CN117978665A - Service data arrangement method and device - Google Patents
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Abstract
The invention provides a business data arrangement method and a device, wherein the method comprises the following steps: initializing and generating a plurality of service arrangement results according to service information needing arrangement; according to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space; calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; updating the speed and the position of each particle to obtain updated particles; checking whether the iteration accords with the ending condition, if not, continuing to execute the steps of calculating the individual fitness value and the global optimal fitness value of each updated particle according to the business data corresponding to each updated particle, and if so, taking the particle corresponding to the global optimal fitness value as the optimal solution.
Description
Technical Field
The invention relates to the technical field of industrial Internet, in particular to a business data arrangement method and device.
Background
In the application scenario of the 5G industrial internet, there are many machine-to-machine connection corresponding delay-sensitive services, such as mechanical arm control, video monitoring, logistics transportation, assembly tracking services, and the like.
The current mainstream scheme is to build an integer programming model of the programming problem and then calculate the optimal solution using a solver. The proposal has the advantages of obtaining the optimal arrangement result in the global sense, and has the disadvantages of consuming a great deal of time and computational resources when processing large-scale arrangement, and having low efficiency, and the situation facing the 5G industrial scene is the latter.
Disclosure of Invention
The invention provides a business data arrangement method and a business data arrangement device, which are used for solving the defect that in the prior art, the efficiency of calculating an optimal solution by an integer programming model is low.
The invention provides a business data arrangement method, which comprises the following steps:
Initializing and generating a plurality of service arrangement results according to service information needing arrangement;
According to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles, and tracking optimal particles in the solution space;
Calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value;
Updating the speed and the position of each particle to obtain updated particles;
Checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
According to the service data arranging method provided by the invention, the service data comprises the following steps: the service data packet size, service correlation and service execution start time of each service;
according to the business data corresponding to each particle, calculating the individual fitness value of each particle by utilizing a predefined integer linear programming model, wherein the method comprises the following steps:
And maximizing the total quantity of the service data packets by utilizing a predefined integer linear programming model according to the size of the service data packets, the service correlation degree and the service execution starting time corresponding to each particle, minimizing the difference between the time equivalent of the service correlation degree and the time equivalent of the service dispersion degree, and taking the final minimized value as the individual fitness value of each particle.
According to the business data arrangement method provided by the invention, the integer linear programming model is realized by the following formula:
and, the constraint includes:
The total traffic volume per time slot is less than the maximum bearer capacity:
the total number of terminals per time slot is the maximum number of bearers:
count(is)≤Nslot,s＝1,2,…,2000
The time slot is an uplink time slot:
is an integer
Wherein N slot represents the maximum number of bearers per unit time slot;
D slot denotes the maximum bearer capacity of service data per unit time slot;
n and m represent uplink time slots and downlink time slots respectively;
M represents the ratio of uplink time slots to downlink time slots, wherein the d k th time slot is the downlink time slot;
s i,j represents the size of a service data packet, wherein i represents a terminal number and j represents a service number;
p i,j represents a service correlation degree, wherein i represents a terminal number and j represents a service number;
t i,j denotes a service execution start time, where i denotes a terminal number and j denotes a service number.
According to the service data arrangement method provided by the invention, after calculating the individual fitness value of each updated particle, the method further comprises the following steps:
Comparing the individual fitness value of each updated particle with the current individual fitness value;
If the individual fitness value of each updated particle is better than the current individual fitness value, taking the individual fitness value of the updated particle as the current individual fitness value of the particle, and taking the position of the updated particle as the individual optimal position;
if the individual fitness value of each updated particle is different from the current individual fitness value, the current individual fitness value and the individual optimal position are kept unchanged.
