acquisition scheme F to be simulated m Selecting the random number epsilon one by one m For each selected random number epsilon m Determination of random number epsilon by roulette selection algorithm m Corresponding data acquisition scheme F to be simulated m For a selected random number epsilon m Corresponding data acquisition scheme F to be simulated m Executing updating operation of a data acquisition scheme to be simulated;
determination of random number epsilon by roulette selection algorithm m Corresponding data acquisition scheme F to be simulated m The specific implementation content is as follows: all the data to be simulated are acquired according to the scheme F m Corresponding following probabilityThe data acquisition schemes to be simulated are respectively marked as F on the assumption that the data acquisition schemes to be simulated are arranged from small to large 1 ，F 2 ，F 3 ，F 4 ，F 5 The corresponding following probabilities are 0.3,0.1,0.2,0.3,0.1,5 data acquisition schemes to be simulated respectively, and F is arranged according to the following probabilities from small to large 2 ，F 5 ，F 3 ，F 1 ，F 4 Accumulating the following probabilities to obtain the data acquisition scheme F to be simulated 2 The corresponding selection range is 0-0.1; data acquisition scheme F to be simulated 5 The corresponding selection range is 0.1-0.2; data acquisition scheme F to be simulated 3 The corresponding selection range is 0.2-0.4; data acquisition scheme F to be simulated 1 The corresponding selection range is 0.4-0.7; data acquisition scheme F to be simulated 4 The corresponding selection range is 0.7-1; when the random number is 0.26 and falls between 0.2 and 0.4, the data acquisition scheme to be simulated corresponding to the random number 0.26 is F 3 ；
S6: all data to be simulated are acquired according to the scheme F m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the selected collection of the population;
s7: acquiring all update times w m (g) And will beWith a number w of updates m (g) Comparing the values with the update threshold W one by one, wherein the update threshold W is set by a user, and if the update threshold W is the same as the update threshold W, the update threshold W is the same as the update threshold W m (g) All are smaller than the update threshold W, and S8 is entered; otherwise, the updating times w are output one by one m (g) M value corresponding to the update threshold W is larger than the update threshold W, and the data acquisition scheme F to be simulated corresponding to the m value is carried out m Storing into the group candidate set, when the number of times w of updating m (g) When the value is larger than the updating threshold value W, the data acquisition scheme F to be simulated corresponding to the value m is described m The fitness is higher, the simulation method is selected and updated by following bees for a plurality of times, the simulation method has the potential of tending to the global optimal solution, is a good sample for subsequent genetic algorithm simulation calculation, and is used for initializing and generating a data acquisition scheme to be simulated to replace a data acquisition scheme F to be simulated corresponding to m values m S8, entering;
s8: judging whether 'g < M' is established, if so, explaining a data acquisition scheme F to be simulated of the content of the group candidate set, wherein the iteration times are insufficient m Insufficient to generate a new population, assigning g+1 to g, and returning to S4; if 'g < M' is not established, the data acquisition scheme F to be simulated for the content of the group to be selected is described m Sufficient to generate a new population, proceeding to S9;
s9: acquiring a population candidate set, and randomly selecting M data acquisition schemes F to be simulated from the population candidate set m Forming a second population, and marking the data acquisition scheme to be simulated in the second population as H m ，H m Is in the form ofWill->Is marked as a gene fragment, and the gene fragment,the value of (2) and->Consistent;
because the genetic algorithm initializes the population firstly in the traditional use process, and the initialized population is random, the genetic algorithm converges slowly in the initial simulation calculation process, and a great deal of time is consumed; the bee colony algorithm has less calculation amount of the model per se, has strategies such as information sharing, local searching and the like, can search data acquisition schemes to be simulated with high adaptability and good potential, takes the data acquisition schemes to be simulated with high adaptability and good potential as an initial population of a genetic algorithm, can reduce the time consumed by convergence of the genetic algorithm in an initial stage, and improves the efficiency of calculating the optimal solution of the whole; on the other hand, multi-objective optimization is directly performed through the swarm algorithm, but in the whole, the swarm algorithm performs a large amount of local search in the process of calculating the optimal solution, so that the convergence speed is slow, the optimal solution is calculated through the swarm algorithm instead of the swarm algorithm, the data acquisition scheme to be simulated with high fitness and good potential is obtained through the swarm algorithm for M times, and the optimal solution is calculated by taking the data acquisition scheme to be simulated with high fitness and good potential as an initial population of a genetic algorithm, so that the effect of reducing the calculation time is achieved.
