CN110737871A - Intelligent street lamp big data distributed computing and scheduling method based on improved ant colony algorithm - Google Patents
Intelligent street lamp big data distributed computing and scheduling method based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention relates to the field of intelligent street lamp and big data distributed computing, in particular to intelligent street lamp big data distributed computing and dispatching methods based on an improved ant colony algorithm, which are different in that the method comprises the steps of S1, classifying tasks according to city street lamp big data collected by a data collection module to form a task set, S2, classifying distributed computing units in a data processing module to form a computing unit set, and S3, performing iterative distribution of computing tasks according to the improved ant colony algorithm to form an optimal solution of distributed computing and dispatching.
Description
Technical Field
The invention relates to the field of intelligent street lamps and big data distributed computing, in particular to intelligent street lamp big data distributed computing and scheduling methods based on an improved ant colony algorithm.
Background
At present, the construction of a smart city is greatly promoted by the state, the smart street lamp is used as a ubiquitous terminal tentacle in the city, is a foundation of the smart city, is a 'complex' of ports of the Internet of things and is also an part of the concept of the smart city, the construction and management level of the city is improved by series informatization technical means, illumination according to needs is realized by automatic brightness adjustment of roads, energy conservation and consumption reduction are realized, the traditional manual patrol mode is changed by intelligent monitoring of equipment, the operation and maintenance cost is reduced, public resources are fully utilized by integration of -based rod pieces with multiple functions, repeated construction is avoided, an optimal carrier is provided for covering of future 5G signals, power supply and network interfaces are reserved, and the comprehensive management platform of the smart street lamp is used for realizing multistage intelligent control of a system and providing -th hand data for city management.
Therefore, distributed calculation is carried out on the urban big data collected by the intelligent street lamps, improved ant colony algorithms are adopted to carry out quick and effective distribution of calculation tasks, calculation efficiency is improved, system performance is improved, and the method has important significance for interconnection and intercommunication of intelligent urban data and efficient urban management.
In view of this, in order to overcome the technical defects, it is an urgent problem in the art to provide intelligent street lamp big data distributed computing and scheduling methods based on the improved ant colony algorithm.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides intelligent street lamp big data distributed computing and scheduling methods and systems based on an improved ant colony algorithm, so that the city big data acquired by the intelligent street lamp can be rapidly and effectively computed and analyzed, and the computing efficiency of the system is improved.
In order to solve the technical problems, the invention adopts the technical scheme that intelligent street lamp big data distributed computing and scheduling methods based on an improved ant colony algorithm are characterized by comprising the following steps:
s1, carrying out task classification on the urban street lamp big data collected by the data collection module to form a task set;
s2, classifying distributed computing units in the data processing module to form a computing unit set;
and S3, performing iterative distribution of calculation tasks according to the improved ant colony algorithm to form a distributed calculation scheduling optimal solution.
According to the above scheme, the task set in step S1 is Tasks [0,1, … N ], the subscript of the array indicates the number of the task, and the value of the array indicates the length of the task.
In the above scheme, the set of computing units in step S2 is Cells [0,1,2, … M ], the subscript of the array indicates the number of the computing unit, and the array value indicates the processing speed of the computing unit.
According to the scheme, the specific steps of the step S3 are as follows:
s31, initializing a task set and a computing unit set;
s32, initializing a pheromone matrix N < M > and setting all pheromone initial values to be 1;
and S33, iterative searching.
According to the scheme, the specific steps of the step S33 are as follows:
s331, calculating the task execution time, wherein the calculation formula is a task execution time matrix timeMatrix [ i ] [ j ] = Tasks [ i ]/Cells [ j ], namely the time of the task i in the calculation unit j;
the method comprises the steps of S332, carrying out iteration, wherein in each iteration, all ants need to complete the distribution of all tasks, carrying out Num times of circulation, wherein each circulation is participated by a total of nANT ants, each ant is task dispatchers, each ant in each iteration needs to complete the distribution of all tasks, and a feasible solution of iterations is formed;
s333, calculating the task execution time and recording the task execution time into a task execution time matrix timeArray _ oneIt;
s334, updating pheromone matrix N [ M ];
and S335, repeating the steps S332 and S to carry out iCount iterative calculation for the total times to form an optimal solution.
