CN114936808A - Cloud-edge cooperative task management system and method for substation fault detection - Google Patents
Cloud-edge cooperative task management system and method for substation fault detection Download PDFInfo
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Abstract
The invention discloses a cloud-edge collaborative task management system and method for substation fault detection, which relate to the technical field of power system fault detection and comprise a data acquisition module, a data analysis module, a load prediction module, a task scheduling module and a communication module which are sequentially connected, wherein the task scheduling module is also connected with a resource monitoring module, a fault detection module and a fault alarm module; according to the invention, a cloud-edge cooperative task management mode is adopted, so that the network transmission delay is reduced, and the smooth completion of urgent and time-sensitive tasks is facilitated; a load prediction mode is adopted to predict the system load condition at the next moment, which is beneficial to improving the resource utilization efficiency; the invention has simple structure, easy operation and convenient popularization.
Description
Technical Field
The invention relates to the technical field of power system fault detection, in particular to a cloud-edge collaborative task management system and method for substation fault detection.
Background
Under novel electric power system background, the intelligent monitoring equipment in the transformer substation is numerous, and the data presentation form is also different, has the digital coding data of collection such as temperature, humidity transducer, has the picture data of patrolling and examining the collection of making a video recording, also has the voiceprint data that sound collection equipment gathered, has formed the heterogeneous transformer substation's fault detection data set of multisource.
How to process and analyze mass data to realize data value and provide data support for stable operation of a power system is worth paying attention. The calculation resources needed for analyzing various types of data are different, the time requirements and the emergency degree of the transformer substation for removing different types of faults are also different, all the data are uploaded to the cloud end for analysis and processing in the traditional method, and the emergency faults can not be removed in time due to communication congestion and other reasons possibly in a result issuing mode, so that potential safety hazards are brought to the transformer substation.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a cloud-edge collaborative task management system and method for substation fault detection, and solves the problems in the background technology.
In order to achieve the purpose, the invention is realized by the following technical scheme: a cloud-edge collaborative task management system for substation fault detection comprises a data acquisition module, a data analysis module, a load prediction module, a task scheduling module and a communication module which are sequentially connected, wherein the communication module is also connected with the data acquisition module, and the task scheduling module is also sequentially connected with a resource monitoring module, a fault detection module and a fault alarm module;
the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;
the data analysis module carries out primary classification processing on the acquired data;
the load prediction module adopts a load prediction method based on similar days, the altitude of the transformer substation, the voltage level of the transformer substation, the daily average temperature, the humidity and the weather type are used as similar daily evaluation criteria, and a training set and a test set are screened out according to the degree of association; a long-short term memory network algorithm is adopted, the service time and the fault rate of electrical equipment in the transformer substation are combined, the scale of data collected by a data collection module is used as an adjustment reference, and the load condition at the next moment is predicted;
the task scheduling module distributes the fault detection task to computing nodes on the cloud side or the edge side by adopting a swarm intelligence algorithm according to the required resources and the time urgency degree of the task;
the resource monitoring module comprises a resource monitor and a monitoring alarm, and the resource monitor monitors resource states of cloud computing nodes and edge computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm gives an alarm, and the task scheduling module redistributes the task which cannot be executed at present to other nodes;
the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault processing measures for reference of operation and maintenance personnel;
the fault alarm module sends an alarm to operation and maintenance personnel after a fault occurs, and sends fault analysis data to the cloud backup to be used as historical data for post-accident fault analysis and prediction in advance at the next moment;
the communication module comprises a power wired private network and a 5G power virtual private network, wherein the power wired private network is used for data transmission of wired data acquisition equipment and wired transmission among edge computing nodes; the 5G electric virtual private network is used for data transmission of the mobile data acquisition equipment, wireless transmission between edge sides and cloud computing nodes and between modules of the system, and is used as a standby communication mode when the electric wired private network fails.