S10: acquiring a data acquisition scheme H to be simulated in a second population m Corresponding fitness ζ m Fitness ζ m Is calculated and adapted delta m Consistent, calculate and wait for analog data acquisition scheme H m Corresponding selection probability，/>Refers to an mth data acquisition scheme H to be simulated m Corresponding selection probability based on selection probability +.>Selecting M/2 data acquisition schemes H to be simulated from a second population by adopting a roulette selection algorithm m Forming a father set of a data acquisition scheme to be simulated;
s11: to-be-simulated data acquisition scheme H in to-be-simulated data acquisition scheme male parent m Performing cross recombination operation, and acquiring a data acquisition scheme H to be simulated, which is output through the cross recombination operation m Forming a sub-set of data acquisition schemes to be simulated;
the cross recombination operation specifically comprises the following contents: randomly selecting two data acquisition schemes H to be simulated from male parents of data acquisition schemes H to be simulated m Generating two random integers between 1 and J through a random function, wherein the larger random integer is recorded as eta 1 The smaller random integer is noted as eta 2 Two data acquisition schemes H to be simulated m The value of j in the middle is eta 1 And eta 2 All gene fragments in between are replaced;
s12: judging whether 'g < D' is established, wherein D is a variation threshold value, and if so, entering S13; if 'g < D' is not satisfied, the data acquisition scheme H to be simulated in the sub-set of the data acquisition scheme to be simulated m Performing mutation operation, updating the sub-set of the analog data acquisition scheme, and entering S13; because the second generation population is generated by an improved bee colony algorithm, the genetic algorithm is sunk into local optimum prematurely in the simulation calculation process, so that the variation operation of a data acquisition scheme to be simulated needs to be performed timely;
the mutation operation specifically comprises the following contents: selecting the data acquisition schemes H to be simulated in the sub-set of the simulation data acquisition schemes one by one m For the selected data acquisition scheme H to be simulated m Generating a random number lambda between 0 and 1 by a random function, and judging that lambda > P c Whether or not it is true, P c For the variation probability, if "lambda > P c "true", two random integers between 1 and J are generated by a random function, denoted μ respectively 1 Sum mu 2 Data acquisition scheme H to be simulated m Wherein j takes the value mu 1 Sum mu 2 Is replaced by a gene fragment of (a);
s13: judging whether 'G < G' is established, if 'G < G' is established, combining the father set of the data acquisition scheme to be simulated and the child set of the data acquisition scheme to be simulated and replacing the father set and the child set of the data acquisition scheme to be simulated with a second population, judging whether an optimal simulated data acquisition scheme exists, if yes, judging whether the optimal simulated data acquisition scheme existsSelecting all data acquisition schemes F to be simulated in the second population without the existence of the optimal simulated data acquisition scheme m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the data as an optimal simulation data acquisition scheme; if the optimal simulated data acquisition scheme exists, selecting all the data acquisition schemes F to be simulated in the second population m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Replacing the optimal simulation data acquisition scheme for storage, assigning g+1 to g, and returning to S10; if "G < G" is not satisfied, the process proceeds to S14;
s14: and selecting an optimal simulation data acquisition scheme as a data acquisition scheme for output.
According to the method, the improved bee colony algorithm is used for simulating the data acquisition scheme to be simulated, the data acquisition scheme to be simulated with high fitness and high updating times is recorded, the data acquisition scheme to be simulated can be more similar to the optimal solution of the data acquisition scheme, the data acquisition scheme to be simulated can be used as the first generation population of a good genetic algorithm, and the time for simulating and calculating the genetic algorithm is saved.
Embodiment 2, a government big data platform data acquisition optimization system adopting the data acquisition optimization method, as shown in fig. 2, includes:
the acquisition node acquisition module is used for acquiring acquisition nodes and numbering;
the CPU computing force acquisition module is used for acquiring the CPU computing force corresponding to the acquisition node;
the acquisition task acquisition module is used for acquiring acquisition nodes and numbering;
the acquisition task data volume acquisition module is used for acquiring acquisition task data volume corresponding to an acquisition task;
the data acquisition scheme simulation module is used for carrying out optimization simulation on the data acquisition task through the improved genetic algorithm based on the acquisition node, the CPU computing power, the acquisition task and the data quantity of the acquisition task and outputting a data acquisition scheme;
and the data acquisition module is used for distributing the acquisition tasks to the acquisition nodes according to the data acquisition scheme to acquire social network data.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims. Parts of the specification not described in detail belong to the prior art known to those skilled in the art.

Claims (8)

1. The government affair big data platform data acquisition optimization method is characterized by comprising the following steps of:
acquisition node C i And acquisition node C i Corresponding CPU calculation force Q i I=1, 2,3 · the contents of which are (I), I is the total number of acquisition nodes, and the CPU calculates the force Q i From the collection node C i CPU performance determination of (2);
setting acquisition task T j And acquisition task T j Corresponding acquisition task data amount N j J=1, 2,3 · the contents of the components are as follows, J is the total number of acquisition tasks;
based on acquisition node C i CPU computing power Q i Acquisition task T j And collecting task data volume N j Performing optimization simulation on the data acquisition task through the improved genetic algorithm, and outputting a data acquisition scheme;
j acquisition tasks T according to a data acquisition scheme j And distributing the data to the I acquisition nodes for social network data acquisition.