According to the above scheme, the timeMatrix matrix in step S331 is an initialized task execution time matrix.
According to the above scheme, the timeArray _ oneIt matrix in step S333 is a task execution time matrix that is updated by continuously performing data iterative computation in the iterative computation process.
According to the above scheme, the iCount value in step S335 is set to 1000, and optimization and adjustment can be performed according to the experimental result.
According to the scheme, the Num value in step S332 is set to 1000, the nAnt value is set to 50, and optimization and adjustment can be performed according to the experimental result.
According to the scheme, the task execution allocation strategy in the step S332 is improved aiming at the ant colony algorithm; wherein, the first 60% ants execute the allocation strategy according to the concentration of the pheromone, and the last 40% ants randomly allocate the calculation units according to the task.
Compared with the prior art, the invention has the beneficial characteristics that: the city big data collected by the intelligent street lamp is quickly and effectively calculated and analyzed, the calculation efficiency of the system is improved, and the consumption of system resources and performance is reduced.
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FIG. 1 is a schematic flow chart of an embodiment of the present invention;
fig. 2 is a block diagram of the structure of the embodiment of the present invention.
Detailed Description
For purposes of making the objects, aspects and advantages of the present invention more apparent, the present invention is described in further detail with reference to the accompanying drawings and the specific embodiments, it being understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting.
Many aspects of the invention are better understood with reference to the following drawings. The components in the drawings are not necessarily to scale. Instead, emphasis is placed upon clearly illustrating the components of the present invention. Moreover, in the several views of the drawings, like reference numerals designate corresponding parts.
The word "exemplary" or "illustrative" as used herein means serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" or "illustrative" is not necessarily to be construed as preferred or advantageous over other embodiments. All of the embodiments described below are exemplary embodiments provided to enable persons skilled in the art to make and use the examples of the disclosure and are not intended to limit the scope of the disclosure, which is defined by the claims. In other instances, well-known features and methods are described in detail so as not to obscure the invention. For purposes of the description herein, the terms "upper," "lower," "left," "right," "front," "rear," "vertical," "horizontal," and derivatives thereof shall relate to the invention as oriented in fig. 1. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description. It is also to be understood that the specific devices and processes illustrated in the attached drawings, and described in the following specification are simply exemplary embodiments of the inventive concepts defined in the appended claims. Hence, specific dimensions and other physical characteristics relating to the embodiments disclosed herein are not to be considered as limiting, unless the claims expressly state otherwise.
Referring to fig. 1 and fig. 2, the intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm of the present invention is different in that the method includes the following steps:
s1, carrying out task classification on the urban street lamp big data collected by the data collection module to form a task set;
s2, classifying distributed computing units in the data processing module to form a computing unit set;
and S3, performing iterative distribution of calculation tasks according to the improved ant colony algorithm to form a distributed calculation scheduling optimal solution.
According to the above scheme, the task set in step S1 is Tasks [0,1, … N ], the subscript of the array indicates the number of the task, and the value of the array indicates the length of the task.
In the above scheme, the set of computing units in step S2 is Cells [0,1,2, … M ], the subscript of the array indicates the number of the computing unit, and the array value indicates the processing speed of the computing unit.
According to the scheme, the specific steps of the step S3 are as follows:
s31, initializing a task set and a computing unit set;
s32, initializing a pheromone matrix N < M > and setting all pheromone initial values to be 1;
and S33, iterative searching.
According to the scheme, the specific steps of the step S33 are as follows:
s331, calculating the task execution time, wherein the calculation formula is a task execution time matrix timeMatrix [ i ] [ j ] = Tasks [ i ]/Cells [ j ], namely the time of the task i in the calculation unit j;
the method comprises the steps of S332, carrying out iteration, wherein in each iteration, all ants need to complete the distribution of all tasks, carrying out Num times of circulation, wherein each circulation is participated by a total of nANT ants, each ant is task dispatchers, each ant in each iteration needs to complete the distribution of all tasks, and a feasible solution of iterations is formed;
s333, calculating the task execution time and recording the task execution time into a task execution time matrix timeArray _ oneIt;
s334, updating pheromone matrix N [ M ];
and S335, repeating the steps S332 and S to carry out iCount iterative calculation for the total times to form an optimal solution.
