CN108815726A - A kind of phased array mode supersonic detection method - Google Patents

Abstract

The invention relates to a phased array ultrasonic detection method, belonging to the field of ultrasonic detection, including S1: obtaining body posture information, including age and fat thickness; S2: designing an input mode according to the body posture, and using a particle swarm multi-objective optimization algorithm to adjust each chip Transmit ultrasonic frequency, power and sound wave direction; S3: output the corresponding ultrasonic frequency, power and sound wave direction to form the corresponding focal area size; S4: collect emission area, time, human body feeling and focal area temperature to form a data pool, feedback adjustment, Improve the algorithm in S2; S5: According to the database, form a learning mechanism, and use Pareto-based multi-objective genetic algorithm to optimize the data, so as to retain the optimal ultrasonic frequency, power and sound wave direction. The invention adjusts the frequency, power and direction of the ultrasonic waves emitted by each chip, thereby adjusting the size of the focal area, detecting the human body, and realizing the purpose of treatment.

Description

A Phased Array Ultrasonic Testing Method

technical field

The invention belongs to the technical field of ultrasonic detection, and relates to a phased array ultrasonic detection method.

Background technique

Phased array ultrasound is a combination of ultrasonic probe chips. It consists of multiple piezoelectric chips arranged according to certain rules, and then excites each chip one by one according to a predetermined delay time. The ultrasonic waves emitted by all the chips form a whole wave front, which can effectively The shape and direction of the emitted ultrasonic beam (wave front) can be accurately controlled, and the beam scanning, deflection and focusing of the ultrasonic wave can be realized. At present, the size of the focal area of the phased-array ultrasonic therapy equipment on the market cannot be adjusted, so it is very necessary to study a method that can adjust the size of the focal area of the phased-array ultrasonic therapy.

Contents of the invention

In view of this, the object of the present invention is to provide a phased array ultrasonic detection method. The method adopts the ultrasonic wavelength, power and sound wave pointing control technology emitted by each chip in the phased array ultrasound to adjust the size of the focal area of the phased array ultrasound, so as to achieve the purpose of treatment. To achieve the above object, the present invention provides the following technical solutions:

A phased array ultrasonic detection method, comprising the following steps:

S1: Obtain body information, including age and fat thickness;

S2: Design the input mode according to the posture, and use the particle swarm multi-objective optimization algorithm to adjust the ultrasonic frequency, power and sound wave direction emitted by each chip;

S3: Output the corresponding ultrasonic frequency, power and sound wave direction to form the corresponding focal area size;

S4: Collect the emission area, time, human body feeling and temperature in the focal area to form a data pool, feedback adjustment, and improve the algorithm in S2;

S5: According to the database, a learning mechanism is formed, and the Pareto-based multi-objective genetic algorithm is used to optimize the data, so as to retain the optimal ultrasonic frequency, power and sound wave direction.

Further, in step S2, the particle swarm multi-objective optimization algorithm includes the following steps:

S21: Randomly generate the initial particle position and initial velocity;

S22: Verify whether the particle position and velocity meet the upper and lower limit constraints;

S23: Substituting the position of each particle into the optimization fitness function f ₁ (x) and f ₂ (x);

S24: Updating the respective individual extremum points when optimizing the two objective functions and

S25: update respective global extremum points g1 ^k and g2 ^k ;

when hour, unchanged; otherwise, Here, XOR represents an exclusive OR operation, and the smaller the XOR value, the closer the optimized solutions of the two objective functions are;

S26: Update particle velocity and position:

In the formula, the individual extremum point The global extremum point g ^k = round((g1 ^k +g2 ^k )/2), round() is a rounding function, which completes the rounding function to meet the switch characteristics of the array element; w is the speed inertia weight, and the value is 0.4 ～0.9; cognition weight c ₁ and social learning weight c ₂ take a value of 2;

S27: Judging whether the iteration end condition is satisfied, if not, return to step S21, if satisfied, end the iteration, and the global extremum point is taken as the optimal solution;

Selection of frequency, power and sonic direction:

①The improved particle swarm algorithm simulation is used to realize all levels of frequency, power and sound wave direction;

② Under the condition of satisfying a certain detection probability, according to the size of the target reflection section and the distance of the target, the frequency, power and sound wave direction to be transmitted are determined by formula (1);

③Judge the frequency, power, and sound wave pointing interval position where the frequency, power, and sound wave pointing are located, and launch the corresponding frequency, power, and sound wave pointing to the target.

