Radio frequency thermal therapy equipment data supervision system and method

Abstract

The invention discloses a radio frequency thermal therapy equipment data supervision system and method, and relates to the technical field of data management, and the method comprises the steps: obtaining image data, collecting the temperature value of each image at a corresponding moment, and extracting an image set in the image data in combination with a focus region and a probe; obtaining the generation time in the image set, collecting the parameter value of the transmitted electromagnetic wave, and obtaining the first feature degree of the image set according to the temperature value; obtaining a second feature degree of the image set according to the position of the probe in the generation time in the image set; and obtaining a total feature degree, classifying the image set into a corresponding classification level, and storing the image set according to the classification level. According to the method, the current image data is analyzed, the parameter value and the temperature corresponding to the image data are synthesized, the classification level of image data storage is judged, and the problems that an image without reference value occupies too much memory, and the difficulty in image extraction is large are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and particularly relates to a radio frequency hyperthermia equipment data supervision system and method. BACKGROUND

[0002] Radio frequency hyperthermia is a technology of generating heat by using high-frequency electromagnetic field to make the temperature of diseased tissue rise, so as to achieve the treatment purpose. With many advantages, radio frequency hyperthermia has become an important non-surgical treatment method in the fields of tumor treatment, pain management and rehabilitation physiotherapy. During the use of the radio frequency hyperthermia equipment, a large amount of image medical data is generated. Subsequently, by analyzing these image data, it is helpful to improve the operation process, and relevant personnel can also learn skills and operation specifications by watching the image data. Therefore, the image data needs to be saved in time.

[0003] However, in the radio frequency hyperthermia treatment, a large amount of image data is generated, among which there are a large amount of repeated and non-referential image data. If all the image data is stored without data screening and extraction, the large amount of repeated and non-referential image data will occupy a large amount of memory space, and when the image data information needs to be extracted, the extraction time and difficulty will also be increased. SUMMARY

[0004] The present application aims to provide a radio frequency hyperthermia equipment data supervision system and method to solve the problems in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] A radio frequency hyperthermia equipment data supervision method, comprising the following steps:

[0007] Step S100: acquiring image data generated in the use process of the radio frequency hyperthermia equipment, collecting the temperature value of each image at the corresponding time through the temperature sensor arranged at the position of the probe of the radio frequency hyperthermia equipment, and analyzing the corresponding lesion area in the image and the movement of the probe to extract the image set in the image data;

[0008] Step S200: acquiring the generation time corresponding to each image in the image set, collecting the parameter value of the electromagnetic wave emitted by the radio frequency hyperthermia equipment at the generation time, and obtaining the first characteristic degree corresponding to the image set according to the temperature value corresponding to the generation time;

[0009] Step S300: obtaining the corresponding target pixel and target time in the image set according to the position of the probe at the generation time in the image set; and obtaining the second characteristic degree corresponding to the image set based on the ratio of the number of target pixels to the number of pixels in the lesion area and the target time length;

[0010] Step S400: According to the first feature degree and the second feature degree of the image set, the total feature degree of the image set is obtained, the image set is classified into a corresponding classification level according to the total feature degree, and different storage is performed according to different classification levels.

[0011] Further, step S110: deploying a temperature sensor at the probe position of the radiofrequency hyperthermia device, collecting the temperature value of the radiofrequency hyperthermia device at each moment in the use process in real time by setting the frequency and power of the electromagnetic wave emitted by the probe; obtaining a pre-set normal temperature range [Q min , Q max ],Q min is the pre-set minimum normal temperature, and Q max is the pre-set maximum normal temperature.

[0012] Step S120: dividing the lesion region in each image data and the probe region corresponding to the probe position; starting from the first image Img1 in the time-continuous image data collected, searching backward until the mth image Img m is found which first satisfies the intersection of the probe region and the lesion region is not empty, and starting from the image Img m searching backward until the nth image Img n is found which first satisfies the intersection of the probe region and the lesion region is empty or the temperature value at the corresponding moment is not in the normal temperature range, 1≤m<n, then all images between the image Img m and the image Img n are combined into an image set, and then a plurality of image sets in the image data are obtained.

