A method for adaptively adjusting and controlling the intensity of physical therapy light

Abstract

The present invention discloses a method for adaptively regulating and controlling the intensity of physiotherapy light, and relates to the technical field of optical instruments. The method includes: constructing a first relationship mapping model between scattered output power and photosensor light intensity and a second relationship mapping model between human epidermal light intensity and photosensor light intensity; determining a target photosensor light intensity parameter group according to a target human epidermal light intensity parameter group and a second relationship mapping model; determining a sensor light intensity error parameter group according to the target photosensor light intensity parameter group and a real-time photosensor light intensity parameter group; judging whether scattered output power adjustment needs to be performed according to the sensor light intensity error parameter group; if necessary, determining a target scattered output power parameter group according to the first relationship mapping model; and determining a light source power according to the target scattered output power parameter group. The present invention realizes real-time monitoring and adjustment of light source power, ensures that the light intensity received by various parts of the human body is always within the optimal range, and improves the convenience and safety of light therapy.

Description

Technical Field

[0001] The present invention relates to the technical field of optical instruments, and in particular to a method for adaptively regulating and controlling the intensity of physiotherapy light. Background Art

[0002] LED red light therapy technology is widely used in different fields, including medical treatment, skin care and sports medicine. In the medical field, it is often used in physical therapy and rehabilitation to assist in the treatment of various diseases. In the beauty industry, this technology is used to promote collagen production and improve skin quality; in sports medicine, athletes use red light therapy to speed up recovery and reduce sports injuries.

[0003] Traditional phototherapy equipment is often based on a constant light source power output, and it is difficult to adaptively adjust the light intensity according to the real-time action area of ​​the human body. The operation is complicated and the accuracy is not high. In addition, different patients have different skin characteristics and treatment area requirements, so phototherapy equipment needs to be able to adaptively adjust in real time. If the light intensity is too high, it may cause skin burns or discomfort; if the light intensity is too low, the treatment effect may not be achieved. The above factors have put forward higher requirements on the light intensity of physical therapy light equipment. Summary of the invention

[0004] The present invention provides a method for adaptively adjusting and controlling the intensity of physiotherapy light to solve at least one of the problems mentioned in the above background technology.

[0005] The specific technical solutions provided by this application are as follows:

[0006] A method for adaptively adjusting and controlling the intensity of physiotherapy light, comprising the steps of:

[0007] Constructing a first relationship mapping model between scattered output power and photosensor light intensity and a second relationship mapping model between human epidermal light intensity and photosensor light intensity;

[0008] Determine a target photosensor light intensity parameter group according to a target human epidermal light intensity parameter group and a second relationship mapping model;

[0009] Determine a sensor light intensity error parameter group according to the target photosensitive sensor light intensity parameter group and the real-time photosensitive sensor light intensity parameter group; determine whether it is necessary to perform scattered output power adjustment according to the sensor light intensity error parameter group;

[0010] If it is necessary to perform scattered output power adjustment, determining a target scattered output power parameter group according to the first relationship mapping model;

[0011] The light source power is determined according to the target scattered output power parameter group.

[0012] As a preferred solution, the first relationship mapping model between the scattered output power and the photosensor light intensity is constructed by the following steps:

[0013] Setting a scattered output power test parameter group; the scattered output power test parameter group includes a plurality of scattered output powers;

[0014] Select a diffuser, and calibrate the linear influence parameters of the selected diffuser on the photosensors of each second photosensor group; repeat the process until the linear influence parameters of all diffusers on the photosensors of all second photosensor groups have been calibrated;

[0015] A first relationship mapping model is constructed according to all linear influence parameters; the first relationship mapping model stores the linear influence parameters of each diffuser on the photosensors of each second photosensor group.

[0016] As a preferred solution, the calibration of the selected diffuser for each second photosensitive sensor The linear influence parameters of the group's photosensors are specifically executed in a loop:

[0017] Select a scattered output power in the scattered output power test parameter group; set the scatterer to work under the selected scattered output power state, and obtain the photosensor light intensity corresponding to each photosensor of the second photosensor group;

[0018] If all scattered output powers in the scattered output power test parameter group have been calibrated, linear influence parameters are fitted according to all scattered output powers and photosensor light intensity data, and the cycle ends.

