CN110568140A - Pollution source exploration positioning method based on machine bionic fish - Google Patents
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Abstract
A pollution source exploration positioning method based on machine bionic fish comprises the steps of firstly establishing a general multi-modal water body pollution diffusion model; randomly placing the machine bionic fish into a water area, and initializing parameters; performing water quality detection to obtain and share position information and pollutant concentration, updating the maximum concentration value of the node, and taking the initial random position as an initial local optimal value to obtain a global optimal value; operating a general multi-mode water body pollution source diffusion model by taking the position of the global optimal value as the origin of the simulated pollution source to obtain the pollutant concentration of the current position and calculate the pheromone concentration; according to a next hop node formula, selecting a next hop node of the bionic fish by the selector, calculating the node transfer probability, and judging whether the node is transferred or not; and moving the machine bionic fish to the next position, repeating the steps, and continuously updating the coordinates, the local optimal values and the global optimal values of the machine bionic fish until the conditions are met, wherein the coordinates of the global optimal values are the position of the pollution source. The invention has good stability and accurate positioning.
Description
Technical Field
The invention relates to the technical field of pollution source positioning, in particular to a pollution source exploring and positioning method based on a machine bionic fish.
background
Water resources are an important natural resource, and in recent years, with the deepening of industrialization degree, the discharge of industrial, agricultural and domestic sewage becomes a source of water pollution and causes irreversible influence on the ecological environment. The pollution caused by human factors needs to be treated from a source, water quality monitoring is one of important means for detecting water pollution, and when the pollution occurs, how to quickly position the position of a pollution source has direct significance on water pollution treatment work.
in recent years, with the rapid development of communication technology, internet of things technology, sensor technology and embedded technology, wireless sensor Network technology and theory are gradually improved, and the rapid development of Underwater Wireless Sensor Networks (UWSNs) further promotes the application of data monitoring networks in water areas.
The mobile exploration and the exploration based on the wireless sensor network become the main exploration mode at present, and the mobile exploration refers to the water quality monitoring in different water areas by using movable water quality monitoring equipment. Common mobile equipment includes land mobile monitoring vehicles, wireless water quality monitoring ships, offline control robots and the like. Due to the high cost of mobile monitoring vehicles and mobile monitoring ships, more and more students are developing research aiming at low-cost and small-sized mobile water quality monitoring equipment, wherein small unmanned monitoring ships and machine-simulated fish are the current research hotspots.
most of the current water pollution source positioning research depends on theoretical analysis such as mathematical modeling, simulation verification is mostly adopted in the conclusion, the application and analysis of actually acquired water quality data are less, and effective information in the water quality data cannot be completely extracted. The group intelligence is judged according to the current collected data without referring to the pollution source diffusion model, the judgment accuracy and the stability are relatively low, and the large-scale search error in the early stage is large. The research focus still tends to the path planning problem of the mobile monitor, and the searching and positioning of the pollution source are not solved. Most of the research results are obtained by theoretical analysis based on collected data, and individual research can also carry out positioning through a wireless sensor network, but the research on the pollution exploration positioning method of the mobile monitoring system is not very deep.
therefore, in the current complex underwater environment, it is urgently needed to provide an effective positioning method for water quality detection.