According to the above scheme, the timeMatrix matrix in step S331 is an initialized task execution time matrix.
According to the above scheme, the timeArray _ oneIt matrix in step S333 is a task execution time matrix that is updated by continuously performing data iterative computation in the iterative computation process.
According to the above scheme, the iCount value in step S335 is set to 1000, and optimization and adjustment can be performed according to the experimental result. In order to avoid excessive iteration, the iteration number iCount can be set to 1000, the current local optimal solution is taken as the global optimal solution, and optimization adjustment can be performed according to an experimental result.
According to the above scheme, the Num value in step S332 may be set to 1000 first, and the nAnt value may be set to 50 first, and the optimization and adjustment may be performed according to the experimental result.
According to the scheme, the task execution allocation strategy in the step S332 is improved aiming at the ant colony algorithm; wherein, the first 60% ants execute the allocation strategy according to the concentration of the pheromone, and the last 40% ants randomly allocate the calculation units according to the task.
The intelligent street lamp system comprises the data acquisition module, the data processing module and the data storage module, and through the strategy improvement of the ant colony algorithm, the quick and effective calculation and analysis processing of the big data of the urban street lamp are realized, the calculation efficiency of the system is improved, and the consumption of system resources and performance is reduced.
It will be apparent to those skilled in the art that many more modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the following claims .
Claims (10)
1. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm is characterized by comprising the following steps of:
s1, carrying out task classification on the urban street lamp big data collected by the data collection module to form a task set;
s2, classifying distributed computing units in the data processing module to form a computing unit set;
and S3, performing iterative distribution of calculation tasks according to the improved ant colony algorithm to form a distributed calculation scheduling optimal solution.
2. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 1, wherein: the task set in step S1 is Tasks [0,1, … N ], the subscript of the array indicates the number of the task, and the value of the array indicates the length of the task.
3. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 2, wherein: the set of computing units in step S2 is Cells [0,1,2, … M ], the subscript of the array indicates the number of the computing unit, and the array indicates the processing speed of the computing unit.
4. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 3, wherein: the specific steps of step S3 are as follows:
s31, initializing a task set and a computing unit set;
s32, initializing a pheromone matrix N < M > and setting all pheromone initial values to be 1;
and S33, iterative searching.
5. The intelligent street lamp big data distributed computing and dispatching method based on the improved ant colony algorithm as claimed in claim 4, wherein the specific steps of step S33 are as follows:
s331, calculating the task execution time, wherein the calculation formula is a task execution time matrix timeMatrix [ i ] [ j ] = Tasks [ i ]/Cells [ j ], namely the time of the task i in the calculation unit j;
the method comprises the steps of S332, carrying out iteration, wherein in each iteration, all ants need to complete the distribution of all tasks, carrying out Num times of circulation, wherein each circulation is participated by a total of nANT ants, each ant is task dispatchers, each ant in each iteration needs to complete the distribution of all tasks, and a feasible solution of iterations is formed;
s333, calculating the task execution time and recording the task execution time into a task execution time matrix timeArray _ oneIt;
s334, updating pheromone matrix N [ M ];
and S335, repeating the steps S332 and S to carry out iCount iterative calculation for the total times to form an optimal solution.
6. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein: the timeMatrix in step S331 is an initialized task execution time matrix.
7. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein: the timeArray _ oneIt matrix in step S333 is a task execution time matrix that is updated by continuously performing data iterative computation during the iterative computation.
8. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein: the iCount value in step S335 is set to 1000, and may be optimized and adjusted according to the experimental result.
9. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein: the Num value in step S332 is set to 1000, and the nAnt value is set to 50, and optimization and adjustment can be performed according to the experimental result.
10. The intelligent street lamp big data distributed computing and scheduling method based on the improved ant colony algorithm as claimed in claim 5, wherein: in the step S332, the task executes a distribution policy, and performs policy improvement for the ant colony algorithm; wherein, the first 60% ants execute the allocation strategy according to the concentration of the pheromone, and the last 40% ants randomly allocate the calculation units according to the task.
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