[0019] As a preferred solution, the second relationship mapping model between human epidermal light intensity and photosensor light intensity is constructed by the following steps:

[0020] A first photosensitive sensor group and a second photosensitive sensor group are provided; the first photosensitive sensor group and the second photosensitive sensor group respectively include a plurality of photosensitive sensors;

[0021] Acquire the light intensity of the photosensor of the first photosensor group and the light intensity of the photosensor of the second photosensor group;

[0022] Construct and train a neural network model, wherein the input of the neural network model is configured as the photosensored light intensity of the first photosensor group, and the output is configured as the photosensored light intensity of the second photosensor group; and construct a second relationship mapping model based on the neural network model.

[0023] As a preferred embodiment, the second relationship mapping model includes a light intensity distance compensation model and a neural network model; the light intensity distance compensation model is configured to receive the target human epidermal light intensity parameter group as input, and calculate the photosensor light intensity of the first photosensor group as output; the neural network model is configured to receive the photosensor light intensity of the first photosensor group as input, and calculate the photosensor light intensity of the second photosensor group as output.

[0024] As a preferred solution, the step of determining the target photosensor light intensity parameter group according to the target human epidermal light intensity parameter group and the second relationship mapping model comprises the following steps:

[0025] When the user is lying down, the distance between the human epidermis and the scatterer at each physiotherapy monitoring position is obtained through the distance measuring sensor group;

[0026] Based on the light intensity distance compensation model, the light intensity of the photosensor at each physical therapy monitoring position is calculated according to the distance between the human epidermis and the scatterer at each physical therapy monitoring position and the target light intensity parameter group of the photosensor;

[0027] The photosensor light intensity of each physical therapy monitoring position is input into the neural network model to obtain the ideal value of the photosensor light intensity of the second photosensor group and record it as the target photosensor light intensity parameter group.

[0028] As a preferred solution, the light intensity distance compensation model is expressed as:

[0029] ,

[0030] Wherein, i represents the number of the physical therapy monitoring position, and is also the number of the photosensor in the first photosensor group; represents the human epidermal light intensity corresponding to the i-th physiotherapy monitoring position, Indicates the light intensity of the photosensor at the i-th physiotherapy monitoring position; represents the distance between the photosensor and the diffuser at the i-th physiotherapy monitoring position, Represents the distance between the human epidermis and the scatterer at the i-th physiotherapy monitoring position.

[0031] As a preferred solution, the step of determining the target scattered output power parameter group according to the first relationship mapping model comprises the following steps:

[0032] Set the initial value of the scattered output power parameter group;

[0033] Set the particle swarm parameters according to the initial value of the scattering output power parameter group, and randomly initialize the position and velocity of each particle;

[0034] Calculate the objective function for each particle;

[0035] Update the personal best position of each particle and the group best position of the particle group;

[0036] Update the velocity and position of each particle according to the position constraints, the individual best position and the group best position;

[0037] Iterate until the maximum number of iterations is reached or the objective function value is less than the set threshold, and output the optimal position of the particle swarm as the target scattering output power parameter group.

[0038] As a preferred solution, the objective function is expressed as:

[0039] ,

[0040] ,

[0041] ,

[0042] ,

[0043] in, represents the target photosensitive sensor light intensity of the jth photosensitive sensor, represents the light intensity of the jth photosensitive sensor of the current particle; L represents the number of scatterers, k represents the number of scatterers, represents the kth scatterer output power of the current particle, represents a linear influence parameter of the kth scatterer and the jth photosensor of the second photosensor group; represents the difference between the target photosensitive sensor light intensity of the j-th photosensitive sensor and the photosensitive sensor light intensity of the j-th photosensitive sensor of the current particle, and They are the preset difference sum weight parameter and variance weight parameter respectively.

[0044] As a preferred solution, after constructing the first relationship mapping model between the scattered output power and the photosensor light intensity and the second relationship mapping model between the human epidermal light intensity and the photosensor light intensity, it also includes constructing a third relationship mapping model between the light source output power and the scattered output power; the third relationship mapping model is a polynomial regression model;

[0045] Determining the light source power compensation value according to the target scattered output power parameter group is specifically determining the light source power according to the target scattered output power parameter group and a third mapping relationship model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present application discloses an adaptive adjustment and control method for therapeutic light intensity, which determines a target photosensor light intensity parameter group through a target human epidermal light intensity parameter group and a second relationship mapping model, determines a target scattered output power parameter group according to the first relationship mapping model, and determines a light source power according to the target scattered output power parameter group, thereby achieving real-time monitoring and adjustment of the light source power, ensuring that the light intensity received by various parts of the human body is always within an optimal range; achieving automatic adjustment, reducing human intervention, and improving the convenience and safety of light therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0050] Figure 1 A schematic flow chart of a method for adaptively adjusting and controlling the intensity of physiotherapy light provided in an embodiment of the present invention;