According to the business data arrangement method provided by the invention, the global optimal fitness value is determined according to the updated individual fitness values of the particles, and the business data arrangement method comprises the following steps:
comparing the updated individual fitness values of the plurality of particles with the current global optimal fitness value respectively;
And if the highest value of the individual fitness values of the plurality of particles after updating is better than the current global optimal fitness value, taking the highest value as the current global fitness value of the plurality of particles, and taking the position of the particle corresponding to the highest value as the global optimal position.
According to the service data arrangement method provided by the invention, the speed and the position of each particle are updated to obtain updated particles, and the method comprises the following steps:
The method for updating the speed of each particle comprises the following steps:
Defining a linear decreasing weight;
and obtaining the updated speed of each particle according to the current speed, the linear decreasing weight, the current global optimal position of the particle, the current individual optimal position of the particle and the current position of the particle.
According to the business data arrangement method provided by the invention, the speed update of each particle is realized by the following formula:
Wherein, Representing the updated velocity of each particle; /(I)Representing the current velocity of each particle, c 1 representing the individual learning factor, c 2 representing the population learning factor, ω representing the linearly decreasing weight, r 1,r2 representing the random number within interval [0,1], increasing the randomness of the search, p id representing the current individual optimum position of the particle, p gd representing the current global optimum position of the particle,/>Indicating the current position of the particle;
Wherein ω max is an initial weight, ω min is a final weight, k is a current iteration number, and iter max is a maximum iteration number;
the location update for each particle is achieved by the following formula:
Wherein, Representing updated position,/>Representing the current position of the particle,/>Representing the updated velocity of each particle.
The invention also provides a service data arrangement device, which comprises:
The business arrangement module is used for initializing and generating a plurality of business arrangement results according to business information which is arranged as required;
The initialization module is used for initializing the position and the speed of each particle in a solution space by taking each service arrangement result as the particle according to a predefined particle swarm algorithm, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles and tracking the optimal particle in the solution space;
The optimal value calculation module is used for calculating the individual fitness value of each particle by utilizing a predefined integer linear programming model according to the service data corresponding to each particle, and determining the global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value;
the particle updating module is used for updating the speed and the position of each particle to obtain updated particles;
And the iteration module is used for checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the service data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the business data orchestration method according to any one of the preceding claims when the program is executed.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the business data orchestration method according to any one of the preceding.
The invention also provides a computer program product comprising a computer program which when executed by a processor performs the steps of a business data orchestration method according to any one of the preceding.
According to the business data arrangement method and device, the integral packet sending data quantity is maximized on the premise of guaranteeing bandwidth limitation and time delay requirements, in order to prevent low solving efficiency of the integral linear programming model, an arrangement method based on a particle swarm algorithm is designed, after individual fitness values and global optimal fitness values of particles are obtained by utilizing a predefined integral linear programming model, iterative computation is continued, the individual fitness values and the global optimal fitness values of the particles are updated until final global optimal fitness values are obtained, and efficient solving of arrangement problems is achieved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a business data arrangement method according to the present invention;
FIG. 2 is a second flow chart of the business data arrangement method according to the present invention;
FIG. 3 is a third flow chart of the business data arrangement method according to the present invention;
FIG. 4 is a flow chart of a business data arrangement method according to the present invention;
FIG. 5 is a flowchart of a business data arrangement method according to the present invention;
FIG. 6 is a schematic diagram of a service data arrangement apparatus according to the present invention;
Fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The service data orchestration method and apparatus of the present invention are described below in connection with fig. 1-6.
The embodiment of the invention discloses a service data arrangement method, which is shown in fig. 1 and comprises the following steps:
101. and initializing and generating a plurality of service arrangement results according to the service information which is required to be arranged.
Wherein, the service information comprises: terminal number, task number, period, and resource block size.
For a plurality of pieces of service information, service arrangement is required to determine the packet sending time of each piece of service information. In the step, a plurality of service arrangement results are generated, and iterative computation is performed in the subsequent step to determine the optimal service arrangement result.
