CN110446174B - Message optimization method based on coal mine working face positioning wireless sensor network - Google Patents

Message optimization method based on coal mine working face positioning wireless sensor network Download PDF

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CN110446174B
CN110446174B CN201910764945.3A CN201910764945A CN110446174B CN 110446174 B CN110446174 B CN 110446174B CN 201910764945 A CN201910764945 A CN 201910764945A CN 110446174 B CN110446174 B CN 110446174B
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message
population
messages
sequence
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CN110446174A (en
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赵小虎
张凯
方祖浩
有鹏
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China University of Mining and Technology CUMT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic control in data switching networks
    • H04L47/50—Queue scheduling
    • H04L47/62—Queue scheduling characterised by scheduling criteria
    • H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
    • H04L47/6275—Queue scheduling characterised by scheduling criteria for service slots or service orders based on priority
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L49/00—Packet switching elements
    • H04L49/90—Buffering arrangements
    • H04L49/9057—Arrangements for supporting packet reassembly or resequencing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
    • H04W28/065—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information using assembly or disassembly of packets
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

The invention relates to a message optimization method based on a coal mine working face positioning wireless sensor network, belongs to the technical field of network transmission scheduling, and solves the problem that the prior art cannot simultaneously consider the effectiveness and the reliability of the network. The method comprises the following steps: initializing a message population to be transmitted based on a coal mine working face positioning wireless sensor network, and sequentially arranging each message on a current channel; identifying the size and the damage degree of each message on the current channel, and sequencing all the messages according to the priorities established by the size and the damage degree of the messages; performing local sequential adjustment on messages with equivalent damage degrees and adjacent priorities through a multi-level iterative greedy algorithm until the algorithm converges, and finishing the adjustment; and identifying whether the adjusted message population is unstable or out of order, if so, performing local order adjustment again and identifying, and if not, outputting the obtained message population as an optimal solution.

Description

Message optimization method based on coal mine working face positioning wireless sensor network
Technical Field
The invention relates to the technical field of network transmission scheduling, in particular to a message optimization method based on a coal mine working face positioning wireless sensor network.
Background
The wireless sensor network consists of a large number of sensor nodes deployed in a monitoring area, and is a multi-hop self-organizing network system formed in a wireless communication mode, and the wireless sensor network aims to cooperatively sense, acquire and process information of a sensed object in a network coverage area and send the information to an observer. Currently, research on wireless sensor networks has been diverted from basic research to application research, such as: emerging industrial wireless sensor networks, Internet of things, smart power grids and the like.
At present, in each scene of a coal mine, the flexibility and the safety requirement of a network are high, especially in a working face of the coal mine, along with the continuous advance of production, the working face is also continuously advanced, which makes the deployment of a wired sensor network difficult, so in recent years, the application of a wireless sensor network in the coal mine is more and more common. The application of wireless sensor networks in coal mines is becoming more and more embodied and specialized, for example, the wireless sensor networks are positioned, and the appearance of the sensor networks with different functions endows the wireless sensor networks with diversity in application.
The diversity of applications places more stringent requirements on the performance of wireless sensor networks. The availability and reliability of networks has been a pair of contradictions. In the deployment of the positioning wireless sensor network on the coal mine working face, the positioning precision is improved, the service life of the network is prolonged, however, the improvement of the reliability is obtained by sacrificing the effectiveness of the network. The goodness criterion for the overall performance of the network is a dynamic balance of availability and reliability, rather than at the expense of one party.
Disclosure of Invention
In view of the foregoing analysis, embodiments of the present invention provide a message optimization method based on a coal mine working plane positioning wireless sensor network, so as to solve the problem that the prior art cannot consider both the validity and the reliability of the network.