Disclosure of Invention
Aiming at the condition that the current research is too much focused on path planning and the research on water pollution exploration positioning of a mobile monitoring system is insufficient, the invention provides a pollution source positioning method based on the machine bionic fish with good stability and accurate positioning,
The technical scheme adopted by the invention for solving the technical problems is as follows:
A method for locating a pollution source based on a machine-simulated fish, the method comprising the steps of:
(1) Establishing a general multi-modal water pollution source diffusion model;
(2) randomly placing M machine bionic fishes to M1×M2Initializing model parameters in the target two-dimensional water area;
(3) Performing water quality detection on the robotic bionic fish to obtain and share position information xi(t) with contaminant concentration Ci(t) updating the maximum concentration value P of the nodebest(i) Taking the initial random position as an initial local optimal value to obtain a global optimal value Gbest(i)；
(4) With global optimum value Gbest(i) the position of the model is the origin of the simulated pollution source, a general multi-mode water body pollution source diffusion model is operated, and the pollutant concentration C of the current position is obtainedS(t) and calculating pheromone concentration τi(t)；
(5) According to a next hop node formula, selecting a next hop node of the bionic fish by the selector, calculating the node transfer probability, and judging whether the node is transferred or not;
(6) The machine bionic fish moves to the next position, the steps (3) to (5) are repeated, and the coordinates and the local optimal value P of each machine bionic fish are continuously updatedbest(i) And global optimum Gbest(i) Until the iteration times reach the set times or the difference between the actually-measured concentration and the simulated concentration of the pollutant is smaller than a certain threshold value, stopping the operation of the algorithm, and then obtaining a global optimal value Gbest(i) the coordinates of (a) are the position of the contamination source.
Further, the process of step 1 is as follows:
step 101, establishing a two-dimensional coordinate system by using a water area plane, and continuously feeding concentration C at a speed Q from a time when t is 0 at any point (xi, eta) on a horizontal plane0The amount of contaminant, dM ═ C, added at time t ═ τ, is then0Qdτ；
Step 102, obtaining a concentration function C (x, y, t) generated at the time point (x, y) where t is tau, wherein C (x, y, t) satisfies the following formula,
Wherein Dx，Dydiffusion coefficient in x and y directions, xi, eta are initial coordinates, C0the pollutant concentration is adopted, and Q is the putting speed;
103, considering the flow velocity of water flow, the water quality monitoring system can be applied to different water areas, when aiming at some river channels in cities, the influence of river banks on pollution sources on two sides is large, so that double boundaries need to be introduced, the water channels are narrow, the influence of far boundaries is ignored, in addition, considering comprehensive degradation coefficients, a general multi-mode water body pollution source diffusion model is obtained by solving through an image source method, wherein C (x, y, t) satisfies the following formula,
Wherein, muxflow velocity in x direction, μyThe flow velocity in the y direction, l is the river width, b is the distance between the pollution source and the near bank, K1To synthesize a degradation coefficient, C0The pollutant concentration and Q the throwing speed.
Still further, in step 4, the process of calculating the pheromone concentration strategy is as follows:
the mechanical bionic fish can detect the actual pollutant concentration CRthe current global optimal value coordinate is taken as a pollution source, a general multi-modal pollutant concentration model is introduced, and the simulated pollutant concentration C of the current point is calculatedSAccording to the pheromone concentration updating strategy of pollutant concentration, the smaller the difference between the simulated concentration and the measured concentration is, the closer to the pollution source is shown, the updating mode is as follows,
where ρ is pheromone volatility constant, τiFor pheromone concentrations, additional additions were given to pheromone concentrations at global optima, taking into account the attractiveness of global optima to other machine-simulated fish, further update strategies are shown below,
wherein eli is pheromone addition parameter, usually a constant between 0-1, CRas actual contaminant concentration, CSIs the simulated pollutant concentration at the current point, rho is the pheromone volatilization constant, tauiIs the pheromone concentration.
Further, the process of step 5 is as follows:
step 501, selecting a next-hop node, referring to the variation trend of the maximum pollutant concentration, and based on the local optimal value PbestAnd global optimum Gbestto obtain a vectorThe unit vector is calculated to obtain the direction of the next node, the calculation method is as follows,
Wherein the content of the first and second substances,Selecting nodes according to the direction as a direction vector;
Step 502, after the bionic fish obtains the position of the next jump node, calculating the transition probability to judge whether to jump to the node, the calculation method is as follows,
wherein, tauGbestFor the global optimum point pheromone concentration, τiis the current point pheromone concentration, piIs the transition probability;
Step 503, adopting a fixed step length as the step length for the jumping of the machine-simulated fish, jumping when the node transfer probability is larger than a constant p, when the node transfer probability is smaller than the constant p and the difference between the current concentration of the information points and the global optimal concentration of the pheromone is not large, thinking that the position is in the vicinity of the maximum pollution point, properly adjusting the moving step length, finding a more accurate solution, wherein the step length updating strategy is as follows,
xi(t+1)＝xi(t)+vi(t+1)，
Wherein R is a fixed step length of the movement of the machine bionic fish, xi(t +1) is the distance of the next hop, vi(t +1) is the weighted calculation step length, w is the moving step length weight, is a constant related to the transition probability, and the value is taken as follows,
wherein lambda is a step length adjustment factor and is a constant between 0 and 1, and piFor transition probabilities, p is a constant.