In the process of establishing a deterministic network, three main indicators are mainly considered in this embodiment: delay, jitter, and packet loss. The time delay refers to the time required for data to be transmitted from one end of a network to the other end, and the low time delay has wide application in industrial production, such as quick response and positioning of anomalies when detecting parts by using monitoring videos in equipment manufacturing workshops; when the multiple mechanical arms work cooperatively, some actions need to be synchronized in microsecond level. Consider that 5GNR employs time division duplexing (Time Division Duplexing, TDD) technology, i.e., different timeslots using the same frequency carrier are received and transmitted as bearers for a channel on one channel, while traffic on the terminal side can only be sent on the uplink timeslots. Therefore, the uplink time slot in relative time can be determined according to the time slot proportion, and when the service data arrangement is carried out, the packet is sent in the uplink time slot as much as possible, so that the purpose of reducing the time delay is achieved.
102. And initializing the position and the speed of each particle in a solution space by taking each service arrangement result as the particle according to a predefined particle swarm algorithm, wherein the particle swarm algorithm is used for carrying out group iterative calculation on a plurality of particles and tracking the optimal particles in the solution space.
The service data arrangement problem belongs to the combination optimization problem and is an NP-hard problem. The problem of scheduling for small scales can be solved by means of a solver by means of integer programming. In order to ensure that when the service scale of the terminal is large, the algorithm can still give an arrangement result in a short time, a solution idea is designed by a heuristic algorithm by means of the teaching of the biological world rule, and a better solution is found in the global range. The particle swarm algorithm simulates the foraging behavior of the bird swarm, tracks optimal particles in a solution space based on swarm iteration, and has remarkable effect on nonlinear continuous optimization and combination optimization.
The location of the initial search point and its velocity are typically randomly generated within an allowable range,
103. Calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value.
In this embodiment, an integer linear programming model is proposed, and the objective of the problem model is to maximize the total amount of data packets that can be sent over all uplink timeslots. To maximize the total amount of packet data, traffic is ordered as fully as possible for each slot, which is equivalent to minimizing the maximum traffic peak in all slots, i.e., minimizing the global optimum. Finally, the smaller the maximum value of the uplink service data of the unit time slot in the design 1s is, the better the arrangement effect is. Considering that certain relevancy exists between services, the distance between services with higher relevancy needs to be ensured not to exceed a tolerance value, a penalty term is set, namely a correlation function between time equivalent of the service relevancy and the service dispersity is set, and the smaller the value of the penalty term is, the better the service starting time is arranged to meet the service relevancy.
104. And updating the speed and the position of each particle to obtain updated particles.
For the particles, the positions are changed after one update, and the corresponding individual fitness values may be changed, and the corresponding global fitness values may be changed, so that the fitness values need to be recalculated.
And for updating the particle speed, the particle granularity traversed in the characterization optimizing process is characterized in that the speed is too high to be easily sunk into a local optimal solution, and the convergence speed is too low if the speed is too low, so that the particle speed needs to be updated along with the updating of the position.
105. Checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
The iteration condition may be various, for example, the number of iterations reaches a threshold, or the globally optimal solution is not updated any more, etc.
After the global optimal solution is obtained, that is, the global optimal fitness value and the particles corresponding to the global optimal fitness value are determined, and then the optimal service arrangement result is determined. And according to the optimal service arrangement result, the service data can be packaged in uplink time slots according to the arrangement result.
In addition, two indexes in actual production are considered in the embodiment: jitter and packet loss.
In actual production, the terminal side is limited by the field environment, and cannot send packets according to the set starting time and period in percentage, and jitter can occur around the starting time, so that the overall arrangement effect is affected, and packet loss is possibly caused. In order to avoid the above situation, the present embodiment further provides a buffering mechanism, where the data packet with the timestamp is stored in the local buffering space first, and sent out immediately when the set time is reached. Similar to the concept of a waiting hall, passengers can wait for a departure from a bus by holding a ticket at a platform, and the departure can be achieved as soon as the time arrives, so that the situation that the passengers arrive for a departure in advance is avoided.