In one aspect, an embodiment of the present invention provides a message optimization method based on a coal mine working face positioning wireless sensor network, including the following steps:
initializing a message population to be transmitted based on a coal mine working face positioning wireless sensor network, and sequentially arranging each message on a current channel;
identifying the size and the damage degree of each message on the current channel, and sequencing all the messages according to the priorities established by the size and the damage degree of the messages;
carrying out local sequential adjustment on messages with equivalent damage degrees and adjacent priorities through a multi-level iterative greedy algorithm until the algorithm is converged, and finishing the adjustment;
and identifying whether the adjusted message population is unstable or out-of-order, if so, performing local order adjustment on the unstable or out-of-order message again through a multi-level iterative greedy algorithm, identifying again, and if not, outputting the obtained message population as an optimal solution.
The beneficial effects of the above technical scheme are as follows: the method can effectively optimize the transmission scheduling problem of the Positioning Wireless Sensor Network (PWSN) facing the coal mine working face, reduce the end-to-end time of message transmission, reduce the packet loss rate in the transmission process, effectively improve the overall effective performance of the network and have outstanding use value.
Based on the further improvement of the method, the initialization processing is carried out on the message population to be transmitted based on the coal mine working face positioning wireless sensor network, and the method further comprises the following steps:
acquiring transmission constraint of a message on a channel;
obtaining the transmission time of each message on each channel according to the transmission constraint;
the message sequence in the message population is optimized by minimizing the maximum transmission time and conforming to the message transmission blocking relation, so that the initialization processing of the message population is realized.
The beneficial effects of the above further improved scheme are: the scheme is an effective scheme summarized by a large number of tests, and can effectively and stably arrange the message populations on the channel, so that the cost can be reduced by direct application.
Further, the constraining includes: each channel can only send one message at a certain time, and the message can only be blocked on the current channel before the transmission on the current channel is finished and the next channel is released;
the maximum transmission time CmaxIs composed of
Cmax＝max(Ri,j|i＝1,…,n)
In the formula, Ri,jIs the ith stripThe end time of the message transmission in the jth channel is determined, and n is the message number of the message population;
the message transmission blocking relation is
Ri,k≥Ri-1,k+1。
The beneficial effects of the above further improved scheme are: the constraint is actually a constraint on the propagation blocking relationship. By the constraint of the transmission blocking relation, the adverse phenomena of queue insertion, queue falling and queue disorder of the messages, which influence the message transmission, can be eliminated.
Further, the transmission time of each message on each channel is obtained by the following formula
R1,1＝A1,1
Figure BDA0002171635340000031
Ri,1＝max(Ri-1,1+Ai,1, Ri-1,2),i＝2,…,n
Ri,k＝max(Ri,k-1+Ai,k, Ri-1,k+1),i＝1,…,m, k＝2,…,m-1
Ri,m＝Ri,m-1+Ai,m,i＝2,…,n
In the formula, Ri,jIndicating the end time of the ith message in the jth channel transmission, Ai,jThe transmission time of the ith message on the jth channel is represented, i is 1, …, n, j is 1, …, m and m represent the number of channels.
The beneficial effects of the above further improved scheme are: the transmission time of each message in each channel is further specifically quantized, so that the step of intuitively finding which can shorten the transmission time is convenient.
Further, the optimizing the message sequence in the message population by minimizing the maximum transmission time and conforming to the message propagation blocking relationship further comprises the following steps:
arranging all messages in a message population into a column;
cutting the arranged message population into a front sequence and a rear sequence through a preset cutting point;
sequentially extracting each message from the next sequence, and calculating the sum of idle time and blocking time generated when the message is used as an adjacent subsequent message of a certain message of the previous sequence for transmission;
and obtaining the message of the latter sequence with the minimum sum as the subsequent adjacent message of the former sequence, and repeating the steps until the message sequence is unchanged, thereby completing the message sequence optimization in the message population.
The beneficial effects of the above further improved scheme are: by optimizing the message sequence in the message population, the maximum transmission time is minimized, propagation blockage is avoided, mutual correspondence between the message population and the channel at the same moment is further realized, and the improvement of user experience is facilitated.
Further, the j-th message of the latter sequence is obtained by the following formula and is used as the sum of the idle time and the blocking time generated when the next subsequent message of the i-th message of the former sequence is transmittedj
Figure BDA0002171635340000041
Wherein k represents a channel number, Ri,kIndicating the end time, R, of the transmission of the ith message in the kth channelj,kEnd time of the jth message in the kth channel transmission, Aj,kThe transmission time of the jth message in the kth channel is shown, and m represents the number of channels.