[0013] Further, step S210: obtaining the generation time corresponding to each image in a certain image set G, the parameter value of the electromagnetic wave emitted by the radiofrequency hyperthermia device includes frequency and power, randomly obtaining any two adjacent generation times T1 and T2 therefrom as a time combination X, wherein time T1 is earlier than time T2, and the frequencies corresponding to time T1 and time T2 are the same, both are H0, and then the power P1 and the temperature value Q1 corresponding to time T1 are obtained, and the power P2 and the temperature value Q2 corresponding to time T2 are obtained.

[0014] Step S220: obtaining the reference image data used for learning skills and operation specifications in history, extracting the reference image set S in the reference image data, the length of the time period D S corresponding to the reference image set S is greater than a pre-set length threshold, and the frequency corresponding to each moment in the time period D S is H0, and the power corresponding to each moment in the time period 0 to D0 is P1, and the power corresponding to each moment in the time period D0 to D s is P2.

[0015] Further, according to the temperature value corresponding to each time in the time period 0 to D0, an average value is obtained as a first reference temperature Q1 S , according to the temperature value corresponding to each time in the time period D0 to D s , an average value is obtained as a second reference temperature Q2 S ; then a plurality of reference image sets in the reference image data are extracted, and the first reference temperature and the second reference temperature corresponding to each reference image set are obtained, and the second reference temperature corresponding to the first reference temperature closest to the temperature value Q1 is taken as Q2 ^ , and further the reference temperature range [k1*Q2 ^ , k2*Q2 ^ ] is obtained, wherein k1 is a first reference ratio, k2 is a second reference ratio, 0 < k1 < 1 < k2;

[0016] Step S230: If the temperature value Q2 is within the reference temperature range, mark the time combination X; and further obtain a plurality of time combinations in the image set G, and take the ratio of the marked time combination as the first feature degree Z1 of the image set G.

[0017] In actual use, the radio frequency hyperthermia equipment may appear abnormal state, so the image of the equipment in the abnormal state cannot provide a favorable basis for the following judgment, so in the present scheme, the time when the equipment is not in an abnormal state needs to be selected, that is, the marked time combination in the present scheme, and the specific judgment basis is: during the operation of the radio frequency hyperthermia equipment, the parameter values of the electromagnetic wave emitted by the equipment include power and frequency, if the power and frequency are consistent with the reference image data used to learn skills and operation specifications, but the temperature change does not conform to the reference temperature range, it means that the equipment at this time is in an abnormal state, and the more abnormal states, the more adverse effects on subsequent calculation, resulting in unreliable calculation results, so the marked time combination needs to be selected, and the first feature degree of the image set is set.

[0018] Further, step S310: obtaining the position of each generated time probe in a certain image set G, and randomly obtaining any two adjacent generated times t1 and t2 as a time combination Y, and taking the distance between the position of the probe at time t1 and the position of the probe at time t2 as the target distance of the time combination Y; extracting M time adjacent time combinations in the image set G, and obtaining M target distances, wherein M is greater than a preset number threshold;

[0019] According to the M target distances, the corresponding variance and average value between the target distances are obtained, if the variance is less than the preset variance threshold, and the average value is less than the preset distance average threshold, the time between the first frame image and the Mth frame image is taken as the target time, and the first frame image and the Mth frame image are converted into gray images to obtain a gray image Img 1 and the gray image Img M , the difference between the gray values in the gray image Img 1 and the gray image Img M is greater than the preset gray difference threshold, and the pixels in the lesion area corresponding to the image set G are taken as target pixels;

[0020] Step S320: Further, the total target time D G in the image set G is obtained, and all the target pixels corresponding in the image set G are obtained, the number of target pixels is taken as N1, and according to the number of lesion area pixels N2, the second feature degree corresponding to the image set G is obtained: Wherein, k0 is a numerical adjustment coefficient.

[0021] Further, the first feature degree Z1 and the second feature degree Z2 corresponding to the image set G are obtained, and according to the preset weight of the first feature degree and the second feature degree, the total feature degree of the image set G is obtained, and the image set G is classified into a corresponding classification level based on the total feature degree, and the image set G is stored according to the classification level.

