CN113945362B - Optical fiber testing system for optimizing light source for power station - Google Patents

Abstract

A fiber optic test system for optimizing the light source for a power station, the system comprises: a spectrometer, a fiber grating, a coupler, and an attenuator. The coupler is connected to a plurality of attenuators, each of which is connected to a fiber grating, and n fiber grating branches are connected to the spectrometer. The light emitted by the light source enters the n fiber grating branches through the coupler, and then is reflected back by the fiber grating. The attenuator is used to adjust the peak value of the reflected light reflected by the fiber grating, and finally forms a reflection spectrum in the spectrometer. The fiber optic test system of the present invention can generate light sources with different spectra; the fiber optic test method optimizes the selection and control of the light source and the spectrum of the light source of the fiber optic test system to achieve the purpose of accurately testing the fiber attenuation.

Description

An optical fiber testing system with optimized light source for power stations

Technical Field

The invention relates to the technical field of optical fiber detection, and in particular to an optical fiber testing system with optimized light source for power stations.

Background Art

Optical fiber communication technology is a communication method that uses light waves as carriers and optical fibers as transmission media. It has high speed and reliability. Optical-electric composite cable is a new type of cable that combines optical cables and electric cables. It integrates optical fiber and power transmission copper wire as a transmission line, which can solve problems such as broadband access, equipment power consumption, and signal transmission. Optical-electric composite cable is suitable for insulated communication optical cables, transportation communication optical cable projects, square optical cable projects, overhead optical cable construction, power optical cable projects, and high-altitude optical cable construction. Performance testing and daily maintenance of optical-electric composite cables to ensure the reliability of power transmission and the stability of information transmission and reduce its adverse effects on the power grid have become one of the current research and application hotspots. Existing research results show that optical-electric composite cables that combine optical fiber application technology and power transmission have played an increasingly important role in the construction of power systems, especially in the construction of smart substations; at the same time, the scientific and technological staff of the power system have an urgent need to quickly and effectively detect and restore optical-electric composite cable faults.

The fiber optic test system can quickly and accurately locate the fault location, making it easier for staff to repair it. The fiber optic test system can quickly and accurately locate the fault location, making it easier for staff to repair it. The fiber optic detection technology in the existing technology is relatively backward. Traditionally, only ordinary lighting sources are used for lighting, and then the staff in the opposite direction make manual judgments. The detection methods and means are relatively rough, and it is difficult to find deep-seated fiber optic attenuation and other problems. In current fiber optic applications and detection, photoelectric converters are indispensable devices. Photoelectric converters have the advantages of anti-electromagnetic interference, small size, high cost performance, and high sensitivity; they can directly or indirectly detect physical quantities such as temperature, displacement, and vibration, but photoelectric converters have a greater dependence on the light source spectrum.

Summary of the invention

In order to solve the above technical problems, the present invention provides an optical fiber testing system with optimized light source for power stations, which can generate light sources with different spectra; the optical fiber testing method optimizes the selection and control of the light source and the light source spectrum of the optical fiber testing system to achieve the purpose of accurately testing the optical fiber attenuation.

The technical solution adopted by the present invention is:

A fiber optic test system for optimizing light sources for power stations includes two parts: a fiber optic test system hardware structure part and a light source spectrum optimization control part. The fiber optic test system hardware structure part includes a spectrometer, a fiber grating, a coupler, and an attenuator. The coupler connects multiple attenuators, each attenuator is connected to a fiber grating, and n fiber grating branches are connected to the spectrometer.

The control method of the light source spectrum optimization control part is described as follows: the light source spectrum optimization control program is installed in the memory of the single-chip computer connected to the light source, and the program control process is: control the light emitted by the light source, the emitted light enters n fiber grating branches through the coupler, and then is reflected back through the fiber grating, and the peak value of the reflected light reflected by the fiber grating is adjusted through the attenuator, and finally a reflection spectrum is formed in the spectrometer.