Further, in step S5, the multi-objective genetic algorithm based on Pareto comprises the following steps:

S51: Initialization of the population: determine the population size M, crossover probability Pc, mutation probability pm, chromosome length N, and the maximum number of iterations K; randomly initialize the chromosomes, and give the emission area, time, human body feeling and focal temperature;

S52: Fitness function construction: the objective function and constraints of ultrasonic frequency, power and sound wave direction are expressed as:

Objective function:

Among them, S represents the emission area, T represents time, C represents the human body feeling; R represents the temperature in the focal area; the coefficients in front of S, T, C and R represent their proportion in the objective function;

The constraints of ultrasonic frequency, power and sound wave direction are as follows:

20KHz<frequency<100KHz;

35W<power<40W;

0°<sonic direction<45°;

S53: Non-dominated sorting: first find out all non-dominated solution sets in the population, and regard them as the same layer, assign 1 to the non-dominated rank value i _rank of all individuals in this layer, and record it as the first non-dominated layer F1; then in the population In addition to all the remaining individuals in this layer, find out other non-dominated solution sets in the remaining individuals, and regard it as another layer, assign the non-dominated rank value i _rank of all individuals in this layer to 2, and record it as the second non-dominated solution set Layer F2; finally repeat the above operation, so that each individual in the population is stratified;

S54: Calculation of congestion degree:

A. Initialize the individual in the population, so that the crowding degree of individual i is 0, denoted as L[i] _d =0;

B. Carry out non-dominated sorting according to the fitness value of each individual, set the crowding degree value of individuals at both ends to a large number, denoted as L[0] _d = L[I] _D = ∞, where I is in the set number of individuals;

C. After the non-dominated sorting process, the calculation formula of the individual crowding degree in the middle is:

D. For multiple different objective functions, repeat steps B and C to obtain the degree of congestion L[i] _d of each individual i;

S55: Elite selection: Here, the competition method is used to select elite individuals, and the parent population P _i and the offspring population Q _i , both of size N, are merged into a population R _i of size 2N; the population R _i is stratified by non-dominated sorting , to get the i _rank of each individual, and calculate the crowding degree of the individual in the non-dominated layer F _j ; according to the i _rank of each individual in the population R _i , the optimal selection is made, when the total number of selected individuals reaches N, Form a new parent population R _i+1 ;

S56: Crossover operation: judge whether the chromosome is a live chromosome, if it is a live chromosome, then cross the chromosome, adopt a one-point crossover method, and the crossover probability is Pc, the specific operation is to randomly set a crossover point in the individual string, and execute When crossing, the partial structures of the two individuals before or after the point are exchanged, and two new individuals are generated;

S57: Mutation operation: Chromosome mutation adopts the method of site mutation, changing the mutated bit 1 of the chromosome to 0, 0 to 1, and keeping the other bits unchanged; the mutation probability is Pm, and the purpose of the mutation is to make the mutated The fitness is greater than or equal to its original fitness; first select a mutant to mutate, and then calculate its fitness to see if it is greater than or equal to its original fitness, if not, re-select the mutant to mutate;

S58: Perform non-dominated sorting, crowding degree calculation, elite selection, crossover, and mutation operations on the population in sequence to obtain new individuals. When the maximum number of iterations is reached, the algorithm ends and the result is obtained; otherwise, non-dominated sorting, crowded degree calculation, elite selection , crossover, and mutation operations until the maximum number of iterations is reached.

The beneficial effects of the present invention are: the method of the present invention adopts the ultrasonic wavelength, power and sound wave pointing control technology emitted by each chip in the phased array ultrasonic, and uses the particle swarm multi-objective optimization algorithm to adjust the ultrasonic frequency, power and sound wave emitted by each chip. The sound wave points to form the corresponding focal area size, and detects the human body to achieve the purpose of treatment; the Pareto-based multi-objective genetic algorithm is used to optimize the data, so as to retain the optimal ultrasonic frequency, power, and sound wave pointing.

