WO2022257243A1 - Water quality early-warning method and system - Google Patents
Water quality early-warning method and system Download PDFInfo
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Definitions
- the invention belongs to the field of water quality early warning, and in particular relates to a water quality early warning method and system.
- Water quality is the abbreviation of water quality, which is generally measured by the physical (such as color, turbidity, odor, etc.), chemical (inorganic and organic content) and biological (bacteria, microorganisms, plankton, benthic organisms) of the water body. .
- physical such as color, turbidity, odor, etc.
- chemical inorganic and organic content
- biological bacteria, microorganisms, plankton, benthic organisms
- the object of the present invention is to provide a water quality early warning method and system, which can improve the accuracy of water quality early warning and has the advantage of strong global search capability.
- the present invention provides following scheme:
- a water quality early warning method comprising:
- the current biological movement characteristic data of the water area to be measured is input into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model owned.
- the water quality early warning model before inputting the current biological movement characteristic data of the water area to be tested into the water quality early warning model to obtain the early warning result of the water area to be tested, it also includes:
- the historical data of biological movement characteristics in the water area to be tested is used as a training set, and the support vector machine model is trained by using the differential evolution algorithm and the gray wolf optimization algorithm to obtain a water quality early warning model.
- the historical data of biological movement characteristics of the water area to be tested is used as a training set, and the support vector machine model is trained using a differential evolution algorithm and a gray wolf optimization algorithm to obtain a water quality early warning model, which specifically includes:
- Both the training wolves and the training set are input into the support vector machine model, and the individual objective function value of each individual in the training wolves is calculated;
- the individuals in the training wolf group are sorted in descending order, and the first three individuals are respectively regarded as ⁇ wolf, ⁇ wolf and ⁇ wolf;
- the individual objective function value of the alpha wolf the individual objective function value of the beta wolf and the individual objective function value of the delta wolf, update the optimal wolf group objective function value;
- Update the position of each individual in the training wolves increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the support vector machine model, calculate the Train the individual objective function value of each individual in the wolf pack" until the first iteration number reaches the first iteration number threshold, and use the position of the individual corresponding to the optimal wolf pack objective function value as the support vector
- the parameters of the machine model are obtained to obtain the water quality early warning model; the parameters of the support vector machine model include: penalty factors and kernel function coefficients.
- the process of using the differential evolution algorithm to process the initial wolf group to obtain the optimal initial wolf group specifically includes:
- Both the parent population and the training set are input into the support vector machine model, and the population objective function value of the parent population is calculated;
- Both the offspring population and the training set are input into the support vector machine model, and the population objective function value of the offspring population is calculated;
- updating the optimal wolf group objective function value according to the individual objective function value of the alpha wolf, the individual objective function value of the beta wolf, and the individual objective function value of the delta wolf specifically includes :
- the optimal wolf group objective function value is updated to the individual objective function value of ⁇ wolf.
- the updating the position of each individual in the training wolf group specifically includes:
- the updated position of ⁇ wolf the updated position of ⁇ wolf and the updated position of ⁇ wolf, using the formula
- the alpha wolf is updated again to obtain the second update position of the alpha wolf; and the second update position of the alpha wolf is used as the position of the alpha wolf in the next iteration;
- X p (t+1) is the position of individual p at the t+1th iteration
- X p (t) is the position of individual p at the tth iteration
- A is the convergence factor
- D is the distance from the individual to the prey Distance
- D
- X(t) is the prey position at the tth iteration
- C 2r 1
- C is the coefficient constant
- r 1 is the distance between [0,1] Random number
- X ⁇ (t+1) is the position of ⁇ -wolf at the t+1th iteration
- X 1 , X 2 and X 3 are the updated positions of ⁇ -wolf, ⁇ -wolf and ⁇ -wolf at the t-th iteration respectively .
- a water quality early warning system comprising:
- the current data acquisition module is used to acquire the current biological movement characteristic data of the water area to be measured
- the water quality early warning module is used to input the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses differential evolution algorithm and gray wolf optimization algorithm to The support vector machine model is trained.
- the system also includes:
- a support vector machine model building module used to set up a support vector machine model
- the historical data acquisition module is used to acquire the historical data of biological movement characteristics in the water area to be measured
- the water quality early warning model determination module is used to use the historical data of biological movement characteristics of the water area to be tested as a training set, and use the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model to obtain a water quality early warning model.
- the water quality early warning model determination module specifically includes:
- the initial wolf pack construction unit is used to build the initial wolf pack
- the optimal initial wolf pack determination unit is used to process the initial wolf pack using a differential evolution algorithm to obtain an optimized initial wolf pack, use the optimized initial wolf pack as a training wolf pack and initialize the optimal wolf pack objective function value;
- An individual objective function value calculation unit used to input the training wolves and the training set into the support vector machine model, and calculate the individual objective function value of each individual in the training wolves;
- a sorting unit configured to sort the individuals in the training wolf group in descending order according to the individual objective function value, and use the first three individuals as ⁇ wolves, ⁇ wolves, and ⁇ wolves respectively;
- An optimal wolf pack objective function value updating unit configured to update the optimal wolf pack according to the individual objective function values of the alpha wolves, the individual objective function values of the beta wolves, and the individual objective function values of the delta wolves objective function value;
- the water quality early warning model determination unit is used to update the position of each individual in the training wolves, increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the Support vector machine model, calculate the individual objective function value " of each individual in the described training wolf group ", until the first iteration number reaches the first iteration number threshold value, and will correspond to the optimal wolf group objective function value
- the location of the individual is used as a parameter of the support vector machine model to obtain a water quality early warning model; the parameters of the support vector machine model include: a penalty factor and a kernel function coefficient.
- the optimal initial wolf group determination unit specifically includes:
- the parent population constructs a subunit, which is used to construct the parent population according to the initial wolf group;
- the first population objective function value calculation subunit is used to input both the parent population and the training set into the support vector machine model, and calculate the population objective function value of the parent population;
- the offspring population constructs a subunit, which is used to perform crossover and mutation processing on the parent population by using a differential evolution algorithm to obtain the offspring population;
- the second population objective function value calculation subunit is used to input both the offspring population and the training set into the support vector machine model, and calculate the population objective function value of the offspring population;
- the first judging subunit is used to judge whether the population objective function value of the child population is greater than the population objective function value of the parent population, and obtain the first judgment result; if the first judgment result is yes, then execute the first The parent population update subunit; if the first judgment result is no, execute the second parent generation population update subunit;
- the first parent population update subunit is configured to update the parent population to the child population
- the second parent population update subunit is used to retain the parent population
- the optimal initial wolf group determines the child unit, which is used to increase the value of the second iteration number by 1, and returns to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the above The second iteration number reaches the second iteration number threshold, and the parent population is determined as the optimized initial wolf group.
- the present invention provides a water quality early warning method and system.
- the method includes: obtaining the current biological movement characteristic data of the water area to be measured; inputting the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result; the water quality early warning
- the model is obtained by using the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model.
- the present invention trains the support vector machine model to obtain the water quality early warning model through the differential evolution algorithm and the gray wolf optimization algorithm, and uses the water quality early warning model to carry out water quality early warning in the water area to be measured, which can improve the accuracy of the water quality early warning and has the advantage of strong global search ability.
- Fig. 1 is the flow chart of the water quality early warning method provided by the embodiment of the present invention.
- Fig. 2 is the flow chart of the water quality early warning model training method provided by the embodiment of the present invention.
- Fig. 3 is the gradation diagram of wolves provided by the embodiment of the present invention.
- Fig. 4 is a schematic diagram of the hunting process of wolves provided by the embodiment of the present invention.
- Fig. 5 is the GA-SVM model fitness graph that the embodiment of the present invention provides
- Fig. 6 is the GWO-SVM model fitness curve chart provided by the embodiment of the present invention.
- Fig. 7 is a DEGWO-SVM model fitness curve diagram provided by the embodiment of the present invention.
- Fig. 8 is a schematic structural diagram of the water quality early warning system provided by the embodiment of the present invention.
- the object of the present invention is to provide a water quality early warning method and system, which can improve the accuracy of water quality early warning and has the advantage of strong global search capability.
- Fig. 1 is the flow chart of the water quality early warning method provided by the embodiment of the present invention, as shown in Fig. 1, the present invention provides a kind of water quality early warning method, comprising:
- Step 101 Obtain the current biological movement characteristic data of the water area to be tested
- Step 102 Input the current biological movement characteristic data of the water area to be tested into the water quality early warning model to obtain the early warning result of the water area to be tested; the water quality early warning model is obtained by using the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model.
- the water quality early warning method provided by the present invention, before step 102, also includes:
- the historical data of biological movement characteristics in the water area to be tested is used as the training set, and the support vector machine model is trained by using the differential evolution algorithm and the gray wolf optimization algorithm to obtain the water quality early warning model.
- the historical data of biological movement characteristics in the water area to be tested is used as a training set, and the support vector machine model is trained using the differential evolution algorithm and the gray wolf optimization algorithm to obtain a water quality early warning model, which specifically includes:
- Both the training wolf group and the training set are input into the support vector machine model, and the individual objective function value of each individual in the training wolf group is calculated; the individual objective function value takes the individual position in the training wolf group as the support vector machine model parameter to perform The fitness function obtained by training.
- the individuals in the training wolf pack are sorted in descending order, and the first three individuals are respectively regarded as ⁇ wolf, ⁇ wolf and ⁇ wolf;
- the individual objective function value of ⁇ wolf the individual objective function value of ⁇ wolf and the individual objective function value of ⁇ wolf, update the optimal wolf group objective function value;
- Update the position of each individual in the training wolf group increase the value of the first iteration by 1, and return to the step "Input the training wolf group and the training set into the support vector machine model, and calculate the individual target of each individual in the training wolf group function value" until the first iteration number reaches the first iteration number threshold, and the position of the individual corresponding to the optimal wolf group objective function value is used as the parameter of the support vector machine model to obtain the water quality early warning model; the parameters of the support vector machine model Including: penalty factor and kernel function coefficient.
- the initial wolf group is processed by using the differential evolution algorithm to obtain the optimized initial wolf group, which specifically includes:
- Both the parent population and the training set are input into the support vector machine model, and the population objective function value of the parent population is calculated; the population objective function value is the fitness function obtained by training the training set with the population parameter as the support vector machine model parameter.
- the parent population is crossed and mutated to obtain the offspring population
- Both the offspring population and the training set are input into the support vector machine model, and the population objective function value of the offspring population is calculated;
- the water quality early warning method provided by the present invention updates the optimal wolf group objective function value according to the individual objective function value of ⁇ wolf, the individual objective function value of ⁇ wolf and the individual objective function value of ⁇ wolf, specifically including:
- Update the position of each individual in the training pack including:
- the updated position of ⁇ wolf According to the updated position of ⁇ wolf, the updated position of ⁇ wolf and the updated position of ⁇ wolf, using the formula Update the ⁇ wolf again to obtain the secondary update position of the ⁇ wolf; and use the secondary update position of the ⁇ wolf as the position of the ⁇ wolf in the next iteration;
- X p (t+1) is the position of individual p at the t+1th iteration
- X p (t) is the position of individual p at the tth iteration
- A is the convergence factor
- D is the distance from the individual to the prey Distance
- D
- X(t) is the prey position at the tth iteration
- C 2r 1
- C is the coefficient constant
- r 1 is the distance between [0,1] Random number
- X ⁇ (t+1) is the position of ⁇ -wolf at the t+1th iteration
- X 1 , X 2 and X 3 are the updated positions of ⁇ -wolf, ⁇ -wolf and ⁇ -wolf at the t-th iteration respectively .
- Fig. 2 is the flow chart of the water quality early warning model training method provided by the embodiment of the present invention, as shown in Fig. 2, the water quality early warning model training method in the present invention is as follows:
- the motion characteristic behavior parameters of the obtained fish schools were normalized and standardized, and the training set and test set were constructed.
- the kernel function in the SVM model uses the Gaussian radial basis kernel function Among them, x i and x j are any point in the space, and ⁇ is the width parameter of the kernel function, which controls the radial range of the function.
- the initial value of the parameters of the SVM model (the penalty factor C, the parameter ⁇ of the kernel function) is the coordinate of the wolf ⁇ in the gray wolf optimization algorithm, and the initial fitness function value of each gray wolf is set to an infinite amount (inf).
- Step (2) Use the DE algorithm (differential evolution algorithm) to initialize the gray wolf population
- the evolution process of the differential evolution algorithm mainly has three operations, namely mutation, crossover and selection.
- the mutation operation refers to adding a vector different from the original vector to the original vector. That is, randomly select the position vectors of two different gray wolf individuals in the initial gray wolf population, adjust the ratio of their vectors and synthesize them with the individual vectors to be mutated.