In the optical fiber test system, the reflection spectrum of the i-th fiber Bragg grating is g _i (λ _Bi ), which is expressed as:

Where: λ _Bi is the central wavelength of the i-th fiber Bragg grating; _ri is the reflectivity of the i-th fiber Bragg grating, _0≤ri≤1 ; _BG is the 3dB bandwidth, _gi (λ _Bi ) is the i-th reflection power spectrum;

Then the superimposed spectrum in the spectrometer is expressed as:

Among them, R is the function of superimposed spectrum, that is, sampling spectrum; g is super Gaussian function; N is the random noise in the system; n is the number of fiber Bragg gratings in a group;

The superimposed spectrum in formula (2) may overlap completely, partially, or not overlap. In order to facilitate the demodulation of the central wavelength, the spectrum is reconstructed. The reconstructed spectrum is expressed as:

Where, _si represents the central wavelength of the reconstructed spectrum of the i-th fiber Bragg grating;

Comparing R _v and R, the objective function of the degree of spectral overlap is expressed as:

f(s)＝[RR _v ] ² (4);

When s _i =λ _Bi , the objective function f(s) reaches the minimum value, and the process of solving the central wavelength is transformed into solving the optimal value. When the original spectrum and the reconstructed spectrum reach the optimal match, the central wavelength value is obtained, and the central wavelength value is used as the central spectrum value of the test light source.

The light source spectrum optimization control method based on animal foraging method comprises the following steps:

Step 1: Initialize the optical fiber test system;

Step 2: Evaluate all groups of carnivorous animals whose test optical fiber effectively responds to the original spectrum of the light source, and select the individual with the largest effective response value in the group, i.e., the leader.

Step 3: Based on the randomly disturbed wandering behavior of carnivores when preying, update the position of the probe according to equations (5) and (6), and determine the value of the probe fitness function. If _Yi > _Ylead , update the leader of the group; otherwise, repeat the wandering behavior until the number of wandering times reaches the maximum number _Tmax , and then execute step 4.

β＝[1-2rand()] (6);

Among them, β takes an integer, as a random perturbation, taking 0, 1 or -1 to randomly perturb the wandering behavior of the probe to improve the optimization range and speed; is the probe position at the next moment; x _id is the probe position at the current moment; is the walking step length of the probe in the d-dimensional space; p is the number of directions the current probe is walking in; h is the total number of directions the probe can walk in; rand() is a random number between 0 and 1.

Step 4: Summoning behavior:

Update the attacker's position according to formula (7) and determine the attacker's fitness function value. If _Yi >Y _lead , update the leader. Otherwise, when the distance between the attacker and the leader is less than d _near according to formula (8), execute step 5.

In formula (7), is the attacker’s running step length in d-dimensional space. is the position of the k-th generation leader in the d-dimensional space. The position of the attacker at the next moment, The attacker's position at the current moment.

In formula (8), w is the distance determination factor. D represents the dimension of the wandering space; d represents the dimension of the current space; d _max represents the maximum value of the space dimension range; d _min represents the minimum value of the space dimension range.

Step 5: Besiege behavior: Besiege the prey according to formula (9). Determine the fitness function value of the attacker. If _Yi >Y _lead , execute step 6.

In formula (9), is the attack step length in the d-dimensional space, and λ is a random number between [-1,1]. The attacker's position at the next moment; is the attacker's current position, The current position of the leader can be considered as the position of the prey.

Step 6: Calculate the probability judgment standard according to formula (10). If rand() < γ, attacker i replaces the leader; otherwise, execute step 5.

Among them, γ≤1, _Yi represents the function fitness of the current attacker, and Y _lead represents the function fitness of the current leader.

If rand() < γ, attacker i replaces the leader, otherwise the replacement operation is not performed. Through the simulated annealing algorithm judgment process, attackers with fitness greater than the leader are subjected to simulated annealing inspection to prevent the algorithm from falling into a local optimum.

Step 7, Carnivore group elimination and update operation: select individuals with high fitness in the carnivore group as leaders, eliminate individuals with low fitness and supplement them.

Step 8: Determine whether the number of iterations has been reached. If the conditions are met, output the optimal solution; otherwise, execute step 3.

The present invention provides an optical fiber testing system for optimizing light sources for power stations, and the technical effects are as follows:

1) The optical fiber testing system with optimized light source of the present invention is provided with various modules for light source spectrum control and is capable of generating light sources with different spectra.

2) The light source spectrum optimization control method of the present invention can optimize the spectrum response of the optical fiber including the photoelectric converter tested by the optical fiber testing system, that is, the original spectrum generated by the test light source and the reconstructed spectrum obtained after the photoelectric converter are optimally matched, thereby obtaining the central spectrum value of the test light source.