Description of drawings

In order to make the purpose, technical scheme and beneficial effect of the present invention clearer, the present invention provides the following drawings for illustration:

Fig. 1 is the overall flowchart of the phased array mode ultrasonic detection method described in the embodiment of the present invention;

Fig. 2 is the flowchart of particle swarm multi-objective optimization algorithm described in the embodiment of the present invention;

Fig. 3 is a flow chart of the Pareto-based multi-objective genetic algorithm described in the embodiment of the present invention.

Detailed ways

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

As shown in Figure 1, a phased array ultrasonic detection method, in this method firstly obtains the patient's posture information (age, fat thickness, etc.); then designs the input mode according to the patient's posture, and then outputs the corresponding ultrasonic frequency, power, The sound wave points to form the corresponding focal area size, and then performs treatment, and then collects the emission area, time, patient feeling, and focal area temperature to form a data pool, and feedbacks to adjust and improve the algorithm in S2; according to the database, a learning mechanism is formed, and Pareto-based multi- The objective genetic algorithm optimizes the data so as to retain the optimal ultrasonic frequency, power, and sound wave direction.

Specifically, the method includes the following steps: S1: Obtain the patient's body information (age, fat thickness, etc.);

S2: Design the input mode according to the patient's posture: use the particle swarm multi-objective optimization algorithm to adjust the ultrasonic frequency, power, and sound wave direction emitted by each chip; S3: output the corresponding ultrasonic frequency, power, and sound wave direction to form the corresponding focal area size; S4 : Collect emission area, time, patient experience, and focal temperature to form a data pool, feedback adjustment, and improve the algorithm in S2; S5: According to the database, form a learning mechanism, and use Pareto-based multi-objective genetic algorithm to optimize the data, so as to retain the most Excellent ultrasonic frequency, power, and sound wave direction.

As shown in Figure 2, the specific steps in this embodiment are as follows:

Step 1: Randomly generate the initial particle position and initial velocity.

Step 2: Verify whether the particle position and velocity meet the upper and lower limit constraints.

Step 3: Substitute the position of each particle into the optimization fitness function f ₁ (x) and f ₂ (x).

Step 4: Update the individual extreme points of the two objective function optimizations and

Step 5: Update respective global extremum points g1 ^k and g2 ^k .

when hour, unchanged; otherwise, Here, XOR represents an exclusive OR operation, and the smaller the XOR value, the closer the optimized solutions of the two objective functions are.

Step 6: Update Particle Velocity and Position

In the formula, the individual extremum point The global extremum point g ^k =round((g1 ^k +g2 ^k )/2), where round() is a rounding function, which completes the rounding function to satisfy the switch characteristics of the array element. w is the weight of speed inertia, usually 0.4~0.9; cognitive weight c ₁ and social learning weight c ₂ . Usually take 2.

Step 7: Determine whether the iteration end condition is satisfied. If it is not satisfied, return to step 2, and if it is satisfied, end the iteration. The global extremum point is regarded as the optimal solution.

As shown in Figure 3, the specific steps in this embodiment are as follows:

S51: Initialization of the population: determine the population size M, crossover probability Pc, mutation probability pm chromosome length N and the maximum number of iterations K; randomly initialize the chromosomes, and give the emission area, time, patient experience, and focal temperature;

S52: Fitness function construction: the objective function and constraints of ultrasonic frequency, power, and sound wave direction can be expressed as:

Objective function:

Among them, S represents the emission area, T represents time, C represents the patient's feeling; R represents the temperature in the focal region; the coefficients in front of S, T, C and R represent their proportion in the objective function;

The constraints of ultrasonic frequency, power, and sound wave direction are as follows:

20KHz<frequency<100KHz;

35W<power<40W;

0°<sonic direction<45°;

S53: Non-dominated sorting: first find out all non-dominated solution sets in the population, and regard them as the same layer, assign 1 to the non-dominated rank value i _rank of all individuals in this layer, and record it as the first non-dominated layer F1; then in the population In addition to all the remaining individuals in this layer, find out other non-dominated solution sets in the remaining individuals, and regard it as another layer, assign the non-dominated rank value i _rank of all individuals in this layer to 2, and record it as the second non-dominated solution set Layer F2; finally repeat the above operation, so that each individual in the population is stratified.