[0022] A radio frequency hyperthermia equipment data supervision system, comprising an image set extraction module, a first feature degree calculation module, a second feature degree calculation module and an image set storage module;

[0023] The image set extraction module is used to acquire image data generated by the radio frequency hyperthermia equipment during use, collect temperature values at each image corresponding time through temperature sensors deployed at the probe position of the radio frequency hyperthermia equipment, and analyze the corresponding lesion area in the image and the movement of the probe to extract the image set in the image data;

[0024] The first feature degree calculation module is used to acquire the generation time corresponding to each image in the image set, collect parameter values of electromagnetic waves emitted by the radio frequency hyperthermia equipment at the generation time, and obtain the first feature degree corresponding to the image set according to the temperature value corresponding to the generation time;

[0025] The second feature degree calculation module is used to obtain the target pixels and the target time corresponding in the image set according to the position of the probe at the generation time in the image set; based on the ratio of the number of target pixels to the number of lesion area pixels and the target time length, the second feature degree corresponding to the image set is obtained;

[0026] The image set storage module is configured to obtain a total feature degree of the image set according to the first feature degree and the second feature degree of the image set, classify the image set into a corresponding classification level according to the total feature degree, and store the image set according to different classification levels.

[0027] Further, the first feature degree calculation module comprises a time combination determination unit, a reference temperature range determination unit, and a first feature degree calculation unit.

[0028] The time combination determination unit is configured to obtain a generation time corresponding to each image in the image set, obtain a parameter value of the electromagnetic wave emitted by the radio frequency hyperthermia device, the parameter value comprising a frequency and a power, and randomly obtain any two adjacent generation times as a time combination.

[0029] The reference temperature range determination unit is configured to obtain reference image data used for learning skills and operation specifications, extract a reference image set in the reference image data, obtain a first reference temperature and a second reference temperature corresponding to each reference image set, and further obtain a reference temperature range.

[0030] The first feature degree calculation unit is configured to determine whether to mark the time combination according to the reference temperature range, obtain a plurality of time combinations in the image set, and obtain a proportion of the marked time combination as the first feature degree of the image set.

[0031] Further, the second feature degree calculation module comprises a target pixel analysis unit and a second feature degree calculation unit.

[0032] The target pixel analysis unit is configured to obtain a position of a probe at each generation time in the image set, obtain a time combination, obtain a target distance of the time combination, and further obtain a target time and a target pixel corresponding to the image set.

[0033] The second feature degree calculation unit is configured to obtain a total target time in the image set, and all target pixels corresponding to the image set, and obtain a second feature degree corresponding to the image set.

[0034] Compared with the prior art, the present application has the beneficial effects that the present application provides a radio frequency hyperthermia equipment data supervision system and method, which comprises the following steps: acquiring image data, collecting temperature values corresponding to each image at a moment, and combining a lesion area and a probe to extract an image set in the image data; acquiring a generation time in the image set, collecting parameter values of emitted electromagnetic waves, and obtaining a first characteristic degree of the image set according to the temperature values; obtaining a second characteristic degree of the image set according to the position of the probe at the generation time; and then obtaining a total characteristic degree, classifying the image set into a corresponding classification level, and storing the image set according to the classification level. The present application analyzes the current image data, comprehensively judges the classification level of the image data storage by combining the corresponding parameter values and temperatures of the image data, effectively solves the problem of excessive memory occupation of images without reference value, and the problem of great difficulty in extracting images. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is a flowchart of the radio frequency hyperthermia equipment data supervision method of the present application.

[0036] Figure 2 It is a structural diagram of the radio frequency hyperthermia equipment data supervision system of the present application. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0038] Embodiment: As shown in the figure, the present application provides a radio frequency hyperthermia equipment data supervision method technical solution, which comprises the following steps: Figure 1

[0039] Step S100: Acquire image data generated by the radio frequency hyperthermia equipment during use, collect temperature values corresponding to each image at a moment through temperature sensors arranged at the position of the probe of the radio frequency hyperthermia equipment, and extract an image set in the image data by analyzing the corresponding lesion area in the image and the movement of the probe;

[0040] Step S110: Deploy temperature sensors at the position of the probe of the radio frequency hyperthermia equipment, collect temperature values at each moment during use of the radio frequency hyperthermia equipment in real time by setting the frequency and power of electromagnetic waves emitted by the probe, acquire a pre-set normal temperature range [Q min ,Q max ], Q min is the pre-set minimum normal temperature, and Q​max is a preset maximum normal temperature;

[0041] Step S120: dividing the lesion region in each image data and the probe region corresponding to the probe position; starting from the first image Img1 in the time-continuous image data collected, searching backward until the mth image Img m is found which first satisfies that the probe region and the lesion region intersect non-empty, and starting from the image Img m searching backward until the nth image Img n is found which first satisfies that the probe region and the lesion region intersect empty, or the temperature value corresponding to the moment is not in the normal temperature range, 1≤m<n, then all images between the image Img m and the image Img n are combined into an image set, and then a plurality of image sets in the image data are obtained.