3) The present invention uses the optimized light source spectrum to perform batch testing on such optical fibers, which can obtain both faster testing efficiency and the most optimized testing results.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the structure of an optical fiber testing system of the present invention;

FIG2 is a flow chart of a light source spectrum optimization control method based on a carnivore foraging method;

Fig. 3 is a diagram of the demodulation of three fiber Bragg grating center wavelengths by the carnivore foraging method;

FIG4 is a diagram showing the average error of the central wavelengths of four fiber Bragg gratings demodulated;

FIG5 is a graph showing the average time consumption for demodulating the central wavelengths of four fiber Bragg gratings.

DETAILED DESCRIPTION

An optical fiber test system for optimizing light sources for power stations, the system includes a spectrometer, a fiber grating, a coupler, and an attenuator. The coupler is connected to multiple attenuators, each attenuator is connected to a fiber grating, and n fiber grating branches are connected to the spectrometer. The coupler is used to divide the light generated by the light source into n paths, which enter each fiber grating branch in the system respectively;

The spectrometer is used to receive the reflected light reflected by the fiber grating to form a reflection spectrum.

Fiber Bragg grating is equivalent to forming a narrowband (transmission or reflection) filter or reflector in the core of the optical fiber.

The attenuator is used to adjust the peak value of the reflected light reflected by the fiber grating.

Through the above system, the optical fiber testing system can generate optical fiber testing light sources with different spectra.

The spectrometer adopts Zeeman S2000 general-purpose spectrometer;

The fiber Bragg grating adopts YIBOHNB-FRC-310-I.

The coupler uses EB USB Hi-Speed SC-SC coupler.

The attenuator used is 5WSMA-KK 0-30dB 0-3GHz attenuator.

In the present invention, the light source is connected to an operating module for controlling the start and pause of the light source emission, and the operating module adopts an STM32F103RCT6 single chip microcomputer. The light source spectrum optimization control program is installed in the memory of the single chip microcomputer.

The structure diagram of the optical fiber test system is shown in Figure 1. In the optical fiber test system, the light emitted by the light source enters n branches through the coupler, and then is reflected back through the fiber grating. The attenuator can adjust the reflection peak, and finally form a reflection spectrum in the spectrum module. The photoelectric converter obtains the change in external conditions by identifying the reflection spectrum. Through the fiber grating spectrum shape multiplexing in the spectrum module, the spectrum shape information is collected, and the photoelectric converter is converted and processed to obtain the sensor measurement information. When the fiber grating senses a change in external conditions, the spectrum shape remains unchanged, and the wavelength drifts to a certain extent.

In this fiber optic test system, the light emitted by the light source enters n fiber grating branches through the coupler and is then reflected back through the fiber grating. The attenuator is used to adjust the peak value of the reflected light reflected by the fiber grating, and finally forms a reflection spectrum in the spectrometer. The change in external conditions is obtained by identifying the reflection spectrum.

Assume that the reflection spectrum of the i-th fiber Bragg grating is g _i (λ _Bi ), and the expression is:

Where: λ _Bi is the central wavelength of the i-th fiber Bragg grating; _ri is the reflectivity of the i-th fiber Bragg grating, _0≤ri≤1 ; _BG is the 3dB bandwidth, generally 0.2nm; _gi (λ _Bi ) is the i-th reflected power spectrum;

Then the superimposed spectrum in the spectrum module is expressed as:

Among them, R is the function of superimposed spectrum, that is, sampling spectrum; g is super Gaussian function; N is the random noise in the system; n is the number of fiber Bragg gratings in a group;

The spectrum in formula (2) may overlap completely, partially, or not overlap. In order to facilitate the demodulation of the central wavelength, the original spectrum is reconstructed. The reconstructed spectrum is expressed as:

Where, _si represents the central wavelength of the reconstructed spectrum of the i-th fiber Bragg grating;

Comparing R _v and R, the objective function of the degree of spectral overlap is expressed as:

f(s)＝[RR _v ] ² (4);

When s _i =λ _Bi , the objective function f(s) reaches the minimum value, and the process of solving the central wavelength is transformed into solving the optimal value. When the original spectrum and the reconstructed spectrum reach the optimal match, the central wavelength value is obtained, and the central wavelength value is used as the central spectrum value of the test light source.