- the specific formula is as follows:
- T is the number of iterations
- F is the scaling factor
- X n is the population individual before mutation
- D i (T+1) is the population individual after mutation.
- j is the dimension
- i is the i-th individual of this dimension
- CR is the crossover probability
- r is the random probability between [0,1]
- D n is the space dimension.
- Xi (T+1) is the parent population of the T+1 iteration
- Xi ( T) is the parent population of the T iteration
- U i ( T+1) is the child population of the T iteration
- f(U i (T+1)) is the fitness value of the offspring population of the T-th iteration
- f(X i (T)) is the fitness value of the parent population of the T-th iteration.
- Step (4) Construct the gray wolf social hierarchy model, calculate the fitness function value and select ⁇ wolf, ⁇ wolf, and ⁇ wolf respectively;
- Fig. 3 is a gradation diagram of wolves provided by the embodiment of the present invention.
- ⁇ wolf is defined as the first level, which is the head wolf in the wolves and is mainly responsible for making decisions on predation activities.
- the wolf with the best management ability in the group the ⁇ wolf is defined as the second level, obeying the alpha wolf, and assisting the alpha wolf to make decisions;
- the delta wolf is defined as the third level, obeying the alpha wolf and the beta wolf; the rest of the wolves Set to ⁇ wolf, this is the lowest level, obeying the wolves of the first three levels.
- Wolves strictly abide by this hierarchy and their own responsibilities when hunting.
- the initial fitness function value of each gray wolf is set to infinite quantity (inf), and the objective function values of the top three individuals are set to X ⁇ , X ⁇ and X ⁇ respectively.
- Step (5) update the wolf pack position, train the SVM model and predict;
- Figure 4 is a schematic diagram of the hunting process of wolves provided by the embodiment of the present invention. As shown in Figure 4, ⁇ wolves, ⁇ wolves, and ⁇ wolves approach and surround prey constantly, and then command candidate wolves ⁇ wolves to capture prey, and the hunting of gray wolves Behaviors include steps such as searching, approaching, surrounding, hunting, and attacking, as follows:
- the final position of the prey is its global optimal solution, so the distance between the individual gray wolf and the prey is very important, and the distance formula between the two is as follows:
- t is the number of iterations
- X p (t) is the position of the wolf individual at the t-th iteration
- X(t) is the position of the prey at the t-th iteration
- C is the coefficient constant
- C 2r 1 , r 1 It is a random number between [0,1].
- X ⁇ (t), X ⁇ (t) and X ⁇ (t) are respectively the positions of ⁇ wolf, ⁇ wolf, and ⁇ wolf in the t-th iteration, then the mathematical model of gray wolf individual position update is as follows:
- X p (t+1) is the position of individual p at the t+1th iteration
- X p (t) is the position of individual p at the tth iteration
- t is the number of first iterations
- D is the distance from the individual to the prey.
- the distance A is the convergence factor
- a 1 , A 2 and A 3 are the values of the convergence factors of ⁇ wolf, ⁇ wolf, and ⁇ wolf in iterations respectively. These three values will change with the number of iterations.
- X ⁇ (t+1) is the t-th +1 optimal solution vector, the formula is:
- the iterative optimal solution vector is used as the parameter combination of the SVM prediction model and substituted into the SVM model for training.
- Step (8) The final result is used as the parameter combination of the SVM prediction model.
- the final position of ⁇ wolf is taken as the value of the SVM model parameter combination (C, ⁇ ), and the test data set is substituted into the trained SVM model for prediction. Because the value of the parameter combination (C, ⁇ ) is optimized by the gray wolf optimization algorithm, the coordinates of ⁇ wolves in the initial population of gray wolves are random, and this coordinate is used as the value of (C, ⁇ ), and the accurate value after SVM training is After the rate is used as the fitness function of each wolf, the coordinates of the ⁇ wolf are getting closer and closer to the optimal (C, ⁇ ) value, and the final position of the ⁇ wolf is the optimal value obtained by the gray wolf optimization algorithm.
- the water quality early warning method proposed by the present invention uses the DE algorithm and the gray wolf optimization algorithm to improve the parameter combination value of the SVM model, and improves the predictive performance of the SVM algorithm in biological water quality early warning, which improves the defects of premature maturity and local optimum, and With global search capability.
- the DE differential evolution algorithm is introduced to generate the optimal value of the initial population of the gray wolf optimization algorithm.
- the initial population is an important factor affecting the convergence speed of continuous iterations.
- the quality of the initial population also affects the optimal value of the prediction model.
- Carry out a simulation experiment after tracking the fish body trajectory under normal water quality and abnormal water quality conditions in the early stage, obtain the five movement characteristic behavior parameters of the fish school (speed, acceleration, average swimming speed, average swimming distance and fish dispersion ), mark the data of these 1000 sample points, including 500 sample points with normal water quality and 500 sample points with abnormal water quality. From these 1000 sample points, 800 sample data points are selected as the training set, and the remaining 200 sample data points are used as the test set.
- the initial gray wolf population N is set to 30, the search space dimension D n is set to 2, the scale factor range is [0.01,100], the crossover probability CR is 0.2, and the maximum number of iterations T is 1000.
- the water quality early warning model obtained by using the DEGWO-SVM algorithm (that is, the present invention uses differential evolution algorithm and gray wolf optimization algorithm to train the support vector machine model to obtain the water quality early warning model) has better classification accuracy , the average classification accuracy rate of the test set is 94.4%, and the generalization performance of the model is good, so the model is suitable for the biological water quality early warning system.
- the model parameter ⁇ changes greatly after each operation, which shows that the fixed model parameters are not suitable for all classification situations.
- the values of the model parameters are also reserved to 6 decimal places, which shows that the value range of the model parameters is wider and more accurate.
- the introduction of the differential evolution algorithm and the gray wolf optimization algorithm can find the best parameter combination of the model in a larger range. Find the global optimal solution for the model parameters.
- Table 2 is the genetic algorithm improved support vector machine model (Genetic Algorithm-Support Vector Machines, GA-SVM), gray wolf optimization algorithm improved support vector machine model (Grey Wolf Optimization-Support Vector Machines, GWO-SVM) and in the present invention
- n is the number of output samples
- f i and y i are the predicted output value and actual value respectively. Run each algorithm 10 times and take the mean value as the prediction result of the model.
- Table 3 shows the comparison results of the prediction error of the three algorithms: GA-SVM, GWO-SVM and DEGWO-SVM.
- DEGWO-SVM has higher advantages in the three evaluation criteria, indicating that the DEGWO-SVM algorithm has the smallest test error and the highest prediction accuracy.
- the fitness curves of GA-SVM, GWO-SVM, and DEGWO-SVM that is, the relationship between the average fitness and the number of iterations are shown in Figure 5-7.
- the optimal fitness value of DEGWO-SVM is smaller than that of GA-SVM and GWO-SVM algorithms, and the optimal fitness value of GA-SVM algorithm drops sharply at the 50th iteration, and then at the 100th iteration It basically remains stable when iterating. Compared with the other two algorithms, it is premature and easy to fall into local optimum.
- the GWO-SVM algorithm fluctuates violently as the number of iterations increases, indicating that the algorithm is unstable.
- the DEGWO-SVM algorithm curve shows a downward trend before 200 iterations, after which the change is small and tends to be stable.
- the improved algorithm of the present invention has higher classification accuracy, the lowest three error evaluation criteria, relatively stable performance, high global search ability, and great significance for biological water quality early warning.
- Fig. 8 is a schematic structural diagram of the water quality early warning system provided by the embodiment of the present invention. As shown in Fig. 8, the present invention provides a water quality early warning system, including:
- the current data acquisition module 901 is used to acquire the current biological movement characteristic data of the water area to be measured
- the water quality early warning module 902 is used to input the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model is to use the differential evolution algorithm and the gray wolf optimization algorithm to carry out obtained by training.
- the water quality early warning system provided by the present invention also includes:
- a support vector machine model building module used to set up a support vector machine model
- the historical data acquisition module is used to acquire the historical data of biological movement characteristics in the water area to be measured
- the water quality early warning model determination module is used to use the historical data of biological movement characteristics in the water area to be tested as a training set, and use the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model to obtain the water quality early warning model.
- Water quality early warning model determination module including:
- the initial wolf pack construction unit is used to build the initial wolf pack
- the optimal initial wolf group determination unit is used to process the initial wolf group using the differential evolution algorithm to obtain an optimized initial wolf group, use the optimized initial wolf group as a training wolf group and initialize the optimal wolf group objective function value;
- the individual objective function value calculation unit is used to input the training wolves and the training set into the support vector machine model, and calculates the individual objective function value of each individual in the training wolves;
- the sorting unit is used to sort the individuals in the training wolf group in descending order according to the individual objective function value, and use the first three individuals as ⁇ wolf, ⁇ wolf and ⁇ wolf respectively;
- the optimal wolf group objective function value updating unit is used to update the optimal wolf group objective function value according to the individual objective function value of ⁇ wolf, the individual objective function value of ⁇ wolf and the individual objective function value of ⁇ wolf;
- the determination unit of the water quality early warning model is used to update the position of each individual in the training wolf group, increase the value of the first iteration number by 1, and return to the step "input the training wolf group and the training set into the support vector machine model, and calculate the training wolf
- the individual objective function value of each individual in the group" until the first iteration reaches the first iteration threshold, and the position of the individual corresponding to the optimal wolf pack objective function value is used as a parameter of the support vector machine model to obtain a water quality warning Model; the parameters of the support vector machine model include: penalty factor and kernel function coefficient.
- the optimal initial wolf pack determination unit specifically includes:
- the parent population constructs a subunit, which is used to construct the parent population according to the initial wolf group;
- the first population objective function value calculation subunit is used to input both the parent population and the training set into the support vector machine model to calculate the population objective function value of the parent population;
- the offspring population constructs a subunit, which is used to perform crossover and mutation processing on the parent population by using the differential evolution algorithm to obtain the offspring population;
- the second population objective function value calculation subunit is used to input both the offspring population and the training set into the support vector machine model to calculate the population objective function value of the offspring population;
- the first judging subunit is used to judge whether the population objective function value of the child population is greater than the population objective function value of the parent population to obtain the first judgment result; if the first judgment result is yes, then perform the first parent population update A subunit; if the first judgment result is no, then execute the second parent generation population update subunit;
- the first parent population update subunit is used to update the parent population to the child population
- the second parent generation population updates the sub-unit for retaining the parent generation population
- the optimal initial wolf group determines the child unit, which is used to increase the value of the second iteration number by 1, and returns to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the second iteration number The second iteration threshold is reached, and the parent population is determined as the optimized initial wolf population.
- the optimal wolf pack objective function value updating unit specifically includes:
- the second judging subunit is used to judge whether the optimal wolf group objective function value is less than the individual objective function value of ⁇ wolf, and obtain the second judgment result; if the second judgment result is yes, then execute the first optimal wolf group objective function A value update subunit; if the second judgment result is no, execute the third judgment subunit;
- the first optimal wolf group objective function value update subunit is used to update the optimal wolf group objective function value to the individual objective function value of ⁇ wolf;
- the third judging subunit is used to judge whether the optimal wolf group objective function value is less than the individual objective function value of the ⁇ wolf, and obtain the third judgment result; if the third judgment result is yes, then execute the second optimal wolf group objective function A value update subunit; if the third judgment result is no, execute the third optimal wolf pack objective function value update subunit;
- the second optimal wolf group objective function value update subunit is used to update the optimal wolf group objective function value to the individual objective function value of the ⁇ wolf;
- the third optimal wolf pack objective function value updating subunit is used to update the optimal wolf pack objective function value to the individual objective function value of ⁇ wolf;
- the determination unit of the water quality early warning model includes:
- the second position update subunit is used to use the formula Update the ⁇ wolf again to obtain the secondary update position of the ⁇ wolf; and use the secondary update position of the ⁇ wolf as the position of the ⁇ wolf in the next iteration;
- X p (t+1) is the position of individual p at the t+1th iteration
- X p (t) is the position of individual p at the tth iteration
- A is the convergence factor
- D is the distance from the individual to the prey Distance
- D
- X(t) is the prey position at the tth iteration
- C 2r 1
- C is the coefficient constant
- r 1 is the distance between [0,1] Random number
- X ⁇ (t+1) is the position of ⁇ -wolf at the t+1th iteration
- X 1 , X 2 and X 3 are the updated positions of ⁇ -wolf, ⁇ -wolf and ⁇ -wolf at the t-th iteration respectively .