In another case, after the packet delay, the delayed data packet is accumulated on a subsequent uplink time slot, and the upper limit of the transmittable data packet of the time slot may be exceeded, so that the packet is lost. The method is to locate a group of three time slots in the programming algorithm, only consider the packet sending in the first time slot and the fault tolerance in the last two time slots.
According to the business data arrangement method provided by the embodiment of the invention, the whole packet sending data volume is maximized on the premise of guaranteeing bandwidth limitation and time delay requirements by establishing the integer linear programming model, in order to prevent low solving efficiency of the integer linear programming model, an arrangement method based on a particle swarm algorithm is designed, after individual fitness values and global optimal fitness values of particles are obtained by utilizing the predefined integer linear programming model, iterative computation is continued, and the individual fitness values and the global optimal fitness values of the particles are updated until final global optimal fitness values are obtained, so that efficient solving of arrangement problems is realized.
Specifically, the service data includes: the service data packet size, service correlation and service execution start time of each service;
according to the business data corresponding to each particle, calculating the individual fitness value of each particle by utilizing a predefined integer linear programming model, wherein the method comprises the following steps:
And maximizing the total quantity of the service data packets by utilizing a predefined integer linear programming model according to the size of the service data packets, the service correlation degree and the service execution starting time corresponding to each particle, minimizing the difference between the time equivalent of the service correlation degree and the time equivalent of the service dispersion degree, and taking the final minimized value as the individual fitness value of each particle.
Specifically, in this embodiment, the service data corresponding to each particle may include: service data packet size, service correlation, and service execution start time.
Specifically, the integer linear programming model is realized by the following formula (1):
wherein max s∑ssi,j represents the maximum total amount of packet data, And representing a penalty term, namely, a correlation function between the time equivalent of the service relativity and the service dispersivity.
And, the constraint includes:
The total traffic volume per time slot is less than the maximum bearer capacity:
the total number of terminals per time slot is the maximum number of bearers:
count(is)≤Nslot,s＝1,2,…,2000
The time slot is an uplink time slot:
is an integer
Wherein N slot represents the maximum number of bearers per unit time slot;
D slot denotes the maximum bearer capacity of service data per unit time slot;
n and m represent uplink time slots and downlink time slots respectively;
M represents the ratio of uplink time slots to downlink time slots, wherein the d k th time slot is the downlink time slot;
s i,j represents the size of a service data packet, wherein i represents a terminal number and j represents a service number;
p i,j represents a service correlation degree, wherein i represents a terminal number and j represents a service number;
t i,j denotes a service execution start time, where i denotes a terminal number and j denotes a service number.
The determination of individual fitness values for each particle can be achieved by an integer linear programming model.
Specifically, in this embodiment, referring to fig. 2, after calculating the individual fitness value of each updated particle, the method further includes:
201. the individual fitness value of each updated particle is compared to the current individual fitness value.
202. And if the individual fitness value of each updated particle is better than the current individual fitness value, taking the individual fitness value of the updated particle as the current individual fitness value of the particle, and taking the position of the updated particle as the individual optimal position.
203. If the individual fitness value of each updated particle is different from the current individual fitness value, the current individual fitness value and the individual optimal position are kept unchanged.
For example, the individual fitness values of 3 particles are B1, B2, and B3, respectively, and the updated individual fitness values of the particles are B1', B2', and B3', respectively, and the individual fitness values B1 and B1', B2 and B2', and B3' are compared. If B1 'is better than B1, taking B1' as the current individual fitness value of the particle; if B2 'is worse than B2 and B3' is worse than B3, the current individual fitness values B2 and B3 are kept unchanged.
Through steps 201 to 203, the updating of the individual fitness value of the updated particles and the updating of the individual optimal position can be realized. The individual optimum position of the particle is a position where the individual fitness value of the particle is optimum, and does not refer to the current position of the particle. It is possible that the current position of the particle is a, but that the individual optimal position is the corresponding position a' in the last iteration.
