CN117398231A - Medical liquid storage device temperature control system and method based on data analysis - Google Patents
Medical liquid storage device temperature control system and method based on data analysis Download PDFInfo
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Abstract
The invention relates to the technical field of temperature control, in particular to a medical liquid storage device temperature control system and method based on data analysis, wherein the specific method comprises the following steps: collecting abdominal cavity depth data and weight data of a patient, and recording physical state data of the patient, thermal treatment environment temperature data and working data of a medical liquid storage device in real time in a thermal treatment process; collecting an infrared radiation image of the abdominal cavity of a patient in real time; the infrared radiation image is input into a neural network model after background threshold segmentation treatment, and gradient division marking is carried out on a temperature region; constructing a temperature data fitting model, and fitting temperature prediction curves of different temperature areas in real time; and judging gray value risks according to different temperature areas, and performing temperature control treatment on the medical liquid storage device. The invention solves the problems of uneven temperature regulation and control and poor predictability of temperature control of the medical liquid storage device caused by large measurement error of the temperature in the patient in the hyperthermia therapy in the prior art.
Description
Technical Field
The invention relates to the technical field of temperature control, in particular to a medical liquid storage device temperature control system and method based on data analysis.
Background
Thermal perfusion is also known as hyperthermia, and in some cancer treatment methods, thermal perfusion is used to heat and infuse an anticancer drug, and by heating the drug to a high temperature and then perfusing it into local tumor tissue, the permeability and thermal sensitivity of the drug can be enhanced, improving the therapeutic effect. Hyperthermia is also often used in organ protection, where oxygen and nutrients can be provided by heating and perfusing protective solutions into the organ, promoting cell survival, and reducing organ ischemia reperfusion injury following allogeneic organ transplantation. The temperature control of the medical liquid storage device in the thermotherapy treatment often affects the key factors of the curative effect of the medicine.
In the prior art, as disclosed in the patent with application publication number CN114740924a, a method, a device, an apparatus and a storage medium for controlling the temperature of a dialysate are disclosed, including obtaining a first temperature value, a second temperature value, a third temperature value, a first state and a historical heating state; determining a first temperature difference value and a second temperature difference value according to the first temperature value, the second temperature value and the third temperature value, wherein the first temperature difference value is used for representing the temperature difference value between the third temperature value and the first temperature value, and the second temperature difference value is used for representing the difference value between the second temperature value and the first temperature value; determining a current heating state of the dialysate according to the first temperature difference, the second temperature difference, the first state and the historical heating state; and determining the power level of the heating element according to the first temperature difference value, the second temperature difference value and the current heating state. The logic of the invention is easy to understand, easy to realize, easy to adjust the parameters, and test and verify for different heating states.
The above patent determines the power level of the heating element by the temperature difference and the heating state, but does not consider the environment, and the influence of the patient on the temperature of the medical liquid has the problems of inaccurate temperature control and poor predictability of temperature control.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The invention aims to solve the technical problems that in the prior art, the measurement accuracy of the temperature in a patient is insufficient, the error is larger, the tolerance of different organ areas of different patients to the temperature of the liquid medicine is different, the temperature distribution of the liquid medicine is uneven in the thermal therapy process, the temperature control step of a medical liquid storage device in the prior art is simple, the temperature rising treatment is usually carried out when the temperature is detected to be reduced by a temperature measuring point in the thermal cycle process, the curative effect of the medicine is reduced by the temperature rising time difference generated in the middle, and the problem of poor predictability of temperature control exists in the prior art, so the temperature control system and the temperature control method of the medical liquid storage device based on data analysis are provided.
In order to achieve the above purpose, the technical scheme of the medical liquid storage device temperature control method based on data analysis of the invention comprises the following steps:
s1: collecting abdominal cavity depth data and weight data of a patient, and recording physical state data of the patient, thermal treatment environment temperature data and working data of a medical liquid storage device in real time in a thermal treatment process;
s2: the infrared camera is used for collecting infrared radiation images of the abdominal cavity of the patient in real time, and continuously monitoring the temperature distribution data of the abdominal cavity thermotherapy of the patient;
s3: the method comprises the steps of performing background threshold segmentation on an infrared radiation image of an abdominal cavity of a patient acquired in real time, inputting the image into a neural network model, and performing gradient division marking on a temperature region;
s4: constructing a temperature data fitting model, and fitting temperature prediction curves of different temperature areas in real time;
s5: and judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermotherapy process, and performing temperature control treatment on the medical liquid storage device.