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Abstract
A water quality early-warning method and system. The method comprises: acquiring current biological motion feature data of a water area to be tested (101); and inputting the current biological motion feature data of said water area into a water quality early-warning model, so as to obtain an early-warning result (102), wherein the water quality early-warning model is obtained by means of training a support vector machine model by using a differential evolution algorithm and a gray wolf optimization algorithm. By means of the method and the system, a support vector machine model is trained by means of a differential evolution algorithm and a gray wolf optimization algorithm, so as to obtain a water quality early-warning model, and water quality early-warning is performed, by using the water quality early-warning model, on a water area to be tested, such that the accuracy of water quality early-warning can be improved. The method and the system have the advantage of a high global search capability.
Description
本申请要求于2021年06月07日提交中国专利局、申请号为202110629371.6、发明名称为“一种水质预警方法及系统”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application claims the priority of the Chinese patent application submitted to the China Patent Office on June 07, 2021, the application number is 202110629371.6, and the invention title is "A Water Quality Early Warning Method and System", the entire content of which is incorporated by reference in this application .
本发明属于水质预警领域,具体涉及一种水质预警方法及系统。The invention belongs to the field of water quality early warning, and in particular relates to a water quality early warning method and system.
水质是水体质量的简称,一般通过水体的物理(如色度、浊度、臭味等)、化学(无机物和有机物的含量)和生物(细菌、微生物、浮游生物、底栖生物)来衡量。目前,随着科技发展和人类生活水平的提高,人类对水质的关注度越来越高。水质预警成为科研人员争相研究的话题。Water quality is the abbreviation of water quality, which is generally measured by the physical (such as color, turbidity, odor, etc.), chemical (inorganic and organic content) and biological (bacteria, microorganisms, plankton, benthic organisms) of the water body. . At present, with the development of science and technology and the improvement of human living standards, human beings pay more and more attention to water quality. Water quality early warning has become a hot topic for researchers.
目前水质预警取得了一定的成果,根据生物(主要是鱼群)轨迹特征参数,采用参数加权求和或者机器学习算法进行生物水质预警。但是参数加权求和的方法,需要进行大量的实验求得最优权值;而机器学习方法大部分都基于SVM(支持向量机,Support Vector Machine)模型对生物特征数据进行二分类得到预警结果,但是,目前的SVM模型和SVM改进模型虽然有不错的局部搜索能力,但是在全局搜索时存在稳定性差、准确率低的问题。At present, water quality early warning has achieved certain results. According to the characteristic parameters of biological (mainly fish) trajectory, parameter weighted summation or machine learning algorithm is used for biological water quality early warning. However, the method of parameter weighted summation requires a large number of experiments to obtain the optimal weight value; and most machine learning methods are based on the SVM (Support Vector Machine, Support Vector Machine) model for binary classification of biological feature data to obtain early warning results. However, although the current SVM model and the improved SVM model have good local search capabilities, they have problems of poor stability and low accuracy in global search.
因此,亟需一种水质预警技术,具有准确性高、全局搜索能力强的优点。Therefore, there is an urgent need for a water quality early warning technology, which has the advantages of high accuracy and strong global search ability.
发明内容Contents of the invention
本发明的目的是提供一种水质预警方法及系统,能够提高水质预警的准确性,具有全局搜索能力强的优点。The object of the present invention is to provide a water quality early warning method and system, which can improve the accuracy of water quality early warning and has the advantage of strong global search capability.
为实现所述目的,本发明提供了如下方案:To achieve said object, the present invention provides following scheme:
一种水质预警方法,包括:A water quality early warning method, comprising:
获取待测水域的当前生物运动特征数据;Obtain the current biological movement characteristic data of the water area to be tested;
将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果;所述水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。The current biological movement characteristic data of the water area to be measured is input into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model owned.
可选的,在所述将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果之前,还包括:Optionally, before inputting the current biological movement characteristic data of the water area to be tested into the water quality early warning model to obtain the early warning result of the water area to be tested, it also includes:
建立支持向量机模型;Build a support vector machine model;
获取待测水域的生物运动特征历史数据;Obtain the historical data of biological movement characteristics in the water area to be tested;
将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型。The historical data of biological movement characteristics in the water area to be tested is used as a training set, and the support vector machine model is trained by using the differential evolution algorithm and the gray wolf optimization algorithm to obtain a water quality early warning model.
可选的,所述将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型,具体包括:Optionally, the historical data of biological movement characteristics of the water area to be tested is used as a training set, and the support vector machine model is trained using a differential evolution algorithm and a gray wolf optimization algorithm to obtain a water quality early warning model, which specifically includes:
构建初始狼群;Build an initial pack of wolves;
利用差分进化算法对所述初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;Utilize differential evolution algorithm to process described initial wolf pack, obtain optimized initial wolf pack, use optimized initial wolf pack as training wolf pack and initialize optimal wolf pack objective function value;
将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值;Both the training wolves and the training set are input into the support vector machine model, and the individual objective function value of each individual in the training wolves is calculated;
根据所述个体目标函数值对所述训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;According to the individual objective function value, the individuals in the training wolf group are sorted in descending order, and the first three individuals are respectively regarded as α wolf, β wolf and δ wolf;
根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值;According to the individual objective function value of the alpha wolf, the individual objective function value of the beta wolf and the individual objective function value of the delta wolf, update the optimal wolf group objective function value;
更新所述训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值”,直至所述第一迭代次数达到第一迭代次数阈值,并将与所述最优狼群目标函数值对应的个体的位置作为所述支持向量机模型的参数,得到水质预警模型;所述支持向量机模型的参数包括:惩罚因子和核函数系数。Update the position of each individual in the training wolves, increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the support vector machine model, calculate the Train the individual objective function value of each individual in the wolf pack" until the first iteration number reaches the first iteration number threshold, and use the position of the individual corresponding to the optimal wolf pack objective function value as the support vector The parameters of the machine model are obtained to obtain the water quality early warning model; the parameters of the support vector machine model include: penalty factors and kernel function coefficients.
可选的,所述利用差分进化算法对所述初始狼群进行处理,得到最优初始狼群,具体包括:Optionally, the process of using the differential evolution algorithm to process the initial wolf group to obtain the optimal initial wolf group specifically includes:
根据所述初始狼群构建父代种群;Construct parent generation population according to described initial wolf group;
将所述父代种群和所述训练集均输入所述支持向量机模型,计算所述父代种群的种群目标函数值;Both the parent population and the training set are input into the support vector machine model, and the population objective function value of the parent population is calculated;
采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群;Using a differential evolution algorithm, performing crossover and mutation processing on the parent population to obtain the offspring population;
将所述子代种群和所述训练集均输入所述支持向量机模型,计算所述子代种群的种群目标函数值;Both the offspring population and the training set are input into the support vector machine model, and the population objective function value of the offspring population is calculated;
判断所述子代种群的种群目标函数值是否大于所述父代种群的种群目标函数值,得到第一判断结果;judging whether the population objective function value of the offspring population is greater than the population objective function value of the parent population to obtain a first judgment result;
若第一判断结果为是,则将所述父代种群更新为所述子代种群;If the first judgment result is yes, updating the parent population to the child population;
若第一判断结果为否,则保留所述父代种群;If the first judgment result is no, then retain the parent population;
令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群”直至所述第二迭代次数达到第二迭代次数阈值,并将所述父代种群确定为优化后的初始狼群。Increase the value of the second iteration number by 1, and return to the step "Use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the second iteration number reaches the second iteration number threshold, And determine the parent population as the optimized initial wolf population.
可选的,所述根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值,具体包括:Optionally, updating the optimal wolf group objective function value according to the individual objective function value of the alpha wolf, the individual objective function value of the beta wolf, and the individual objective function value of the delta wolf, specifically includes :
判断所述最优狼群目标函数值是否小于α狼的个体目标函数值,得到第二判断结果;judging whether the optimal wolf pack objective function value is less than the individual objective function value of α wolf, and obtaining a second judgment result;
若第二判断结果为是,则将所述最优狼群目标函数值更新为α狼的个体目标函数值;If the second judgment result is yes, then update the optimal wolf group objective function value to the individual objective function value of α wolf;
若第二判断结果为否,则判断所述最优狼群目标函数值是否小于β狼的个体目标函数值,得到第三判断结果;If the second judgment result is no, then judge whether the optimal wolf group objective function value is less than the individual objective function value of the β wolf, and obtain the third judgment result;
若第三判断结果为是,则将所述最优狼群目标函数值更新为β狼的个体目标函数值;If the third judgment result is yes, then update the optimal wolf group objective function value to the individual objective function value of β wolf;
若第三判断结果为否,则将所述最优狼群目标函数值更新为δ狼的个体目标函数值。If the third judgment result is no, the optimal wolf group objective function value is updated to the individual objective function value of δ wolf.
可选的,所述更新所述训练狼群中每个个体的位置,具体包括:Optionally, the updating the position of each individual in the training wolf group specifically includes:
根据所述训练狼群中每个个体的位置,利用公式X
p(t+1)=X
p(t)-A.D对所述训练狼群中每个个体进行更新,得到所述训练狼群中每个个体的一 次更新位置;并将所述训练狼群中除α狼以外每个个体的一次更新位置作为下一次迭代时除α狼以外每个个体的位置;
According to the position of each individual in the training wolves, use the formula X p (t+1)=X p (t)-AD to update each individual in the training wolves to obtain the One update position of each individual; and one update position of each individual except α wolf in the training wolf group as the position of each individual other than α wolf during the next iteration;
根据α狼的一次更新位置、β狼的一次更新位置和δ狼的一次更新位置,利用公式
对α狼进行再次更新,得到α狼的二次更新位置;并将所述α狼的二次更新位置作为下一次迭代时α狼的位置;
According to the updated position of α wolf, the updated position of β wolf and the updated position of δ wolf, using the formula The alpha wolf is updated again to obtain the second update position of the alpha wolf; and the second update position of the alpha wolf is used as the position of the alpha wolf in the next iteration;
式中,X
p(t+1)为第t+1次迭代时个体p的位置;X
p(t)为第t次迭代时个体p的位置;A为收敛因子;D为个体到猎物的距离,D=|CX
p(t)-X(t)|,X(t)为第t次迭代时猎物位置,C=2r
1,C为系数常量,r
1为[0,1]间的随机数;X
α(t+1)为第t+1次迭代时α狼的位置;X
1、X
2和X
3分别为第t次迭代时α狼、β狼和δ狼的一次更新位置。
In the formula, X p (t+1) is the position of individual p at the t+1th iteration; X p (t) is the position of individual p at the tth iteration; A is the convergence factor; D is the distance from the individual to the prey Distance, D=|CX p (t)-X(t)|, X(t) is the prey position at the tth iteration, C=2r 1 , C is the coefficient constant, r 1 is the distance between [0,1] Random number; X α (t+1) is the position of α-wolf at the t+1th iteration; X 1 , X 2 and X 3 are the updated positions of α-wolf, β-wolf and δ-wolf at the t-th iteration respectively .
一种水质预警系统,包括:A water quality early warning system comprising:
当前数据获取模块,用于获取待测水域的当前生物运动特征数据;The current data acquisition module is used to acquire the current biological movement characteristic data of the water area to be measured;
水质预警模块,用于将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果;所述水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。The water quality early warning module is used to input the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses differential evolution algorithm and gray wolf optimization algorithm to The support vector machine model is trained.
可选的,所述系统,还包括:Optionally, the system also includes:
支持向量机模型建立模块,用于建立支持向量机模型;A support vector machine model building module, used to set up a support vector machine model;
历史数据获取模块,用于获取待测水域的生物运动特征历史数据;The historical data acquisition module is used to acquire the historical data of biological movement characteristics in the water area to be measured;
水质预警模型确定模块,用于将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型。The water quality early warning model determination module is used to use the historical data of biological movement characteristics of the water area to be tested as a training set, and use the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model to obtain a water quality early warning model.