[0051] Figure 2 A schematic flow chart of a first relationship mapping model between scattered output power and photosensor light intensity provided in an embodiment of the present invention;

[0052] Figure 3 A schematic flow chart of a second relationship mapping model between human epidermal light intensity and photosensor light intensity provided in an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of a process for determining a target photosensor light intensity parameter group according to a target human epidermal light intensity parameter group and a second relationship mapping model provided in an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of a flow chart of determining a target scattered output power parameter group according to the first relationship mapping model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0057] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0058] LED red light therapy technology is widely used in different fields, including medical treatment, skin care and sports medicine. In the medical field, it is often used in physical therapy and rehabilitation to assist in the treatment of various diseases. In the beauty industry, this technology is used to promote collagen production and improve skin quality; in sports medicine, athletes use red light therapy to speed up recovery and reduce sports injuries.

[0059] LED red light therapy is a modern technology based on the treatment of red light with a specific wavelength (usually in the range of 600-900 nanometers). Its equipment design uses high-intensity light-emitting diodes (LEDs) to ensure the effectiveness of the light while taking into account uniform irradiation to avoid heat concentration and thus protect the skin.

[0060] The treatment principle mainly involves photobiomodulation. Red light can penetrate the skin and act on the mitochondria of cells to promote the production of ATP, thereby increasing cell metabolism and promoting tissue repair and regeneration. During the design process of the equipment, the optimal light-emitting wavelength will be selected according to the treatment goal to ensure the effect. When applied, LED red light therapy can be used for local targeted irradiation or whole-body irradiation, and the specific choice depends on the treatment needs. For example, local irradiation is suitable for relieving pain and promoting wound healing, while whole-body irradiation can be used for overall recovery and beauty. The treatment plan will include frequency (such as the number of treatments per day or week) and the duration of each irradiation (usually 10-30 minutes) to maximize the treatment effect.

[0061] Traditional phototherapy equipment is often based on a constant light source power output, and it is difficult to adaptively adjust the light intensity according to the real-time action area of ​​the human body. The operation is complicated and the accuracy is not high. In addition, different patients have different skin characteristics and treatment area requirements, so phototherapy equipment needs to be able to adaptively adjust in real time. If the light intensity is too high, it may cause skin burns or discomfort; if the light intensity is too low, the treatment effect may not be achieved. The above factors have put forward higher requirements on the light intensity of physical therapy light equipment.

[0062] To solve the above problems, please refer to Figure 1 The present invention provides a method for adaptively adjusting and controlling the intensity of physiotherapy light, comprising the steps of:

[0063] Constructing a first relationship mapping model between scattered output power and photosensor light intensity and a second relationship mapping model between human epidermal light intensity and photosensor light intensity;

[0064] Determine a target photosensor light intensity parameter group according to a target human epidermal light intensity parameter group and a second relationship mapping model;

[0065] Determine a sensor light intensity error parameter group according to the target photosensitive sensor light intensity parameter group and the real-time photosensitive sensor light intensity parameter group; determine whether it is necessary to perform scattered output power adjustment according to the sensor light intensity error parameter group;

[0066] If it is necessary to perform scattered output power adjustment, determining a target scattered output power parameter group according to the first relationship mapping model;

[0067] The light source power is determined according to the target scattered output power parameter group.

[0068] The present application discloses an adaptive adjustment and control method for therapeutic light intensity, which determines a target photosensor light intensity parameter group through a target human epidermal light intensity parameter group and a second relationship mapping model, determines a target scattered output power parameter group according to the first relationship mapping model, and determines a light source power according to the target scattered output power parameter group, thereby achieving real-time monitoring and adjustment of the light source power, ensuring that the light intensity received by various parts of the human body is always within an optimal range; achieving automatic adjustment, reducing human intervention, and improving the convenience and safety of light therapy.