As shown in FIG2 , the light source spectrum optimization control method based on the carnivore foraging method includes the following steps:

Step 1: Initialize the optical fiber test system.

Step 2: Evaluate all groups of carnivorous animals whose test optical fiber effectively responds to the original spectrum of the light source, and select the individual with the largest effective response value in the group, i.e., the leader.

Step 3: Based on the randomly disturbed wandering behavior of carnivores when preying, update the position of the probe according to equations (5) and (6), and determine the value of the probe fitness function. If _Yi > _Ylead , update the leader of the group; otherwise, repeat the wandering behavior until the number of wandering times reaches the maximum number _Tmax , and then execute step 4.

β＝[1-2rand()] (6);

Among them, β takes an integer, as a random perturbation, taking 0, 1 or -1 to randomly perturb the wandering behavior of the probe to improve the optimization range and speed; is the probe position at the next moment; x _id is the probe position at the current moment; is the walking step length of the probe in the d-dimensional space; p is the number of directions the current probe is walking in; h is the total number of directions the probe can walk in; rand() is a random number between 0 and 1.

Step 4: Summoning behavior:

Update the attacker's position according to formula (7) and determine the attacker's fitness function value. If _Yi >Y _lead , update the leader. Otherwise, when the distance between the attacker and the leader is less than d _near according to formula (8), execute step 5.

In formula (7), is the attacker’s running step length in d-dimensional space. is the position of the k-th generation leader in the d-dimensional space. The position of the attacker at the next moment, The attacker's position at the current moment.

In formula (8), w is the distance determination factor. D represents the dimension of the wandering space; d represents the dimension of the current space; d _max represents the maximum value of the space dimension range; d _min represents the minimum value of the space dimension range.

Step 5: Besiege behavior: Besiege the prey according to formula (9). Determine the fitness function value of the attacker. If _Yi >Y _lead , execute step 6.

In formula (9), is the attack step length in the d-dimensional space, and λ is a random number between [-1,1]. The attacker's position at the next moment; is the attacker's current position, The current position of the leader can be considered as the position of the prey.

Step 6: Calculate the probability judgment standard according to formula (10). If rand() < γ, attacker i replaces the leader; otherwise, execute step 5.

Among them, γ≤1, _Yi represents the function fitness of the current attacker, and Y _lead represents the function fitness of the current leader.

If rand() < γ, attacker i replaces the leader, otherwise the replacement operation is not performed. Through the simulated annealing algorithm judgment process, attackers with fitness greater than the leader are subjected to simulated annealing inspection to prevent the algorithm from falling into a local optimum.

Step 7, Carnivore group elimination and update operation: select individuals with high fitness in the carnivore group as leaders, eliminate individuals with low fitness and supplement them.

Step 8: Determine whether the number of iterations has been reached. If the conditions are met, output the optimal solution; otherwise, execute step 3.

The light source spectrum optimization control method can optimize the spectrum response of the optical fiber including the photoelectric converter tested by the optical fiber test system, that is, the original spectrum generated by the test light source and the reconstructed spectrum obtained after the photoelectric converter are optimally matched to obtain the central spectrum value of the test light source. Then, the optimized light source spectrum is used to perform batch testing on the optical fiber, which can obtain both faster test efficiency and the most optimized test results.

Example:

(I) Overlap of two fiber Bragg grating spectra:

The experimental platform was built according to Figure 1. The first group of experiments used fiber Bragg grating 1 and fiber Bragg grating 2 sensors, whose central wavelengths were 1541.398nm and 1541.743nm respectively. Fiber Bragg grating 2 was placed in a room temperature environment of 25℃ and the temperature was kept constant; fiber Bragg grating 1 was placed in a constant temperature box and the temperature was adjusted to change between 25℃ and 70℃. The response attenuation reflectivity _r1 and _r2 were adjusted to be 1 and 0.82 respectively.

The spectrum was reconstructed using the carnivore foraging method. The central wavelength of each fiber Bragg grating was obtained by performing 10 operations at 25℃-70℃ and taking the average value.

Table 1 Demodulation of the central wavelength and error of two fiber Bragg gratings by the carnivore foraging method

As shown in Table 1, when the spectra of two fiber Bragg gratings overlap, the carnivore foraging algorithm can identify the central wavelength. Its identification error is within 5 pm, and the demodulation spectrum has high accuracy.