The beneficial effects of the above further improved scheme are: not only considering the influence of blocking time (R)i,k) The influence of idle time is also taken into account (A)j,k) So that the result (sum) obtainedj) The method is more convincing and more real, and the effect of using the method as the basis of the message optimization sequence is better.
Further, the said ranking all messages according to the priority established according to their size and damage degree includes the following steps:
comparing the size and the damage degree of each message with a preset threshold value, and determining the number d of damaged messages;
randomly selecting d messages from the message population after the message sequence optimization is completed;
inserting each selected message in turn to make maximum transmission time CmaxA minimum position;
performing the following operation if the inserted message group CmaxMessage population C after completing message sequence optimizationmaxIf the message is not improved, adjusting the total number d 'of damaged messages'
d'＝d+r (r＞1)
Judging again until improving, otherwise, replacing the message population after completing message sequence optimization with the message population obtained after inserting, and completing the priority sequence arrangement of all messages.
The beneficial effects of the above further improved scheme are: in fact, the relatively poor quality message population is replaced by the good quality message population, so that the population is updated, and the transmission effect is better.
Further, the local order adjustment is performed on the messages with the equivalent damage degree and the adjacent priorities through a multi-level iterative greedy algorithm, and the method further comprises the following steps:
selecting all possible combinations consisting of d' messages from the message populations with the priority sequence arrangement;
for each selected message combination, inserting each message in turn to make maximum transmission time CmaxThe minimum position, carry on the local sequence adjustment;
obtaining the maximum transmission time C after insertionmaxThe insertion position corresponding to the minimum message combination and the message population after the local sequence adjustment;
and changing the value d', repeating the steps until the sequence of the messages in the message population is unchanged, judging the convergence of the multi-level iterative greedy algorithm, and finishing the local sequence adjustment of the message population without improving all the messages.
The beneficial effects of the above further improved scheme are: through the operations of exchange and insertion, whether the whole message population is in a convergence state or not can be judged, the message population is suitable for information transmission, if the whole message population is not converged (namely, the multi-level iterative greedy algorithm is not converged), the d' value is changed, and the judgment is carried out again until the whole message population is converged.
Further, the value of d' is changed twice according to the following formula
d”＝d'+ri (r＞1,i＝1,2)
r2＞r1
Wherein d 'represents d', r after modificationiRepresents the amplitude of variation;
if the maximum transmission time CmaxAnd judging the convergence of the multi-level iterative greedy algorithm without change.
The beneficial effects of the above further improved scheme are: a number of experiments have shown that changing the value of d' twice is generally sufficient to keep the entire message population in a converged state. After sequential adjustment and multiple iterations of a multi-level iterative greedy algorithm, the message population is caused to jump out of local convergence, and a reliable message population can be obtained.
Further, the identifying whether the adjusted packet population is unstable or out of order further includes the following steps:
detecting the transmission condition of the message in the adjusted message population in the channel;
according to the transmission condition, acquiring a population distribution map with the abscissa as the number of X channel messages and the ordinate as the number of Y channel messages;
judging whether the adjusted message population is unstable or out of order according to the population distribution map; when the distribution of the messages is approximate to a straight line with a fixed slope, the adjusted message population is judged to be stable and not to have disorder; otherwise, the adjusted message population is judged to be unstable and out of order.
The beneficial effects of the above further improved scheme are: the basis for further judging whether the whole message population is in a converged state and is suitable for transmission is limited, and a test mode is combined. If there is no instability or disorder, signal transmission may be performed, otherwise even if transmitted, the information obtained may be inaccurate and require further processing or discarding.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic diagram of a message optimization method for positioning a wireless sensor network based on a coal mine working face according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram of initial packet population distribution on two channels in embodiment 3 of the present invention;
fig. 3 is a schematic diagram of the distribution of the message population after the optimization processing in embodiment 3 of the present invention;
fig. 4 is a schematic diagram of a packet loss rate test result in embodiment 3 of the present invention;
fig. 5 is a schematic diagram of end-to-end time test results in embodiment 3 of the present invention.