[0069] Specifically, the steps of a method for adaptively adjusting and controlling the intensity of physiotherapy light according to the present invention are described in detail through the following contents:

[0070] A method for adaptively adjusting and controlling the intensity of physiotherapy light, comprising the steps of:

[0071] S1, constructing a first relationship mapping model between scattered output power and photosensor light intensity and a second relationship mapping model between human epidermal light intensity and photosensor light intensity;

[0072] The method for adaptively adjusting and controlling the intensity of physiotherapy light in this embodiment involves a physiotherapy device designed as a cabin structure in the application scenario. The physiotherapy device includes a plurality of light sources and diffusers connected one by one with the light sources, and the diffusers are installed on the top of the cabin. Based on the cabin structure, the patient can comfortably enter and remain in a lying state; when the physiotherapy device is started, the specific wavelength red light provided by the plurality of light sources is uniformly output through the corresponding diffusers, covering the target area where the patient lies, and realizing whole-body or local physiotherapy light.

[0073] For further information, see Figure 2 The first relationship mapping model between the scattered output power and the photosensor light intensity is constructed by the following steps:

[0074] S111, setting a scattered output power test parameter group; the scattered output power test parameter group includes a plurality of scattered output powers; in one embodiment, the sequence of the plurality of scattered output powers in the scattered output power test parameter group is an arithmetic progression;

[0075] S112, selecting a scatterer, and calibrating the linear influence parameters of the selected scatterer on the photosensors of each second photosensor group; repeating the process until the linear influence parameters of all scatterers on the photosensors of all second photosensor groups have been calibrated;

[0076] Furthermore, the calibration of the linear influence parameters of the selected diffuser on the photosensors of each second photosensor group is specifically performed in a loop:

[0077] Select a scattered output power in the scattered output power test parameter group; set the scatterer to work under the selected scattered output power state, and obtain the photosensor light intensity corresponding to each photosensor of the second photosensor group;

[0078] If all scattered output powers in the scattered output power test parameter group have been calibrated, linear influence parameters are fitted according to all scattered output powers and photosensor light intensity data, and the cycle ends.

[0079] The light sensors in the second light sensor group are installed near the positions corresponding to the head, torso and limbs of the user in the cabin, and ensure that the second light sensor group is not blocked when the user is in use.

[0080] S113. Construct a first relationship mapping model according to all linear influence parameters; the first relationship mapping model stores the linear influence parameters of each diffuser on each photosensor of each second photosensor group.

[0081] The first relationship mapping model obtained by this embodiment is specifically a linear influence parameter mapping table, which uses the scatterer numbers as rows and the photosensitive sensor numbers of the second photosensitive sensor group as columns. The linear influence parameters are recorded as:

[0082] ,

[0083] Wherein, k represents the diffuser number, j represents the sensor number in the second photosensitive sensor group; represents the scattered output power of the kth scatterer, Represents the photosensor light intensity of the jth photosensor.

[0084] For further information, see Figure 3 The second relationship mapping model between the human epidermal light intensity and the photosensor light intensity is constructed by the following steps:

[0085] S121. Set up a first photosensor group and a second photosensor group; the first photosensor group and the second photosensor group respectively include a number of photosensors; the photosensors in the first photosensor group are respectively installed at the physiotherapy monitoring positions in the cabin, and the physiotherapy monitoring positions are set and numbered by professionals based on prior knowledge; the photosensors in the second photosensor group are installed in the cabin near the positions corresponding to the head, torso and limbs when the user is in use, and ensure that the second photosensor group is not blocked when the user is in use.

[0086] S122, obtaining the light intensity of the photosensors of the first photosensor group and the light intensity of the photosensors of the second photosensor group;

[0087] S123, constructing and training a neural network model, wherein the input of the neural network model is configured as the photosensored light intensity of the first photosensor group, and the output is configured as the photosensored light intensity of the second photosensor group; constructing a second relationship mapping model according to the neural network model.

[0088] As a preferred embodiment, the second relationship mapping model includes a light intensity distance compensation model and a neural network model; the light intensity distance compensation model is configured to receive the target human epidermal light intensity parameter group as input, and calculate the photosensor light intensity of the first photosensor group as output; the neural network model is configured to receive the photosensor light intensity of the first photosensor group as input, and calculate the photosensor light intensity of the second photosensor group as output.

[0089] During the process of building and training the neural network model, the first photosensor group used is installed at the bottom of the therapy chamber, and the scatterer is installed at the top of the therapy chamber. There is an error in the distance between the first photosensor group and the scatterer and the distance between the illuminated human epidermis when the user is lying down and the scatterer. Therefore, the photosensor light intensity of the first photosensor group cannot accurately represent the human epidermis light intensity at the corresponding therapy monitoring position. In order to compensate for the error between the photosensor light intensity of the first photosensor group obtained by the neural network model during actual application and the human epidermis light intensity at the corresponding therapy monitoring position, this embodiment compensates the output of the neural network model through a light intensity distance compensation model, and the light intensity distance compensation model is constructed through the relationship between light intensity and distance.