2. The government affair big data platform data acquisition optimization method according to claim 1 is characterized by carrying out optimization simulation on a data acquisition task through an improved genetic algorithm, and specifically comprising the following steps:
s1: let g=1, G be used to record the iteration number, set the maximum iteration number G;
s2: initializing and generating M data acquisition schemes F to be simulated m ，m=1，2，3······M，F m Is in the form ofWill->Is marked as a gene segment, and one gene segment represents the acquisition task T j The corresponding allocation would be an I×J matrix, wherein +.>The value of (2) is 0 or 1, when +.>When representing the acquisition task T j Distributed to collection node C i On the basis of->When representing the acquisition task T j Not assigned to acquisition node C i And M data acquisition schemes F to be simulated m Forming a first population;
s3: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Calculating a data acquisition scheme F to be simulated m Corresponding fitness delta m ；
S4: selecting the data acquisition schemes F to be simulated in the first population one by one m Selecting a data acquisition scheme F to be simulated for each m Executing the updating operation of the data acquisition scheme to be simulated, and when all the data acquisition schemes to be simulated F m After updating, entering S5; otherwise, selecting the data acquisition scheme to be simulated to carry out F m Executing updating operation of a data acquisition scheme to be simulated;
s5: according to the data acquisition scheme F to be simulated m Corresponding fitness delta m Calculating each data acquisition scheme F to be simulated m Corresponding following probability，/>Represents the mth analog number to be simulatedAccording to acquisition scheme F m Corresponding following probability, generating M random numbers epsilon between 0 and 1 through random function m Selecting the random number epsilon one by one m For each selected random number epsilon m Determination of random number epsilon by roulette selection algorithm m Corresponding data acquisition scheme F to be simulated m For a selected random number epsilon m Corresponding data acquisition scheme F to be simulated m Executing updating operation of a data acquisition scheme to be simulated;
s6: all data to be simulated are acquired according to the scheme F m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the selected collection of the population;
s7: acquiring all update times w m (g) And will update all times w m (g) Comparing with the update threshold W one by one, if it is all the update times W m (g) All are smaller than the update threshold W, and S8 is entered; otherwise, the updating times w are output one by one m (g) M value corresponding to the update threshold W is larger than the update threshold W, and the data acquisition scheme F to be simulated corresponding to the m value is carried out m Storing the data collection into a group candidate collection, and initializing to generate a data collection scheme F to be simulated, wherein the data collection scheme F to be simulated corresponds to the m value of the data collection scheme to be simulated m S8, entering;
s8: judging whether 'g < M' is established, if 'g < M' is established, assigning g+1 to g, and returning to S4; if "g < M" is not satisfied, entering S9;
s9: acquiring a population candidate set, and randomly selecting M data acquisition schemes F to be simulated from the population candidate set m Forming a second population, and marking the data acquisition scheme to be simulated in the second population as H m ，H m Is in the form ofWill->Is marked as a gene fragment, and the gene fragment,the value of (2) and->Consistent;
s10: acquiring a data acquisition scheme H to be simulated in a second population m Corresponding fitness ζ m Fitness ζ m Is calculated and adapted delta m Consistent, calculate and wait for analog data acquisition scheme H m Corresponding selection probability，Refers to an mth data acquisition scheme H to be simulated m Corresponding selection probability based on selection probability +.>Selecting M/2 data acquisition schemes H to be simulated from a second population by adopting a roulette selection algorithm m Forming a father set of a data acquisition scheme to be simulated; s11: to-be-simulated data acquisition scheme H in to-be-simulated data acquisition scheme male parent m Performing cross recombination operation, and acquiring a data acquisition scheme H to be simulated, which is output through the cross recombination operation m Forming a sub-set of data acquisition schemes to be simulated;
s12: judging whether 'g < D' is established, wherein D is a variation threshold, if 'g < D' is established, entering S13; if 'g < D' is not satisfied, the data acquisition scheme H to be simulated in the sub-set of the data acquisition scheme to be simulated m Performing mutation operation, updating the sub-set of the analog data acquisition scheme, and entering S13;
s13: judging whether 'G < G' is established, if 'G < G' is established, forming a father set of the data acquisition scheme to be simulated and a child set of the data acquisition scheme to be simulated into a second population, judging whether an optimal simulated data acquisition scheme exists or not, if so, selecting all the data acquisition schemes F to be simulated in the second population m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Storing the data as an optimal simulation data acquisition scheme; if the optimal simulated data acquisition scheme exists, selecting all the data acquisition schemes F to be simulated in the second population m Degree of middle adaptation delta m Minimum data acquisition scheme F to be simulated m Replacing the optimal simulation data acquisition scheme for storage, assigning g+1 to g, and returning to S10; if "G < G" is not satisfied, the process proceeds to S14;
s14: and selecting an optimal simulation data acquisition scheme as a data acquisition scheme for output.