Specifically, in the present embodiment, referring to fig. 3, determining a global optimal fitness value according to the updated individual fitness values of the plurality of particles includes:
301. And comparing the updated individual fitness values of the plurality of particles with the current global optimal fitness value respectively.
302. And if the highest value of the individual fitness values of the plurality of particles after updating is better than the current global optimal fitness value, taking the highest value as the current global fitness value of the plurality of particles, and taking the position of the particle corresponding to the highest value as the global optimal position.
For example, the individual fitness values of the plurality of particles after updating are B1, B2, B3, and B4, respectively, where B4 is highest, the current global optimum is C1, and both B3 and B4 are greater than C1. Then B4 is taken as the current global fitness value of the plurality of particles. Thereby completing the updating of the global fitness value.
303. If the individual fitness values of the plurality of particles after updating are all worse than the current global optimal fitness value, the current global optimal fitness value is kept unchanged.
Through steps 301 to 303, the global optimum fitness value of the updated particle and the global optimum position can be updated.
Specifically, in this embodiment, referring to fig. 4, in step 104, the method for updating the velocity of each particle includes:
401. A linearly decreasing weight is defined.
402. And obtaining the updated speed of each particle according to the current speed, the linear decreasing weight, the current global optimal position of the particle, the current individual optimal position of the particle and the current position of the particle.
The particle swarm algorithm simulates the foraging behavior of the bird swarm, tracks optimal particles in a solution space based on swarm iteration, and has remarkable effect on nonlinear continuous optimization and combination optimization. However, the basic particle swarm algorithm has a problem of slow convergence, and this embodiment defines a linearly decreasing weight as the following formula (2):
Where ω max is the initial weight, ω min is the final weight, k is the current iteration number, and iter max is the maximum iteration number. Omega is linearly decreasing, decreasing with increasing iteration number. In the initial stage of iteration, the set omega is larger, so that the algorithm maintains stronger global searching capability; and in the later iteration stage, omega is smaller, so that the capability of local optimization of an algorithm is enhanced.
Specifically, the velocity update for each particle is achieved by the following formula (3):
Wherein, Representing the updated velocity of each particle; /(I)Representing the current velocity of each particle, c 1 representing the individual learning factor, c 2 representing the population learning factor, ω representing the linearly decreasing weight, r 1,r2 representing the random number within interval [0,1], increasing the randomness of the search, p id representing the current individual optimum position of the particle, p gd representing the current global optimum position of the particle,/>Indicating the current position of the particle;
the location update for each particle is achieved by the following equation (4):
Wherein, Representing updated position,/>Representing the current position of the particle,/>Representing the updated velocity of each particle.
In order to facilitate understanding of the technical solution of the present embodiment, an embodiment of the present invention discloses a service data arrangement method, referring to fig. 5, including:
501. Initializing. And initializing and generating a plurality of service arrangement results according to the service information which is required to be arranged.
502. According to a predefined particle swarm algorithm, each business arrangement result is taken as a particle, and the position and the speed of each particle are initialized in a solution space.
The position z 0 and its velocity v 0 as the initial of the particles are typically randomly generated within the allowed range, the p id coordinate of each particle is set to the current position of the particle, and its corresponding individual fitness value is calculated, and the global optimum fitness value is the best one of the individual fitness values. And recording the particle serial number of the global optimal fitness value, and setting p gd as the current position of the particle corresponding to the global optimal fitness value.
503. And maximizing the total quantity of the service data packets by utilizing a predefined integer linear programming model according to the size of the service data packets, the service correlation degree and the service execution starting time corresponding to each particle, minimizing the difference between the time equivalent of the service correlation degree and the time equivalent of the service dispersion degree, taking the final minimized value as the individual fitness value of each particle, and determining the global optimal fitness value according to the individual fitness values of a plurality of particles.