[0090] Based on the above, the light intensity distance compensation model is expressed as:

[0091] ,

[0092] Wherein, i represents the number of the physical therapy monitoring position, and is also the number of the photosensor in the first photosensor group; represents the human epidermal light intensity corresponding to the i-th physiotherapy monitoring position, Indicates the light intensity of the photosensor at the i-th physiotherapy monitoring position; represents the distance between the photosensor and the diffuser at the i-th physiotherapy monitoring position, Represents the distance between the human epidermis and the scatterer at the i-th physiotherapy monitoring position.

[0093] Among them, the distance between the human epidermis and the scatterer at each physical therapy monitoring position is obtained through a ranging sensor group, which is installed on the top of the cabin. The number of ranging sensor groups is the same as that of physical therapy monitoring positions and they correspond one to one. The line connecting the ranging sensor and the corresponding physical therapy monitoring position is perpendicular to the bottom plane of the cabin.

[0094] S2, determining a target photosensor light intensity parameter group according to the target human epidermis light intensity parameter group and the second relationship mapping model;

[0095] The target human epidermal light intensity parameter group refers to the light intensity that needs to be achieved when the light source irradiates the human epidermis during phototherapy. Since the target human epidermal light intensity parameter group directly affects the efficacy and safety, it needs to be precisely controlled.

[0096] Based on the thickness, sensitivity, tissue structure and treatment goals of human skin, the required light intensity for each part is different. For example, the stratum corneum on the back and soles of the feet is thicker and more tolerant, so a higher light intensity can be used appropriately. The skin around the eyes and lips is more sensitive and easily irritated, so a lower light intensity is required. For example, when removing pigment spots and treating erythema, the absorption characteristics of the target area are different, and the required light intensity is also different. The target human surface light intensity involved in this embodiment depends on the setting of professionals based on prior knowledge.

[0097] For further information, see Figure 4 The method of determining the target photosensitive sensor light intensity parameter group according to the target human epidermal light intensity parameter group and the second relationship mapping model comprises the steps of:

[0098] S21, when the user is lying down, obtaining the distance between the human epidermis and the scatterer at each physiotherapy monitoring position through the distance measuring sensor group;

[0099] S22, based on the light intensity distance compensation model, calculating the light intensity of the photosensor at each physical therapy monitoring position according to the distance between the human epidermis and the scatterer at each physical therapy monitoring position and the target photosensor light intensity parameter group;

[0100] The target human body surface light intensity is specifically a group of light intensity parameter groups stored according to the numbers of the physical therapy monitoring positions, expressed as .

[0101] The light intensity distance compensation model is expressed as:

[0102] ,

[0103] Wherein, i represents the number of the physical therapy monitoring position, and is also the number of the photosensor in the first photosensor group; represents the human epidermal light intensity corresponding to the i-th physiotherapy monitoring position, Indicates the light intensity of the photosensor at the i-th physiotherapy monitoring position; represents the distance between the photosensor and the diffuser at the i-th physiotherapy monitoring position, Represents the distance between the human epidermis and the scatterer at the i-th physiotherapy monitoring position.

[0104] S23, input the light intensity of the photosensors at each physiotherapy monitoring position into the neural network model, obtain the ideal value of the light intensity of the photosensors of the second photosensor group and record it as the target light intensity parameter group of photosensors. The target light intensity parameter group of photosensors is expressed as , M represents the number of photosensors in the second photosensor group.

[0105] S3, determining a sensor light intensity error parameter group according to the target photosensitive sensor light intensity parameter group and the real-time photosensitive sensor light intensity parameter group; judging whether it is necessary to perform scattered output power adjustment according to the sensor light intensity error parameter group;

[0106] Based on the target photosensitive sensor light intensity parameter group obtained in step S2 and the collected real-time photosensitive sensor light intensity, a sensor light intensity error parameter group is calculated. The sensor light intensity error parameter group is expressed as: ,in, ; represents the sensing light intensity error of the jth photosensor; represents the target photosensitive sensing light intensity of the jth photosensitive sensor; Represents the real-time photosensor light intensity of the jth photosensor.