[0042] Step S200: obtaining the generation time corresponding to each image in the image set, collecting the parameter value of the electromagnetic wave emitted by the radiofrequency hyperthermia device at the generation time, and obtaining the first characteristic degree corresponding to the image set according to the temperature value corresponding to the generation time;

[0043] Step S210: obtaining the generation time corresponding to each image in a certain image set G, the parameter value of the electromagnetic wave emitted by the radiofrequency hyperthermia device including frequency and power, randomly obtaining any two adjacent generation times T1 and T2 as a time combination X, wherein the time T1 is earlier than the time T2, and the frequencies corresponding to the time T1 and the time T2 are the same, both being H0, and then obtaining the power P1 and the temperature value Q1 corresponding to the time T1, and the power P2 and the temperature value Q2 corresponding to the time T2;

[0044] Step S220: obtaining the reference image data used for learning skills and operation specifications in history, extracting the reference image set S in the reference image data, the reference image set S corresponding to a time period D S with a length greater than a preset length threshold, and the frequency corresponding to each moment in the time period D S is H0, and the power corresponding to each moment in the time period 0 to D0 is P1, and the power corresponding to each moment in the time period D0 to D s is P2;

[0045] then averaging the temperature values corresponding to each moment in the time period 0 to D0 to obtain a first reference temperature Q1 S , and averaging the temperature values corresponding to each moment in the time period D0 to D s to obtain a second reference temperature Q2 S; then extract a plurality of reference image sets in the reference image data, and obtain a first reference temperature and a second reference temperature corresponding to each reference image set, and take the second reference temperature corresponding to the first reference temperature closest to the temperature value Q1 as Q2 ^ , and further obtain a reference temperature range of [k1*Q2 ^ , k2*Q2 ^ ], wherein k1 is a first reference ratio, k2 is a second reference ratio, and 0<k1<1<k2;

[0046] Step S230: If the temperature value Q2 is within the reference temperature range, mark the time combination X; and further obtain a plurality of time combinations in the image set G, and take the proportion of the marked time combination as the first feature degree Z1 of the image set G.

[0047] In actual use, the radio frequency hyperthermia equipment may appear abnormal state, so the image of the equipment in the abnormal state cannot provide a favorable basis for the following judgment, therefore, in the present scheme, the time when the equipment is not in an abnormal state, that is, the marked time combination in the present scheme, needs to be selected, and the specific judgment basis is: during the operation of the radio frequency hyperthermia equipment, the parameter values of the electromagnetic wave emitted by the equipment include power and frequency, if the power and frequency are consistent with the reference image data used to learn skills and operation specifications, but the temperature change does not conform to the reference temperature range, it indicates that the equipment at this moment is in an abnormal state, and the more abnormal states, the more adverse effects on the subsequent calculation, resulting in unreliable calculation results, therefore, the marked time combination needs to be selected, and the first feature degree of the image set is set.

[0048] Step S300: According to the position of the probe in the image set at the generation time, obtain the corresponding target pixel and target time in the image set; based on the ratio of the number of target pixels to the number of pixels in the lesion area and the target time length, obtain the second feature degree corresponding to the image set;

[0049] Step S310: Obtain the position of the probe at each generation time in a certain image set G, and randomly obtain any two adjacent generation times t1 and t2 from them as a time combination Y, and take the distance between the position of the probe at time t1 and the position of the probe at time t2 as the target distance of the time combination Y; extract M time-adjacent time combinations in the image set G, and obtain M target distances, wherein M is greater than a preset number threshold;