The technical concept of the invention is to provide a general multi-mode pollution source diffusion model, and introduce an ant colony algorithm pheromone strategy to solve and obtain the position of a pollution source by taking a pollutant concentration difference value as an optimization object. And (3) regarding each mobile monitoring machine bionic fish as one ant in the ant colony, and constructing an pheromone updating method based on the measured concentration and the simulated concentration. Meanwhile, the global optimal point and the local optimal point are considered in the selection of the next hop node and the hop probability calculation, and the machine bionic fish approaches to the global optimal point to achieve the positioning target.
The invention has the following beneficial effects: the method provided by the invention has good stability and more accurate positioning, and the machine bionic fish advances in a fixed step length to accelerate the running speed, and when the optimal point is approached, the step length is reduced by weighting, so that the search is prevented from falling into local optimization, and the accuracy is improved.
Drawings
fig. 1 is a flow chart of a method for locating a source of pollution based on a machine-simulated fish.
Detailed Description
the invention will be further explained with reference to the drawings,
Referring to fig. 1, a pollution source positioning method based on a machine-simulated fish includes the following steps:
(1) Establishing a general multi-modal water pollution source diffusion model;
(2) Randomly placing M machine bionic fishes to M1×M2initializing model parameters in the target two-dimensional water area;
(3) performing water quality detection on the robotic bionic fish to obtain and share position information xi(t) with contaminant concentration Ci(t) updating the maximum concentration value P of the nodebest(i) Taking the initial random position as an initial local optimal value to obtain a global optimal value Gbest(i)；
(4) With global optimum value Gbest(i) The position of the model is the origin of the simulated pollution source, a general multi-mode water body pollution source diffusion model is operated, and the pollutant concentration C of the current position is obtainedS(t) and calculating pheromone concentration τi(t)；
(5) According to a next hop node formula, selecting a next hop node of the bionic fish by the selector, calculating the node transfer probability, and judging whether the node is transferred or not;
(6) The machine bionic fish moves to the next position, the steps (3) to (5) are repeated, and the coordinates and the local optimal value P of each machine bionic fish are continuously updatedbest(i) And global optimum Gbest(i) And stopping the operation of the algorithm until the iteration times reach the set times or the difference value between the actually-measured pollutant concentration and the simulated pollutant concentration is smaller than a certain threshold value. Global optimum value G at this timebest(i) The coordinates of (a) are the position of the contamination source.
Further, the process of step 1 is as follows:
Step 101, establishing a two-dimensional coordinate system by using a water area plane, and continuously feeding concentration C at a speed Q from a time when t is 0 at any point (xi, eta) on a horizontal plane0A contaminant. At time t τ, the amount of pollutant charged is dM ═ C0Qdτ；
step 102, obtaining a concentration function C (x, y, t) generated at the time point (x, y) where t is tau, wherein C (x, y, t) satisfies the following formula,
wherein Dx，DyDiffusion coefficient in x and y directions, xi, eta are initial coordinates, C0The pollutant concentration is adopted, and Q is the putting speed;
103, considering the flow velocity of water flow, the water quality monitoring system can be applied to different water areas, when the water quality monitoring system is applied to some river channels in cities, the influence of river banks on pollution sources on two sides is large, so that double boundaries need to be introduced, the water channels are narrow, the influence of far boundaries is ignored, and in addition, considering comprehensive degradation coefficients, a general multi-mode water body pollution source diffusion model is obtained by solving through an image source method, wherein C (x, y, t) meets the following formula,
Wherein, muxflow velocity in x direction, μyThe flow velocity in the y direction, l is the river width, b is the distance between the pollution source and the near bank, K1To synthesize a degradation coefficient, C0The pollutant concentration and Q the throwing speed.