The invention also provides a cloud-edge cooperative task management method for substation fault detection, which comprises the following steps of:
s1: collecting data of electrical equipment in a transformer substation, including digital coding data, image data and sound pattern data of a transformer, a mutual inductor, a circuit breaker, a disconnecting switch and other equipment;
s2: preliminarily analyzing the collected data, and classifying according to sources;
s3: predicting the load condition at the next moment by adopting a load prediction method based on similar days;
s4: distributing the fault detection task to a proper computing node on a cloud side or an edge side by adopting a group intelligence-based task management method;
s5: monitoring the resource use condition of a task execution node, judging whether a computing node is abnormal or not, and if the computing node is abnormal, re-scheduling the task;
s6: further analyzing the tasks distributed by the nodes, judging whether faults exist, and if so, analyzing specific fault contents and fault occurrence points; if not, ending the task;
s7: judging whether an alarm needs to be sent out or not according to a fault detection result, and if so, immediately alarming to inform operation and maintenance personnel; if not, ending the task;
s8: and uploading the fault information to a cloud for storage.
Preferably, the step S3 includes the following substeps:
s3.1: inputting historical samples and influence factor data of a day to be predicted, wherein the influence factor data comprises the altitude of a transformer substation, the voltage level of the transformer substation, the service time of electrical equipment in the transformer substation, the comprehensive failure rate of the electrical equipment in the transformer substation, the daily average air temperature, the humidity and the weather type;
s3.2: selecting a data set required by load prediction, wherein the data set comprises a training set and a testing set;
s3.3: and predicting the time sequence by adopting a long-term and short-term memory network algorithm, and predicting the load condition at the next moment, wherein the load condition comprises calculation, storage and network resources required at the next moment.
Preferably, the step S3.2 of selecting the data set required for load prediction includes the following steps:
s3.2.1: constructing a similar daily influence factor matrix comprising a subsequence and a mother sequence;
s3.2.2: carrying out initialization processing on the subsequence and the mother sequence;
s3.2.3: selecting a test set, taking the influence factor data of a day to be predicted as a mother sequence, randomly selecting the influence factor data of a sample with a specified proportion from historical samples as subsequences, calculating the association degree between each subsequence and the mother sequence, and selecting the subsequences before the association degree is sorted from high to low
Taking the sample of the bits as a test set, and selecting the sample with the highest correlation degree as a training label day;
s3.2.4: selecting a training set, taking the influence factor data of a training label day as a mother sequence, taking the influence factor data of the rest samples in the historical samples as subsequences, calculating the association degree between each subsequence and the mother sequence, and selecting the sequences with the association degree from high to low before the sequences are ordered
The samples of bits are used as a training set.
Preferably, in step S3.2.3, the influence factor data of 40% of the historical samples are randomly selected as the subsequence.
Preferably, the group intelligent task management method in step S4 includes the steps of:
s4.1: inputting the preliminarily parsed data in step S2 and the load prediction situation at the next time in step S3;
s4.2: calculating a cloud edge cooperative task evaluation index;
s4.3: comparing the evaluation results of the tasks deployed to the cloud computing nodes with the evaluation results of the tasks deployed to the edge computing nodes, deploying the tasks to the cloud if the evaluation results of the tasks deployed to the cloud are smaller, and deploying the tasks to the edge if the evaluation results of the tasks deployed to the edge computing nodes are smaller;
s4.4: and according to the task deployment result in the step S4.3, further distributing the fault detection task to a proper computing node on the cloud side or the edge side by adopting a swarm intelligence algorithm.
Preferably, the colony intelligent algorithm of step S4.4 is an ant colony optimization algorithm, which includes the following steps:
s4.4.1: initializing parameters to be
Only ants are randomly assigned to
A computing node joining the departure node to the first
Taboo watch of only ants
In, the number of iterations is set
；
S4.4.2: is calculated at
Time slave node
To the node
Probability of (2)
Selecting new computing node and adding new node to tabu table
Performing the following steps;
s4.4.3: updating a node
Residual information in between;
s4.4.4: to be provided with
Selecting an optimal computing node for the target with the minimum sum of network topological distances in the ants;
s4.4.5: updating residual information on the optimal network topology path;
s4.4.6: and judging whether the set iteration times are reached, if not, repeating the process, otherwise, obtaining an optimal task allocation scheme.