504. And updating the speed and the position of each particle to obtain updated particles.
505. Checking whether the iteration meets the end condition, if not, executing step 506 according to the updated particles; if so, go to step 507.
506. Comparing the individual fitness value of each updated particle with the current individual fitness value and the current global optimal fitness value, finding the individual optimal fitness value p best of the particle, finding the global optimal fitness value g best of the particle, and returning to execute step 504.
For specific comparison steps, refer to the foregoing steps 201 to 203 and steps 301 to 303, and the description of this embodiment is omitted.
507. And taking the particles corresponding to the global optimal fitness value as an optimal solution.
See tables 1 and 2 below. Table 1 is the service information to be laid out, and table 2 is the final service layout result.
TABLE 1
| Terminal number | Task number | Cycle time | Resource block size |
| CLIENT_8E040BCE | PLC_TAKS1 | 30000 | 150 |
| CLIENT_8E040BCE | PLC_TAKS2 | 20000 | 120 |
| CLIENT_8E040BCE | PLC_TAKS3 | 45000 | 230 |
| CLIENT_8E040BCE | PLC_TAKS4 | 5000 | 180 |
| CLIENT_8E040BCE | PLC_TAKS5 | 12000 | 110 |
TABLE 2
| Terminal number | Task number | Cycle time | Time of hair pack |
| CLIENT_8E040BCE | PLC_TAKS1 | 30000 | 4000 |
| CLIENT_8E040BCE | PLC_TAKS2 | 20000 | 14000 |
| CLIENT_8E040BCE | PLC_TAKS3 | 45000 | 19500 |
| CLIENT_8E040BCE | PLC_TAKS4 | 5000 | 2000 |
| CLIENT_8E040BCE | PLC_TAKS5 | 12000 | 2000 |
The service data arranging device provided by the invention is described below, and the service data arranging device described below and the service data arranging method described above can be correspondingly referred to each other.
The invention provides a service data arrangement device, see fig. 6, comprising:
The service arrangement module 601 is configured to generate a plurality of service arrangement results in an initialized manner according to the service information that needs to be arranged;
An initialization module 602, configured to initialize a position and a speed of each particle in a solution space with each service arrangement result as a particle according to a predefined particle swarm algorithm, where the particle swarm algorithm is configured to perform a population iterative computation on a plurality of particles, and track an optimal particle in the solution space;
The optimal value calculating module 603 is configured to calculate an individual fitness value of each particle according to the service data corresponding to each particle by using a predefined integer linear programming model, and determine a global optimal fitness value according to the individual fitness values of the plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value;
a particle update module 604, configured to update a speed and a position of each particle to obtain updated particles;
And the iteration module 605 is configured to check whether the iteration meets an end condition, if not, continue to execute the step of calculating an individual fitness value of each updated particle by using a predefined integer linear programming model according to the service data corresponding to each updated particle, and determine a global optimal fitness value according to the individual fitness values of the updated plurality of particles, if yes, take the particle corresponding to the global optimal fitness value as an optimal solution.
Optionally, the service data includes: the service data packet size, service correlation and service execution start time of each service;
The optimal value calculating module 603 is specifically configured to: and maximizing the total quantity of the service data packets by utilizing a predefined integer linear programming model according to the size of the service data packets, the service correlation degree and the service execution starting time corresponding to each particle, minimizing the difference between the time equivalent of the service correlation degree and the time equivalent of the service dispersion degree, and taking the final minimized value as the individual fitness value of each particle.
Optionally, the apparatus further comprises an individual fitness value determining module for: after calculating the individual fitness value of each updated particle, comparing the individual fitness value of each updated particle with the current individual fitness value;
If the individual fitness value of each updated particle is better than the current individual fitness value, taking the individual fitness value of the updated particle as the current individual fitness value of the particle, and taking the position of the updated particle as the individual optimal position;
if the individual fitness value of each updated particle is different from the current individual fitness value, the current individual fitness value and the individual optimal position are kept unchanged.