可选的,所述水质预警模型确定模块,具体包括:Optionally, the water quality early warning model determination module specifically includes:
初始狼群构建单元,用于构建初始狼群;The initial wolf pack construction unit is used to build the initial wolf pack;
最优初始狼群确定单元,用于利用差分进化算法对所述初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;The optimal initial wolf pack determination unit is used to process the initial wolf pack using a differential evolution algorithm to obtain an optimized initial wolf pack, use the optimized initial wolf pack as a training wolf pack and initialize the optimal wolf pack objective function value;
个体目标函数值计算单元,用于将所述训练狼群和所述训练集均输入 所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值;An individual objective function value calculation unit, used to input the training wolves and the training set into the support vector machine model, and calculate the individual objective function value of each individual in the training wolves;
排序单元,用于根据所述个体目标函数值对所述训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;A sorting unit, configured to sort the individuals in the training wolf group in descending order according to the individual objective function value, and use the first three individuals as α wolves, β wolves, and δ wolves respectively;
最优狼群目标函数值更新单元,用于根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值;An optimal wolf pack objective function value updating unit, configured to update the optimal wolf pack according to the individual objective function values of the alpha wolves, the individual objective function values of the beta wolves, and the individual objective function values of the delta wolves objective function value;
水质预警模型确定单元,用于更新所述训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值”,直至所述第一迭代次数达到第一迭代次数阈值,并将与所述最优狼群目标函数值对应的个体的位置作为所述支持向量机模型的参数,得到水质预警模型;所述支持向量机模型的参数包括:惩罚因子和核函数系数。The water quality early warning model determination unit is used to update the position of each individual in the training wolves, increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the Support vector machine model, calculate the individual objective function value " of each individual in the described training wolf group ", until the first iteration number reaches the first iteration number threshold value, and will correspond to the optimal wolf group objective function value The location of the individual is used as a parameter of the support vector machine model to obtain a water quality early warning model; the parameters of the support vector machine model include: a penalty factor and a kernel function coefficient.
可选的,所述最优初始狼群确定单元,具体包括:Optionally, the optimal initial wolf group determination unit specifically includes:
父代种群构建子单元,用于根据所述初始狼群构建父代种群;The parent population constructs a subunit, which is used to construct the parent population according to the initial wolf group;
第一种群目标函数值计算子单元,用于将所述父代种群和所述训练集均输入所述支持向量机模型,计算所述父代种群的种群目标函数值;The first population objective function value calculation subunit is used to input both the parent population and the training set into the support vector machine model, and calculate the population objective function value of the parent population;
子代种群构建子单元,用于采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群;The offspring population constructs a subunit, which is used to perform crossover and mutation processing on the parent population by using a differential evolution algorithm to obtain the offspring population;
第二种群目标函数值计算子单元,用于将所述子代种群和所述训练集均输入所述支持向量机模型,计算所述子代种群的种群目标函数值;The second population objective function value calculation subunit is used to input both the offspring population and the training set into the support vector machine model, and calculate the population objective function value of the offspring population;
第一判断子单元,用于判断所述子代种群的种群目标函数值是否大于所述父代种群的种群目标函数值,得到第一判断结果;若第一判断结果为是,则执行第一父代种群更新子单元;若第一判断结果为否,则执行第二父代种群更新子单元;The first judging subunit is used to judge whether the population objective function value of the child population is greater than the population objective function value of the parent population, and obtain the first judgment result; if the first judgment result is yes, then execute the first The parent population update subunit; if the first judgment result is no, execute the second parent generation population update subunit;
第一父代种群更新子单元,用于将所述父代种群更新为所述子代种群;The first parent population update subunit is configured to update the parent population to the child population;
第二父代种群更新子单元,用于保留所述父代种群;The second parent population update subunit is used to retain the parent population;
最优初始狼群确定子单元,用于令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对所述父代种群进行交叉和变异处理,得到 子代种群”直至所述第二迭代次数达到第二迭代次数阈值,并将所述父代种群确定为优化后的初始狼群。The optimal initial wolf group determines the child unit, which is used to increase the value of the second iteration number by 1, and returns to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the above The second iteration number reaches the second iteration number threshold, and the parent population is determined as the optimized initial wolf group.
本发明的有益效果是:The beneficial effects of the present invention are:
本发明提供了一种水质预警方法及系统,方法包括:获取待测水域的当前生物运动特征数据;将待测水域的当前生物运动特征数据,输入水质预警模型,得到预警结果;所述水质预警模型是采用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。本发明通过差分进化算法和灰狼优化算法对支持向量机模型进行训练得到水质预警模型,使用水质预警模型对待测水域进行水质预警,能够提高水质预警的准确性,具有全局搜索能力强的优点。The present invention provides a water quality early warning method and system. The method includes: obtaining the current biological movement characteristic data of the water area to be measured; inputting the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result; the water quality early warning The model is obtained by using the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model. The present invention trains the support vector machine model to obtain the water quality early warning model through the differential evolution algorithm and the gray wolf optimization algorithm, and uses the water quality early warning model to carry out water quality early warning in the water area to be measured, which can improve the accuracy of the water quality early warning and has the advantage of strong global search ability.
说明书附图Instructions attached
为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the embodiments. Obviously, the accompanying drawings in the following description are only some of the present invention. Embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without paying creative labor.
图1为本发明实施例所提供的水质预警方法流程图;Fig. 1 is the flow chart of the water quality early warning method provided by the embodiment of the present invention;
图2为本发明实施例所提供的水质预警模型训练方法流程图;Fig. 2 is the flow chart of the water quality early warning model training method provided by the embodiment of the present invention;
图3为本发明实施例所提供的狼群等级划分图;Fig. 3 is the gradation diagram of wolves provided by the embodiment of the present invention;
图4为本发明实施例所提供的狼群猎捕过程示意图;Fig. 4 is a schematic diagram of the hunting process of wolves provided by the embodiment of the present invention;
图5为本发明实施例所提供的GA-SVM模型适应度曲线图;Fig. 5 is the GA-SVM model fitness graph that the embodiment of the present invention provides;
图6为本发明实施例所提供的GWO-SVM模型适应度曲线图;Fig. 6 is the GWO-SVM model fitness curve chart provided by the embodiment of the present invention;
图7为本发明实施例所提供的DEGWO-SVM模型适应度曲线图;Fig. 7 is a DEGWO-SVM model fitness curve diagram provided by the embodiment of the present invention;
图8为本发明实施例所提供的水质预警系统结构示意图。Fig. 8 is a schematic structural diagram of the water quality early warning system provided by the embodiment of the present invention.
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的 范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
本发明的目的是提供一种水质预警方法及系统,能够提高水质预警的准确性,具有全局搜索能力强的优点。The object of the present invention is to provide a water quality early warning method and system, which can improve the accuracy of water quality early warning and has the advantage of strong global search capability.
为使本发明的所述目的、特征和优点能够更加明显易懂,下面结合附图和具体实施方式对本发明作进一步详细的说明。In order to make the purpose, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.
图1为本发明实施例所提供的水质预警方法流程图,如图1所示,本发明提供了一种水质预警方法,包括:Fig. 1 is the flow chart of the water quality early warning method provided by the embodiment of the present invention, as shown in Fig. 1, the present invention provides a kind of water quality early warning method, comprising:
步骤101:获取待测水域的当前生物运动特征数据;Step 101: Obtain the current biological movement characteristic data of the water area to be tested;
步骤102:将待测水域的当前生物运动特征数据,输入水质预警模型,得到待测水域的预警结果;水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。Step 102: Input the current biological movement characteristic data of the water area to be tested into the water quality early warning model to obtain the early warning result of the water area to be tested; the water quality early warning model is obtained by using the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model.
本发明提供的水质预警方法,在步骤102之前,还包括:The water quality early warning method provided by the present invention, before step 102, also includes:
建立支持向量机模型;Build a support vector machine model;
获取待测水域的生物运动特征历史数据;Obtain the historical data of biological movement characteristics in the water area to be tested;
将待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对支持向量机模型进行训练,得到水质预警模型。The historical data of biological movement characteristics in the water area to be tested is used as the training set, and the support vector machine model is trained by using the differential evolution algorithm and the gray wolf optimization algorithm to obtain the water quality early warning model.
具体的,将待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对支持向量机模型进行训练,得到水质预警模型,具体包括:Specifically, the historical data of biological movement characteristics in the water area to be tested is used as a training set, and the support vector machine model is trained using the differential evolution algorithm and the gray wolf optimization algorithm to obtain a water quality early warning model, which specifically includes:
构建初始狼群;Build an initial pack of wolves;
利用差分进化算法对初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;Use the differential evolution algorithm to process the initial wolf group to obtain the optimized initial wolf group, use the optimized initial wolf group as the training wolf group and initialize the optimal wolf group objective function value;
将训练狼群和训练集均输入支持向量机模型,计算训练狼群中每个个体的个体目标函数值;个体目标函数值为以训练狼群中个体位置为支持向量机模型参数对训练集进行训练得到的适应度函数。Both the training wolf group and the training set are input into the support vector machine model, and the individual objective function value of each individual in the training wolf group is calculated; the individual objective function value takes the individual position in the training wolf group as the support vector machine model parameter to perform The fitness function obtained by training.
根据个体目标函数值对训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;According to the individual objective function value, the individuals in the training wolf pack are sorted in descending order, and the first three individuals are respectively regarded as α wolf, β wolf and δ wolf;
根据α狼的个体目标函数值、β狼的个体目标函数值和δ狼的个体目标函数值,更新最优狼群目标函数值;According to the individual objective function value of α wolf, the individual objective function value of β wolf and the individual objective function value of δ wolf, update the optimal wolf group objective function value;
更新训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将训练狼群和训练集均输入支持向量机模型,计算训练狼群中每个个体的个体目标函数值”,直至第一迭代次数达到第一迭代次数阈值,并将与最优狼群目标函数值对应的个体的位置作为支持向量机模型的参 数,得到水质预警模型;支持向量机模型的参数包括:惩罚因子和核函数系数。Update the position of each individual in the training wolf group, increase the value of the first iteration by 1, and return to the step "Input the training wolf group and the training set into the support vector machine model, and calculate the individual target of each individual in the training wolf group function value" until the first iteration number reaches the first iteration number threshold, and the position of the individual corresponding to the optimal wolf group objective function value is used as the parameter of the support vector machine model to obtain the water quality early warning model; the parameters of the support vector machine model Including: penalty factor and kernel function coefficient.
其中,利用差分进化算法对初始狼群进行处理,得到优化后的初始狼群,具体包括:Among them, the initial wolf group is processed by using the differential evolution algorithm to obtain the optimized initial wolf group, which specifically includes:
根据初始狼群构建父代种群;Construct the parent population based on the initial wolf population;
将父代种群和训练集均输入支持向量机模型,计算父代种群的种群目标函数值;种群目标函数值为以种群参数为支持向量机模型参数对训练集进行训练得到的适应度函数。Both the parent population and the training set are input into the support vector machine model, and the population objective function value of the parent population is calculated; the population objective function value is the fitness function obtained by training the training set with the population parameter as the support vector machine model parameter.
采用差分进化算法,对父代种群进行交叉和变异处理,得到子代种群;Using the differential evolution algorithm, the parent population is crossed and mutated to obtain the offspring population;
将子代种群和训练集均输入支持向量机模型,计算子代种群的种群目标函数值;Both the offspring population and the training set are input into the support vector machine model, and the population objective function value of the offspring population is calculated;
判断子代种群的种群目标函数值是否大于父代种群的种群目标函数值,得到第一判断结果;Judging whether the population objective function value of the offspring population is greater than the population objective function value of the parent population, and obtaining the first judgment result;
若第一判断结果为是,则将父代种群更新为子代种群;If the first judgment result is yes, update the parent population to the child population;
若第一判断结果为否,则保留父代种群;If the first judgment result is no, the parent population is retained;
令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对父代种群进行交叉和变异处理,得到子代种群”直至第二迭代次数达到第二迭代次数阈值,并将父代种群确定为优化后的初始狼群。Increase the value of the second iteration number by 1, and return to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the second iteration number reaches the second iteration number threshold, and the parent generation The population is determined as the optimized initial wolf group.
本发明提供的水质预警方法,根据α狼的个体目标函数值、β狼的个体目标函数值和δ狼的个体目标函数值,更新最优狼群目标函数值,具体包括:The water quality early warning method provided by the present invention updates the optimal wolf group objective function value according to the individual objective function value of α wolf, the individual objective function value of β wolf and the individual objective function value of δ wolf, specifically including:
判断最优狼群目标函数值是否小于α狼的个体目标函数值,得到第二判断结果;Judging whether the optimal wolf group objective function value is less than the individual objective function value of α wolf, and obtaining the second judgment result;
若第二判断结果为是,则将最优狼群目标函数值更新为α狼的个体目标函数值;If the second judgment result is yes, update the optimal wolf group objective function value to the individual objective function value of α wolf;
若第二判断结果为否,则判断最优狼群目标函数值是否小于β狼的个体目标函数值,得到第三判断结果;If the second judgment result is no, it is judged whether the optimal wolf group objective function value is less than the individual objective function value of the β wolf, and the third judgment result is obtained;
若第三判断结果为是,则将最优狼群目标函数值更新为β狼的个体目标函数值;If the third judgment result is yes, update the optimal wolf group objective function value to the individual objective function value of β wolf;
若第三判断结果为否,则将最优狼群目标函数值更新为δ狼的个体目标函数值。If the third judgment result is no, update the optimal wolf group objective function value to the individual objective function value of δ wolf.