According to the invention, a task management mode of cloud-edge cooperation is adopted to carry out layered processing on different types of data, so that the situation that emergency faults cannot be timely removed due to communication congestion and the like and potential safety hazards are brought to a transformer substation is avoided to a great extent. The cloud computing is good at non-real-time, long-period and particularly high-requirement data processing and analysis on computing capacity, and the edge computing is suitable for real-time and short-period data processing and analysis and can better support real-time intelligent decision and execution of local services. The data analysis and processing tasks are distributed to the cloud and the edge side nodes according to the characteristics to be executed, so that the resources are reasonably utilized, and the life cycle management and the value mining of the data are efficiently carried out.
According to the invention, a cloud-edge cooperative task management mode is adopted, so that the network transmission delay is reduced, and the smooth completion of urgent and time-sensitive tasks is facilitated; a load prediction mode is adopted to predict the system load condition at the next moment, which is beneficial to improving the resource utilization efficiency; the invention has simple structure, easy operation and convenient popularization.
Drawings
FIG. 1 is a flow chart of a load clustering method of the present invention;
FIG. 2 is a flow chart of the load data preprocessing of the present invention;
FIG. 3 is a flow chart of singular value decomposition method for dimension reduction of load data according to the present invention;
FIG. 4 is a flow chart of the improved K-means algorithm of the present invention that considers density.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1, the cloud-side collaborative task management system for substation fault detection according to the present invention includes a data acquisition module, a data analysis module, a load prediction module, a task scheduling module, and a communication module, which are connected in sequence, wherein the task scheduling module is further connected with a resource monitoring module, a fault detection module, and a fault alarm module;
the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;
the data analysis module carries out primary classification processing on the acquired data;
the load prediction module adopts a load prediction method based on similar days, the altitude of the transformer substation, the voltage level of the transformer substation, the daily average temperature, the humidity and the weather type are used as similar daily evaluation criteria, and a training set and a test set are screened out according to the degree of association; the method comprises the steps that a long-term and short-term memory network algorithm is adopted, the service time and the fault rate of electrical equipment in a transformer substation are combined, the scale of data collected by a data collection module is used as an adjustment reference, and the load condition at the next moment is predicted;
the task scheduling module distributes the fault detection task to computing nodes on the cloud side or the edge side by adopting a swarm intelligence algorithm according to the required resources and the time urgency degree of the task;
the resource monitoring module comprises a resource monitor and a monitoring alarm, and the resource monitor monitors resource states of cloud computing nodes and edge computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm gives an alarm, and the task scheduling module redistributes the task which cannot be executed at present to other nodes;
the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault processing measures for reference of operation and maintenance personnel;
the fault alarm module sends an alarm to operation and maintenance personnel after a fault occurs, and sends fault analysis data to the cloud backup to be used as historical data for post fault analysis and prediction in advance at the next moment;
the communication module comprises a wired power private network and a 5G power virtual private network, and the wired power private network is used for data transmission of wired data acquisition equipment and wired transmission among edge computing nodes; the 5G electric virtual private network is used for data transmission of the mobile data acquisition equipment, wireless transmission between edge sides and cloud computing nodes and between modules of the system, and is used as a standby communication mode when the electric wired private network fails.
The invention discloses a transformer substation fault detection-oriented cloud-side collaborative task management method, which comprises the following steps of:
s1: collecting data of electrical equipment in a transformer substation, including digital coding data, image data and sound pattern data of a transformer, a mutual inductor, a circuit breaker, a disconnecting switch and other equipment;
s2: preliminarily analyzing the collected data, and classifying according to sources;
s3: predicting the load condition at the next moment by adopting a load prediction method based on similar days;
s4: distributing the fault detection task to a proper computing node at a cloud end or an edge side by adopting a group intelligence-based task management method;
s5: monitoring the resource use condition of a task execution node, judging whether a computing node is abnormal or not, and if the computing node is abnormal, re-scheduling the task;
s6: further analyzing the tasks distributed by the nodes, judging whether faults exist, and if so, analyzing specific fault contents and fault occurrence points; if not, ending the task;
s7: judging whether an alarm needs to be sent out or not according to a fault detection result, and if so, immediately alarming to inform operation and maintenance personnel; if not, ending the task;
s8: and uploading the fault information to a cloud for storage.