S54: Calculation of congestion degree:

A. Initialize the individual in the population, make the crowding degree of individual i be 0, denoted as L[i] _d =0.

B. Carry out non-dominated sorting according to the fitness value of each individual, set the crowding degree value of individuals at both ends to a large number, denoted as L[0] _d = L[I] _D = ∞, where I is in the set number of individuals.

C. After the non-dominated sorting process, the formula for calculating the crowding degree of the individual in the middle is

D. For multiple different objective functions, repeat steps B and C to obtain the crowding degree L[i] _d of each individual i.

S55: Elite selection: Here, the competition method is used to select elite individuals, and the parent population P _i and the offspring population Q _i , both of size N, are merged into a population R _i of size 2N; the population R _i is stratified by non-dominated sorting , to get the i _rank of each individual, and calculate the crowding degree of the individual in the non-dominated layer F _j ; according to the i _rank of each individual in the population R _i , the optimal selection is made, when the total number of selected individuals reaches N, Form a new parent population R _i+1 .

S56: Crossover operation: judge whether the chromosome is a live chromosome, if it is a live chromosome, then cross the chromosome, adopt a one-point crossover method, and the crossover probability is Pc, the specific operation is to randomly set a crossover point in the individual string, and execute When crossing, the partial structures of the two individuals before or after the point are exchanged, and two new individuals are generated.

S57: Mutation operation: Chromosome mutation adopts the method of site mutation, changing the mutated bit 1 of the chromosome to 0, 0 to 1, and keeping the other bits unchanged; the mutation probability is Pm, and the purpose of the mutation is to make the mutated The fitness is greater than or equal to its original fitness; first select a mutated bit to mutate, and then calculate its fitness to see if it is greater than or equal to its original fitness, if not, re-select the mutated bit to mutate;

S58: Perform non-dominated sorting, crowding degree calculation, elite selection, crossover, and mutation operations on the population in sequence to obtain new individuals. When the maximum number of iterations is reached, the algorithm ends and the result is obtained; otherwise, non-dominated sorting, crowded degree calculation, elite selection , crossover, and mutation operations until the maximum number of iterations is reached.

Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that it can be described in terms of form and Various changes may be made in the details without departing from the scope of the invention defined by the claims.

Claims (3)

1. a kind of phased array mode supersonic detection method, it is characterised in that：Include the following steps：

S1：Obtain posture information, including age and fat thickness；

S2：Input pattern is designed according to posture, every piece of chip is adjusted using population multi-objective optimization algorithm and emits ultrasonic wave frequency Rate, power and sound wave are directed toward；

S3：It exports corresponding ultrasonic frequency, power and sound wave to be directed toward, forms corresponding focal regions size；

S4：It acquires emitting area, time, human feeling and focal regions temperature and forms data pool, feedback adjustment improves algorithm in S2；

S5：According to database, study mechanism is formed, data are optimized using the multi-objective genetic algorithm based on Pareto, It is directed toward to retain optimal ultrasonic frequency, power and sound wave.

2. phased array mode supersonic detection method according to claim 1, it is characterised in that：In step s 2, population Multi-objective optimization algorithm includes the following steps：

S21：Primary position and initial velocity is randomly generated；

S22：Whether verifying particle position and speed meet upper and lower limit constraint；

S23：Each particle position is substituted into optimization fitness function f₁(x) and f₂(x)；

S24：Update respective individual extreme point when two objective function optimizationsWith

S25：Update respective global extreme point g1^kAnd g2^k；

WhenWhen,Do not change；Otherwise, Here, XOR indicates that XOR operation, the optimization solution that exclusive or is worth two objective functions of smaller explanation are closer；

S26：Update particle rapidity and position：

In formula, individual extreme pointGlobal extreme point g^k=round ((g1^k+g2^k)/2), Round () is bracket function, completes the function of rounding up, to meet array element switching characteristic；W is speed inertia weight, value It is 0.4~0.9；Recognize weight c₁With social learning weight c₂, value 2；