[0050] According to the M target distances, the variance and the average value corresponding to the target distances are obtained, if the variance is less than a preset variance threshold value, and the average value is less than a preset distance average threshold value, the time between the first frame of image and the Mth frame of image is taken as the target time, and the first frame of image and the Mth frame of image are converted into gray scale images to obtain a gray scale image Img 1 and the gray scale image Img M , the difference between the gray scale values in the gray scale image Img 1 and the gray scale image Img M is greater than a preset gray scale difference threshold value, and the pixel in the lesion area corresponding to the image set G is taken as the target pixel;

[0051] Step S320: Further, the total target time D G in the image set G is obtained, and all the target pixels corresponding to the image set G are obtained, the number of target pixels is taken as N1, and according to the number of pixels N2 in the lesion area, the second feature degree corresponding to the image set G is obtained: Wherein, k0 is a numerical adjustment coefficient.

[0052] In the present scheme, the first feature degree and the second feature degree represent the value of the image set G, and the greater the first feature degree and the second feature degree, the higher the value of the image set G; since the formula y = 1-e -x When x takes a value of x≥0, y takes a value of [0,1), which is a function of y growing with x, so when D G is greater, the second feature degree Z2 is also greater, since the number of pixels N2 in the lesion area is constant, when the number of target pixels N1 is greater, the second feature degree Z2 is also greater, that is, the treatment time in the image set G is longer, and the treatment area is larger, which indicates that the image set G is more valuable.

[0053] Step S400: According to the first feature degree and the second feature degree of the image set, the total feature degree of the image set is obtained, the image set is classified into a corresponding classification level according to the total feature degree, and different storage is performed according to different classification levels.

[0054] Step S400 includes: obtaining the first feature degree Z1 and the second feature degree Z2 corresponding to a certain image set G, and obtaining the total feature degree of the image set G according to the preset weight values of the first feature degree and the second feature degree, and classifying the image set G into a corresponding classification level based on the total feature degree, and storing the image set G according to the classification level.

[0055] In the embodiment, the weights of the first feature degree and the second feature degree are respectively W1 and W2, and W1+W2=1, since the value ranges of the first feature degree and the second feature degree are both 0 to 1, then the total feature degree Z of the image set G is Z=W1×Z1+W2×Z2. Since the value range of the total feature degree Z is also 0 to 1, then in the embodiment, when the total feature degree Z is 0 to 0.4, the image set G is classified into a low-value classification level, and is regularly cleaned, stored lightly or filtered out quickly; when the total feature degree Z is 0.4 to 0.7, the image set G is classified into a medium-value classification level, and is reserved and extracted for use as needed; when the total feature degree Z is 0.7 to 1, the image set G is classified into a high-value classification level, and is stored for a long time, and is analyzed with high precision and deeply mined for value.

[0056] The application further provides a radio frequency hyperthermia equipment data supervision system, as shown in the figure, comprising an image set extraction module, a first feature degree calculation module, a second feature degree calculation module and an image set storage module. Figure 2

[0057] The image set extraction module is used for acquiring image data generated by the radio frequency hyperthermia equipment in the use process, collecting temperature values at each image corresponding time through temperature sensors arranged at the probe positions of the radio frequency hyperthermia equipment, and analyzing the corresponding lesion regions in the images and the movement of the probe to extract the image set in the image data.

[0058] The first feature degree calculation module is used for acquiring the generation time corresponding to each image in the image set, collecting parameter values of electromagnetic waves emitted by the radio frequency hyperthermia equipment at the generation time, and obtaining the first feature degree corresponding to the image set according to the temperature values corresponding to the generation time.

[0059] The second feature degree calculation module is used for obtaining the corresponding target pixel and target time in the image set according to the position of the probe at the generation time in the image set, and obtaining the second feature degree corresponding to the image set based on the ratio of the target pixel quantity to the pixel quantity of the lesion region and the target time length.

[0060] The image set storage module is used for obtaining the total feature degree of the image set according to the first feature degree and the second feature degree of the image set, classifying the image set into a corresponding classification level according to the total feature degree, and storing the image set according to different classification levels.

[0061] ​It will be apparent to those skilled in the art that the application is not limited to the details of the above-exemplified embodiments and that the present application can be implemented in other particular forms without departing from the spirit or essential characteristics of the present application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the above description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. No reference signs in the claims should be considered as limiting the scope of the claims with respect to the figures of the patent document.