3. The government affair big data platform data acquisition optimization method according to claim 2, characterized in that the method comprises the steps of initializing and generating a data acquisition scheme F to be simulated m The method specifically comprises the following steps: traversing all acquisition tasks T j For each acquisition task T j Order-making，/>Wherein k is a random integer between 1 and I, x satisfies x ε {1,2,3 }. I } and x+.k; when all acquisition tasks T j After traversing, generating a corresponding data acquisition scheme F to be simulated m 。
4. The government affair big data platform data acquisition optimization method according to claim 3, wherein the data acquisition scheme F to be simulated is calculated m Corresponding fitness delta m The method specifically comprises the following steps: calculating a data acquisition scheme F to be simulated m Corresponding node delayThe method comprises the steps of carrying out a first treatment on the surface of the Wherein node delay->To adopt the data acquisition scheme F to be simulated m Post-acquisitionNode C i Processing all assignments to acquisition node C i Time spent for acquisition tasks of (1) selecting all nodes delay +.>Maximum node delay ∈>As a fitness delta m 。
5. The government affair big data platform data acquisition optimization method according to claim 4 is characterized in that the updating operation of the data acquisition scheme to be simulated specifically comprises the following steps: selecting the data acquisition schemes F to be simulated one by one n N.epsilon {1,2, 3.M } and n.noteq.m, for each selected data acquisition scheme F to be simulated n Generating a random integer gamma between 1 and J by a random function, and acquiring a data acquisition scheme F to be simulated m In (a) and (b)Alternative to data acquisition scheme F to be simulated n Middle->Calculating the replaced data acquisition scheme to be simulated +.>Is->Fitness +.>Degree of adaptation delta from the original m Comparing the sizes, if the fitness is +.>Is greater than the original fitness delta m Fitness +.>As a fitness delta m Storing and replacing the data acquisition scheme to be simulated +.>As a data acquisition scheme F to be simulated m Storing; if it is fitness->Not greater than the original fitness delta m No operation is performed; at the same time record the update times w m (g) Number of updates w m (g) Initial 0, each time data acquisition scheme F to be simulated m When the updating operation of the data acquisition scheme to be simulated is executed, if the replaced data acquisition scheme to be simulated is executed +.>As a data acquisition scheme F to be simulated m Store w m (g) Assigning +1 to w m (g) Otherwise, no operation is performed.
6. The government affair big data platform data acquisition optimization method according to claim 5 is characterized in that the cross reorganization operation specifically comprises the following steps: randomly selecting two data acquisition schemes H to be simulated from male parents of data acquisition schemes H to be simulated m Generating two random integers between 1 and J through a random function, wherein the larger random integer is recorded as eta 1 The smaller random integer is noted as eta 2 Two data acquisition schemes H to be simulated m The value of j in the middle is eta 1 And eta 2 All gene fragments in between are replaced.
7. The government affair big data platform data acquisition optimization method according to claim 6, wherein the mutation operation specifically comprises the following steps: selecting the data acquisition schemes to be simulated in the sub-set of the simulation data acquisition schemes one by oneH m For the selected data acquisition scheme H to be simulated m Generating a random number lambda between 0 and 1 by a random function, and judging that lambda > P c Whether or not it is true, P c For the variation probability, if "lambda > P c "true", two random integers between 1 and J are generated by a random function, denoted μ respectively 1 Sum mu 2 Data acquisition scheme H to be simulated m Wherein j takes the value mu 1 Sum mu 2 Is replaced by a gene fragment of (a).
8. The government affair big data platform data collection and optimization method according to claim 7, further comprising a government affair big data platform data collection and optimization system adopting the government affair big data platform data collection and optimization method, wherein the system comprises:
the acquisition node acquisition module is used for acquiring acquisition nodes and numbering;
the CPU computing force acquisition module is used for acquiring the CPU computing force corresponding to the acquisition node;
the acquisition task acquisition module is used for acquiring acquisition nodes and numbering;
the acquisition task data volume acquisition module is used for acquiring acquisition task data volume corresponding to an acquisition task;
the data acquisition scheme simulation module is used for carrying out optimization simulation on the data acquisition task through the improved genetic algorithm based on the acquisition node, the CPU computing power, the acquisition task and the data quantity of the acquisition task and outputting a data acquisition scheme;
and the data acquisition module is used for distributing the acquisition tasks to the acquisition nodes according to the data acquisition scheme to acquire social network data.
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