CN116405874A - Reservoir ecological environment real-time monitoring method - Google Patents

Reservoir ecological environment real-time monitoring method Download PDF

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CN116405874A
CN116405874A CN202310657666.3A CN202310657666A CN116405874A CN 116405874 A CN116405874 A CN 116405874A CN 202310657666 A CN202310657666 A CN 202310657666A CN 116405874 A CN116405874 A CN 116405874A
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CN116405874B (en
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赵艳
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Hunan Water Planning And Design Institute Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • 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
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • 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
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
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Abstract

The invention relates to the technical field of ecological environment monitoring, and discloses a method for monitoring the ecological environment of a reservoir in real time, which comprises the following steps: acquiring sensor deployable positions of a target area, and constructing sensor perception content distribution of each sensor deployable position; performing discrete optimization solution on the reservoir ecological sensor grouping environment monitoring model based on the objective function to obtain a sensor grouping result and a sensor deployment position; and constructing a self-adaptive route discovery model to obtain a data packet transmission route of the current running sensor and carrying out route transmission on the data packet. The invention combines the sensing content distribution of the sensor deployable positions to carry out grouping processing on the existing sensor deployable positions, combines the mutual information of the sensing content of each group of sensors to measure the monitoring effect of each group of sensors on the target area, and only operates one group of sensors at the same time under the condition of ensuring that the lowest monitoring effect of each group of sensors reaches the current maximum, thereby prolonging the service life of the sensors.

Description

Reservoir ecological environment real-time monitoring method
Technical Field
The invention relates to the technical field of ecological environment monitoring, in particular to a real-time monitoring method for the ecological environment of a reservoir.
Background
In recent years, the global climate is continuously changed, the environmental problem is outstanding, the eutrophication problem of lakes and reservoirs is still outstanding, and the large-area propagation of algal bloom is one of the water environment problems in China and worldwide. Phytoplankton plays an important role in the aquatic ecosystem, and is not only a primary producer, but also a basis of an aquatic food network, and has close relation with energy flow, material circulation and information transmission of the aquatic ecosystem. In addition, the environmental impact on the ecosystem can be reflected by structural changes in the phytoplankton, which can indicate the health and stability of the water ecosystem. The water quality and the water ecology are used as the most important monitoring indexes of the drinking water source, and even if the concentration of the monitored pollutant is very low, the long-term low-dose exposure still can cause certain harm to the health of a human body, so that the real-time monitoring of the sensor layout of the reservoir ecological environment is needed.
Disclosure of Invention
In view of the above, the invention provides a method for monitoring the ecological environment of a reservoir in real time, which aims at: 1) Constructing a reservoir ecological sensor grouping environment monitoring model based on probability distribution of sensor deployable position sensing content, grouping the current sensor deployable positions, wherein each group comprises a plurality of sensors, measuring the monitoring effect of each group of sensors on a target area by combining mutual information of the sensor deployable content of each group, operating only one group of sensors at the same time under the condition of guaranteeing the lowest monitoring effect of each group of sensor sensing content, effectively prolonging the service life of the sensors, guaranteeing the monitoring effect on the reservoir ecological environment, determining a data packet transmission path in real time for the current sensor in an operating state by adopting an adaptive route discovery strategy combining hop count and sensor electric quantity, and transmitting and analyzing data packets acquired by the sensors to obtain real-time reservoir environment information; 2) And carrying out discrete optimization solving on the reservoir ecological sensor grouping environment monitoring model based on a double-layer objective function, wherein parameters to be solved of the objective function are sensor deployment position sets contained in each group of grouping results, determining each group of sensor deployment positions for monitoring the reservoir ecological environment based on the solving result of the objective function, maximizing the sensor perception content distribution minimum value in the grouping results, enabling each group of sensors to independently complete environment monitoring of a target area, carrying out discrete representation on the groups of grouping results by constructing a discrete coding representation scheme, constructing upper discrete particles to represent each group of grouping results, determining a plurality of main body schemes with the best overall monitoring effect in an upper optimization part, and selecting the main body scheme with the largest upper discrete particle minimum fitness cost in the main body schemes as a discrete optimization solving result for the current plurality of groups of main body schemes, thereby rapidly realizing sensor grouping deployment of the target reservoir area.
The invention provides a reservoir ecological environment real-time monitoring method, which comprises the following steps:
s1: acquiring sensor deployable positions of a target area, and constructing sensor perception content distribution of each sensor deployable position;
s2: constructing a reservoir ecological sensor grouping environment monitoring model, wherein the constructed model takes the deployable positions of the existing sensors and the number of sensors as input, takes the grouping results of the sensors and the deployment positions of the sensors as output, and takes the maximized sensor sensing content distribution as an objective function;
s3: performing discrete optimization solution on the reservoir ecological sensor grouping environment monitoring model based on the objective function to obtain a sensor grouping result and a sensor deployment position;
s4: constructing a self-adaptive route discovery model for a sensor set operated by a current group, wherein the constructed model takes the position of the sensor operated currently as input and takes a data packet transmission route as output;
s5: and carrying out route transmission on the data packet acquired by the current running sensor according to the calculated data packet transmission route.