更新训练狼群中每个个体的位置,具体包括:Update the position of each individual in the training pack, including:
根据训练狼群中每个个体的位置,利用公式X
p(t+1)=X
p(t)-A.D对训 练狼群中每个个体进行更新,得到训练狼群中每个个体的一次更新位置;并将训练狼群中除α狼以外每个个体的一次更新位置作为下一次迭代时除α狼以外每个个体的位置;
According to the position of each individual in the training wolves, use the formula X p (t+1)=X p (t)-AD to update each individual in the training wolves, and get an update of each individual in the training wolves position; and the updated position of each individual in the training wolf group except α wolf as the position of each individual except α wolf in the next iteration;
根据α狼的一次更新位置、β狼的一次更新位置和δ狼的一次更新位置,利用公式
对α狼进行再次更新,得到α狼的二次更新位置;并将α狼的二次更新位置作为下一次迭代时α狼的位置;
According to the updated position of α wolf, the updated position of β wolf and the updated position of δ wolf, using the formula Update the α wolf again to obtain the secondary update position of the α wolf; and use the secondary update position of the α wolf as the position of the α wolf in the next iteration;
式中,X
p(t+1)为第t+1次迭代时个体p的位置;X
p(t)为第t次迭代时个体p的位置;A为收敛因子;D为个体到猎物的距离,D=|CX
p(t)-X(t)|,X(t)为第t次迭代时猎物位置,C=2r
1,C为系数常量,r
1为[0,1]间的随机数;X
α(t+1)为第t+1次迭代时α狼的位置;X
1、X
2和X
3分别为第t次迭代时α狼、β狼和δ狼的一次更新位置。
In the formula, X p (t+1) is the position of individual p at the t+1th iteration; X p (t) is the position of individual p at the tth iteration; A is the convergence factor; D is the distance from the individual to the prey Distance, D=|CX p (t)-X(t)|, X(t) is the prey position at the tth iteration, C=2r 1 , C is the coefficient constant, r 1 is the distance between [0,1] Random number; X α (t+1) is the position of α-wolf at the t+1th iteration; X 1 , X 2 and X 3 are the updated positions of α-wolf, β-wolf and δ-wolf at the t-th iteration respectively .
图2为本发明实施例所提供的水质预警模型训练方法流程图,如图2所示本发明中的水质预警模型训练方法如下所示:Fig. 2 is the flow chart of the water quality early warning model training method provided by the embodiment of the present invention, as shown in Fig. 2, the water quality early warning model training method in the present invention is as follows:
步骤(1):初始化相关参数Step (1): Initialize relevant parameters
将得到的鱼群的运动特征行为参数进行数据归一标准化处理,并构建训练集和测试集。SVM模型中的核函数选用高斯径向基核函数
其中,x
i与x
j为空间中的任一点,γ为核函数的宽度参数,控制了函数的径向作用范围。SVM模型的参数(惩罚因子C、核函数的参数γ)初始值为灰狼优化算法中α狼的坐标,每只灰狼的初始适应度函数值设为无穷大量(inf),对灰狼优化算法中的空间维数D
n、狼群数量N、最大迭代次数T
max、系数常量C、收敛因子A,差分进化算法中的交叉概率CR、上下搜索界限、最大迭代次数T′
max和比例因子F(其值范围在[0,2]之间)都进行初始化,假设参数a=2,r
1、r
2是0~1间的随机数。
The motion characteristic behavior parameters of the obtained fish schools were normalized and standardized, and the training set and test set were constructed. The kernel function in the SVM model uses the Gaussian radial basis kernel function Among them, x i and x j are any point in the space, and γ is the width parameter of the kernel function, which controls the radial range of the function. The initial value of the parameters of the SVM model (the penalty factor C, the parameter γ of the kernel function) is the coordinate of the wolf α in the gray wolf optimization algorithm, and the initial fitness function value of each gray wolf is set to an infinite amount (inf). Space dimension D n , number of wolves N, maximum number of iterations T max , coefficient constant C, convergence factor A in the algorithm, crossover probability CR, upper and lower search limits, maximum number of iterations T′ max and scaling factor in the differential evolution algorithm F (its value range is between [0,2]) are all initialized, assuming that the parameter a=2, r 1 and r 2 are random numbers between 0 and 1.
步骤(2):使用DE算法(差分进化算法)初始化灰狼种群Step (2): Use the DE algorithm (differential evolution algorithm) to initialize the gray wolf population
差分进化算法的进化过程主要有3个操作,即变异、交叉和选择。The evolution process of the differential evolution algorithm mainly has three operations, namely mutation, crossover and selection.
变异:变异操作是指在原向量的基础上加上一个与原向量不同的向量。即随机选取初始灰狼种群中两个不同的灰狼个体的位置向量,将其向量调整比例后与待变异个体向量进行合成,具体公式如下:Mutation: The mutation operation refers to adding a vector different from the original vector to the original vector. That is, randomly select the position vectors of two different gray wolf individuals in the initial gray wolf population, adjust the ratio of their vectors and synthesize them with the individual vectors to be mutated. The specific formula is as follows:
D
i(T+1)=X
r1(T)+F*(X
r2(T)-X
r3(T))
D i (T+1)=X r1 (T)+F*(X r2 (T)-X r3 (T))
其中:T为迭代次数,F为缩放因子,X
n为变异前种群个体,D
i(T+1) 为变异后的种群个体。
Among them: T is the number of iterations, F is the scaling factor, X n is the population individual before mutation, D i (T+1) is the population individual after mutation.
交叉:变异完成后,对第T代种群个体X
i,j(T)及其变异的个体D
i,j(T+1)进行交叉操作,数学模型公式如下:
Crossover: After the mutation is completed, the crossover operation is performed on the T generation population individual X i,j (T) and its mutated individual D i,j (T+1). The mathematical model formula is as follows:
其中:j为维度,i为该维的第i个个体,CR为交叉概率,r为[0,1]间的随机概率,D
n为空间维度。
Among them: j is the dimension, i is the i-th individual of this dimension, CR is the crossover probability, r is the random probability between [0,1], and D n is the space dimension.
选择:为了保证种群的优良性,使用贪心算法来选择进入下一代的个体。当新一代比父代优时(新一代适应度值比父代适应度值低),新一代被保留,否则父代被保留,选择公式如下:Selection: In order to ensure the goodness of the population, a greedy algorithm is used to select individuals to enter the next generation. When the new generation is better than the parent generation (the fitness value of the new generation is lower than the fitness value of the parent generation), the new generation is retained, otherwise the parent generation is retained. The selection formula is as follows:
X
i(T+1)为第T+1次迭代的父代种群,X
i(T)为第T次迭代的父代种群,U
i(T+1)为第T次迭代的子代种群,f(U
i(T+1))为第T次迭代的子代种群的适应度值,f(X
i(T))为第T次迭代的父代种群的适应度值。
Xi (T+1) is the parent population of the T+1 iteration, Xi ( T) is the parent population of the T iteration, U i ( T+1) is the child population of the T iteration , f(U i (T+1)) is the fitness value of the offspring population of the T-th iteration, and f(X i (T)) is the fitness value of the parent population of the T-th iteration.
步骤(3):判断是否达到最大迭代次数T
max;若达到,执行步骤4;若未达到,令T=T+1,并执行步骤(2)。
Step (3): Judging whether the maximum number of iterations T max is reached; if yes, execute step 4; if not, set T=T+1, and execute step (2).
步骤(4):构建灰狼社会等级层次模型,计算适应度函数值并分别选取α狼、β狼、δ狼;Step (4): Construct the gray wolf social hierarchy model, calculate the fitness function value and select α wolf, β wolf, and δ wolf respectively;
图3为本发明实施例所提供的狼群等级划分图,如图3所示,α狼被定义为第一等级,是狼群中的头狼,主要负责对捕食活动做出决策,是狼群中管理能力最好的狼;β狼被定义为第二等级,服从于α狼,并协助α狼做出决策;δ狼被定义为第三等级,服从α狼、β狼;其余的狼设置为ω狼,这个是最低等级,服从前三等级的狼。狼群在狩猎时严格遵守着这个等级制度和自己的职责。Fig. 3 is a gradation diagram of wolves provided by the embodiment of the present invention. As shown in Fig. 3, α wolf is defined as the first level, which is the head wolf in the wolves and is mainly responsible for making decisions on predation activities. The wolf with the best management ability in the group; the β wolf is defined as the second level, obeying the alpha wolf, and assisting the alpha wolf to make decisions; the delta wolf is defined as the third level, obeying the alpha wolf and the beta wolf; the rest of the wolves Set to ω wolf, this is the lowest level, obeying the wolves of the first three levels. Wolves strictly abide by this hierarchy and their own responsibilities when hunting.
每只灰狼的初始适应度函数值设为无穷大量(inf),将排名前三的3个个体的目标函数值分别设置为X
α、X
β和X
δ。
The initial fitness function value of each gray wolf is set to infinite quantity (inf), and the objective function values of the top three individuals are set to X α , X β and X δ respectively.
步骤(5):更新狼群位置,训练SVM模型并预测;Step (5): update the wolf pack position, train the SVM model and predict;
图4为本发明实施例所提供的狼群猎捕过程示意图,如图4所示,α狼、β狼、δ狼不断靠近包围猎物,然后指挥候选狼ω狼抓捕猎物,灰狼的狩猎行为包括搜索、接近、包围、狩猎、攻击等步骤,具体如下:Figure 4 is a schematic diagram of the hunting process of wolves provided by the embodiment of the present invention. As shown in Figure 4, α wolves, β wolves, and δ wolves approach and surround prey constantly, and then command candidate wolves ω wolves to capture prey, and the hunting of gray wolves Behaviors include steps such as searching, approaching, surrounding, hunting, and attacking, as follows:
对于优化问题,猎物的最终位置是其全局最优解,所以灰狼个体和猎 物的距离十分重要,两者间的距离公式如下:For the optimization problem, the final position of the prey is its global optimal solution, so the distance between the individual gray wolf and the prey is very important, and the distance formula between the two is as follows:
D=|C.X
p(t)-X(t)|
D=|CX p (t)-X(t)|
t为迭代次数,X
p(t)为灰狼个体在第t次迭代时的位置,X(t)为猎物在第t次迭代时的位置,C为系数常量,C=2r
1,r
1是[0,1]间的随机数。
t is the number of iterations, X p (t) is the position of the wolf individual at the t-th iteration, X(t) is the position of the prey at the t-th iteration, C is the coefficient constant, C=2r 1 , r 1 It is a random number between [0,1].
α狼、β狼、δ狼和猎物的距离,分别用D
α、D
β、D
δ表示,公式如下所示:
The distances between α-wolves, β-wolves, δ-wolves and their prey are represented by D α , D β , and D δ respectively, and the formulas are as follows:
D
α=|C
1.X
α(t)-X(t)|
D α =|C 1 .X α (t)-X(t)|
D
β=|C
2.X
β(t)-X(t)|
D β =|C 2 .X β (t)-X(t)|
D
δ=|C
3.X
δ(t)-X(t)|
D δ =|C 3 .X δ (t)-X(t)|
X
α(t),X
β(t)和X
δ(t)分别为α狼、β狼、δ狼在第t次迭代时的位置,那么灰狼个体位置更新的数学模型如下:
X α (t), X β (t) and X δ (t) are respectively the positions of α wolf, β wolf, and δ wolf in the t-th iteration, then the mathematical model of gray wolf individual position update is as follows:
X(t+1)=X
p(t)-A.D
X(t+1) = Xp(t)-AD
其中:X
p(t+1)为第t+1次迭代时个体p的位置;X
p(t)为第t次迭代时个体p的位置;t为第一迭代次数,D为个体到猎物的距离A是收敛因子,由式A=2ar
2-a可得到A的初始值,r
2为[0,1]之间的随机数,a=2,随着迭代次数的增加线性递减到0。对于收敛因子A而言,A越大,全局搜索能力越强,局部搜索能力越弱;A越小,全局搜索能力越弱,局部搜索能力越强。α狼、β狼、δ狼具体位置由如下公式所示:
Among them: X p (t+1) is the position of individual p at the t+1th iteration; X p (t) is the position of individual p at the tth iteration; t is the number of first iterations, and D is the distance from the individual to the prey. The distance A is the convergence factor, the initial value of A can be obtained by the formula A=2ar 2 -a, r 2 is a random number between [0, 1], a=2, and linearly decreases to 0 with the increase of the number of iterations . For the convergence factor A, the larger A is, the stronger the global search ability is, and the weaker the local search ability is; the smaller A is, the weaker the global search ability is, and the stronger the local search ability is. The specific positions of α wolves, β wolves, and δ wolves are shown by the following formula:
X
1=X
α(t)一A
1.D
α
X 1 =X α (t)-A 1 .D α
X
2=X
β(t)一A
2.D
β
X 2 =X β (t)-A 2 .D β
X
3=X
δ(t)一A
3.D
δ
X 3 =X δ (t)-A 3 .D δ
A
1,A
2和A
3分别为α狼、β狼、δ狼在迭代时收敛因子的值,这三个值会随着迭代次数的变化而变化,X
α(t+1)为第t+1次的最优解向量,公式为:
A 1 , A 2 and A 3 are the values of the convergence factors of α wolf, β wolf, and δ wolf in iterations respectively. These three values will change with the number of iterations. X α (t+1) is the t-th +1 optimal solution vector, the formula is:
将这次迭代最优解向量作为SVM预测模型的参数组合,代入SVM模型进行训练。The iterative optimal solution vector is used as the parameter combination of the SVM prediction model and substituted into the SVM model for training.