MA-cultural genetic algorithm; DE- (ABC) -differential evolution artificial bee colony algorithm;
GCS-the message optimization method described in embodiment 2 of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Example 1
The embodiment of the invention discloses a message optimization method based on a coal mine working face positioning wireless sensor network, which comprises the following steps as shown in figure 1:
s1, initializing a message population to be transmitted based on a coal mine working face positioning wireless sensor network, and sequentially arranging each message on a current channel;
s2, identifying the size and the damage degree of each message on the current channel, and sequencing all the messages according to the priorities established by the size and the damage degree of the messages;
s3, carrying out local sequence adjustment on messages with equivalent damage degrees and adjacent priorities through a multi-level iterative greedy algorithm until the algorithm is converged, and finishing the local sequence adjustment;
and S4, identifying whether the adjusted message population is unstable or disordered, if so, performing local sequence adjustment on the unstable or disordered message again through a multi-level iterative greedy algorithm, identifying again, and if not, outputting the obtained message population as an optimal solution.
Compared with the prior art, the method provided by the embodiment can effectively optimize the transmission scheduling problem of the Positioning Wireless Sensor Network (PWSN) facing the coal mine working face, reduce the end-to-end time of message transmission, reduce the packet loss rate in the transmission process, effectively improve the overall effective performance of the network, and has outstanding use value.
Example 2
Optimization is performed on the basis of embodiment 1, and in step S1, the initializing process of the message population to be transmitted based on the coal mine working face positioning wireless sensor network further includes the following steps:
s11, acquiring transmission constraint of a message on a channel;
s12, obtaining the transmission time of each message on each channel according to the transmission constraint;
and S13, optimizing the message sequence in the message population by minimizing the maximum transmission time and conforming to the message transmission blocking relation, thereby realizing the initialization processing of the message population.
Preferably, in the step S11, the constraining includes: 1) each channel can only send one message at a certain time; 2) the message is transmitted on the current channel and can only be blocked on the current channel before the next channel is released.
Maximum transmission time CmaxIs composed of
Cmax＝max(Ri,j|i＝1,…,n) (1)
In the formula, Ri,jAnd n is the message number of the message population, and is the transmission ending time of the ith message in the jth channel.
The message propagation blocking relationship is
Ri,k≥Ri-1,k+1 (2)
Indicating that if the subsequent channel k +1 is not idle, the message is blocked on the channel k until k +1 is idle.
Preferably, in step S12, the transmission start time of the 1 st packet in the 1 st channel is set to 0, and the transmission time of each packet in each channel is obtained by the following formula
R1,1＝A1,1
Figure BDA0002171635340000091
Ri,1＝max(Ri-1,1+Ai,1, Ri-1,2),i＝2,…,n
Ri,k＝max(Ri,k-1+Ai,k, Ri-1,k+1),i＝1,…,m,k＝2,…,m-1
Ri,m＝Ri,m-1+Ai,m,i＝2,…,n (3)
In the formula, Ri,jIndicating the end time of the ith message in the jth channel transmission, Ai,jThe transmission time of the ith message on the jth channel is represented, i is 1, …, n, j is 1, …, m and m represent the number of channels.
Preferably, in step S13, the optimizing the message sequence in the message population by minimizing the maximum transmission time and conforming to the message propagation blocking relationship further includes the following steps:
s131, arranging all messages in the message group into a line;
s132, cutting the arranged message population into a front sequence and a rear sequence through a preset cutting point;
s133, sequentially extracting each message from the next sequence, and calculating the sum of idle time and blocking time generated when the message is used as an adjacent subsequent message of a certain message of the previous sequence for transmission;
s134, obtaining the message of the next sequence with the minimum sum as the next adjacent message of the previous sequence, repeating the steps until the message sequence is not changed, and finishing the message sequence optimization in the message population.