As a further improvement of the present invention: optionally, the step S1 acquires the sensor deployable positions of the target area, and constructs a sensor-aware content distribution of each sensor deployable position, including:
Sensor deployable location coordinate set for acquiring a target region V
Figure SMS_1
Wherein the total number of sensor deployable positions in the target area V is K,/or%>
Figure SMS_2
Representing the K-th sensor deployable position, K, in the target region V>n, n represents the minimum number of sensor deployable locations, and the target area represents a reservoir environmental area; the positions of the existing sensors are adjusted within the same time range, and the sensors with the adjusted positions are utilized to obtain the sensor sensing contents of different deployable positions in the target area V, wherein the sensing content mean value and standard deviation of the deployable position of the kth sensor in the target area in the sensing time are respectively as follows
Figure SMS_3
Wherein->
Figure SMS_4
Mean value representing perceived content of kth sensor deployable position within perceived time, +.>
Figure SMS_5
The standard deviation of the perceived content of the deployable position of the kth sensor in the perceived time is represented, and the perceived content of the sensor represents reservoir environment information acquired by the sensor, wherein the environment information comprises soil humidity, soil temperature, the proportions of elements and chemicals in the soil, air temperature, air humidity and the content of particles in the air;
in the embodiment of the invention, the sensing time of the sensor at each sensor-deployable position is the same;
Constructing a sensor-aware content distribution for each sensor-deployable location:
Figure SMS_6
；
Figure SMS_7
；
wherein:
t represents a transpose;
Figure SMS_8
a standard deviation matrix representing the perceived content of the deployment sensor;
Figure SMS_9
representing a probability that a kth sensor-deployable location perceives an observation s representing +.>
Figure SMS_10
Reservoir environmental information s is collected.
Optionally, constructing a reservoir ecological sensor grouping environment monitoring model in the step S2 includes:
constructing a reservoir ecological sensor grouping environment monitoring model according to sensor sensing content distribution of sensor deployable positions, wherein the constructed model takes the existing sensor deployable positions and the number of sensors as input, and takes sensor grouping results and sensor deployment positions as output;
determining a sensor packet number based on the target area size:
Figure SMS_11
；
wherein:
m represents the number of sensor groups, S represents the area of the target region V, R represents the information sensing radius of the sensor,
Figure SMS_12
representing decision coefficients, will->
Figure SMS_13
Set to 3;
wherein the number of the existing sensors to be deployed is num, the number of the sensor groups is M, and the set of the sensor deployable positions contained in the M-th group of group results is
Figure SMS_14
The number of sensors contained in the m-th group of grouping results is +. >
Figure SMS_15
，/>
Figure SMS_16
。
Optionally, in the step S2, the objective function of the reservoir ecological sensor grouping environment monitoring model is:
Figure SMS_17
；
Figure SMS_18
；
Figure SMS_19
；
Figure SMS_20
；
Figure SMS_21
；
Figure SMS_22
；
wherein:
Figure SMS_23
sensor-perceived content distribution representing the result of the m-th group of packets,>
Figure SMS_24
a sensor representing the M-group packet result senses a content distribution minimum;
Figure SMS_25
perceived content mean value representing K sensor-deployable locations,/->
Figure SMS_26
A perceived content mean representing the m-th group of results;
and the parameters to be solved of the objective function are M groups of grouping results, each group of grouping results comprises a sensor deployment position set, each group of sensor deployment positions for monitoring the ecological environment of the reservoir are determined based on the solving result of the objective function, and maximization of the minimum value of the sensor perception content distribution in the M groups of grouping results is realized, so that each group of sensors can independently complete the environment monitoring of the target area.
Optionally, in the step S3, discrete optimization solution is performed on the reservoir ecological sensor grouping environment monitoring model based on the objective function, including:
performing discrete optimization solving on the reservoir ecological sensor grouping environment monitoring model based on an objective function, wherein the discrete optimization solving flow is as follows:
s31: setting the optimal solving frequency as Max, generating G upper discrete coding representation schemes, wherein each upper discrete coding representation scheme comprises M particles, each particle corresponds to a sensor deployable position set in a group of grouping results, and the first discrete coding representation scheme comprises M particles
Figure SMS_27
The form of the discrete coded representation scheme is:
Figure SMS_28
；
Figure SMS_29
；
wherein:
Figure SMS_30
indicate->
Figure SMS_31
A form of a discrete coded representation scheme;
Figure SMS_32
representation->
Figure SMS_33
The mth particle of the group corresponds to the mth grouping result;
Figure SMS_34
representation->
Figure SMS_35
Middle->
Figure SMS_36
A coded representation of a set of sensor deployable locations, wherein each particle comprises a different sensor deployable location from each other and each particle comprises a greater than 0 number of sensor deployable locations;
s32: determining a fitness function of the upper layer discrete particles:
Figure SMS_37
；
wherein:
Figure SMS_38
representing the distribution of the perceived content of the computing sensor;
Figure SMS_39
representing the fitness function of the upper discrete particles, wherein the calculation result of the fitness function is the fitness cost of the upper discrete particles;
calculating the fitness cost of the discrete coding representation scheme:
Figure SMS_40
；
wherein:
Figure SMS_41
indicate->
Figure SMS_42
The fitness cost of the individual discrete code representation schemes;
s33: the U discrete coding representation schemes with the largest fitness cost are selected as main schemes, the other schemes are used as auxiliary schemes, and the normalized fitness cost of the U-th main scheme is calculated:
Figure SMS_43
；
wherein:
Figure SMS_44
representing the maximum fitness cost in the U main body schemes; />
Figure SMS_45
Indicating the fitness cost of the u-th subject regimen, < ->
Figure SMS_46
Representing normalized fitness costs for a nth subject scheme;
S34: calculating the number of subsidiary schemes owned by the u-th subject scheme
Figure SMS_47
：
Figure SMS_48
；
Wherein:
Figure SMS_49