Claims

1. A method for monitoring radiofrequency hyperthermia equipment data, characterized in that: The following steps are involved: Step S100: Obtaining image data generated by the radiofrequency hyperthermia device during use, using a temperature sensor deployed at the probe position of the radiofrequency hyperthermia device to collect the temperature value at the corresponding moment of each image, and analyzing the corresponding lesion area in the image and the movement of the probe to extract an image set from the image data; Step S200: obtaining a generation time corresponding to each image in the image set, collecting parameter values ​​of electromagnetic waves emitted by the radiofrequency hyperthermia device at the generation time, and obtaining a first characteristic degree corresponding to the image set based on the temperature value corresponding to the generation time; Step S300: obtaining the corresponding target pixel and target time in the image set according to the position of the probe at the generation time in the image set; Based on the ratio of the number of target pixels to the number of pixels in the lesion area and the target time duration, a second characteristic degree corresponding to the image set is obtained; Step S400: Obtain the total feature degree of the image set according to the first feature degree and the second feature degree of the image set, classify the image set into corresponding classification levels according to the total feature degree, and store them differently according to different classification levels.

2. A radiofrequency hyperthermia equipment data monitoring method according to claim 1, characterized in that: Step S100 includes: Step S110: Deploy a temperature sensor at the probe position of the radiofrequency hyperthermia device, and collect the temperature value of the radiofrequency hyperthermia device at every moment during use by setting the frequency and power of the electromagnetic wave emitted by the probe; obtain the preset normal temperature range [Q min ,Q max ], Q min is the preset minimum normal temperature, Q max is the preset maximum normal temperature; Step S120: Divide the lesion regions in each image data and the probe regions corresponding to the probe positions; starting from the first frame of image Img1 in a number of continuously acquired image data, search backward until the m-th frame of image Img is found that first satisfies the condition that the intersection of the probe region and the lesion region is not empty. m and start searching backward from the image Img m until the n-th frame of image Img is found that first satisfies the condition that the intersection of the probe region and the lesion region is empty or the temperature value at the corresponding moment is not within the normal temperature range. n , where 1 ≤ m < n, then combine all the images between the image Img m and the image Img n into an image set, and thus obtain several image sets in the image data.

3. The method for monitoring radiofrequency hyperthermia equipment data according to claim 1, characterized in that: Step S200 includes: Step S210: Obtain the generation time corresponding to each image in a certain image set G. The parameter values ​​of the electromagnetic waves emitted by the radiofrequency hyperthermia device include frequency and power. Randomly obtain any two adjacent generation times T1 and T2 as the time combination X, where time T1 is earlier than time T2, and the frequencies corresponding to time T1 and time T2 are the same, both H0. Then, obtain the power P1 and temperature Q1 corresponding to time T1, and the power P2 and temperature Q2 corresponding to time T2; Step S220: Obtain historical benchmark image data used for learning skills and operating specifications, extract a benchmark image set S from the benchmark image data, and the benchmark image set S corresponds to the time period D S The duration of D is greater than the preset duration threshold, and the time period D S The frequency corresponding to each moment in the time period is H0, and the power corresponding to each moment in the time period from 0 to D0 is P1. s The power corresponding to each moment is P2; Then, according to the temperature values ​​corresponding to each moment in time period 0 to D0, the average value is calculated as the first reference temperature Q1 S , according to the time period D0 to D s The temperature value corresponding to each moment is averaged and used as the second reference temperature Q2 S Then extract several reference image sets from the reference image data, and obtain the first reference temperature and the second reference temperature corresponding to each reference image set, and take the second reference temperature corresponding to the first reference temperature closest to the temperature value Q1 as Q2 ^ , and then the reference temperature range is [k1*Q2 ^ ,k2*Q2 ^ ], where k1 is the first reference ratio, k2 is the second reference ratio, 0 <k1<1<k2; Step S230 : If the temperature value Q2 is within the reference temperature range, mark the time combination X; then obtain several time combinations in the image set G, and use the proportion of the marked time combinations as the first characteristic level Z1 of the image set G.