步骤(6):重新计算适应度函数,更新α狼、β狼、δ狼的位置;并判断是否达到最大迭代次数T′
max;每只狼都由X(t+1)=X
p(t)-A.D更 新,但是α狼位置每次迭代后会被
公式替代,在该次迭代中,SVM模型的预测精度会作为一个新的适应度函数值,以错误率最小化为目标,最终适应度函数值与100做差取绝对值。如果该适应度函数值小于α狼的适应度函数值,则将α狼的适应度函数值更新为该适应度函数值,如果该适应度函数值介于α狼和β狼的适应度函数值之间,则将β狼的适应度函数值更新为该适应度函数值,如果该适应度函数值介于β狼和δ狼的适应度函数值之间,则将δ狼的适应度函数值更新为该适应度函数值。更新狼群位置,判断是否达到最大迭代次数T′
max,若达到,α狼最终的位置就是支持向量机中参数组合(惩罚因子C,核函数参数γ)的值。如果没有达到,T=T+1,并行步骤(4)。
Step (6): Recalculate the fitness function, update the positions of α wolf, β wolf, and δ wolf; and judge whether the maximum number of iterations T′ max is reached; each wolf is determined by X(t+1)=X p (t )-AD is updated, but the alpha wolf position will be replaced by In this iteration, the prediction accuracy of the SVM model will be used as a new fitness function value, with the goal of minimizing the error rate, and the difference between the final fitness function value and 100 will be taken as the absolute value. If the fitness function value is less than the fitness function value of α wolf, update the fitness function value of α wolf to the fitness function value, if the fitness function value is between the fitness function value of α wolf and β wolf between, update the fitness function value of β wolf to the fitness function value, if the fitness function value is between the fitness function value of β wolf and δ wolf, then update the fitness function value of δ wolf Update to the fitness function value. Update the position of the wolves, and judge whether the maximum number of iterations T′ max is reached. If so, the final position of the α wolf is the value of the parameter combination (penalty factor C, kernel function parameter γ) in the support vector machine. If not, T=T+1, parallel step (4).
步骤(8):将最后得出结果作为SVM预测模型的参数组合。Step (8): The final result is used as the parameter combination of the SVM prediction model.
将最后得出α狼的位置作为SVM模型参数组合(C,γ)的值,将测试数据集代入训练后的SVM模型进行预测。因为用灰狼优化算法优化的就是参数组合(C,γ)的值,灰狼初始种群中α狼的坐标是随机的,将这个坐标作为(C,γ)的值,将SVM训练后的准确率作为每只狼的适应度函数后,α狼的坐标就越来越接近较优的(C,γ)值,α狼的最终位置就是灰狼优化算法得出来的最优值。The final position of α wolf is taken as the value of the SVM model parameter combination (C, γ), and the test data set is substituted into the trained SVM model for prediction. Because the value of the parameter combination (C, γ) is optimized by the gray wolf optimization algorithm, the coordinates of α wolves in the initial population of gray wolves are random, and this coordinate is used as the value of (C, γ), and the accurate value after SVM training is After the rate is used as the fitness function of each wolf, the coordinates of the α wolf are getting closer and closer to the optimal (C, γ) value, and the final position of the α wolf is the optimal value obtained by the gray wolf optimization algorithm.
本发明提出的水质预警方法,利用DE算法和灰狼优化算法改进SVM模型的参数组合值,提升SVM算法在生物水质预警中的预测性能,它改善了过早成熟和局部最优的缺陷,且具有全局搜索能力。针对经典灰狼算法前期寻优速度较慢的缺陷,通过在引入DE差分进化算法,用于生成灰狼寻优算法初始种群的最优值,初始种群是影响不断迭代时收敛速度的重要因素,且初始种群的质量也影响着预测模型的最优值。所以,直接优化初始种群能够提高灰狼优化算法寻找最优值的能力,克服灰狼优化算法的初始种群随机生成的局限性,使灰狼优化算法具有更加良好的寻优能力,提高了SVM生物水质预警模型的分类准确率。为了验证本发明的效果,技术人员作了如下工作:The water quality early warning method proposed by the present invention uses the DE algorithm and the gray wolf optimization algorithm to improve the parameter combination value of the SVM model, and improves the predictive performance of the SVM algorithm in biological water quality early warning, which improves the defects of premature maturity and local optimum, and With global search capability. Aiming at the defect of the classic gray wolf algorithm's slow optimization speed in the early stage, the DE differential evolution algorithm is introduced to generate the optimal value of the initial population of the gray wolf optimization algorithm. The initial population is an important factor affecting the convergence speed of continuous iterations. And the quality of the initial population also affects the optimal value of the prediction model. Therefore, directly optimizing the initial population can improve the ability of the gray wolf optimization algorithm to find the optimal value, overcome the limitations of the random generation of the initial population of the gray wolf optimization algorithm, make the gray wolf optimization algorithm have a better search ability, and improve the SVM biological The classification accuracy of the water quality early warning model. In order to verify the effect of the present invention, technicians have done the following work:
(1)提高准确率(1) Improve accuracy
进行仿真实验,在前期跟踪到正常水质和异常水质情况下的鱼体轨迹后,获取鱼群的五个运动特征行为参数(速度、加速度、平均游动速度、平均游动距离及鱼群离散度),对这1000个样本点的数据进行标记,其中水质正常样本点500个,水质异常样本点500个。从这1000个样本点中选择800个样本数据点作为训练集,其余200个样本数据点作为测试集。初始的灰狼种群数目N定为30,搜索空间维度D
n定为2,比例因子范围为[0.01,100],交叉概率CR为0.2,最大迭代次数T为1000。
Carry out a simulation experiment, after tracking the fish body trajectory under normal water quality and abnormal water quality conditions in the early stage, obtain the five movement characteristic behavior parameters of the fish school (speed, acceleration, average swimming speed, average swimming distance and fish dispersion ), mark the data of these 1000 sample points, including 500 sample points with normal water quality and 500 sample points with abnormal water quality. From these 1000 sample points, 800 sample data points are selected as the training set, and the remaining 200 sample data points are used as the test set. The initial gray wolf population N is set to 30, the search space dimension D n is set to 2, the scale factor range is [0.01,100], the crossover probability CR is 0.2, and the maximum number of iterations T is 1000.
由于选择训练集和测试集时,是随机选择的,在相同环境下进行10次实验,实验结果如表1所示,表中记录模型参数γ和C的值以及测试误差。Since the training set and test set were selected randomly, 10 experiments were carried out in the same environment. The experimental results are shown in Table 1. The values of the model parameters γ and C and the test error are recorded in the table.
表1 DEGWO-SVM算法测试准确率Table 1 DEGWO-SVM algorithm test accuracy
由表1能够看出,采用DEGWO-SVM算法(即本发明利用差分进化算法和灰狼优化算法对支持向量机模型进行训练,得到水质预警模型)得到的水质预警模型具有较好的分类准确性,测试集分类准确率平均值为94.4%,模型泛化性能好,因此该模型适用于生物水质预警系统。此外,可以看出,每次运算后模型参数γ的变化较大,这就说明固定的模型参数并不适合所有的分类情况。模型参数的取值也保留到了小数点后6位,说明模型参数的取值范围比较宽也更加精确,引入差分进化算法和灰狼优化算法能够在更大的范围内寻找模型的最佳参数组合,找到模型参数的全局最优解。As can be seen from Table 1, the water quality early warning model obtained by using the DEGWO-SVM algorithm (that is, the present invention uses differential evolution algorithm and gray wolf optimization algorithm to train the support vector machine model to obtain the water quality early warning model) has better classification accuracy , the average classification accuracy rate of the test set is 94.4%, and the generalization performance of the model is good, so the model is suitable for the biological water quality early warning system. In addition, it can be seen that the model parameter γ changes greatly after each operation, which shows that the fixed model parameters are not suitable for all classification situations. The values of the model parameters are also reserved to 6 decimal places, which shows that the value range of the model parameters is wider and more accurate. The introduction of the differential evolution algorithm and the gray wolf optimization algorithm can find the best parameter combination of the model in a larger range. Find the global optimal solution for the model parameters.
为验证DEGWO-SVM算法的水质预警模型的准确率和收敛性,使用另外两种改进SVM模型的优化算法与发明改进优化的SVM模型进行对比。表2为遗传算法改进支持向量机模型(Genetic Algorithm-Support Vector Machines,GA-SVM)、灰狼优化算法改进支持向量机模型(Grey Wolf Optimization-Support Vector Machines,GWO-SVM)与于本发明中的DEGWO-SVM算法模型各运行100次算法后,将预测结果的平均准确率进行对比,对比结果如表2所示。In order to verify the accuracy and convergence of the water quality early warning model of the DEGWO-SVM algorithm, two other optimization algorithms of the improved SVM model were used to compare with the improved and optimized SVM model of the invention. Table 2 is the genetic algorithm improved support vector machine model (Genetic Algorithm-Support Vector Machines, GA-SVM), gray wolf optimization algorithm improved support vector machine model (Grey Wolf Optimization-Support Vector Machines, GWO-SVM) and in the present invention After the DEGWO-SVM algorithm model of DEGWO-SVM runs the algorithm 100 times, the average accuracy rate of the prediction results is compared, and the comparison results are shown in Table 2.
表2平均准确率对比表Table 2 Average accuracy comparison table
从表2可知,DEGWO-SVM的平均准确率比其他两种分类算法平均准确率高。实验采用平均绝对误差(MAE)、均方误差(MSE)、平均绝对百分比误差(MAPE)作为预测方法的评价标准,三种评价标准定义如下:It can be seen from Table 2 that the average accuracy of DEGWO-SVM is higher than the average accuracy of the other two classification algorithms. The experiment uses mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) as the evaluation criteria of the prediction method. The three evaluation criteria are defined as follows:
式中,n为输出样本的个数,f
i、y
i分别为预测输出值和实际值。将每个算法运行10次取均值作为模型的预测结果。GA-SVM、GWO-SVM和DEGWO-SVM三种算法的预测结果误差对比结果如表3所示。
In the formula, n is the number of output samples, f i and y i are the predicted output value and actual value respectively. Run each algorithm 10 times and take the mean value as the prediction result of the model. Table 3 shows the comparison results of the prediction error of the three algorithms: GA-SVM, GWO-SVM and DEGWO-SVM.
表3预测结果误差对比表Table 3 Prediction result error comparison table
根据表3可知,DEGWO-SVM与其它两种算法相比,三种评价标准都有较高的优势,表明DEGWO-SVM算法的测试误差最小,预测准确性最高。According to Table 3, compared with the other two algorithms, DEGWO-SVM has higher advantages in the three evaluation criteria, indicating that the DEGWO-SVM algorithm has the smallest test error and the highest prediction accuracy.
(2)改善过早收敛,加快收敛速度(2) Improve premature convergence and speed up convergence
GA-SVM、GWO-SVM、DEGWO-SVM的适应度曲线,即平均适应度与迭代次数间的关系如图5-7所示。随着迭代次数的增加,DEGWO-SVM的最佳适应度值小于GA-SVM和GWO-SVM算法,GA-SVM算法的最佳适应度值在第50次迭代时猛然下降,然后在第100次迭代时基本保持稳定,与其他两种算法相比过早熟,容易陷入局部最优。GWO-SVM算法随着迭代次数的增加剧烈波动,表明该算法具有不稳定。而DEGWO-SVM算法曲线在200次迭代之前呈下降的趋势,在这之后变化较小、趋于稳定。通过实验对比及分析,本发明改进算法具有较高的分类准确率,三种误差评价准则最低,且性能较为稳定,有较高的全局搜索能力,对生物水质预警具有重大意义。The fitness curves of GA-SVM, GWO-SVM, and DEGWO-SVM, that is, the relationship between the average fitness and the number of iterations are shown in Figure 5-7. As the number of iterations increases, the optimal fitness value of DEGWO-SVM is smaller than that of GA-SVM and GWO-SVM algorithms, and the optimal fitness value of GA-SVM algorithm drops sharply at the 50th iteration, and then at the 100th iteration It basically remains stable when iterating. Compared with the other two algorithms, it is premature and easy to fall into local optimum. The GWO-SVM algorithm fluctuates violently as the number of iterations increases, indicating that the algorithm is unstable. However, the DEGWO-SVM algorithm curve shows a downward trend before 200 iterations, after which the change is small and tends to be stable. Through experimental comparison and analysis, the improved algorithm of the present invention has higher classification accuracy, the lowest three error evaluation criteria, relatively stable performance, high global search ability, and great significance for biological water quality early warning.