[0107] After obtaining the sensor light intensity error parameter group, calculate the ratio of the sensor light intensity error of each photosensitive sensor to the target photosensitive sensor light intensity The error rate is recorded as error rate. If the error rate of at least one photosensitive sensor is greater than the ratio threshold set by the photosensitive sensor, the scattered output power needs to be adjusted. The ratio threshold set for each photosensitive sensor can be the same ratio threshold, or different ratio thresholds can be set according to the sensitivity of each part.

[0108] S4. If it is necessary to perform scattered output power adjustment, determine a target scattered output power parameter group according to the first relationship mapping model;

[0109] As a preferred embodiment, please refer to Figure 5 The step of determining the target scattered output power parameter group according to the first relationship mapping model is specifically determining the target scattered output power parameter group according to the first relationship mapping model based on a particle swarm optimization algorithm, including the steps of:

[0110] S41, setting the initial value of the scattered output power parameter group;

[0111] The scattered output power parameter group includes the scattered output power corresponding to each scatterer; the initial value of the scattered output power parameter group may be the scattered output power corresponding to each current scatterer.

[0112] S42, setting the particle swarm parameters according to the initial value of the scattering output power parameter group, and randomly initializing the position and speed of each particle; the position of each particle is used to represent a parameter group.

[0113] The particle swarm parameters include the number of particles, inertia weight, learning factor, maximum number of iterations and parameter range of the parameter group; wherein the parameter range is to set the value range of each parameter in combination with the initial value of the scattering output power parameter group, so that each parameter in the initial value of the scattering output power parameter group is located at an appropriate position within the value range, such as the center of the value range.

[0114] The position of a particle corresponds to a point in the solution space, representing a potential solution to the optimization problem. For this embodiment, the position of a particle represents a scattering output power parameter group, which can also be interpreted as a vector containing parameter values ​​of multiple scattering output powers. The velocity of the particle is used to determine the moving direction and speed of the particle in the solution space. The velocity is also a vector, which has the same dimension as the position vector, i.e., the number of scatterers, and each component represents the rate of change of the corresponding parameter. If the velocity component of the particle is positive, it means that the parameter value in this dimension will increase; if the component is negative, the parameter value will decrease.

[0115] S43, calculating the objective function of each particle;

[0116] The objective function is expressed as:

[0117] ,

[0118] ,

[0119] ,

[0120] ,

[0121] in, represents the target photosensitive sensor light intensity of the jth photosensitive sensor, represents the light intensity of the jth photosensitive sensor of the current particle; L represents the number of scatterers, k represents the number of scatterers, represents the kth scatterer output power of the current particle, represents a linear influence parameter of the kth scatterer and the jth photosensor of the second photosensor group; represents the difference between the target photosensitive sensor light intensity of the j-th photosensitive sensor and the photosensitive sensor light intensity of the j-th photosensitive sensor of the current particle, and They are the preset difference sum weight parameter and variance weight parameter respectively.

[0122] Based on the difference sum weight parameter and the variance weight parameter, the objective function of this embodiment can weigh the difference sum of the photosensitive sensing light intensity of each particle and the discreteness of the difference, thereby guiding the iteration direction of the particles and the performance of the final result.

[0123] S44, updating the personal best position of each particle and the group best position of the particle group.

[0124] The personal best position refers to the position where the particle has the smallest objective function value among the positions it has experienced in history. Each particle will save this information during the search process as its best position in history. In each iteration of the particle swarm algorithm, the current particle position is compared with the personal best position. If the current position is better, the personal best position is updated to the current position.

[0125] The group best position refers to the position with the smallest objective function value among the individual best positions of all particles in the entire particle swarm. The group best position is global information, and all particles in the particle swarm will use the group best position to update their own speed and position. Similar to the individual best position, if a better position is found in each iteration, the group best position will be updated.

[0126] S45. Update the speed and position of each particle according to the position constraint, the individual best position and the group best position.

[0127] The position constraint is set according to the ratio threshold set by each photosensitive sensor; the position constraint is used to constrain the error rate of each photosensitive sensor in response to the scattered output power parameter group represented by the particle. The ratio threshold value set by the photosensitive sensor is not greater than that of the particle. This ensures that the solution represented by the particle is always feasible during the entire search process, avoiding the generation of invalid solutions that do not conform to the actual situation, thus meeting the actual needs and limitations of the problem.