Specifically, the patient physical state data includes: real-time body temperature, heart rate data, blood pressure data, blood oxygen saturation and abdominal blood flow velocity of a patient.
Specifically, in S1, the collecting of the working data of the medical liquid storage device includes: collecting real-time temperature data T of inflow liquid medicine at a time point T by placing a temperature sensor at the liquid medicine inlet of the medical liquid storage device at the liquid medicine inlet 1,t The method comprises the steps of carrying out a first treatment on the surface of the The middle part of the liquid medicine is collected by placing a temperature sensor at the center of the height of the column body of the medical liquid storage device, and the real-time temperature data T of the stored liquid medicine is obtained when the time point is T 2,t The method comprises the steps of carrying out a first treatment on the surface of the The temperature sensor is arranged at the liquid medicine outlet of the medical liquid storage device to collect real-time temperature data T of the flowing liquid medicine at the liquid medicine outlet when the time point is T 3,t 。
Specifically, the continuous monitoring of the temperature distribution data of the abdominal cavity thermotherapy of the patient comprises the following specific steps:
s201: placing an infrared thermal imager at a position which is two meters away from the thermal therapy workbench, wherein the placement height of a camera of the infrared thermal imager is one meter higher than the thermal therapy workbench, and the placement angle is thirty degrees downwards;
s202: collecting infrared radiation images of the abdominal cavity of a patient every 5 minutes;
s203: and (3) carrying out image denoising, size adjustment and normalization treatment on the infrared radiation image of the abdominal cavity of the patient.
Specifically, the step S3 includes the following specific steps:
s301: graying an infrared radiation image of the abdominal cavity of a patient acquired in real time, wherein the gray value of a pixel point with the temperature of i is R (i);
s302: according to S301, performing threshold segmentation processing on the infrared radiation image subjected to graying processing to obtain a common X-level temperature monitoring area;
s303: and constructing a neural network model, and sequentially marking the region grades of different temperature regions according to the gray value sum of all rows of the temperature regions from small to large through the neural network model.
Specifically, in S302, the segmentation rule of the threshold segmentation process is as follows:
s321: the lower left corner of the infrared radiation image of the abdominal cavity of the patient is marked as the origin of coordinates, a coordinate system is constructed, wherein the width of the infrared radiation image of the abdominal cavity of the whole patient is L, each interval distance c is marked as one row, the infrared radiation image is divided into J rows from bottom to top, J is an integer, and the maximum value is
S322: calculating the gray average value of all pixel points in each row, wherein P j The gray average value of the J-th row is 1-J;
s323: when the gray difference value phi between the adjacent rows is smaller than the segmentation threshold value K, the two adjacent rows are divided into the same-level temperature areas, wherein the gray difference value phi is calculated according to the following strategy: phi=p j -P j-1 。
Specifically, the functional expression of the temperature prediction curve is as follows:
wherein W is x,t The predicted temperature of the x-th-stage temperature monitoring area at the time point t is obtained;
L 1 in the thermal therapy equipment, the liquid medicine outlet of the medical liquid storage device is connected with the length of an infusion pipeline of the abdominal cavity of a patient; l (L) 2,x The abdominal cavity depth of the x-th stage temperature monitoring area of the patient;
λ 1 ,λ 2 the temperature loss coefficient of the thermotherapy transfusion pipeline and the temperature loss coefficient of the abdominal cavity channel of the patient are respectively;
v is the expected arrival time of the liquid medicine from the liquid medicine outlet to the first-stage temperature monitoring area;
θ is a heat exchange interference coefficient to the external environment in the process of patient thermotherapy;
T t is the body temperature of the patient at time t; t (T) t ' is the temperature of the thermal therapy environment at the time point tDegree.