Preferably, the sum of the idle time and the blocking time generated when the j-th message of the next sequence in step S133 is transmitted as the immediately subsequent message of the i-th message of the previous sequence is obtained by the following formulaj
Figure BDA0002171635340000101
Wherein k represents a channel number, Ri,kIndicating the end time, R, of the transmission of the ith message in the kth channelj,kEnd time of the jth message in the kth channel transmission, Aj,kThe transmission time of the jth message in the kth channel is shown, and m represents the number of channels.
Preferably, in step S2, the size and damage degree of the message may be identified by the Fluke network analyzer. The method for arranging all messages according to the priorities established according to the sizes and the damage degrees of the messages comprises the following steps:
s21, comparing the size and the damage degree of each message with a preset threshold value, and determining the number d of damaged messages;
s22, randomly selecting d messages from the message population after the message sequence optimization is completed;
s23, inserting each selected message in sequence to enable maximum transmission time CmaxA minimum position;
s24, executing the following operation, if the inserted message group CmaxMessage population C after completing message sequence optimizationmaxWithout improvement, i.e. if the inserted message group CmaxGreater than or equal to newspaperMessage population C after text sequence optimizationmaxAdjusting the total number d 'of damaged messages'
d'＝d+r (r＞1) (5)
Judging again until improving, otherwise, replacing the message population after completing message sequence optimization with the message population obtained after inserting, and completing the priority sequence arrangement of all messages.
Preferably, in step S3, the local order adjustment is performed on the messages with the equivalent damage degree and the adjacent priorities by a multi-level iterative greedy algorithm, which further includes the following steps:
s31, selecting all possible combinations consisting of d' messages from the message populations with the priority sequence arrangement;
s32, for each selected message combination, inserting each message in sequence to enable maximum transmission time CmaxThe minimum position, carry on the local sequence adjustment;
s33, obtaining the maximum transmission time C after insertionmaxThe insertion position corresponding to the minimum message combination and the message population after the local sequence adjustment;
and S34, changing the value d', repeating the steps until the sequence of the messages in the message population is not changed, judging that the multilevel iterative greedy algorithm is converged, and finishing the local sequence adjustment of the message population without improving all the messages.
It should be noted that the combination of d' packets is randomly selected, and there is no variation rule.
The purpose of local order adjustment is to find the optimal individuals within the neighborhood and then order them. The obtained solution is more accurate by adopting a multilevel iteration greedy algorithm, and the larger the iteration frequency is, the more accurate the obtained result is. The idea of each layer iteration (steps S31-S33) is consistent.
Preferably, in step S34, the value of d' is changed twice according to the following formula
d”＝d'+ri (r＞1,i＝1,2) (6)
r2＞r1
Wherein d "represents a changeAfter d', riIndicating the magnitude of the change.
Preferably, in step S4, the identifying whether the adjusted packet population is unstable or out of order further includes the following steps:
s41, detecting the transmission condition of the message in the adjusted message population in the channel;
s42, acquiring a population distribution map with the abscissa as the number of the X channel messages and the ordinate as the number of the Y channel messages according to the transmission condition;
s43, judging whether the adjusted message population is unstable or out of order according to the population distribution map; when the distribution of the messages is approximate to a straight line with a fixed slope, the adjusted message population is judged to be stable and not to have disorder; otherwise, the adjusted message population is judged to be unstable and out of order.
If the message population is stable and has no disorder, the message transmission can be carried out, otherwise, the message needs to be further optimized (optionally, local sequence adjustment is carried out by returning through a multi-level iterative greedy algorithm, and judgment is carried out again) or discarded.
Compared with the embodiment 1, the method provided by the embodiment updates the message population, performs local exchange and insertion operations, judges whether the population converges, finally obtains the stable and unordered message population suitable for channel transmission, can meet the current user requirement to a great extent, and improves the user experience.
Example 3
In order to clearly show the technical effects of the present invention, the following description will be given in detail by taking an example.
Data of different scales in the public data set Taillard are selected as an initial message population, and then the method (algorithm), the cultural gene algorithm and the differential evolution artificial bee colony algorithm which are described in the embodiment 2 of the invention are utilized to carry out processing in sequence.