The load prediction method based on similar days in step S3, as shown in fig. 3, includes the following sub-steps:
s3.1: inputting historical samples and influence factor data of a day to be predicted, wherein the influence factor data comprise the altitude of a transformer substation, the voltage level of the transformer substation, the service time of electrical equipment in the transformer substation, the comprehensive failure rate of the electrical equipment in the transformer substation, the daily average air temperature, the humidity and the weather type;
s3.2: selecting a data set required by load prediction, wherein the data set comprises a training set and a testing set, and the specific steps are as follows:
s3.2.1: constructing a similar daily influence factor matrix, wherein subsequences
Is as follows
Of heaven
A vector composed of a plurality of influencing factors,
，
and
respectively representing the number of days of the historical sample and the number of influencing factors; mother sequence
For days to be tested or training label days
A vector of influencing factors.
S3.2.2: performing initial value processing on the subsequence and the mother sequence, namely dividing the influence factor data in each sequence by the first data in the sequence, and respectively using the processed subsequence and the mother sequence
And
and (4) showing.
S3.2.3: selecting a test set, taking the influence factor data of the day to be predicted as a mother sequence, randomly selecting 40% of the influence factor data of the samples in the historical samples as subsequences, and calculating the association degree between each subsequence and the mother sequence
Before selecting the order of the degree of association from high to low
And taking the sample of the bits as a test set, and selecting the sample with the highest relevance as a training label day.
The correlation degree is calculated in the manner of
Wherein,
is as follows
First of a subsequence
The first influencing factor and the mother sequence
The correlation coefficient of each influence factor;
for the initial mother sequence and
first of a subsequence
The absolute value of the difference between the individual influencing factors,
for the initial mother sequence and
the minimum difference absolute value of two poles of the subsequence,
for the initial mother sequence and
maximum difference absolute value of two poles of subsequence;
for the resolution factor, the value range is (0, 1), usually 0.5.
S3.2.4: selecting a training set, taking the influence factor data of the training label day as a mother sequence, taking the influence factor data of the rest 60% of samples in the historical samples as subsequences, and calculating the association degree between each subsequence and the mother sequence
Before selecting the order of the degree of association from high to low
The samples of bits are used as a training set.
S3.3: and predicting the time sequence by adopting a long-term and short-term memory network algorithm, and predicting the load condition at the next moment, wherein the load condition comprises calculation, storage and network resources required at the next moment.
The mathematical expression of the iterative process of the forgetting gate, the input gate and the output gate in the long-short term memory network unit is
Wherein,
、
、
respectively the outputs of the forgetting gate, the input gate and the output gate,
、
and
which are the inputs of the respective gates, respectively,
and
the weight and offset corresponding to each gate,
the function is activated for Sigmoid.
As shown in fig. 4, the task management method based on group intelligence in step S4 includes the following steps:
s4.1: inputting the preliminarily parsed data in step S2 and the load prediction situation at the next time in step S3;
s4.2: computing cloud edge collaborative task evaluation index
；
Wherein,
time consumed for transmitting the task to the cloud or edge side computing node is consumed;
the emergency degree is classified according to sources, wherein the major fault data source comprises a transformer, the emergency fault data source comprises a mutual inductor and a circuit breaker, the common fault data source comprises an isolating switch and other equipment tasks, the calculated values deployed to the cloud are respectively 0.3, 0.5 and 1, and the calculated values deployed to the edge side by the tasks are respectively 0.2, 0.5 and 1;
load balance degree for deploying tasks to a cloud side or an edge side;
s4.3: comparing evaluation results of tasks deployed to cloud computing nodes
And the evaluation result deployed to the edge side computing node
The size of the task is smaller, if the evaluation result of deploying the task to the cloud end is smaller, the task is deployed to the cloud end, and the task is not deployed to the cloud endThen deployed to the edge side;
wherein,
is as follows
The evaluation results of the tasks deployed to the cloud end are calculated in the mode of the sum of the evaluation index items;
is as follows
The evaluation results of the tasks deployed to the edge side are calculated in the same way as the cloud side;
s4.4: according to the task deployment result of the step S4.3, further distributing the fault detection task to a proper computing node on the cloud side or the edge side by adopting a swarm intelligence algorithm, wherein the swarm intelligence algorithm is an ant colony optimization algorithm and specifically comprises the following steps:
s4.4.1: initializing parameters to be
Ants only (i.e. a
Task) is randomly assigned to
A computing node joining the departure node to the first
Taboo watch of only ants
In, the number of iterations is set
；
S4.4.2: is calculated according to equation (8)
Time slave node
To the node
Selecting a new computing node and adding the new node into a tabu table according to the probability;
wherein,
is composed of
Time of day at a node
Residual information between, the value being a constant at the initial time;
is a node
To the node
The value is the reciprocal of the network topology distance between two nodes;
and
the importance of the residual information and the importance of the heuristic information, respectively.