Optionally, the apparatus further comprises a global fitness value determining module for:
comparing the updated individual fitness values of the plurality of particles with the current global optimal fitness value respectively;
And if the highest value of the individual fitness values of the plurality of particles after updating is better than the current global optimal fitness value, taking the highest value as the current global fitness value of the plurality of particles, and taking the position of the particle corresponding to the highest value as the global optimal position.
Optionally, the particle update module 604 is specifically configured to:
Defining a linear decreasing weight;
and obtaining the updated speed of each particle according to the current speed, the linear decreasing weight, the current global optimal position of the particle, the current individual optimal position of the particle and the current position of the particle.
According to the business data arrangement device provided by the embodiment of the invention, the whole packet sending data volume is maximized on the premise of guaranteeing bandwidth limitation and time delay requirements by establishing the integer linear programming model, in order to prevent low solving efficiency of the integer linear programming model, an arrangement method based on a particle swarm algorithm is designed, after individual fitness values and global optimal fitness values of particles are obtained by utilizing the predefined integer linear programming model, iterative computation is continued, and the individual fitness values and the global optimal fitness values of the particles are updated until final global optimal fitness values are obtained, so that efficient solving of arrangement problems is realized.
Fig. 7 illustrates a physical schematic diagram of an electronic device, as shown in fig. 7, which may include: processor 710, communication interface (Communications Interface) 720, memory 730, and communication bus 740, wherein processor 710, communication interface 720, memory 730 communicate with each other via communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a business data orchestration method comprising: initializing and generating a plurality of service arrangement results according to service information needing arrangement; according to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles, and tracking optimal particles in the solution space; calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value; updating the speed and the position of each particle to obtain updated particles; checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
Further, the logic instructions in the memory 730 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of performing the business data orchestration method provided by the above methods, the method comprising: initializing and generating a plurality of service arrangement results according to service information needing arrangement; according to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles, and tracking optimal particles in the solution space; calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value; updating the speed and the position of each particle to obtain updated particles; checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a business data orchestration method provided by the above methods, the method comprising: initializing and generating a plurality of service arrangement results according to service information needing arrangement; according to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles, and tracking optimal particles in the solution space; calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value; updating the speed and the position of each particle to obtain updated particles; checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of orchestrating traffic data, comprising:
Initializing and generating a plurality of service arrangement results according to service information needing arrangement;
According to a predefined particle swarm algorithm, taking each service arrangement result as particles, and initializing the position and the speed of each particle in a solution space, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles, and tracking optimal particles in the solution space;
Calculating individual fitness values of each particle by utilizing a predefined integer linear programming model according to service data corresponding to each particle, and determining a global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value;
Updating the speed and the position of each particle to obtain updated particles;
Checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the business data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
2. The traffic data orchestration method according to claim 1, wherein the traffic data comprises: the service data packet size, service correlation and service execution start time of each service;
according to the business data corresponding to each particle, calculating the individual fitness value of each particle by utilizing a predefined integer linear programming model, wherein the method comprises the following steps:
And maximizing the total quantity of the service data packets by utilizing a predefined integer linear programming model according to the size of the service data packets, the service correlation degree and the service execution starting time corresponding to each particle, minimizing the difference between the time equivalent of the service correlation degree and the time equivalent of the service dispersion degree, and taking the final minimized value as the individual fitness value of each particle.