Still further, the process of step 4 is as follows:
The mechanical bionic fish can detect the actual pollutant concentration CRthe current global optimal value coordinate is taken as a pollution source, a general multi-modal pollutant concentration model is introduced, and the simulated pollutant concentration C of the current point is calculatedSAccording to the pheromone concentration updating strategy of pollutant concentration, the smaller the difference between the simulated concentration and the measured concentration is, the closer to the pollution source is shown, the updating mode is as follows,
where ρ is pheromone volatility constant, τiFor pheromone concentrations, additional additions were given to pheromone concentrations at global optima, taking into account the attractiveness of global optima to other machine-simulated fish, further update strategies are shown below,
Wherein eli is pheromone addition parameter, usually a constant between 0-1, CRAs actual contaminant concentration, CSis the simulated pollutant concentration at the current point, rho is the pheromone volatilization constant, tauiIs the pheromone concentration.
Further, the process of step 5 is as follows:
step 501, selecting a next-hop node, referring to the variation trend of the maximum pollutant concentration, and based on the local optimal value PbestAnd global optimum GbestTo obtain a vectorcalculation unitThe vector obtains the direction of the next node, the calculation method is as follows,
wherein the content of the first and second substances,Selecting nodes according to the direction as a direction vector;
Step 502, after the bionic fish obtains the position of the next jump node, calculating the transition probability to judge whether to jump to the node, the calculation method is as follows,
Wherein, tauGbestFor the global optimum point pheromone concentration, τiIs the current point pheromone concentration, piIs the transition probability;
Step 503, adopting a fixed step length as the step length for the jumping of the machine-simulated fish, jumping when the node transfer probability is larger than a constant p, when the node transfer probability is smaller than the constant p and the difference between the current concentration of the information points and the global optimal concentration of the pheromone is not large, thinking that the position is in the vicinity of the maximum pollution point, properly adjusting the moving step length, finding a more accurate solution, wherein the step length updating strategy is as follows,
xi(t+1)＝xi(t)+vi(t+1)，
Wherein R is a fixed step length of the movement of the machine bionic fish, xi(t +1) is the distance of the next hop, vi(t +1) is the weighted calculation step length, w is the moving step length weight, is a constant related to the transition probability, and the value is taken as follows,
wherein lambda is a step-length adjustment factor, a constant between 0 and 1, and piFor transition probabilities, p is a constant.
Claims (4)
1. A pollution source exploration positioning method based on machine-simulated fish is characterized by comprising the following steps:
(1) establishing a general multi-modal water pollution source diffusion model;
(2) Randomly placing M machine bionic fishes to M1×M2Initializing model parameters in the target two-dimensional water area;
(3) performing water quality detection on the robotic bionic fish to obtain and share position information xi(t) with contaminant concentration Ci(t) updating the maximum concentration value P of the nodebest(i) Taking the initial random position as an initial local optimal value to obtain a global optimal value Gbest(i)；
(4) With global optimum value Gbest(i) the position of the model is the origin of the simulated pollution source, a general multi-mode water body pollution source diffusion model is operated, and the pollutant concentration C of the current position is obtainedS(t) and calculating pheromone concentration τi(t)；
(5) according to a next hop node formula, selecting a next hop node of the bionic fish by the selector, calculating the node transfer probability, and judging whether the node is transferred or not;
(6) The machine bionic fish moves to the next position, the steps (3) to (5) are repeated, and the coordinates and the local optimal value P of each machine bionic fish are continuously updatedbest(i) And global optimum Gbest(i) Until the iteration times reach the set times or the difference between the actually-measured concentration and the simulated concentration of the pollutant is smaller than a certain threshold value, stopping the operation of the algorithm, and then obtaining a global optimal value Gbest(i) The coordinates of (a) are the position of the contamination source.