Fig. 2 shows arrangement conditions of initial packet populations on two channels of the method X, Y according to embodiment 2 of the present invention, and after the processing by the method according to embodiment 2 of the present invention, packet populations in a stable and ordered arrangement are obtained, as shown in fig. 3.
Setting key parameters: the particle group size is 100, and the number of iterations G of the multilevel iterative greedy algorithm is 2000 when the number of channels is 20, and is 5000 when the number of channels is 50. In the process of simulating the packet loss rate results under different algorithms, because the test is performed under the same data scale, the final packet loss rate can be obtained only by comparing the packet loss numbers under different algorithms.
In the test related to the packet loss rate, when the number of iterations is too small, the difference of the packet loss number is very small, and in order to make the test result have more obvious comparison effect, the low-frequency iteration process is directly skipped, and a multi-frequency iteration process of 100 × 50 is selected. As shown in fig. 4, in the complete transmission process, the packet loss rate of the algorithm in embodiment 2 of the present invention can be reduced by 40% at most and can be reduced by 36.4% on average, compared with the MA algorithm; compared with the DE-ABC algorithm, the method can reduce 44.5 percent at most and 38.9 percent on average.
In the test of comparing the end-to-end time, in order to avoid the contingency of test data and ensure the accuracy of results, more cases are selected for simulation, the case selection range is Taillard080-100, and different iteration times (2000, 5000) are used to obtain different transmission time results. FIG. 5 is a comparison of end-to-end time results of different algorithms, where the end-to-end time of the algorithm in embodiment 2 of the present invention can be reduced by 12.5% at most and 10.7% on average, compared with the MA algorithm; compared with the DE-ABC algorithm, the method can reduce the transmission scheduling process of the whole network by 8.4 percent at most and 6.9 percent on average, because the method described in the embodiment 2 does optimize the transmission scheduling process of the whole network.
The above experiment further verifies that the method described in embodiment 2 of the present invention can effectively optimize the transmission scheduling of the network and maintain higher network effectiveness.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which is stored in a computer readable storage medium, to instruct related hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
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 changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (6)

1. A message optimization method based on a coal mine working face positioning wireless sensor network is characterized by comprising the following steps:
initializing a message population to be transmitted based on a coal mine working face positioning wireless sensor network, and sequentially arranging each message on a current channel;
identifying the size and the damage degree of each message on the current channel, and sequencing all the messages according to the priorities established by the size and the damage degree of the messages;
carrying out local sequential adjustment on messages with equivalent damage degrees and adjacent priorities through a multi-level iterative greedy algorithm until the algorithm is converged, and finishing the adjustment;
identifying whether the adjusted message population is unstable or out-of-order, if so, performing local order adjustment on the unstable or out-of-order message again through a multi-level iterative greedy algorithm, identifying again, and if not, outputting the obtained message population as an optimal solution;
the initialization process further includes the steps of:
acquiring transmission constraint of a message on a channel; the constraint includes that each channel can only send one message at a certain time, and the message can only be blocked on the current channel before the next channel is released after being transmitted on the current channel;
obtaining the transmission time of each message on each channel according to the transmission constraint; a. thei,jIndicating the transmission time of the ith message in the jth channel, Ri,jThe end time of the ith message in the jth channel transmission is represented by i being 1, …, n, j being 1, …, m, m representing the number of channels, and n representing the number of messages in the message group; setting the 1 st message in the 1 st channelThe transmission start time is 0, and the transmission time of each message on each channel is obtained by the following formula
R1,1＝A1,1
Figure FDA0002936314730000011
Ri,1＝max(Ri-1,1+Ai,1,Ri-1,2),i＝2,…,n
Ri,k＝max(Ri,k-1+Ai,k,Ri-1,k+1),i＝1,…,m,k＝2,…,m-1
Ri,m＝Ri,m-1+Ai,m,i＝2,…,n
Optimizing the message sequence in the message population by minimizing the maximum transmission time and conforming to the message transmission blocking relation, thereby realizing the initialization processing of the message population; the maximum transmission time CmaxIs composed of
Cmax＝max(Ri,j|i＝1,…,n)
The message transmission blocking relation is
Ri,k≥Ri-1,k+1
The step of arranging all messages in order according to the priorities established by the sizes and the damage degrees further comprises:
comparing the size and the damage degree of each message with a preset threshold value, and determining the number d of damaged messages;
randomly selecting d messages from the message population after the message sequence optimization is completed;
inserting each selected message in turn to make maximum transmission time CmaxA minimum position;
performing the following operation if the inserted message group CmaxMessage population C after completing message sequence optimizationmaxIf the message is not improved, adjusting the total number d 'of damaged messages'
d'＝d+r,r＞1
Judging again until the message is improved, otherwise, replacing the message population after completing the message sequence optimization with the message population obtained after inserting, and finishing the priority sequence arrangement of all messages;
the messages with the same damage degree and the adjacent priorities are all possible combinations consisting of d' final messages selected from the message groups with the priority sequence.