Specifically, the gray value risk judgment for different temperature areas includes:
s501: extracting gray average values of different temperature areas;
s502: when the gray average value of the x-th level temperature monitoring area is smaller than or equal to one time or larger than the risk level difference A twice consecutively 1 When the temperature monitoring area is monitored, the temperature monitoring is continued;
s503: when the gray average value of the x-th level temperature monitoring area is continuously larger than the risk first-level difference value A three times 1 And is less than or equal to the risk secondary difference A 2 And when the temperature prediction curve is used, the interaction interface pops up a slight risk warning, returns to the step S4, records the monitoring time point as Z at the moment, predicts the target temperature through the function of the temperature prediction curve, automatically triggering to control the temperature rise or the temperature reduction of the medical liquid storage device according to the target temperature value, and performing safe repeated verification after the interval time V;
s504: when the gray average value of the x-th level temperature monitoring area is larger than the risk second level difference value A for the first time 2 And when the medical staff is in use, the interactive interface pops up a major risk warning, gives an alarm and requests the medical staff to perform manual temperature adjustment operation.
Specifically, in S503, the secure duplicate verification includes:
after the interval time V, repeatedly executing the step S5 to judge the gray value risk;
when the gray average value of the x-th-level temperature monitoring area is smaller than or equal to the gray average value, continuing to monitor the temperature of the temperature monitoring area;
when the gray average value of the x-th level temperature monitoring area is larger than the first-level risk difference A 1 At this time, step S504 is performed.
In addition, the medical liquid storage device temperature control system based on data analysis comprises the following modules:
the system comprises a central control module, a data acquisition module, an image acquisition module, a neural network module, a temperature prediction module and a temperature control module;
the central control module is used for managing and monitoring various components and subsystems of the system;
the data acquisition module is used for acquiring abdominal cavity depth data and weight data of a patient and recording physical state data of the patient, thermal treatment environment temperature data and working data of the medical liquid storage device in real time in the thermal treatment process;
the image acquisition module is used for acquiring an infrared radiation image of the abdominal cavity of the patient in real time through the infrared camera and continuously monitoring the data of the thermal treatment temperature distribution of the abdominal cavity of the patient;
the neural network module is used for carrying out background threshold segmentation processing on an infrared radiation image of the abdominal cavity of a patient acquired in real time, inputting the background threshold segmentation processing into a neural network model, and carrying out gradient division marking on a temperature region;
the temperature prediction module is used for constructing a temperature data fitting model and fitting temperature prediction curves of different temperature areas in real time;
the temperature control module is used for judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermal treatment process, and performing temperature control treatment on the medical liquid storage device.
Specifically, the neural network module includes: input layer, convolution layer, sampling layer, pooling layer, full connection layer and output layer, wherein each convolution layer is provided with 10 8×8 convolution kernels.
Compared with the prior art, the invention has the following technical effects:
1. the invention carries out temperature monitoring area division in the process of thermal therapy by collecting the infrared radiation image of the abdominal cavity of a patient in real time, belongs to non-contact temperature measurement, and avoids cross infection caused by the traditional temperature measurement mode in the thermal therapy process.
2. According to the invention, the temperature region grading marking is carried out on the infrared radiation image of the abdominal cavity of the patient through the neural network, and the temperature distribution condition of the abdominal cavity of the patient can be monitored in real time by utilizing the iterative updating capability of the neural network, so that the temperature change condition of the patient in the thermal therapy process is comprehensively known, the temperature abnormal region which is missed due to too few monitoring points is avoided, the accurate visualization of the temperature region division is realized, and the accuracy of the temperature control of the medical liquid storage equipment is improved.
3. According to the invention, through gray value risk judgment of different temperature areas, the automatic control of the medical liquid storage device with small amplitude temperature fluctuation caused by heat transmission loss in the thermal therapy process is realized, the number of times of frequent operation of temperature control equipment for medical staff is reduced by large amplitude fluctuation early warning feedback of manual intervention, meanwhile, the automatic control of the medical liquid storage device is doubly ensured by safe repeated verification of temperature data, and the temperature control sensitivity and predictability of the medical liquid storage device are enhanced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
FIG. 1 is a schematic flow chart of a temperature control method of a medical liquid storage device based on data analysis;
FIG. 2 is a schematic diagram of a temperature control system of a medical liquid storage device based on data analysis according to the present invention;
fig. 3 is a flowchart of gray value risk determination for different temperature regions according to the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Embodiment one:
as shown in fig. 1 and 3, a method for controlling the temperature of a medical liquid storage device based on data analysis according to an embodiment of the present invention, as shown in fig. 1, includes the following specific steps:
s1: collecting abdominal cavity depth data and weight data of a patient, and recording physical state data of the patient, thermal treatment environment temperature data and working data of a medical liquid storage device in real time in a thermal treatment process;
the patient physical state data includes: real-time body temperature, heart rate data, blood pressure data, blood oxygen saturation and abdominal blood flow velocity of a patient.