2. The design method of the pollution source exploring positioning system based on the machine-imitated fish as claimed in claim 1, wherein the process of the step 1 is as follows:
Step 101, establishing a two-dimensional coordinate system by using a water area planeAt any point on the horizontal plane, the concentration C is continuously fed at speed Q from the moment when t is 00The amount of contaminant, dM ═ C, added at time t ═ τ, is then0Qdτ；
step 102, obtaining a concentration function C (x, y, t) generated at the time point (x, y) where t is tau, wherein C (x, y, t) satisfies the following formula,
wherein Dx，DyDiffusion coefficient in x and y directions, xi, eta are initial coordinates, C0the pollutant concentration is adopted, and Q is the putting speed;
103, considering the flow velocity of water flow, applying the water quality monitoring system to different water areas, aiming at some river channels in cities, the influence of river banks on pollution sources at two sides is large, so that double boundaries need to be introduced, the water channels are narrow, the influence of far boundaries is ignored, considering comprehensive degradation coefficients, solving by an image source method to obtain a general multi-mode water body pollution source diffusion model, wherein C (x, y, t) satisfies the following formula,
wherein, muxFlow velocity in x direction, μythe flow velocity in the y direction, l is the river width, b is the distance between the pollution source and the near bank, K1To synthesize a degradation coefficient, C0the pollutant concentration and Q the throwing speed.
3. the design method of the pollution source exploration positioning system based on the machine-imitated fish as claimed in claim 1 or 2, wherein in the step 4, the process of calculating the pheromone concentration strategy is as follows:
The mechanical bionic fish can detect the actual pollutant concentration CRThe current global optimal value coordinate is taken as a pollution source, a general multi-modal pollutant concentration model is introduced, and the simulated pollutant concentration C of the current point is calculatedSAccording to the pheromone concentration updating strategy of pollutant concentration, the smaller the difference between the simulated concentration and the measured concentration is, the closer to the pollution source is shown, the updating mode is as follows,
where ρ is pheromone volatility constant, τiFor pheromone concentrations, additional additions were given to pheromone concentrations at global optima, taking into account the attractiveness of global optima to other machine-simulated fish, further update strategies are shown below,
Wherein eli is pheromone addition parameter, usually a constant between 0-1, CRAs actual contaminant concentration, CSIs the simulated pollutant concentration at the current point, rho is the pheromone volatilization constant, tauiIs the pheromone concentration.
4. The design method of the pollution source exploring positioning system based on the machine-imitated fish as claimed in claim 1 or 2, wherein the step 5 of judging to select the next node for transfer specifically comprises:
Step 501, selecting a next-hop node, referring to the variation trend of the maximum pollutant concentration, and based on the local optimal value Pbestand global optimum Gbestto obtain a vectorThe unit vector is calculated to obtain the direction of the next node, the calculation method is as follows,
Wherein the content of the first and second substances,selecting nodes according to the direction as a direction vector;
Step 502, after the bionic fish obtains the position of the next jump node, calculating the transition probability to judge whether to jump to the node, the calculation method is as follows,
Wherein, tauGbestfor the global optimum point pheromone concentration, τiis the current point pheromone concentration, piIs the transition probability;
Step 503, adopting a fixed step length as the step length for the jumping of the machine-simulated fish, jumping when the node transfer probability is larger than a constant p, when the node transfer probability is smaller than the constant p and the difference between the current concentration of the information points and the global optimal concentration of the pheromone is not large, thinking that the position is in the vicinity of the maximum pollution point, properly adjusting the moving step length, finding a more accurate solution, wherein the step length updating strategy is as follows,
xi(t+1)＝xi(t)+vi(t+1)，
Wherein R is a fixed step length of the movement of the machine bionic fish, xi(t +1) is the distance of the next hop, vi(t +1) is the weighted calculation step length, w is the moving step length weight, is a constant related to the transition probability, and the value is taken as follows,
Wherein lambda is a step-length adjustment factor, a constant between 0 and 1, and pifor transition probabilities, p is a constant.
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