2. The coal mine working face positioning wireless sensor network-based message optimization method according to claim 1, wherein the message sequence in the message population is optimized by minimizing the maximum transmission time and conforming to the message propagation blocking relationship, further comprising the steps of:
arranging all messages in a message population into a column;
cutting the arranged message population into a front sequence and a rear sequence through a preset cutting point;
sequentially extracting each message from the next sequence, and calculating the sum of idle time and blocking time generated when the message is used as an adjacent subsequent message of a certain message of the previous sequence for transmission;
and obtaining the message of the latter sequence with the minimum sum as the subsequent adjacent message of the former sequence, and repeating the steps until the message sequence is unchanged, thereby completing the message sequence optimization in the message population.
3. The method of claim 2, wherein the j-th message in the next sequence is obtained as the sum of the idle time and the blocking time sum of the transmission of the immediately following message of the i-th message in the previous sequencej
Figure FDA0002936314730000031
Wherein k represents a channel number, Ri,kIndicating the end time, R, of the transmission of the ith message in the kth channelj,kEnd time of the jth message in the kth channel transmission, Aj,kThe transmission time of the jth message in the kth channel is shown, and m represents the number of channels.
4. The message optimization method based on the coal mine working face positioning wireless sensor network, according to claim 3, wherein the messages with the equivalent damage degree and the adjacent priority are subjected to local sequence adjustment through a multi-level iterative greedy algorithm, and further comprising the following steps:
selecting all possible combinations consisting of d' messages from the message populations with the priority sequence arrangement;
for each selected message combination, inserting each message in turn to make maximum transmission time CmaxThe minimum position, carry on the local sequence adjustment;
obtaining the maximum transmission time C after insertionmaxThe insertion position corresponding to the minimum message combination and the message population after the local sequence adjustment;
and changing the value d', repeating the steps until the sequence of the messages in the message population is unchanged, judging the convergence of the multi-level iterative greedy algorithm, and finishing the local sequence adjustment of the message population without improving all the messages.
5. The coal mine working face positioning wireless sensor network-based message optimization method according to claim 4, wherein the value of d' is changed twice according to the following formula
d”＝d'+rir＞1,i＝1,2
r2＞r1
Wherein d 'represents d', r after modificationiRepresents the amplitude of variation;
if the maximum transmission time CmaxAnd judging the convergence of the multi-level iterative greedy algorithm without change.
6. The coal mine working face positioning wireless sensor network-based message optimization method according to any one of claims 3 to 5, wherein the step of identifying whether the adjusted message population is unstable or out of order further comprises the steps of:
detecting the transmission condition of the message in the adjusted message population in the channel;
according to the transmission condition, acquiring a population distribution map with the abscissa as the number of X channel messages and the ordinate as the number of Y channel messages;
judging whether the adjusted message population is unstable or out of order according to the population distribution map; when the distribution of the messages is approximate to a straight line with a fixed slope, the adjusted message population is judged to be stable and not to have disorder; otherwise, the adjusted message population is judged to be unstable and out of order.
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