4. The method for monitoring radiofrequency hyperthermia equipment data according to claim 1, characterized in that: Step S300 includes: Step S310: Obtain the position of the probe at each generation time in a certain image set G, and randomly obtain any two adjacent generation times t1 and t2 as a time combination Y, and use the distance between the probe position at time t1 and the probe position at time t2 as the target distance of the time combination Y; extract M time combinations with adjacent time in the image set G, and obtain M target distances, where M is greater than a preset number threshold; According to the M target distances, the corresponding variance and average value between the target distances are obtained. If the variance is less than the preset variance threshold and the average value is less than the preset distance mean threshold, the time between the 1st frame image and the Mth frame image is taken as the target time, and the 1st frame image and the Mth frame image are converted into grayscale images to obtain the grayscale image Img 1 and grayscale image Img M , where the grayscale value is in the grayscale image Img 1 and grayscale image Img M The difference between the two images is greater than the preset grayscale difference threshold, and the pixel in the lesion area corresponding to the image set G is taken as the target pixel; Step S320: Obtain the total target time D in the image set G G , and all corresponding target pixels in the image set G, the number of target pixels is taken as N1, and according to the number of pixels in the lesion area N2, the second characteristic degree corresponding to the image set G is obtained: Among them, k0 is the numerical adjustment coefficient.

5. The method for monitoring radiofrequency hyperthermia equipment data according to claim 1, characterized in that: Step S400 includes: obtaining the first feature degree Z1 and the second feature degree Z2 corresponding to a certain image set G, and obtaining the total feature degree of the image set G according to the pre-set weights of the first feature degree and the second feature degree, and based on the total feature degree, classifying the image set G into the corresponding classification level, and storing the image set G according to the classification level.

6. A radio frequency hyperthermia equipment data monitoring system, used to execute a radio frequency hyperthermia equipment data monitoring method according to any one of claims 1 to 5, characterized in that: The system includes an image set extraction module, a first feature degree calculation module, a second feature degree calculation module and an image set storage module; Image collection extraction module: used to obtain image data generated by the radiofrequency hyperthermia device during use. The temperature sensor deployed at the probe position of the radiofrequency hyperthermia device collects the temperature value at the corresponding moment of each image, and analyzes the corresponding lesion area in the image and the movement of the probe to extract the image collection from the image data. A first characteristic degree calculation module is configured to obtain a generation time corresponding to each image in the image set, collect parameter values ​​of electromagnetic waves emitted by the radiofrequency hyperthermia device at the generation time, and obtain a first characteristic degree corresponding to the image set based on a temperature value corresponding to the generation time; The second feature degree calculation module is used to obtain the corresponding target pixel and target time in the image set according to the position of the probe at the generation time in the image set; Based on the ratio of the number of target pixels to the number of pixels in the lesion area and the target time duration, a second characteristic degree corresponding to the image set is obtained; Image set storage module: used to obtain the total feature degree of the image set according to the first feature degree and the second feature degree of the image set, classify the image set into corresponding classification levels according to the total feature degree, and store them differently according to different classification levels.

7. A radiofrequency hyperthermia equipment data monitoring system according to claim 6, characterized in that: The first characteristic degree calculation module includes a time combination determination unit, a reference temperature range determination unit and a first characteristic degree calculation unit; A time combination determination unit is used to obtain the generation time corresponding to each image in the image set. The parameter values ​​of the electromagnetic waves emitted by the radiofrequency hyperthermia device include frequency and power. Any two adjacent generation times are randomly obtained as the time combination. Reference temperature range determination unit: used to obtain historical reference image data used for learning skills and operating specifications, and extract a reference image set from the reference image data; Obtaining a first reference temperature and a second reference temperature corresponding to each reference image set, and then obtaining a reference temperature range; A first characteristic degree calculation unit is used to determine whether to mark the time combination according to the reference temperature range; A number of time combinations in the image set are obtained, and the proportion of the marked time combinations is used as the first characteristic degree of the image set.

8. The radiofrequency hyperthermia equipment data monitoring system according to claim 6, characterized in that: The second feature degree calculation module includes a target pixel analysis unit and a second feature degree calculation unit; Target pixel analysis unit: used to obtain the location of each generation time probe in the image set, and obtain the time combination, obtain the target distance of the time combination, and then obtain the target time and target pixel corresponding to the image set; The second feature degree calculation unit is used to obtain the total target time in the image set and all corresponding target pixels in the image set, and obtain the second feature degree corresponding to the image set.

Images