S4.4.3: updating a node
Residual information in between;
wherein,
for updated at node
Residual information in between;
the information is persistent;
is a first
Only ants are in the node
Residual information difference between;
is the total concentration of pheromones;
is as follows
The sum of the network topology distances traveled by ants.
S4.4.4: to be provided with
Selecting an optimal computing node for the target with the minimum sum of network topological distances in the ants;
s4.4.5: updating residual information on the optimal network topology path;
wherein,
and
respectively updated residual information and original residual information;
is a global information volatilization coefficient.
S4.4.6: and judging whether the set iteration times are reached, if not, repeating the process, otherwise, obtaining an optimal task allocation scheme.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. The utility model provides a transformer substation fault detection-oriented cloud limit collaborative task management system which characterized in that: the system comprises a data acquisition module, a data analysis module, a load prediction module, a task scheduling module and a communication module which are connected in sequence, wherein the communication module is also connected with the data acquisition module, and the task scheduling module is also connected with a resource monitoring module, a fault detection module and a fault alarm module in sequence;
the data acquisition module is used for collecting data generated by data acquisition equipment of the transformer substation;
the data analysis module carries out primary classification processing on the acquired data;
the load prediction module adopts a load prediction method based on similar days, takes the altitude of the transformer substation, the voltage level of the transformer substation, the daily average temperature, the humidity and the weather type as the basis of similar daily evaluation, and screens out a training set and a test set according to the degree of association; the method comprises the steps that a long-term and short-term memory network algorithm is adopted, the service time and the fault rate of electrical equipment in a transformer substation are combined, the scale of data collected by a data collection module is used as an adjustment reference, and the load condition at the next moment is predicted;
the task scheduling module distributes the fault detection task to computing nodes on the cloud side or the edge side by adopting a swarm intelligence algorithm according to the required resources and the time urgency degree of the task;
the resource monitoring module comprises a resource monitor and a monitoring alarm, and the resource monitor monitors resource states of cloud computing nodes and edge computing nodes in the system; when the computing node is abnormal and cannot execute the current task, the monitoring alarm gives an alarm, and the task scheduling module redistributes the task which cannot be executed at present to other nodes;
the fault detection module further analyzes the data by utilizing the computing node resources to obtain specific fault content and fault occurrence points, and analyzes possible fault reasons and fault processing measures for reference of operation and maintenance personnel;
the fault alarm module sends an alarm to operation and maintenance personnel after a fault occurs, and sends fault analysis data to the cloud backup to be used as historical data for post fault analysis and prediction in advance at the next moment;
the communication module comprises a power wired private network and a 5G power virtual private network, wherein the power wired private network is used for data transmission of wired data acquisition equipment and wired transmission among edge computing nodes; the 5G electric virtual private network is used for data transmission of the mobile data acquisition equipment, wireless transmission between the edge side and the cloud computing node and among modules among systems, and is used as a standby communication mode when the electric wired private network fails.
2. A cloud-edge collaborative task management method for substation fault detection is characterized by comprising the following steps:
s1: collecting data of electrical equipment in a transformer substation, including digital coding data, image data and sound pattern data of a transformer, a mutual inductor, a circuit breaker, a disconnecting switch and other equipment;
s2: preliminarily analyzing the collected data, and classifying according to sources;
s3: predicting the load condition at the next moment by adopting a load prediction method based on similar days;
s4: distributing the fault detection task to a proper computing node at a cloud end or an edge side by adopting a group intelligence-based task management method;
s5: monitoring the resource use condition of a task execution node, judging whether a computing node is abnormal or not, and if the computing node is abnormal, re-scheduling the task;
s6: further analyzing the tasks distributed by the nodes, judging whether faults exist, and if so, analyzing specific fault contents and fault occurrence points; if not, ending the task;
s7: judging whether an alarm needs to be sent out or not according to a fault detection result, and if so, immediately alarming to inform operation and maintenance personnel; if not, ending the task;
s8: and uploading the fault information to a cloud for storage.