representing a rounding function, G representing the total number of discrete coding representation schemes;
random allocation to a nth subject scheme
Figure SMS_50
The method comprises the steps of forming a group of main body scheme fields, wherein the main body scheme fields comprise a main body scheme and a plurality of auxiliary schemes;
s35: taking the auxiliary scheme with the highest fitness cost in the u-th main scheme as the optimal auxiliary scheme of the u-th main scheme; for any subsidiary scheme in any u-th main scheme, generating a random number rand between 0 and 1, and if the rand is smaller than 0.4, updating the selected subsidiary scheme and the main scheme as follows:
Figure SMS_51
；
Figure SMS_52
；
otherwise, the optimal auxiliary scheme of the selected auxiliary scheme and the optimal auxiliary scheme of the u-th main scheme are updated as follows:
Figure SMS_53
；
Figure SMS_54
；
Figure SMS_55
；
wherein:
Figure SMS_56
representing the upper discrete particles with highest fitness cost in the body scheme of the u th type, the +.>
Figure SMS_57
Representing the upper discrete particles with highest fitness cost in the selected subsidiary scheme, +.>
Figure SMS_58
Representing the upper discrete particles with highest fitness cost in the optimal auxiliary scheme corresponding to the main scheme of the u th type;
Figure SMS_59
representation->
Figure SMS_60
Is updated by->
Figure SMS_61
Representation after updating
Figure SMS_62
A coded representation of a set of sensor deployable locations; in the embodiment of the invention, the code representation solution result is in a vector form, and a sensor deployable position which is nearest to the calculated sensor deployable position and is not contained by other discrete particles in the same scheme is selected as the code representation solution result, namely, the sensor deployable position which is calculated by the distance is selected, wherein the plurality of sensor deployable positions are contained by the updated upper discrete particles >
Figure SMS_63
The sensor deployable positions contained in other discrete particles in the nearest and non-u-th main body scheme are used as coding representation solving results;
s36: calculating the adaptability cost of each updated main scheme and auxiliary schemes, and updating the scheme with the highest adaptability cost into a main scheme for any group of main scheme fields; returning to step S35;
s37: repeating steps S35 to S36 to reach preset optimal solution times Max, calculating to obtain the minimum fitness cost of the upper discrete particles in each group of main body schemes for the current U group main body scheme, selecting the main body scheme with the maximum minimum fitness cost of the upper discrete particles in the U group main body scheme as a discrete optimal solution result, wherein the minimum fitness cost of the upper discrete particles is the minimum value of sensor perception content distribution in the M group of results, the discrete optimal solution result is the M group of results, each group of results comprises a plurality of sensor deployable positions, and deploying the sensors based on the discrete optimal solution result.
Optionally, in the step S4, an adaptive route discovery model is built on the sensor set running on the current packet, including: according to the obtained sensor operation grouping result and the sensor deployment position, controlling all sensors in only one group to be in an operation state at the same moment; for all sensors in a grouping result in a current running state, constructing a self-adaptive route discovery model, wherein the constructed model takes the current running sensor position as input and takes a data packet transmission route as output, the data packet transmission route takes the current running sensor position as a starting point and takes a central control node as an end point, and the central control node is responsible for receiving and analyzing the data packet sent by the sensor;
The data packet transmission route determining flow based on the self-adaptive route discovery model is as follows:
s41: the sensor which operates currently is used as a routing node, the sensor of the data packet to be sent is set as an initial routing node, and the central control node is used as a target node;
s42: the initial routing node sends a routing request message to the adjacent routing node, the adjacent routing node receives the routing request message, supplements node record data in the routing request message, and forwards the routing request message to the adjacent routing node until the routing request message reaches the target node; the routing request message comprises node record data, and the hop count between routing nodes and the current sensor electric quantity of the nodes, through which the routing request message passes, are recorded;
s43: when the route request message reaches the target node, the target node sends a route reply message with node record data to the adjacent node, and the adjacent node receives and forwards the route reply message until the route reply message reaches the initial route node;
s44: the initial routing node analyzes the routing reply node to obtain the hop count among different routing nodes and the sensor electric quantity corresponding to the routing nodes;
s45: and deleting the route nodes with the sensor electric quantity lower than the preset threshold value, and traversing the route nodes in the rest route nodes to obtain a transmission route with the shortest hop number, which takes the initial route node as a starting point and the target node as an ending point, and taking the transmission route as the data packet transmission route of the data packet sensor to be sent currently.
Optionally, in the step S5, route transmission is performed on the data packet collected by the current running sensor, including:
calculating a data packet transmission route of the sensor currently in an operation state according to the step S4, and carrying out route transmission on the data packet acquired by the currently operated sensor according to the data packet transmission route;
and the central control node analyzes the data packet according to the received data packet to obtain the current reservoir environment information, so that the reservoir ecological environment is monitored in real time.
In order to solve the above-described problems, the present invention provides an electronic apparatus including:
a memory storing at least one instruction;
the communication interface is used for realizing the communication of the electronic equipment; and the processor executes the instructions stored in the memory to realize the reservoir ecological environment real-time monitoring method.
In order to solve the above problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored, where the at least one instruction is executed by a processor in an electronic device to implement the method for monitoring the ecological environment of a reservoir in real time.