[0128] The velocity of the particle is expressed as:

[0129] ,

[0130] The position of the particle is expressed as:

[0131] ,

[0132] in, is the inertia weight, which represents the influence of the velocity of the previous generation of particles on the velocity of the current generation of particles. and They are the first acceleration constant and the second acceleration constant, respectively. The first acceleration constant is used to control the amplitude of the particle moving toward the individual optimal position, and the second acceleration constant is used to control the amplitude of the particle moving toward the group optimal position. is the individual best position of the particle, and g is the group best position of the group to which the particle belongs. and are all random numbers in the interval [0,1], used to provide randomness for each iteration. h is the number of the particle, represents the position of the hth particle at time t, represents the velocity of the hth particle at time t.

[0133] As a preferred embodiment, the inertia weight is expressed as:

[0134] ,

[0135] in, Indicates the maximum inertia weight set, Indicates the minimum inertia weight set, Indicates the current iteration number, Indicates the maximum number of iterations.

[0136] S46, iterating until the maximum number of iterations is reached or the objective function value is less than a set threshold, and outputting the optimal position of the particle swarm as the target scattering output power parameter group.

[0137] S5. Determine the light source power according to the target scattered output power parameter group.

[0138] In this embodiment, the function of the diffuser is to evenly distribute the red light of a specific wavelength emitted by the light source to the target area where the patient lies. The light source output power refers to the light power emitted by the light source itself, and the light source provides the basic red light energy; the diffuser output power refers to the power output after the light passes through the diffuser, and the diffuser is responsible for diffusing and evenly distributing the light to cover the patient's target area. When entering or passing through the diffuser, part of the light will be reflected back due to interface reflection, or the diffuser material absorbs part of the light energy and converts it into heat energy or other forms of energy instead of outputting it as visible light. Therefore, there is a loss error between the diffuser output power and the light source power.

[0139] As a preferred embodiment, step S1, after constructing the first relationship mapping model between the scattered output power and the light intensity of the photosensor and the second relationship mapping model between the human epidermal light intensity and the photosensor light intensity, also includes constructing a third relationship mapping model between the light source output power and the scattered output power; the third relationship mapping model is a polynomial regression model. In the process of constructing the polynomial regression model, for the red light source of the selected wavelength, the light source power is gradually adjusted from the lowest to the highest, the scattered output power at each power is recorded, and then the scattered output power is measured using a light intensity meter; then, the third relationship mapping model between the light source power and the scattered output power is fitted using a polynomial regression analysis method. For the acquired third relationship mapping model, the accuracy of the model is verified by actually measuring the scattered output power at different light source power values.

[0140] Based on the foregoing embodiment, determining the light source power compensation value according to the target scattered output power parameter group is specifically determining the light source power according to the target scattered output power parameter group and a third mapping relationship model.

[0141] In the embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the modules can be electrical, mechanical or other forms.

[0142] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0143] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software functional modules.

[0144] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

[0145] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for adaptively adjusting and controlling the intensity of physiotherapy light, characterized in that: Includes steps: Constructing a first relationship mapping model between scattered output power and photosensor light intensity and a second relationship mapping model between human epidermal light intensity and photosensor light intensity; Determine a target photosensor light intensity parameter group according to a target human epidermal light intensity parameter group and a second relationship mapping model; Determine a sensing light intensity error parameter group according to the target light sensitive sensing light intensity parameter group and the real-time light sensitive sensing light intensity parameter group; Determine whether to perform scattered output power adjustment according to the sensor light intensity error parameter group; If it is necessary to perform scattered output power adjustment, determining a target scattered output power parameter group according to the first relationship mapping model; Determining the light source power according to the target scattered output power parameter group; The second relationship mapping model between the human epidermal light intensity and the photosensor light intensity is constructed by the following steps: A first photosensitive sensor group and a second photosensitive sensor group are provided; the first photosensitive sensor group and the second photosensitive sensor group respectively include a plurality of photosensitive sensors; Acquire the light intensity of the photosensor of the first photosensor group and the light intensity of the photosensor of the second photosensor group; Construct and train a neural network model, wherein the input of the neural network model is configured as the photosensored light intensity of the first photosensor group, and the output is configured as the photosensored light intensity of the second photosensor group; and construct a second relationship mapping model based on the neural network model.

2. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 1, characterized in that: The first relationship mapping model between the scattered output power and the photosensor light intensity is constructed by the following steps: Setting a scattered output power test parameter group; the scattered output power test parameter group includes a plurality of scattered output powers; Select a diffuser, and calibrate the linear influence parameters of the selected diffuser on the photosensors of each second photosensor group; repeat the process until the linear influence parameters of all diffusers on the photosensors of all second photosensor groups have been calibrated; A first relationship mapping model is constructed according to all linear influence parameters; the first relationship mapping model stores the linear influence parameters of each diffuser on the photosensors of each second photosensor group.

3. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 2, characterized in that: The calibration of the linear influence parameters of the selected diffuser on the photosensors of each second photosensor group is specifically performed in a loop: Select a scattered output power in the scattered output power test parameter group; set the scatterer to work under the selected scattered output power state, and obtain the photosensor light intensity corresponding to each photosensor of the second photosensor group; If all scattered output powers in the scattered output power test parameter group have been calibrated, linear influence parameters are fitted according to all scattered output powers and photosensor light intensity data, and the cycle ends.

4. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 1, characterized in that: The second relationship mapping model includes a light intensity distance compensation model and a neural network model; the light intensity distance compensation model is configured to receive the target human epidermal light intensity parameter group as input, and calculate the photosensor light intensity of the first photosensor group as output; the neural network model is configured to receive the photosensor light intensity of the first photosensor group as input, and calculate the photosensor light intensity of the second photosensor group as output.

5. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 4, characterized in that: Determining the target photosensitive sensor light intensity parameter group according to the target human epidermal light intensity parameter group and the second relationship mapping model comprises the steps of: When the user is lying down, the distance between the human skin and the scatterer at each physiotherapy monitoring position is obtained through the distance measuring sensor group; Based on the light intensity distance compensation model, the light intensity of the photosensor at each physical therapy monitoring position is calculated according to the distance between the human epidermis and the scatterer at each physical therapy monitoring position and the target light intensity parameter group of the photosensor; The photosensor light intensity of each physiotherapy monitoring position is input into the neural network model to obtain the ideal value of the photosensor light intensity of the second photosensor group and record it as the target photosensor light intensity parameter group.

6. A method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 4, characterized in that: The light intensity distance compensation model is expressed as: ； Wherein, i represents the number of the physical therapy monitoring position, and is also the number of the photosensor in the first photosensor group; represents the human epidermal light intensity corresponding to the i-th physiotherapy monitoring position, Indicates the light intensity of the photosensor at the i-th physiotherapy monitoring position; represents the distance between the photosensor and the diffuser at the i-th physiotherapy monitoring position, Represents the distance between the human epidermis and the scatterer at the i-th physiotherapy monitoring position.

7. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 1, characterized in that: The step of determining a target scattered output power parameter group according to the first relationship mapping model comprises the following steps: Set the initial value of the scattered output power parameter group; Set the particle swarm parameters according to the initial value of the scattering output power parameter group, and randomly initialize the position and velocity of each particle; Calculate the objective function for each particle; Update the personal best position of each particle and the group best position of the particle group; Update the velocity and position of each particle according to the position constraints, the individual best position and the group best position; Iterate until the maximum number of iterations is reached or the objective function value is less than the set threshold, and output the optimal position of the particle swarm as the target scattering output power parameter group.

8. A method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 7, characterized in that: The objective function is expressed as: ， ， ， ； in, represents the target photosensitive sensor light intensity of the jth photosensitive sensor, represents the light intensity of the jth photosensitive sensor of the current particle; L represents the number of scatterers, k represents the number of scatterers, represents the kth scatterer output power of the current particle, represents a linear influence parameter of the kth scatterer and the jth photosensor of the second photosensor group; represents the difference between the target photosensitive sensor light intensity of the j-th photosensitive sensor and the photosensitive sensor light intensity of the j-th photosensitive sensor of the current particle, and are respectively a preset difference sum weight parameter and a variance weight parameter; M represents the number of photosensors in the second photosensor group.

9. The method for adaptively adjusting and controlling the intensity of physiotherapy light according to claim 1, characterized in that: After constructing the first relationship mapping model between the scattered output power and the photosensor light intensity and the second relationship mapping model between the human epidermal light intensity and the photosensor light intensity, the method further includes constructing a third relationship mapping model between the light source output power and the scattered output power; the third relationship mapping model is a polynomial regression model; Determining the light source power according to the target scattered output power parameter group is specifically determining the light source power according to the target scattered output power parameter group and a third relationship mapping model.

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