图8为本发明实施例所提供的水质预警系统结构示意图,如图8所示,本发明提供了一种水质预警系统,包括:Fig. 8 is a schematic structural diagram of the water quality early warning system provided by the embodiment of the present invention. As shown in Fig. 8, the present invention provides a water quality early warning system, including:
当前数据获取模块901,用于获取待测水域的当前生物运动特征数据;The current data acquisition module 901 is used to acquire the current biological movement characteristic data of the water area to be measured;
水质预警模块902,用于将待测水域的当前生物运动特征数据,输入水质预警模型,得到待测水域的预警结果;水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。The water quality early warning module 902 is used to input the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model is to use the differential evolution algorithm and the gray wolf optimization algorithm to carry out obtained by training.
本发明提供的水质预警系统,还包括:The water quality early warning system provided by the present invention also includes:
支持向量机模型建立模块,用于建立支持向量机模型;A support vector machine model building module, used to set up a support vector machine model;
历史数据获取模块,用于获取待测水域的生物运动特征历史数据;The historical data acquisition module is used to acquire the historical data of biological movement characteristics in the water area to be measured;
水质预警模型确定模块,用于将待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对支持向量机模型进行训练,得到水质预警模型。The water quality early warning model determination module is used to use the historical data of biological movement characteristics in the water area to be tested as a training set, and use the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model to obtain the water quality early warning model.
水质预警模型确定模块,具体包括:Water quality early warning model determination module, including:
初始狼群构建单元,用于构建初始狼群;The initial wolf pack construction unit is used to build the initial wolf pack;
最优初始狼群确定单元,用于利用差分进化算法对初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;The optimal initial wolf group determination unit is used to process the initial wolf group using the differential evolution algorithm to obtain an optimized initial wolf group, use the optimized initial wolf group as a training wolf group and initialize the optimal wolf group objective function value;
个体目标函数值计算单元,用于将训练狼群和训练集均输入支持向量机模型,计算训练狼群中每个个体的个体目标函数值;The individual objective function value calculation unit is used to input the training wolves and the training set into the support vector machine model, and calculates the individual objective function value of each individual in the training wolves;
排序单元,用于根据个体目标函数值对训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;The sorting unit is used to sort the individuals in the training wolf group in descending order according to the individual objective function value, and use the first three individuals as α wolf, β wolf and δ wolf respectively;
最优狼群目标函数值更新单元,用于根据α狼的个体目标函数值、β 狼的个体目标函数值和δ狼的个体目标函数值,更新最优狼群目标函数值;The optimal wolf group objective function value updating unit is used to update the optimal wolf group objective function value according to the individual objective function value of α wolf, the individual objective function value of β wolf and the individual objective function value of δ wolf;
水质预警模型确定单元,用于更新训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将训练狼群和训练集均输入支持向量机模型,计算训练狼群中每个个体的个体目标函数值”,直至第一迭代次数达到第一迭代次数阈值,并将与最优狼群目标函数值对应的个体的位置作为支持向量机模型的参数,得到水质预警模型;支持向量机模型的参数包括:惩罚因子和核函数系数。The determination unit of the water quality early warning model is used to update the position of each individual in the training wolf group, increase the value of the first iteration number by 1, and return to the step "input the training wolf group and the training set into the support vector machine model, and calculate the training wolf The individual objective function value of each individual in the group" until the first iteration reaches the first iteration threshold, and the position of the individual corresponding to the optimal wolf pack objective function value is used as a parameter of the support vector machine model to obtain a water quality warning Model; the parameters of the support vector machine model include: penalty factor and kernel function coefficient.
最优初始狼群确定单元,具体包括:The optimal initial wolf pack determination unit specifically includes:
父代种群构建子单元,用于根据初始狼群构建父代种群;The parent population constructs a subunit, which is used to construct the parent population according to the initial wolf group;
第一种群目标函数值计算子单元,用于将父代种群和训练集均输入支持向量机模型,计算父代种群的种群目标函数值;The first population objective function value calculation subunit is used to input both the parent population and the training set into the support vector machine model to calculate the population objective function value of the parent population;
子代种群构建子单元,用于采用差分进化算法,对父代种群进行交叉和变异处理,得到子代种群;The offspring population constructs a subunit, which is used to perform crossover and mutation processing on the parent population by using the differential evolution algorithm to obtain the offspring population;
第二种群目标函数值计算子单元,用于将子代种群和训练集均输入支持向量机模型,计算子代种群的种群目标函数值;The second population objective function value calculation subunit is used to input both the offspring population and the training set into the support vector machine model to calculate the population objective function value of the offspring population;
第一判断子单元,用于判断子代种群的种群目标函数值是否大于父代种群的种群目标函数值,得到第一判断结果;若第一判断结果为是,则执行第一父代种群更新子单元;若第一判断结果为否,则执行第二父代种群更新子单元;The first judging subunit is used to judge whether the population objective function value of the child population is greater than the population objective function value of the parent population to obtain the first judgment result; if the first judgment result is yes, then perform the first parent population update A subunit; if the first judgment result is no, then execute the second parent generation population update subunit;
第一父代种群更新子单元,用于将父代种群更新为子代种群;The first parent population update subunit is used to update the parent population to the child population;
第二父代种群更新子单元,用于保留父代种群;The second parent generation population updates the sub-unit for retaining the parent generation population;
最优初始狼群确定子单元,用于令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对父代种群进行交叉和变异处理,得到子代种群”直至第二迭代次数达到第二迭代次数阈值,并将父代种群确定为优化后的初始狼群。The optimal initial wolf group determines the child unit, which is used to increase the value of the second iteration number by 1, and returns to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the second iteration number The second iteration threshold is reached, and the parent population is determined as the optimized initial wolf population.
最优狼群目标函数值更新单元,具体包括:The optimal wolf pack objective function value updating unit, specifically includes:
第二判断子单元,用于判断最优狼群目标函数值是否小于α狼的个体目标函数值,得到第二判断结果;若第二判断结果为是,则执行第一最优狼群目标函数值更新子单元;若第二判断结果为否,则执行第三判断子单元;The second judging subunit is used to judge whether the optimal wolf group objective function value is less than the individual objective function value of α wolf, and obtain the second judgment result; if the second judgment result is yes, then execute the first optimal wolf group objective function A value update subunit; if the second judgment result is no, execute the third judgment subunit;
第一最优狼群目标函数值更新子单元,用于将最优狼群目标函数值更新为α狼的个体目标函数值;The first optimal wolf group objective function value update subunit is used to update the optimal wolf group objective function value to the individual objective function value of α wolf;
第三判断子单元,用于判断最优狼群目标函数值是否小于β狼的个体目标函数值,得到第三判断结果;若第三判断结果为是,则执行第二最优狼群目标函数值更新子单元;若第三判断结果为否,则执行第三最优狼群目标函数值更新子单元;;The third judging subunit is used to judge whether the optimal wolf group objective function value is less than the individual objective function value of the β wolf, and obtain the third judgment result; if the third judgment result is yes, then execute the second optimal wolf group objective function A value update subunit; if the third judgment result is no, execute the third optimal wolf pack objective function value update subunit;
第二最优狼群目标函数值更新子单元,用于将最优狼群目标函数值更新为β狼的个体目标函数值;The second optimal wolf group objective function value update subunit is used to update the optimal wolf group objective function value to the individual objective function value of the β wolf;
第三最优狼群目标函数值更新子单元,用于将最优狼群目标函数值更新为δ狼的个体目标函数值;The third optimal wolf pack objective function value updating subunit is used to update the optimal wolf pack objective function value to the individual objective function value of δ wolf;
水质预警模型确定单元,具体包括:The determination unit of the water quality early warning model includes:
第一位置更新子单元,用于根据训练狼群中每个个体的位置,利用公式X
p(t+1)=X
p(t)-A.D对训练狼群中每个个体进行更新,得到训练狼群中每个个体的一次更新位置;并将训练狼群中除α狼以外每个个体的一次更新位置作为下一次迭代时除α狼以外每个个体的位置;
The first position update subunit is used to update each individual in the training wolves by using the formula X p (t+1)=X p (t)-AD according to the position of each individual in the training wolves to obtain the training The updated position of each individual in the wolf pack; and the updated position of each individual in the training wolf pack except α wolf as the position of each individual except α wolf in the next iteration;
第二位置更新子单元,用于根据α狼的一次更新位置、β狼的一次更新位置和δ狼的一次更新位置,利用公式
对α狼进行再次更新,得到α狼的二次更新位置;并将α狼的二次更新位置作为下一次迭代时α狼的位置;
The second position update subunit is used to use the formula Update the α wolf again to obtain the secondary update position of the α wolf; and use the secondary update position of the α wolf as the position of the α wolf in the next iteration;
式中,X
p(t+1)为第t+1次迭代时个体p的位置;X
p(t)为第t次迭代时个体p的位置;A为收敛因子;D为个体到猎物的距离,D=|CX
p(t)-X(t)|,X(t)为第t次迭代时猎物位置,C=2r
1,C为系数常量,r
1为[0,1]间的随机数;X
α(t+1)为第t+1次迭代时α狼的位置;X
1、X
2和X
3分别为第t次迭代时α狼、β狼和δ狼的一次更新位置。
In the formula, X p (t+1) is the position of individual p at the t+1th iteration; X p (t) is the position of individual p at the tth iteration; A is the convergence factor; D is the distance from the individual to the prey Distance, D=|CX p (t)-X(t)|, X(t) is the prey position at the tth iteration, C=2r 1 , C is the coefficient constant, r 1 is the distance between [0,1] Random number; X α (t+1) is the position of α-wolf at the t+1th iteration; X 1 , X 2 and X 3 are the updated positions of α-wolf, β-wolf and δ-wolf at the t-th iteration respectively .
以上结合附图对本发明的实施例进行了描述,但是本发明并不局限于上述的具体实施方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在本发明的启示下,在不脱离本发明宗旨和权利要求所保护的范围情况下,还可做出很多形式,这些均属于本发明的保护之内。The embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments, which are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, and these all belong to the protection of the present invention.
Claims (10)
- 一种水质预警方法,其特征在于,所述方法,包括:A water quality early warning method is characterized in that said method comprises:获取待测水域的当前生物运动特征数据;Obtain the current biological movement characteristic data of the water area to be tested;将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果;所述水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。The current biological movement characteristic data of the water area to be measured is input into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model owned.
- 根据权利要求1所述的水质预警方法,其特征在于,在所述将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果之前,还包括:The water quality early warning method according to claim 1, wherein, before inputting the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured, it also includes:建立支持向量机模型;Build a support vector machine model;获取待测水域的生物运动特征历史数据;Obtain the historical data of biological movement characteristics in the water area to be tested;将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型。The historical data of biological movement characteristics in the water area to be tested is used as a training set, and the support vector machine model is trained by using the differential evolution algorithm and the gray wolf optimization algorithm to obtain a water quality early warning model.
- 根据权利要求2所述的水质预警方法,其特征在于,所述将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型,具体包括:The water quality early warning method according to claim 2, wherein, the historical data of biological movement characteristics of the water area to be measured is used as a training set, and the support vector machine model is carried out by using a differential evolution algorithm and a gray wolf optimization algorithm. Training to get the water quality early warning model, including:构建初始狼群;Build an initial pack of wolves;利用差分进化算法对所述初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;Utilize differential evolution algorithm to process described initial wolf pack, obtain optimized initial wolf pack, use optimized initial wolf pack as training wolf pack and initialize optimal wolf pack objective function value;将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值;Both the training wolves and the training set are input into the support vector machine model, and the individual objective function value of each individual in the training wolves is calculated;根据所述个体目标函数值对所述训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;According to the individual objective function value, the individuals in the training wolf group are sorted in descending order, and the first three individuals are respectively regarded as α wolf, β wolf and δ wolf;根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值;According to the individual objective function value of the alpha wolf, the individual objective function value of the beta wolf and the individual objective function value of the delta wolf, update the optimal wolf group objective function value;更新所述训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值”,直至所述第一迭 代次数达到第一迭代次数阈值,并将与所述最优狼群目标函数值对应的个体的位置作为所述支持向量机模型的参数,得到水质预警模型;所述支持向量机模型的参数包括:惩罚因子和核函数系数。Update the position of each individual in the training wolves, increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the support vector machine model, calculate the Train the individual objective function value of each individual in the wolf pack" until the first iteration number reaches the first iteration number threshold, and use the position of the individual corresponding to the optimal wolf pack objective function value as the support vector The parameters of the machine model are obtained to obtain the water quality early warning model; the parameters of the support vector machine model include: penalty factors and kernel function coefficients.