Compared with the prior art, the invention provides a real-time monitoring method for the ecological environment of a reservoir, which has the following advantages:
Firstly, the scheme provides a reservoir ecological sensor grouping environment monitoring model for realizing reservoir ecological environment grouping monitoring, wherein an objective function of the reservoir ecological sensor grouping environment monitoring model is as follows:
Figure SMS_64
；
Figure SMS_65
；
Figure SMS_66
；
Figure SMS_67
；
Figure SMS_68
；
Figure SMS_69
；
wherein:
Figure SMS_70
sensor-perceived content distribution representing the result of the m-th group of packets,>
Figure SMS_71
a sensor representing the M-group packet result senses a content distribution minimum; />
Figure SMS_72
Perceived content mean value representing K sensor-deployable locations,/->
Figure SMS_73
Representing the perceived content mean of the m-th set of results. Solving the objective function, and controlling all sensors in only one group to be in an operation state at the same moment according to a sensor operation grouping result obtained by solving and a sensor deployment position; the data packet transmission route determining flow based on the self-adaptive route discovery model is as follows: the sensor which operates currently is used as a routing node, the sensor of the data packet to be sent is set as an initial routing node, and the central control node is used as a target node; the initial routing node sends a routing request message to the adjacent routing node, the adjacent routing node receives the routing request message, supplements node record data in the routing request message, and forwards the routing request message to the adjacent routing node until the routing request message reaches the target node; the routing request message comprises node record data, and the hop count between routing nodes and the current sensor electric quantity of the nodes, through which the routing request message passes, are recorded; on the road The request message reaches a target node, the target node sends a route reply message with node record data to a neighboring node, and the neighboring node receives and forwards the route reply message until the route reply message reaches an initial route node; the initial routing node analyzes the routing reply node to obtain the hop count among different routing nodes and the sensor electric quantity corresponding to the routing nodes; and deleting the route nodes with the sensor electric quantity lower than the preset threshold value, and traversing the route nodes in the rest route nodes to obtain a transmission route with the shortest hop number, which takes the initial route node as a starting point and the target node as an ending point, and taking the transmission route as the data packet transmission route of the data packet sensor to be sent currently. Carrying out route transmission on the data packet acquired by the current running sensor according to the data packet transmission route; and the central control node analyzes the data packet according to the received data packet to obtain the current reservoir environment information, so that the reservoir ecological environment is monitored in real time. According to the scheme, the reservoir ecological sensor grouping environment monitoring model is constructed based on probability distribution of sensor deployable position sensing content, the deployable positions of the existing sensors are subjected to grouping processing, each group comprises a plurality of sensors, the monitoring effect of each group of sensors on a target area is measured by combining mutual information of the sensor deployable content of each group, only one group of sensors is operated at the same time under the condition that the lowest monitoring effect of each group of sensors is guaranteed, the service life of the sensors is effectively prolonged, the monitoring effect on the reservoir ecological environment is guaranteed, a data packet transmission path is determined in real time for the current sensors in an operating state by adopting a self-adaptive route discovery strategy combining hop count and sensor electric quantity, and data packets collected by the sensors are transmitted and analyzed, so that real-time reservoir environment information is obtained.
Meanwhile, the scheme provides a double-layer objective function optimization solving algorithm, parameters to be solved of the objective function are sensor deployment position sets contained in each group of grouping results, each group of sensor deployment positions for monitoring the reservoir ecological environment are determined based on the solving results of the objective function, maximization of minimum sensor perception content distribution values in the grouping results is achieved, each group of sensors can independently complete environment monitoring of a target area, discrete representation is conducted on the groups of grouping results through construction of a discrete coding representation scheme, upper discrete particles are constructed to represent each group of grouping results, a plurality of main body schemes with the best overall monitoring effect are determined in an upper optimization part, and for the current plurality of main body schemes, the main body scheme with the maximum cost of minimum upper discrete particles in the main body scheme is selected as the discrete optimization solving result, so that sensor grouping deployment of the target reservoir area is achieved rapidly.
Drawings
FIG. 1 is a schematic flow chart of a method for monitoring the ecological environment of a reservoir in real time according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an electronic device for implementing a method for monitoring an ecological environment of a reservoir in real time according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a real-time monitoring method for the ecological environment of a reservoir. The execution main body of the reservoir ecological environment real-time monitoring method comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the reservoir ecological environment real-time monitoring method can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1
S1: the sensor deployable locations of the target area are acquired, and the sensor perceived content distribution of each sensor deployable location is constructed.
The step S1 of acquiring the sensor deployable positions of the target area and constructing the sensor perceived content distribution of each sensor deployable position includes:
Sensor deployable location coordinate set for acquiring a target region V
Figure SMS_74
Wherein the total number of sensor deployable positions in the target area V is K,/or%>
Figure SMS_75
Representing the K-th sensor deployable position, K, in the target region V>n, n represents the minimum number of sensor deployable locations, and the target area represents a reservoir environmental area;
the positions of the existing sensors are adjusted within the same time range, and the sensors with the adjusted positions are utilized to obtain the sensor sensing contents of different deployable positions in the target area V, wherein the sensing content mean value and standard deviation of the deployable position of the kth sensor in the target area in the sensing time are respectively as follows
Figure SMS_76
Wherein->
Figure SMS_77
Mean value representing perceived content of kth sensor deployable position within perceived time, +.>
Figure SMS_78
The standard deviation of perceived content of the deployable position of the kth sensor in the perceived time is represented, and the perceived content of the sensor represents reservoir environment information acquired by the sensor;
constructing a sensor-aware content distribution for each sensor-deployable location:
Figure SMS_79
；
Figure SMS_80
；
wherein:
t represents a transpose;
Figure SMS_81
a standard deviation matrix representing the perceived content of the deployment sensor;
Figure SMS_82
representing a probability that a kth sensor-deployable location perceives an observation s representing +. >
Figure SMS_83
Reservoir environmental information s is collected.