(ii) Overlap of 3 fiber Bragg grating spectra:

The second group of experiments used FBG 1, FBG 2, and FBG 3 sensors, whose central wavelengths were 1541.398nm, 1541.743nm, and 1542.223nm, respectively. FBG 1 was placed in a constant temperature box, and the temperature of the constant temperature box was adjusted to change between 25℃ and 70℃; FBG 2 was placed in a room temperature environment of 25℃, and the ambient temperature was kept constant; FBG 3 was placed in a constant temperature box, and the temperature of the constant temperature box was adjusted to change between 20℃ and 25℃. The attenuation reflectivity r ₁ , r _2, and r ₃ were adjusted to 1, 0.82, and 0.63, respectively.

The carnivore foraging algorithm was used to perform 10 operations and take the average value to obtain the central wavelength of each fiber Bragg grating. The demodulation result of the central wavelength is shown in Figure 3. According to Figure 3, the wavelength error of the demodulation effect of the carnivore foraging algorithm is small and has a certain linear relationship.

The average time consumption is shown in Table 2.

Table 2 Average time consumption of demodulating three fiber Bragg gratings by carnivore foraging method

As shown in Table 2, the demodulation time of the carnivore foraging algorithm is only about 2 to 4 seconds, which is fast.

(III) Spectral overlap of four fiber gratings:

The third group of experiments used FBG 1, FBG 2, FBG 3 and FBG 4 sensors, whose central wavelengths were 1541.398nm, 1541.743nm, 1542.223nm and 1542.751nm, respectively. Their adjusted attenuation reflectivities r ₁ , r ₂ , r ₃ and r ₄ were 1, 0.82, 0.63 and 0.4, respectively.

The carnivore foraging method was used to perform 10 operations under random temperature changes and the average value was taken to obtain the central wavelength of each fiber Bragg grating. The average demodulation error of the central wavelength is shown in Figure 4, and the average time consumption is shown in Figure 5.

From Figures 4 and 5, it can be seen that the wavelength error of the demodulated center of gravity of the carnivore foraging algorithm fluctuates around 3pm, and the error is small and stable; the algorithm runs fast, and the average time consumed is kept within 5s.

(IV) Overlap of multiple fiber Bragg grating spectra:

The overlap of 8, 12 and 20 fiber Bragg grating spectra was simulated. Based on the above experiments, fiber Bragg gratings with similar central wavelengths were used, and the experimental environment temperature was adjusted to make multiple fiber Bragg grating spectra completely overlap. The carnivore foraging method was used for demodulation. Ten operations were performed and the average value was taken to obtain the central wavelength of each fiber Bragg grating. The average time and average error are shown in Table 3.

Table 3 Average time and error of demodulating multiple fiber Bragg gratings by carnivore foraging method

It can be seen from Table 3 that as the number of fiber Bragg gratings increases, the precision and running speed of the optimization control method of the present invention still have high precision and speed.

Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

The present invention is described with reference to the flowchart and/or block diagram of the method, device (system), and computer program product according to the embodiment of the present invention. It should be understood that each process and/or box in the flowchart and/or block diagram, as well as the combination of the process and/or box in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

Claims (7)

1. A light source spectrum optimization control method is characterized in that: comprising a power station light source optimized fiber optic test system, the system comprising: the device comprises a spectrometer, a fiber grating, a coupler and an attenuator; the coupler is connected with a plurality of attenuators, each attenuator is connected with a fiber grating, and n fiber grating branches are connected with the spectrometer; light emitted by the light source enters n fiber bragg grating branches through the coupler, and then is reflected back through the fiber bragg gratings, and the attenuator is used for adjusting the peak value of the reflected light reflected by the fiber bragg gratings, so that a reflection spectrum is formed in the spectrometer;

the light source spectrum optimization control method comprises the following steps:

S1, evaluating all individual groups of carnivorous animals with effective response of a test optical fiber to an original spectrum of a light source, and generating an individual with the largest effective response value in the groups as a lead;