3. The business data orchestration method according to claim 2, wherein the integer linear programming model is implemented by the following formula:
and, the constraint includes:
The total traffic volume per time slot is less than the maximum bearer capacity:
the total number of terminals per time slot is the maximum number of bearers:
count(is)≤Nslot,s＝1,2,…,2000
The time slot is an uplink time slot:
is an integer
Wherein N slot represents the maximum number of bearers per unit time slot;
D slot denotes the maximum bearer capacity of service data per unit time slot;
n and m represent uplink time slots and downlink time slots respectively;
M represents the ratio of uplink time slots to downlink time slots, wherein the d k th time slot is the downlink time slot;
s i,j represents the size of a service data packet, wherein i represents a terminal number and j represents a service number;
p i,j represents a service correlation degree, wherein i represents a terminal number and j represents a service number;
t i,j denotes a service execution start time, where i denotes a terminal number and j denotes a service number.
4. The traffic data orchestration method according to claim 1, wherein after calculating the individual fitness value for each updated particle, the method further comprises:
Comparing the individual fitness value of each updated particle with the current individual fitness value;
If the individual fitness value of each updated particle is better than the current individual fitness value, taking the individual fitness value of the updated particle as the current individual fitness value of the particle, and taking the position of the updated particle as the individual optimal position;
if the individual fitness value of each updated particle is different from the current individual fitness value, the current individual fitness value and the individual optimal position are kept unchanged.
5. The traffic data orchestration method according to claim 4, wherein determining a global optimal fitness value from the updated individual fitness values of the plurality of particles comprises:
comparing the updated individual fitness values of the plurality of particles with the current global optimal fitness value respectively;
And if the highest value of the individual fitness values of the plurality of particles after updating is better than the current global optimal fitness value, taking the highest value as the current global fitness value of the plurality of particles, and taking the position of the particle corresponding to the highest value as the global optimal position.
6. The traffic data arranging method according to claim 1, wherein updating the speed and the position of each particle to obtain updated particles comprises:
The method for updating the speed of each particle comprises the following steps:
Defining a linear decreasing weight;
and obtaining the updated speed of each particle according to the current speed, the linear decreasing weight, the current global optimal position of the particle, the current individual optimal position of the particle and the current position of the particle.
7. The traffic data orchestration method according to claim 6, wherein,
The velocity update for each particle is achieved by the following formula:
Wherein, Representing the updated velocity of each particle; /(I)Representing the current velocity of each particle, c 1 representing the individual learning factor, c 2 representing the population learning factor, ω representing the linearly decreasing weight, r 1,r2 representing the random number within interval [0,1], increasing the randomness of the search, p id representing the current individual optimum position of the particle, p gd representing the current global optimum position of the particle,/>Indicating the current position of the particle;
Wherein ω max is an initial weight, ω min is a final weight, k is a current iteration number, and iter max is a maximum iteration number;
the location update for each particle is achieved by the following formula:
Wherein, Representing updated position,/>Representing the current position of the particle,/>Representing the updated velocity of each particle.
8. A business data orchestration device, comprising:
The business arrangement module is used for initializing and generating a plurality of business arrangement results according to business information which is arranged as required;
The initialization module is used for initializing the position and the speed of each particle in a solution space by taking each service arrangement result as the particle according to a predefined particle swarm algorithm, wherein the particle swarm algorithm is used for carrying out group iterative computation on a plurality of particles and tracking the optimal particle in the solution space;
The optimal value calculation module is used for calculating the individual fitness value of each particle by utilizing a predefined integer linear programming model according to the service data corresponding to each particle, and determining the global optimal fitness value according to the individual fitness values of a plurality of particles; the model objective of the integer linear programming model is to minimize a global optimal fitness value;
the particle updating module is used for updating the speed and the position of each particle to obtain updated particles;
And the iteration module is used for checking whether iteration accords with an ending condition, if not, continuing to execute the steps of calculating the individual fitness value of each updated particle by utilizing a predefined integer linear programming model according to the service data corresponding to each updated particle, determining a global optimal fitness value according to the individual fitness values of a plurality of updated particles, and if so, taking the particle corresponding to the global optimal fitness value as an optimal solution.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the business data orchestration method according to any one of claims 1-7 when the program is executed.
10. A non-transitory computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the business data orchestration method according to any one of claims 1 to 7.
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