S2: and constructing a reservoir ecological sensor grouping environment monitoring model, wherein the constructed model takes the deployable positions of the existing sensors and the number of sensors as input, takes the grouping results of the sensors and the deployment positions of the sensors as output, and takes the maximized sensor perception content distribution as an objective function.
And step S2, constructing a reservoir ecological sensor grouping environment monitoring model, which comprises the following steps:
constructing a reservoir ecological sensor grouping environment monitoring model according to sensor sensing content distribution of sensor deployable positions, wherein the constructed model takes the existing sensor deployable positions and the number of sensors as input, and takes sensor grouping results and sensor deployment positions as output;
determining a sensor packet number based on the target area size:
Figure SMS_84
；
wherein: m represents the number of sensor groups, S represents the area of the target region V, R represents the information sensing radius of the sensor,
Figure SMS_85
representing decision coefficients, will->
Figure SMS_86
Set to 3;
wherein the number of the existing sensors to be deployed is num, the number of the sensor groups is M, and the set of the sensor deployable positions contained in the M-th group of group results is
Figure SMS_87
The number of sensors contained in the m-th group of grouping results is +.>
Figure SMS_88
，/>
Figure SMS_89
。
And in the step S2, the objective function of the reservoir ecological sensor grouping environment monitoring model is as follows:
Figure SMS_90
;
Figure SMS_91
;
Figure SMS_92
;
Figure SMS_93
;
Figure SMS_94
;
Figure SMS_95
;
Wherein:
Figure SMS_96
sensor-perceived content distribution representing the result of the m-th group of packets,>
Figure SMS_97
a sensor representing the M-group packet result senses a content distribution minimum;
Figure SMS_98
perceived content mean value representing K sensor-deployable locations,/->
Figure SMS_99
A perceived content mean representing the m-th group of results;
and the parameters to be solved of the objective function are M groups of grouping results, each group of grouping results comprises a sensor deployment position set, each group of sensor deployment positions for monitoring the ecological environment of the reservoir are determined based on the solving result of the objective function, and maximization of the minimum value of the sensor perception content distribution in the M groups of grouping results is realized, so that each group of sensors can independently complete the environment monitoring of the target area.
S3: and carrying out discrete optimization solution on the reservoir ecological sensor grouping environment monitoring model based on the objective function to obtain a sensor grouping result and a sensor deployment position.
In the step S3, discrete optimization solution is carried out on the reservoir ecological sensor grouping environment monitoring model based on an objective function, and the method comprises the following steps:
performing discrete optimization solving on the reservoir ecological sensor grouping environment monitoring model based on an objective function, wherein the discrete optimization solving flow is as follows:
s31: setting the optimal solving frequency as Max, generating G upper discrete coding representation schemes, wherein each upper discrete coding representation scheme comprises M particles, each particle corresponds to a sensor deployable position set in a group of grouping results, and the first discrete coding representation scheme comprises M particles
Figure SMS_100
The form of the discrete coded representation scheme is:
Figure SMS_101
;
Figure SMS_102
;
wherein:
Figure SMS_103
indicate->
Figure SMS_104
A form of a discrete coded representation scheme;
Figure SMS_105
representation->
Figure SMS_106
The mth particle of the group corresponds to the mth grouping result;
Figure SMS_107
representation->
Figure SMS_108
Middle->
Figure SMS_109
A coded representation of a set of sensor deployable locations, wherein each particle comprises a different sensor deployable location from each other and each particle comprises a greater than 0 number of sensor deployable locations;
s32: determining a fitness function of the upper layer discrete particles:
Figure SMS_110
;
wherein:
Figure SMS_111
representing the distribution of the perceived content of the computing sensor;
Figure SMS_112
representing the fitness function of the upper discrete particles, wherein the calculation result of the fitness function is the fitness cost of the upper discrete particles;
calculating the fitness cost of the discrete coding representation scheme:
Figure SMS_113
;
wherein:
Figure SMS_114
indicate->
Figure SMS_115
The fitness cost of the individual discrete code representation schemes;
s33: the U discrete coding representation schemes with the largest fitness cost are selected as main schemes, the other schemes are used as auxiliary schemes, and the normalized fitness cost of the U-th main scheme is calculated:
Figure SMS_116
;
wherein:
Figure SMS_117
representing the maximum fitness cost in the U main body schemes; />
Figure SMS_118
Indicating the fitness cost of the u-th subject regimen, < ->
Figure SMS_119
Representing normalized fitness costs for a nth subject scheme;
S34: calculating the number of subsidiary schemes owned by the u-th subject scheme
Figure SMS_120
：
Figure SMS_121
;
Wherein:
Figure SMS_122
representing a rounding function, G representing the total number of discrete coding representation schemes;
random allocation to a nth subject scheme
Figure SMS_123
The method comprises the steps of forming a group of main body scheme fields, wherein the main body scheme fields comprise a main body scheme and a plurality of auxiliary schemes;
s35: taking the auxiliary scheme with the highest fitness cost in the u-th main scheme as the optimal auxiliary scheme of the u-th main scheme; for any subsidiary scheme in any u-th main scheme, generating a random number rand between 0 and 1, and if the rand is smaller than 0.4, updating the selected subsidiary scheme and the main scheme as follows:
Figure SMS_124
;
Figure SMS_125
;
otherwise, the optimal auxiliary scheme of the selected auxiliary scheme and the optimal auxiliary scheme of the u-th main scheme are updated as follows:
Figure SMS_126
;
Figure SMS_127
;
Figure SMS_128
;
wherein:
Figure SMS_129
representing the upper discrete particles with highest fitness cost in the body scheme of the u th type, the +.>
Figure SMS_130
Representing the upper discrete particles with highest fitness cost in the selected subsidiary scheme, +.>
Figure SMS_131
Representing the upper discrete particles with highest fitness cost in the optimal auxiliary scheme corresponding to the main scheme of the u th type;
Figure SMS_132
representation->
Figure SMS_133
Is updated by->
Figure SMS_134
Representation after updating
Figure SMS_135
A coded representation of a set of sensor deployable locations; in the embodiment of the invention, the code representation solution result is in a vector form, and a sensor deployable position which is nearest to the calculated sensor deployable position and not contained by other discrete particles in the same scheme is selected as the code representation solution result, namely, the sensor deployable position which is calculated by the distance is selected corresponding to a plurality of sensor deployable positions contained by the updated upper layer discrete particles
Figure SMS_136
The sensor deployable positions contained in other discrete particles in the nearest and non-u-th main body scheme are used as coding representation solving results;
s36: calculating the adaptability cost of each updated main scheme and auxiliary schemes, and updating the scheme with the highest adaptability cost into a main scheme for any group of main scheme fields; returning to step S35;
s37: repeating steps S35 to S36 to reach preset optimal solution times Max, calculating to obtain the minimum fitness cost of the upper discrete particles in each group of main body schemes for the current U group main body scheme, selecting the main body scheme with the maximum minimum fitness cost of the upper discrete particles in the U group main body scheme as a discrete optimal solution result, wherein the minimum fitness cost of the upper discrete particles is the minimum value of sensor perception content distribution in the M group of results, the discrete optimal solution result is the M group of results, each group of results comprises a plurality of sensor deployable positions, and deploying the sensors based on the discrete optimal solution result.
S4: and constructing an adaptive route discovery model for the sensor set operated by the current group, wherein the constructed model takes the position of the sensor operated currently as input and takes the data packet transmission route as output.
And in the step S4, an adaptive route discovery model is built for the sensor set operated by the current packet, and the method comprises the following steps:
according to the obtained sensor operation grouping result and the sensor deployment position, controlling all sensors in only one group to be in an operation state at the same moment; for all sensors in a grouping result in a current running state, constructing a self-adaptive route discovery model, wherein the constructed model takes the current running sensor position as input and takes a data packet transmission route as output, the data packet transmission route takes the current running sensor position as a starting point and takes a central control node as an end point, and the central control node is responsible for receiving and analyzing the data packet sent by the sensor;
the data packet transmission route determining flow based on the self-adaptive route discovery model is as follows:
s41: the sensor which operates currently is used as a routing node, the sensor of the data packet to be sent is set as an initial routing node, and the central control node is used as a target node;
s42: the initial routing node sends a routing request message to the adjacent routing node, the adjacent routing node receives the routing request message, supplements node record data in the routing request message, and forwards the routing request message to the adjacent routing node until the routing request message reaches the target node; the routing request message comprises node record data, and the hop count between routing nodes and the current sensor electric quantity of the nodes, through which the routing request message passes, are recorded;
S43: when the route request message reaches the target node, the target node sends a route reply message with node record data to the adjacent node, and the adjacent node receives and forwards the route reply message until the route reply message reaches the initial route node;
s44: the initial routing node analyzes the routing reply node to obtain the hop count among different routing nodes and the sensor electric quantity corresponding to the routing nodes;
s45: and deleting the route nodes with the sensor electric quantity lower than the preset threshold value, and traversing the route nodes in the rest route nodes to obtain a transmission route with the shortest hop number, which takes the initial route node as a starting point and the target node as an ending point, and taking the transmission route as the data packet transmission route of the data packet sensor to be sent currently.
S5: and carrying out route transmission on the data packet acquired by the current running sensor according to the calculated data packet transmission route.
And in the step S5, the data packet acquired by the current operation sensor is transmitted in a routing way, and the method comprises the following steps:
calculating a data packet transmission route of the sensor currently in an operation state according to the step S4, and carrying out route transmission on the data packet acquired by the currently operated sensor according to the data packet transmission route;
and the central control node analyzes the data packet according to the received data packet to obtain the current reservoir environment information, so that the reservoir ecological environment is monitored in real time.