3. The substation fault detection-oriented cloud-edge collaborative task management method according to claim 2, wherein the step S3 includes the following sub-steps:
s3.1: inputting historical samples and influence factor data of a day to be predicted, wherein the influence factor data comprises the altitude of a transformer substation, the voltage level of the transformer substation, the service time of electrical equipment in the transformer substation, the comprehensive failure rate of the electrical equipment in the transformer substation, the daily average air temperature, the humidity and the weather type;
s3.2: selecting a data set required by load prediction, wherein the data set comprises a training set and a testing set;
s3.3: and predicting the time sequence by adopting a long-term and short-term memory network algorithm, and predicting the load condition at the next moment, wherein the load condition comprises calculation, storage and network resources required at the next moment.
4. The substation fault detection-oriented cloud-edge collaborative task management method according to claim 3, wherein the step S3.2 of selecting a data set required for load prediction includes the steps of:
s3.2.1: constructing a similar daily influence factor matrix comprising a subsequence and a mother sequence;
s3.2.2: carrying out initialization processing on the subsequence and the mother sequence;
s3.2.3: selecting a test set, taking the influence factor data of a day to be predicted as a mother sequence, randomly selecting the influence factor data of a sample with a specified proportion from historical samples as subsequences, calculating the association degree between each subsequence and the mother sequence, and selecting the subsequences before the association degree is sorted from high to low
Taking the sample of the bits as a test set, and selecting the sample with the highest correlation degree as a training label day;
s3.2.4: selecting a training set, taking the influence factor data of a training label day as a mother sequence, taking the influence factor data of the rest samples in the historical samples as subsequences, calculating the association degree between each subsequence and the mother sequence, and selecting the subsequences before the association degree is ranked from high to low
Samples of bits are used as a training set.
5. The substation fault detection-oriented cloud-edge collaborative task management method according to claim 4, characterized in that: in step S3.2.3, the influence factor data of 40% of the historical samples are randomly selected as a subsequence.
6. The substation fault detection-oriented cloud-edge collaborative task management method according to claim 2, characterized in that: the group intelligent task management method in step S4 includes the following steps:
s4.1: inputting the preliminarily analyzed data in step S2 and the load prediction situation at the next time in step S3;
s4.2: calculating a cloud edge cooperative task evaluation index;
s4.3: comparing the evaluation results of the tasks deployed to the cloud computing nodes with the evaluation results of the tasks deployed to the edge computing nodes, deploying the tasks to the cloud if the evaluation results of the tasks deployed to the cloud are smaller, and deploying the tasks to the edge if the evaluation results of the tasks deployed to the edge computing nodes are smaller;
s4.4: and according to the task deployment result in the step S4.3, further distributing the fault detection task to a proper computing node on the cloud side or the edge side by adopting a swarm intelligence algorithm.
7. The substation fault detection-oriented cloud-edge collaborative task management method according to claim 6, wherein the swarm intelligence algorithm of step S4.4 is an ant colony optimization algorithm, and comprises the following steps:
s4.4.1: initializing parameters to be
Ants are randomly assigned to
A computing node joining the departure node to the first
Taboo watch of only ants
In, the number of iterations is set
；
S4.4.2: is calculated at
Time slave node
To the node
Probability of (2)
Selecting a new compute node and adding the new node to the tabu table
Performing the following steps;
s4.4.3: updating a node
Residual information in between;
s4.4.4: to be provided with
Selecting an optimal computing node for the target with the minimum sum of network topological distances in the ants;
s4.4.5: updating residual information on the optimal network topology path;
s4.4.6: and judging whether the set iteration times are reached, if not, repeating the process, otherwise, obtaining an optimal task allocation scheme.
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