- 根据权利要求3所述的水质预警方法,其特征在于,所述利用差分进化算法对所述初始狼群进行处理,得到优化后的初始狼群,具体包括:The water quality early warning method according to claim 3, wherein the initial wolf pack is processed using a differential evolution algorithm to obtain an optimized initial wolf pack, specifically comprising:根据所述初始狼群构建父代种群;Construct parent generation population according to described initial wolf group;将所述父代种群和所述训练集均输入所述支持向量机模型,计算所述父代种群的种群目标函数值;Both the parent population and the training set are input into the support vector machine model, and the population objective function value of the parent population is calculated;采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群;Using a differential evolution algorithm, performing crossover and mutation processing on the parent population to obtain the offspring population;将所述子代种群和所述训练集均输入所述支持向量机模型,计算所述子代种群的种群目标函数值;Both the offspring population and the training set are input into the support vector machine model, and the population objective function value of the offspring population is calculated;判断所述子代种群的种群目标函数值是否大于所述父代种群的种群目标函数值,得到第一判断结果;judging whether the population objective function value of the offspring population is greater than the population objective function value of the parent population to obtain a first judgment result;若第一判断结果为是,则将所述父代种群更新为所述子代种群;If the first judgment result is yes, updating the parent population to the child population;若第一判断结果为否,则保留所述父代种群;If the first judgment result is no, then retain the parent population;令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群”直至所述第二迭代次数达到第二迭代次数阈值,并将所述父代种群确定为优化后的初始狼群。Increase the value of the second iteration number by 1, and return to the step "Use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the second iteration number reaches the second iteration number threshold, And determine the parent population as the optimized initial wolf population.
- 根据权利要求3所述的水质预警方法,其特征在于,所述根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值,具体包括:The water quality early warning method according to claim 3, characterized in that, according to the individual objective function value of the alpha wolf, the individual objective function value of the beta wolf and the individual objective function value of the delta wolf, update the Describe the optimal wolf pack objective function value, including:判断所述最优狼群目标函数值是否小于α狼的个体目标函数值,得到第二判断结果;judging whether the optimal wolf pack objective function value is less than the individual objective function value of α wolf, and obtaining a second judgment result;若第二判断结果为是,则将所述最优狼群目标函数值更新为α狼的个体目标函数值;If the second judgment result is yes, then update the optimal wolf group objective function value to the individual objective function value of α wolf;若第二判断结果为否,则判断所述最优狼群目标函数值是否小于β狼的个体目标函数值,得到第三判断结果;If the second judgment result is no, then judge whether the optimal wolf group objective function value is less than the individual objective function value of the β wolf, and obtain the third judgment result;若第三判断结果为是,则将所述最优狼群目标函数值更新为β狼的个体目标函数值;If the third judgment result is yes, then update the optimal wolf group objective function value to the individual objective function value of β wolf;若第三判断结果为否,则将所述最优狼群目标函数值更新为δ狼的个体目标函数值。If the third judgment result is no, the optimal wolf group objective function value is updated to the individual objective function value of δ wolf.
- 根据权利要求3所述的水质预警方法,其特征在于,所述更新所述训练狼群中每个个体的位置,具体包括:The water quality early warning method according to claim 3, wherein the updating the position of each individual in the training wolf pack specifically includes:根据所述训练狼群中每个个体的位置,利用公式X p(t+1)=X p(t)-A.D对所述训练狼群中每个个体进行更新,得到所述训练狼群中每个个体的一次更新位置;并将所述训练狼群中除α狼以外每个个体的一次更新位置作为下一次迭代时除α狼以外每个个体的位置; According to the position of each individual in the training wolves, use the formula X p (t+1)=X p (t)-AD to update each individual in the training wolves to obtain the One update position of each individual; and one update position of each individual except α wolf in the training wolf group as the position of each individual other than α wolf during the next iteration;根据α狼的一次更新位置、β狼的一次更新位置和δ狼的一次更新位置,利用公式 对α狼进行再次更新,得到α狼的二次更新位置;并将所述α狼的二次更新位置作为下一次迭代时α狼的位置; According to the updated position of α wolf, the updated position of β wolf and the updated position of δ wolf, using the formula The alpha wolf is updated again to obtain the second update position of the alpha wolf; and the second update position of the alpha wolf is used as the position of the alpha wolf in the next iteration;式中,X p(t+1)为第t+1次迭代时个体p的位置;X p(t)为第t次迭代时个体p的位置;A为收敛因子;D为个体到猎物的距离,D=|CX p(t)-X(t)|,X(t)为第t次迭代时猎物位置,C=2r 1,C为系数常量,r 1为[0,1]间的随机数;X α(t+1)为第t+1次迭代时α狼的位置;X 1、X 2和X 3分别为第t次迭代时α狼、β狼和δ狼的一次更新位置。 In the formula, X p (t+1) is the position of individual p at the t+1th iteration; X p (t) is the position of individual p at the tth iteration; A is the convergence factor; D is the distance from the individual to the prey Distance, D=|CX p (t)-X(t)|, X(t) is the prey position at the tth iteration, C=2r 1 , C is the coefficient constant, r 1 is the distance between [0,1] Random number; X α (t+1) is the position of α-wolf at the t+1th iteration; X 1 , X 2 and X 3 are the updated positions of α-wolf, β-wolf and δ-wolf at the t-th iteration respectively .
- 一种水质预警系统,其特征在于,所述系统,包括:A water quality early warning system is characterized in that said system includes:当前数据获取模块,用于获取待测水域的当前生物运动特征数据;The current data acquisition module is used to acquire the current biological movement characteristic data of the water area to be measured;水质预警模块,用于将所述待测水域的当前生物运动特征数据,输入水质预警模型,得到所述待测水域的预警结果;所述水质预警模型是利用差分进化算法和灰狼优化算法对支持向量机模型进行训练得到的。The water quality early warning module is used to input the current biological movement characteristic data of the water area to be measured into the water quality early warning model to obtain the early warning result of the water area to be measured; the water quality early warning model uses differential evolution algorithm and gray wolf optimization algorithm to The support vector machine model is trained.
- 根据权利要求7所述的水质预警系统,其特征在于,所述系统,还包括:The water quality early warning system according to claim 7, wherein said system also includes:支持向量机模型建立模块,用于建立支持向量机模型;A support vector machine model building module, used to set up a support vector machine model;历史数据获取模块,用于获取待测水域的生物运动特征历史数据;The historical data acquisition module is used to acquire the historical data of biological movement characteristics in the water area to be measured;水质预警模型确定模块,用于将所述待测水域的生物运动特征历史数据作为训练集,利用差分进化算法和灰狼优化算法对所述支持向量机模型进行训练,得到水质预警模型。The water quality early warning model determination module is used to use the historical data of biological movement characteristics of the water area to be tested as a training set, and use the differential evolution algorithm and the gray wolf optimization algorithm to train the support vector machine model to obtain a water quality early warning model.
- 根据权利要求8所述的水质预警系统,其特征在于,所述水质预警模型确定模块,具体包括:The water quality early warning system according to claim 8, wherein the water quality early warning model determination module specifically includes:初始狼群构建单元,用于构建初始狼群;The initial wolf pack construction unit is used to build the initial wolf pack;最优初始狼群确定单元,用于利用差分进化算法对所述初始狼群进行处理,得到优化后的初始狼群,将优化后的初始狼群作为训练狼群并初始化最优狼群目标函数值;The optimal initial wolf pack determination unit is used to process the initial wolf pack using a differential evolution algorithm to obtain an optimized initial wolf pack, use the optimized initial wolf pack as a training wolf pack and initialize the optimal wolf pack objective function value;个体目标函数值计算单元,用于将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值;An individual objective function value calculation unit, configured to input both the training wolves and the training set into the support vector machine model, and calculate the individual objective function value of each individual in the training wolves;排序单元,用于根据所述个体目标函数值对所述训练狼群中的个体进行降序排序,并将前3个个体分别作为α狼、β狼和δ狼;A sorting unit, configured to sort the individuals in the training wolf group in descending order according to the individual objective function value, and use the first three individuals as α wolves, β wolves, and δ wolves respectively;最优狼群目标函数值更新单元,用于根据所述α狼的个体目标函数值、所述β狼的个体目标函数值和所述δ狼的个体目标函数值,更新所述最优狼群目标函数值;An optimal wolf pack objective function value updating unit, configured to update the optimal wolf pack according to the individual objective function values of the alpha wolves, the individual objective function values of the beta wolves, and the individual objective function values of the delta wolves objective function value;水质预警模型确定单元,用于更新所述训练狼群中每个个体的位置,令第一迭代次数的数值增加1,并返回步骤“将所述训练狼群和所述训练集均输入所述支持向量机模型,计算所述训练狼群中每个个体的个体目标函数值”,直至所述第一迭代次数达到第一迭代次数阈值,并将与所述最优狼群目标函数值对应的个体的位置作为所述支持向量机模型的参数,得到水质预警模型;所述支持向量机模型的参数包括:惩罚因子和核函数系数。The water quality early warning model determination unit is used to update the position of each individual in the training wolves, increase the value of the first iteration number by 1, and return to the step "input the training wolves and the training set into the Support vector machine model, calculate the individual objective function value " of each individual in the described training wolf group ", until the first iteration number reaches the first iteration number threshold, and will be corresponding to the optimal wolf group objective function value The location of the individual is used as a parameter of the support vector machine model to obtain a water quality early warning model; the parameters of the support vector machine model include: a penalty factor and a kernel function coefficient.
- 根据权利要求9所述的水质预警系统,其特征在于,所述最优初始狼群确定单元,具体包括:The water quality early warning system according to claim 9, wherein the optimal initial wolf group determination unit specifically includes:父代种群构建子单元,用于根据所述初始狼群构建父代种群;The parent population constructs a subunit, which is used to construct the parent population according to the initial wolf group;第一种群目标函数值计算子单元,用于将所述父代种群和所述训练集 均输入所述支持向量机模型,计算所述父代种群的种群目标函数值;The first population objective function value calculation subunit is used to input both the parent population and the training set into the support vector machine model, and calculate the population objective function value of the parent population;子代种群构建子单元,用于采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群;The offspring population constructs a subunit, which is used to perform crossover and mutation processing on the parent population by using a differential evolution algorithm to obtain the offspring population;第二种群目标函数值计算子单元,用于将所述子代种群和所述训练集均输入所述支持向量机模型,计算所述子代种群的种群目标函数值;The second population objective function value calculation subunit is used to input both the offspring population and the training set into the support vector machine model, and calculate the population objective function value of the offspring population;第一判断子单元,用于判断所述子代种群的种群目标函数值是否大于所述父代种群的种群目标函数值,得到第一判断结果;若第一判断结果为是,则执行第一父代种群更新子单元;若第一判断结果为否,则执行第二父代种群更新子单元;The first judging subunit is used to judge whether the population objective function value of the child population is greater than the population objective function value of the parent population to obtain a first judgment result; if the first judgment result is yes, then execute the first The parent population update subunit; if the first judgment result is no, execute the second parent generation population update subunit;第一父代种群更新子单元,用于将所述父代种群更新为所述子代种群;The first parent population update subunit is configured to update the parent population to the child population;第二父代种群更新子单元,用于保留所述父代种群;The second parent population update subunit is used to retain the parent population;最优初始狼群确定子单元,用于令第二迭代次数的数值增加1,并返回步骤“采用差分进化算法,对所述父代种群进行交叉和变异处理,得到子代种群”直至所述第二迭代次数达到第二迭代次数阈值,并将所述父代种群确定为优化后的初始狼群。The optimal initial wolf group determines the child unit, which is used to increase the value of the second iteration number by 1, and returns to the step "use the differential evolution algorithm to perform crossover and mutation processing on the parent population to obtain the offspring population" until the The second iteration number reaches the second iteration number threshold, and the parent population is determined as the optimized initial wolf group.
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