Example 2
Fig. 2 is a schematic structural diagram of an electronic device for implementing a method for monitoring an ecological environment of a reservoir in real time according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing real-time monitoring of the ecological environment of a reservoir, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
acquiring sensor deployable positions of a target area, and constructing sensor perception content distribution of each sensor deployable position;
constructing a reservoir ecological sensor grouping environment monitoring model;
performing discrete optimization solution on the reservoir ecological sensor grouping environment monitoring model based on the objective function to obtain a sensor grouping result and a sensor deployment position;
constructing a self-adaptive route discovery model for a sensor set operated by a current group, wherein the constructed model takes the position of the sensor operated currently as input and takes a data packet transmission route as output;
and carrying out route transmission on the data packet acquired by the current running sensor according to the calculated data packet transmission route.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (6)

1. The method for monitoring the ecological environment of the reservoir in real time is characterized by comprising the following steps of:
S1: acquiring sensor deployable positions of a target area, and constructing sensor perception content distribution of each sensor deployable position;
s2: constructing a reservoir ecological sensor grouping environment monitoring model, wherein the constructed model takes the deployable positions of the existing sensors and the number of sensors as input, takes the grouping results of the sensors and the deployment positions of the sensors as output, and takes the maximized sensor sensing content distribution as an objective function;
s3: performing discrete optimization solution on the reservoir ecological sensor grouping environment monitoring model based on the objective function to obtain a sensor grouping result and a sensor deployment position;
s4: constructing a self-adaptive route discovery model for a sensor set operated by a current group, wherein the constructed model takes the position of the sensor operated currently as input and takes a data packet transmission route as output;
s5: according to the calculated data packet transmission route, carrying out route transmission on the data packet acquired by the current running sensor;
the step S1 of acquiring the sensor deployable positions of the target area and constructing the sensor perceived content distribution of each sensor deployable position includes:
sensor deployable location coordinate set for acquiring a target region V
Figure QLYQS_1
Wherein the total number of sensor deployable positions in the target area V is K,/or%>
Figure QLYQS_2
Representing the K-th sensor deployable position, K, in the target region V>n, n represents the minimum number of sensor deployable locations, and the target area represents a reservoir environmental area;
the positions of the existing sensors are adjusted within the same time range, and the sensors with the adjusted positions are utilized to obtain the sensor sensing contents of different deployable positions in the target area V, wherein the sensing content mean value and standard deviation of the deployable position of the kth sensor in the target area in the sensing time are respectively as follows
Figure QLYQS_3
Wherein->
Figure QLYQS_4
Mean value representing perceived content of kth sensor deployable position within perceived time, +.>
Figure QLYQS_5
The standard deviation of perceived content of the deployable position of the kth sensor in the perceived time is represented, and the perceived content of the sensor represents reservoir environment information acquired by the sensor;
constructing a sensor-aware content distribution for each sensor-deployable location:
Figure QLYQS_6
；
Figure QLYQS_7
；
wherein: t represents the transpose of the number,
Figure QLYQS_8
a standard deviation matrix representing the perceived content of the deployment sensor;
Figure QLYQS_9
representing a probability that a kth sensor-deployable location perceives an observation s representing +. >
Figure QLYQS_10
Reservoir environmental information s is collected.
2. The method for monitoring the ecological environment of the reservoir in real time according to claim 1, wherein the step S2 of constructing the reservoir ecological sensor grouping environment monitoring model comprises the following steps:
constructing a reservoir ecological sensor grouping environment monitoring model according to sensor sensing content distribution of sensor deployable positions, wherein the constructed model takes the existing sensor deployable positions and the number of sensors as input, and takes sensor grouping results and sensor deployment positions as output;
determining a sensor packet number based on the target area size:
Figure QLYQS_11
；
wherein:
m represents the number of sensor groups, S represents the area of the target region V, R represents the information sensing radius of the sensor,
Figure QLYQS_12
representing decision coefficients, will->
Figure QLYQS_13
Set to 3;
wherein the number of the existing sensors to be deployed is num, the number of the sensor groups is M, and the set of the sensor deployable positions contained in the M-th group of group results is
Figure QLYQS_14
The number of sensors contained in the m-th group of grouping results is +.>
Figure QLYQS_15
，/>
Figure QLYQS_16
。
3. The method for monitoring the ecological environment of the reservoir in real time according to claim 2, wherein the objective function of the reservoir ecological sensor grouping environment monitoring model in the step S2 is as follows:
Figure QLYQS_17
；
Figure QLYQS_18
；
Figure QLYQS_19
；
Figure QLYQS_20
；
Figure QLYQS_21
；
Figure QLYQS_22
；
Wherein:
Figure QLYQS_23
representing the number of sensors contained in the m-th group of grouping results;
Figure QLYQS_24
sensor-perceived content distribution representing the result of the m-th group of packets,>
Figure QLYQS_25
a sensor representing the M-group packet result senses a content distribution minimum;
Figure QLYQS_26
perceived content mean value representing K sensor-deployable locations,/->
Figure QLYQS_27
A perceived content mean representing the m-th group of results;
and the parameters to be solved of the objective function are M groups of grouping results, each group of grouping results comprises a sensor deployment position set, and each group of sensor deployment positions for monitoring the ecological environment of the reservoir are determined based on the solving result of the objective function.