The collection of the working data of the medical liquid storage device comprises the following steps: collecting real-time temperature data T of inflow liquid medicine at a time point T by placing a temperature sensor at the liquid medicine inlet of the medical liquid storage device at the liquid medicine inlet 1,t The method comprises the steps of carrying out a first treatment on the surface of the The middle part of the liquid medicine is collected by placing a temperature sensor at the center of the height of the column body of the medical liquid storage device, and the real-time temperature data T of the stored liquid medicine is obtained when the time point is T 2,t The method comprises the steps of carrying out a first treatment on the surface of the The temperature sensor is arranged at the liquid medicine outlet of the medical liquid storage device to collect real-time temperature data T of the flowing liquid medicine at the liquid medicine outlet when the time point is T 3,t 。
S2: the infrared camera is used for collecting infrared radiation images of the abdominal cavity of the patient in real time, and continuously monitoring the temperature distribution data of the abdominal cavity thermotherapy of the patient;
the continuous monitoring of the abdominal cavity hyperthermia temperature distribution data of the patient comprises the following specific steps:
s201: placing an infrared thermal imager at a position which is two meters away from the thermal therapy workbench, wherein the placement height of a camera of the infrared thermal imager is one meter higher than the thermal therapy workbench, and the placement angle is thirty degrees downwards;
s202: collecting infrared radiation images of the abdominal cavity of a patient every 5 minutes;
s203: and (3) carrying out image denoising, size adjustment and normalization treatment on the infrared radiation image of the abdominal cavity of the patient.
S3: the method comprises the steps of performing background threshold segmentation on an infrared radiation image of an abdominal cavity of a patient acquired in real time, inputting the image into a neural network model, and performing gradient division marking on a temperature region;
the step S3 comprises the following specific steps:
s301: graying an infrared radiation image of the abdominal cavity of a patient acquired in real time, wherein the gray value of a pixel point with the temperature of i is R (i);
s302: according to S301, performing threshold segmentation processing on the infrared radiation image subjected to graying processing to obtain a common X-level temperature monitoring area;
s303: and constructing a neural network model, and sequentially marking the region grades according to the gray values of all rows of different temperature regions of the neural network model from small to large.
In S302, the segmentation rule of the threshold segmentation process is as follows:
s321: the lower left corner of the infrared radiation image of the abdominal cavity of the patient is marked as the origin of coordinates, a coordinate system is constructed, wherein the width of the infrared radiation image of the abdominal cavity of the whole patient is L, each interval distance c is marked as one row, the infrared radiation image is divided into J rows from bottom to top, J is an integer, and the maximum value is
S322: calculating the gray average value of all pixel points in each row, wherein P j The gray average value of the J-th row is 1-J;
s323: when the gray difference value phi between the adjacent rows is smaller than the segmentation threshold value K, the two adjacent rows are divided into the same-level temperature areas, wherein the gray difference value phi is calculated according to the following strategy: phi=p j -P j-1 。
S4: constructing a temperature data fitting model, and fitting temperature prediction curves of different temperature areas in real time;
the functional expression of the temperature prediction curve is as follows:
wherein W is x,t The predicted temperature of the x-th-stage temperature monitoring area at the time point t is obtained;
L 1 in the thermal therapy equipment, the liquid medicine outlet of the medical liquid storage device is connected with the length of an infusion pipeline of the abdominal cavity of a patient; l (L) 2,x The abdominal cavity depth of the x-th stage temperature monitoring area of the patient;
λ 1 ,λ 2 the temperature loss coefficient of the thermotherapy transfusion pipeline and the temperature loss coefficient of the abdominal cavity channel of the patient are respectively;
v is the expected arrival time of the liquid medicine from the liquid medicine outlet to the first-stage temperature monitoring area;
θ is a heat exchange interference coefficient to the external environment in the process of patient thermotherapy;
T t is the body temperature of the patient at time t; t (T) t ' is the hyperthermia ambient temperature at time point t.
S5: and judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermotherapy process, and performing temperature control treatment on the medical liquid storage device.