S2, based on random disturbance of the predator during predation, updating the position of the probe, judging the fitness function value of the probe, and if Y _i>Y_lead,Y_i represents the fitness of the current attacker, Y _lead represents the fitness of the current leader; updating the leader in the group; otherwise, repeating the walk behavior until the walk times reach the maximum times T _max, and executing the step S3;

s3, updating the position of the attack hand, judging the adaptability function value of the attack hand, and if Y _i>Y_lead is the same, updating the leader; otherwise, executing the step S4 when the distance that the attacker approaches the leader is less than d _near;

S4, carrying out enclosing attack on the prey, judging an attack hand fitness function value, and if Y _i>Y_lead is found, executing a step S5;

s5, calculating a probability judgment standard, and if rand () < gamma, rand () is a random number between 0 and 1; the attack hand i replaces the leader; otherwise, executing the step S4;

If rand () < gamma, replacing the leader by the attacker i, otherwise, not executing the replacement operation, and carrying out simulated annealing investigation on the attacker with the adaptability larger than that of the leader through a simulated annealing algorithm judging process;

s6, eliminating and updating individual groups of carnivorous animals: selecting an individual with high fitness in the individual group of carnivorous animals as a leader, eliminating and supplementing an individual with low fitness;

S7, judging whether iteration times are reached, and if the iteration times are met, outputting an optimal solution; otherwise, step S2 is performed.

2. The light source spectrum optimization control method according to claim 1, wherein: in the optical fiber test system, the reflection spectrum shape of the ith optical fiber grating is g _i(λ_Bi), and the expression is:

Wherein: lambda _Bi is the center wavelength of the ith fiber grating; r _i is the reflectivity of the ith fiber grating, r _i≤1;B_G is 3dB bandwidth, g _i(λ_Bi) the ith reflected power spectrum;

The superimposed spectrum in the spectrometer is expressed as:

Wherein R is a function of the superimposed spectrum, i.e. the sampled spectrum; g is a super Gaussian function; n is random noise in the system; n is the number of fiber gratings in a group;

reconstructing the spectrum in formula (2), the reconstructed spectrum being expressed as:

Wherein s _i represents the center wavelength of the reconstructed spectrum of the ith fiber grating;

Comparing R _v with R, the objective function of the spectrum overlap degree is expressed as:

f(s)＝[R-R_v]² (4)；

When s _i＝λ_Bi, the objective function f(s) takes the minimum value, the process of solving the center wavelength is converted into the process of solving the optimal value, when the original spectrum and the reconstructed spectrum reach the optimal matching, the center wavelength value is obtained, and then the center wavelength value is taken as the center spectrum value of the test light source.

3. The light source spectrum optimization control method according to claim 1, wherein: s2, updating the probe position according to the formula (5) and the formula (6):

β＝[1-2rand()] (6)；

the beta is an integer and is used as a random disturbance, 0,1 or-1 is taken to randomly disturb the walk behavior of the probe, and the optimizing range and speed are improved; The probe position is the next moment; x _id is the current time probe position; a step length of the probe in d-dimensional space; p is the number of the current probe travelling directions; h is the total number of the probes in the movable direction; the rand () is a random number between 0 and 1.

4. The light source spectrum optimization control method according to claim 1, wherein: s3, updating the attack hand position according to the formula (7),

In the formula (7), the amino acid sequence of the compound,To attack the hand's step size in d-dimensional space,For the position of the k-th generation leader in d-dimensional space,For the next moment attack the hand position,The position of the hand is attacked at the current moment.

5. The light source spectrum optimization control method according to claim 1, wherein: s3, when judging that the distance of the attacker approaching the leader is smaller than d _near according to the formula (8), executing the step S4;

In the formula (8), w is a distance judgment factor; d represents the dimension of the walk space; d represents the dimension of the current space; d _max represents the maximum value of the spatial dimension value range; d _min denotes the minimum value of the spatial dimension range of values.

6. The light source spectrum optimization control method according to claim 1, wherein: s4, carrying out the surrounding attack on the prey according to the formula (9):

in the formula (9), the amino acid sequence of the compound, For attack step in d-dimensional space, λ is a random number between [ -1,1 ]; the position of the next moment of the attack hand; to attack the current position of the hand, The position of the hunting object can be considered as the position of the leader at the current moment.

7. A light source spectrum optimization control method according to claim 3, wherein: s5, calculating probability judgment standards according to the formula (10),

Wherein, gamma is less than or equal to 1, Y _i represents the function fitness of the current attack hand, and Y _lead represents the function fitness of the current leader.