4. The method for monitoring the ecological environment of the reservoir in real time according to claim 3, wherein in the step S3, discrete optimization solution is performed on the reservoir ecological sensor grouping environment monitoring model based on an objective function, and the method comprises the following steps:
performing discrete optimization solving on the reservoir ecological sensor grouping environment monitoring model based on an objective function, wherein the discrete optimization solving flow is as follows:
s31: setting the optimal solving frequency as Max, generating G upper discrete coding representation schemes, wherein each upper discrete coding representation scheme comprises M particles, each particle corresponds to a sensor deployable position set in a group of grouping results, and the first discrete coding representation scheme comprises M particles
Figure QLYQS_28
The form of the discrete coded representation scheme is:
Figure QLYQS_29
；
Figure QLYQS_30
；
wherein:
Figure QLYQS_31
indicate->
Figure QLYQS_32
A form of a discrete coded representation scheme;
Figure QLYQS_33
representation->
Figure QLYQS_34
The mth particle of the group corresponds to the mth grouping result;
Figure QLYQS_35
representation->
Figure QLYQS_36
Middle->
Figure QLYQS_37
A coded representation of a set of sensor deployable locations, wherein each particle comprises a different sensor deployable location from each other and each particle comprises a greater than 0 number of sensor deployable locations;
s32: determining a fitness function of the upper layer discrete particles:
Figure QLYQS_38
；
wherein:
Figure QLYQS_39
representing the distribution of the perceived content of the computing sensor;
Figure QLYQS_40
representing the fitness function of the upper discrete particles, wherein the calculation result of the fitness function is the fitness cost of the upper discrete particles;
calculating the fitness cost of the discrete coding representation scheme:
Figure QLYQS_41
；
wherein:
Figure QLYQS_42
indicate->
Figure QLYQS_43
The fitness cost of the individual discrete code representation schemes;
s33: the U discrete coding representation schemes with the largest fitness cost are selected as main schemes, the other schemes are used as auxiliary schemes, and the normalized fitness cost of the U-th main scheme is calculated:
Figure QLYQS_44
；
wherein:
Figure QLYQS_45
representing the maximum fitness cost in the U main body schemes; />
Figure QLYQS_46
Indicating the fitness cost of the u-th subject regimen, < ->
Figure QLYQS_47
Representing normalized fitness costs for a nth subject scheme;
S34: calculating the number of subsidiary schemes owned by the u-th subject scheme
Figure QLYQS_48
：
Figure QLYQS_49
；
Wherein:
Figure QLYQS_50
representing a rounding function, G representing the total number of discrete coding representation schemes;
random allocation to a nth subject scheme
Figure QLYQS_51
The method comprises the steps of forming a group of main body scheme fields, wherein the main body scheme fields comprise a main body scheme and a plurality of auxiliary schemes;
s35: taking the auxiliary scheme with the highest fitness cost in the u-th main scheme as the optimal auxiliary scheme of the u-th main scheme; for any subsidiary scheme in any u-th main scheme, generating a random number rand between 0 and 1, and if the rand is smaller than 0.4, updating the selected subsidiary scheme and the main scheme as follows:
Figure QLYQS_52
；
Figure QLYQS_53
；
otherwise, the optimal auxiliary scheme of the selected auxiliary scheme and the optimal auxiliary scheme of the u-th main scheme are updated as follows:
Figure QLYQS_54
；
Figure QLYQS_55
；
Figure QLYQS_56
；
wherein:
Figure QLYQS_57
representing the upper discrete particles with highest fitness cost in the body scheme of the u th type, the +.>
Figure QLYQS_58
Representing the upper discrete particles with highest fitness cost in the selected subsidiary scheme, +.>
Figure QLYQS_59
Representing the upper discrete particles with highest fitness cost in the optimal auxiliary scheme corresponding to the main scheme of the u th type;
Figure QLYQS_60
representation->
Figure QLYQS_61
Is updated by->
Figure QLYQS_62
Representing post-update +.>
Figure QLYQS_63
A coded representation of a set of sensor deployable locations; s36: calculating the adaptability cost of each updated main scheme and auxiliary schemes, and updating the scheme with the highest adaptability cost into a main scheme for any group of main scheme fields; returning to step S35;
S37: repeating steps S35 to S36 to reach preset optimal solution times Max, calculating to obtain the minimum fitness cost of the upper discrete particles in each group of main body schemes for the current U group main body scheme, selecting the main body scheme with the maximum minimum fitness cost of the upper discrete particles in the U group main body scheme as a discrete optimal solution result, wherein the minimum fitness cost of the upper discrete particles is the minimum value of sensor perception content distribution in the M group of results, the discrete optimal solution result is the M group of results, each group of results comprises a plurality of sensor deployable positions, and deploying the sensors based on the discrete optimal solution result.
5. The method for monitoring the ecological environment of the reservoir in real time according to claim 1, wherein the step S4 is to construct an adaptive route discovery model for the current group-operated sensor set, and the method comprises the following steps:
according to the obtained sensor operation grouping result and the sensor deployment position, controlling all sensors in only one group to be in an operation state at the same moment; for all sensors in a grouping result in a current running state, constructing a self-adaptive route discovery model, wherein the constructed model takes the current running sensor position as input and takes a data packet transmission route as output, the data packet transmission route takes the current running sensor position as a starting point and takes a central control node as an end point, and the central control node is responsible for receiving and analyzing the data packet sent by the sensor;
The data packet transmission route determining flow based on the self-adaptive route discovery model is as follows:
s41: the sensor which operates currently is used as a routing node, the sensor of the data packet to be sent is set as an initial routing node, and the central control node is used as a target node;
s42: the initial routing node sends a routing request message to the adjacent routing node, the adjacent routing node receives the routing request message, supplements node record data in the routing request message, and forwards the routing request message to the adjacent routing node until the routing request message reaches the target node; the routing request message comprises node record data, and the hop count between routing nodes and the current sensor electric quantity of the nodes, through which the routing request message passes, are recorded;
s43: when the route request message reaches the target node, the target node sends a route reply message with node record data to the adjacent node, and the adjacent node receives and forwards the route reply message until the route reply message reaches the initial route node;
s44: the initial routing node analyzes the routing reply node to obtain the hop count among different routing nodes and the sensor electric quantity corresponding to the routing nodes;
s45: and deleting the route nodes with the sensor electric quantity lower than the preset threshold value, and traversing the route nodes in the rest route nodes to obtain a transmission route with the shortest hop number, which takes the initial route node as a starting point and the target node as an ending point, and taking the transmission route as the data packet transmission route of the data packet sensor to be sent currently.
6. The method for monitoring the ecological environment of a reservoir in real time according to claim 5, wherein in the step S5, the data packet collected by the current running sensor is routed, and the method comprises the following steps:
calculating a data packet transmission route of the sensor currently in an operation state according to the step S4, and carrying out route transmission on the data packet acquired by the currently operated sensor according to the data packet transmission route;
and the central control node analyzes the data packet according to the received data packet to obtain the current reservoir environment information.
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