As shown in fig. 3, the gray value risk determination for different temperature areas includes:
s501: extracting gray average values of different temperature areas;
s502: when the gray average value of the x-th level temperature monitoring area is smaller than or equal to one time or larger than the risk level difference A twice consecutively 1 When the temperature monitoring area is monitored, the temperature monitoring is continued;
s503: when the gray average value of the x-th level temperature monitoring area is continuously larger than the risk first-level difference value A three times 1 And is less than or equal to the risk secondary difference A 2 When the temperature prediction curve is used, the interaction interface pops up a slight risk warning, returns to the step S4, records the monitoring time point at the moment as Z, and performs the process through the function of the temperature prediction curveThe target temperature is predicted, the temperature rise or the temperature reduction of the medical liquid storage device is controlled by automatic triggering according to the target temperature value, and safety repeated verification is carried out after the interval time V;
s504: when the gray average value of the x-th level temperature monitoring area is larger than the risk second level difference value A for the first time 2 And when the medical staff is in use, the interactive interface pops up a major risk warning, gives an alarm and requests the medical staff to perform manual temperature adjustment operation.
In S503, the secure duplicate verification includes:
after the interval time V, repeatedly executing the step S5 to judge the gray value risk;
when the gray average value of the x-th level temperature monitoring area is less than or equal to the first-level risk difference value A 1 When the temperature monitoring area is monitored, the temperature monitoring is continued;
when the gray average value of the x-th level temperature monitoring area is larger than the first-level risk difference A 1 At this time, step S504 is performed.
Embodiment two:
as shown in fig. 2, a medical liquid storage device temperature control system based on data analysis according to an embodiment of the present invention, as shown in fig. 2, includes the following modules:
the system comprises a central control module, a data acquisition module, an image acquisition module, a neural network module, a temperature prediction module and a temperature control module;
the central control module is used for managing and monitoring various components and subsystems of the system;
the data acquisition module is used for acquiring abdominal cavity depth data and weight data of a patient and recording physical state data of the patient, thermal treatment environment temperature data and working data of the medical liquid storage device in real time in the thermal treatment process;
the image acquisition module is used for acquiring an infrared radiation image of the abdominal cavity of the patient in real time through the infrared camera and continuously monitoring the data of the thermal treatment temperature distribution of the abdominal cavity of the patient;
the neural network module is used for carrying out background threshold segmentation processing on an infrared radiation image of the abdominal cavity of a patient acquired in real time, inputting the background threshold segmentation processing into a neural network model, and carrying out gradient division marking on a temperature region;
the temperature prediction module is used for constructing a temperature data fitting model and fitting temperature prediction curves of different temperature areas in real time;
the temperature control module is used for judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermal treatment process, and performing temperature control treatment on the medical liquid storage device.
The data acquisition module comprises a patient abdominal cavity depth data acquisition unit, a patient body state data acquisition unit, a temperature data acquisition unit and a working data acquisition unit, wherein the temperature data acquisition unit is used for acquiring thermal treatment environment temperature data, and the working data acquisition unit is used for acquiring working data of the medical liquid storage device;
the working data acquisition unit comprises a liquid medicine inlet acquisition unit, a cylinder height center acquisition unit and a liquid medicine outlet acquisition unit.
The neural network module includes: input layer, convolution layer, sampling layer, pooling layer, full connection layer and output layer, wherein each convolution layer is provided with 10 8×8 convolution kernels.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the partitioning of units is merely one, and there may be additional partitioning in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In summary, compared with the prior art, the technical effects of the invention are as follows:
1. the invention carries out temperature monitoring area division in the process of thermal therapy by collecting the infrared radiation image of the abdominal cavity of a patient in real time, belongs to non-contact temperature measurement, and avoids cross infection caused by the traditional temperature measurement mode in the thermal therapy process.
2. According to the invention, the temperature region grading marking is carried out on the infrared radiation image of the abdominal cavity of the patient through the neural network, and the temperature distribution condition of the abdominal cavity of the patient can be monitored in real time by utilizing the iterative updating capability of the neural network, so that the temperature change condition of the patient in the thermal therapy process is comprehensively known, the temperature abnormal region which is missed due to too few monitoring points is avoided, the accurate visualization of the temperature region division is realized, and the accuracy of the temperature control of the medical liquid storage equipment is improved.