S27：Judge whether to meet iteration termination condition, the return step S21 if being unsatisfactory for, terminates iteration if meeting, entirely Office's extreme point is used as optimal solution；

The selection that frequency, power and sound wave are directed toward：

1. being directed toward using modified particle swarm optiziation the Realization of Simulation frequency at different levels, power and sound wave；

2. under conditions of meeting certain detection probability, according to the size of target reflecting section, the distance of target range, by formula (1) Determine that the frequency for needing to emit, power and sound wave are directed toward；

3. determination frequency, power and sound wave are directed toward the frequency at place, power, sound wave direction section position, corresponding to objective emission Frequency, power, sound wave be directed toward.

3. phased array mode supersonic detection method according to claim 1, it is characterised in that：In step s 5, the base Include the following steps in the multi-objective genetic algorithm of Pareto：

S51：The initialization of group：Determine population scale M, crossover probability Pc, mutation probability pm chromosome length N and greatest iteration Number K；Random initializtion chromosome provides emitting area, time, human feeling and focal regions temperature；

S52：Fitness function construction：The objective function and constraint condition that ultrasonic frequency, power and sound wave are directed toward are expressed as：

Objective function：

Wherein, S represents emitting area, and T represents the time, and C represents human feeling；R represents focal regions temperature；S, it is before T, C and R Number represents their proportions in objective function；

The constraint condition that ultrasonic frequency, power and sound wave are directed toward is as follows：

20KHz<Frequency<100KHz；

35W<Power<40W；

0°<Sound wave is directed toward<45°；

S53：Non-dominated ranking：Find out non-dominant disaggregation all in population first, be considered as same layer, by this layer it is all individual Non-dominant sequence value i_rankAssignment 1 is denoted as first non-dominant layer of F1；Then it removes in this layer of remaining all individuals, looks in population Other non-dominant disaggregation in remaining individual out, are considered as another layer, by the non-dominant sequence value i of this layer of all individuals_rankIt is assigned a value of 2, it is denoted as second non-dominant layer of F2；It finally repeats above operation, is layered each of population individual；

S54：Crowding calculates：

A. individual in population is initialized, makes the crowding 0 of individual i, is denoted as L [i]_d=0；

B. non-dominated ranking is carried out according to the fitness value of each individual, the crowded angle value of setting both ends individual is a big number, note Make L [0]_d=L [I]_D=∞, I is individual amount in set in formula；

C. after non-dominated ranking processing, it is in intermediate individual crowding calculation formula：

D. it is directed to multiple and different objective functions, step B and C is repeated, finds out the crowding L [i] of each individual i_d；

S55：Elitist selection：It is the parent population P of N by size here with competitive bidding match method selection elite individual_iWith filial generation kind Group Q_iIt is merged into the population R that size is 2N_i；To population R_iNon-dominated ranking layering is carried out, the i of each individual is obtained_rank, and count The individual is calculated in affiliated non-dominant layer F_jCrowding；By population R_iIn each individual i_rankOptimum selecting is carried out, when selected When the individual sum selected reaches N, new parent population R is formed_i+1；

S56：Crossover operation：Judge whether chromosome is that chromosome living then hands over chromosome if chromosome living Fork, using some interleaved modes, crossover probability Pc, concrete operations are to set a crosspoint at random in individual string, are carried out When intersection, two individual part-structures before or after the point are interchangeable, and generate two new individuals；

S57：Mutation operation：For chromosomal variation by the way of Mutation, the variation position 1 of chromosome, which is become 0,0, becomes 1, Other positions all remain unchanged；Mutation probability is Pm, and the purpose of variation is that the fitness after making it make a variation is suitable more than or equal to its original Response；First a variation position is selected to make a variation, then calculate its fitness, sees whether it is greater than or equal to its original adaptation Degree, if not if just reselect variation position make a variation；

S58：Population is successively carried out just obtain after non-dominated ranking, crowding calculating, elitist selection, intersection, mutation operation New individual, when the maximum number of iterations is reached algorithm terminate to obtain as a result, otherwise non-dominated ranking, crowding calculate, Jing Yingxuan It selects, intersect, mutation operation, until reaching maximum number of iterations.