3. According to the invention, through gray value risk judgment of different temperature areas, the automatic control of the medical liquid storage device with small amplitude temperature fluctuation caused by heat transmission loss in the thermal therapy process is realized, the number of times of frequent operation of temperature control equipment for medical staff is reduced by large amplitude fluctuation early warning feedback of manual intervention, meanwhile, the automatic control of the medical liquid storage device is doubly ensured by safe repeated verification of temperature data, and the temperature control sensitivity and predictability of the medical liquid storage device are enhanced.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A medical liquid storage device temperature control method based on data analysis is characterized in that: the method comprises the following specific steps:
s1: collecting abdominal cavity depth data of a patient, and recording physical state data of the patient, thermal treatment environment temperature data and working data of a medical liquid storage device in real time in a thermal treatment process;
s2: the infrared camera is used for collecting infrared radiation images of the abdominal cavity of the patient in real time, and continuously monitoring the temperature distribution data of the abdominal cavity thermotherapy of the patient;
s3: the method comprises the steps of performing background threshold segmentation on an infrared radiation image of an abdominal cavity of a patient acquired in real time, inputting the image into a neural network model, and performing gradient division marking on a temperature region;
s4: constructing a temperature data fitting model, and fitting temperature prediction curves of different temperature areas in real time;
s5: and judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermotherapy process, and performing temperature control treatment on the medical liquid storage device.
2. The method of claim 1, wherein in S1, the patient physical state data comprises: real-time body temperature, heart rate data, blood pressure data, blood oxygen saturation and abdominal blood flow velocity of a patient.
3. The method for controlling temperature of a medical fluid storage device based on data analysis according to claim 1, wherein in S1, the collection of the working data of the medical fluid storage device includes: collecting real-time temperature data T of inflow liquid medicine at a time point T by placing a temperature sensor at the liquid medicine inlet of the medical liquid storage device at the liquid medicine inlet 1,t The method comprises the steps of carrying out a first treatment on the surface of the The middle part of the liquid medicine is collected by placing a temperature sensor at the center of the height of the column body of the medical liquid storage device, and the real-time temperature data T of the stored liquid medicine is obtained when the time point is T 2,t The method comprises the steps of carrying out a first treatment on the surface of the The temperature sensor is arranged at the liquid medicine outlet of the medical liquid storage device to collect real-time temperature data T of the flowing liquid medicine at the liquid medicine outlet when the time point is T 3,t 。
4. The method for controlling the temperature of a medical fluid storage device based on data analysis according to claim 1, wherein the continuous monitoring of the temperature distribution data of the abdominal cavity hyperthermia of the patient comprises the following specific steps:
s201: placing an infrared thermal imager at a position which is two meters away from the thermal therapy workbench, wherein the placement height of a camera of the infrared thermal imager is one meter higher than the thermal therapy workbench, and the placement angle is thirty degrees downwards;
s202: collecting infrared radiation images of the abdominal cavity of a patient every 5 minutes;
s203: and (3) carrying out image denoising, size adjustment and normalization treatment on the infrared radiation image of the abdominal cavity of the patient.
5. The method for controlling the temperature of a medical fluid storage device based on data analysis according to claim 1, wherein the step S3 comprises the following specific steps:
s301: graying an infrared radiation image of the abdominal cavity of a patient acquired in real time, wherein the gray value of a pixel point with the temperature of i is R (i);
s302: according to S301, performing threshold segmentation processing on the infrared radiation image subjected to graying processing to obtain a common X-level temperature monitoring area;
s303: and constructing a neural network model, and sequentially marking the region grades of different temperature regions according to the gray value sum of all rows of the temperature regions from small to large through the neural network model.
6. The method for controlling temperature of a medical fluid storage device based on data analysis according to claim 5, wherein in S302, the segmentation rule of the threshold segmentation process is as follows:
s321: the lower left corner of the infrared radiation image of the abdominal cavity of the patient is marked as the origin of coordinates, a coordinate system is constructed, wherein the width of the infrared radiation image of the abdominal cavity of the whole patient is L, each interval distance c is marked as one row, the infrared radiation image is divided into J rows from bottom to top, J is an integer, and the maximum value is
S322: calculating the gray average value of all pixel points in each row, wherein P j The gray average value of the J-th row is 1-J;
s323: when the gray difference value phi between the adjacent rows is smaller than the segmentation threshold value K, the two adjacent rows are divided into the same-level temperature areas, wherein the gray difference value phi is calculated according to the following strategy: phi=p j -P j-1 。
7. The method of claim 1, wherein the temperature prediction curve has a functional expression as follows:
wherein W is x,t The predicted temperature of the x-th-stage temperature monitoring area at the time point t is obtained;
L 1 in the thermal therapy equipment, the liquid medicine outlet of the medical liquid storage device is connected with the length of an infusion pipeline of the abdominal cavity of a patient; l (L) 2,x The abdominal cavity depth of the x-th stage temperature monitoring area of the patient;
λ 1 ,λ 2 the temperature loss coefficient of the thermotherapy transfusion pipeline and the temperature loss coefficient of the abdominal cavity channel of the patient are respectively;
v is the expected arrival time of the liquid medicine from the liquid medicine outlet to the first-stage temperature monitoring area;
θ is a heat exchange interference coefficient to the external environment in the process of patient thermotherapy;
T t is the body temperature of the patient at time t; t (T) t ' is the hyperthermia ambient temperature at time point t.
8. The method for controlling temperature of a medical fluid storage device based on data analysis according to claim 7, wherein the gray value risk determination for different temperature areas comprises:
s501: extracting gray average values of different temperature areas;
s502: when the gray average value of the x-th level temperature monitoring area is smaller than or equal to one time or larger than the risk level difference A twice consecutively 1 When the temperature monitoring area is monitored, the temperature monitoring is continued;
s503: when the gray average value of the x-th level temperature monitoring area is continuously larger than the risk first-level difference value A three times 1 And is less than or equal to the risk secondary difference A 2 And when the temperature prediction curve is used, the interaction interface pops up a slight risk warning, returns to the step S4, records the monitoring time point as Z at the moment, predicts the target temperature through the function of the temperature prediction curve, automatically triggering to control the temperature rise or the temperature reduction of the medical liquid storage device according to the target temperature value, and performing safe repeated verification after the interval time V;
s504: when the gray average value of the x-th level temperature monitoring area is larger than the risk second level difference value A for the first time 2 And when the medical staff is in use, the interactive interface pops up a major risk warning, gives an alarm and requests the medical staff to perform manual temperature adjustment operation.
9. The method for controlling temperature of a medical fluid storage device based on data analysis according to claim 8, wherein in S503, the secure repeated verification includes:
after the interval time V, repeatedly executing the step S5 to judge the gray value risk;
when the gray average value of the x-th-level temperature monitoring area is smaller than or equal to the gray average value, continuing to monitor the temperature of the temperature monitoring area;
when the gray average value of the x-th level temperature monitoring area is larger than the first-level risk difference A 1 At this time, step S504 is performed.
10. A medical fluid storage device temperature control system based on data analysis, which is realized based on a medical fluid storage device temperature control method based on data analysis according to any one of claims 1-9, characterized in that the system comprises the following modules:
the system comprises a central control module, a data acquisition module, an image acquisition module, a neural network module, a temperature prediction module and a temperature control module;
the central control module is used for managing and monitoring various components and subsystems of the system;
the data acquisition module is used for acquiring abdominal cavity depth data and weight data of a patient and recording physical state data of the patient, thermal treatment environment temperature data and working data of the medical liquid storage device in real time in the thermal treatment process;
the image acquisition module is used for acquiring an infrared radiation image of the abdominal cavity of the patient in real time through the infrared camera and continuously monitoring the data of the thermal treatment temperature distribution of the abdominal cavity of the patient;
the neural network module is used for carrying out background threshold segmentation processing on an infrared radiation image of the abdominal cavity of a patient acquired in real time, inputting the background threshold segmentation processing into a neural network model, and carrying out gradient division marking on a temperature region;
the neural network module includes: the system comprises an input layer, a convolution layer, a sampling layer, a pooling layer, a full connection layer and an output layer, wherein each convolution layer is provided with 10 convolution kernels of 8 multiplied by 8;
the temperature prediction module is used for constructing a temperature data fitting model and fitting temperature prediction curves of different temperature areas in real time;
the temperature control module is used for judging gray value risks according to different temperature areas, monitoring and feeding back temperature data in the patient thermal treatment process, and performing temperature control treatment on the medical liquid storage device.
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