Drainage liquid monitoring method and device based on drainage liquid real-time monitoring device

Abstract

The invention relates to the technical field of data processing, and discloses a drainage liquid monitoring method and device based on a drainage liquid real-time monitoring device. The light signal to be analyzed comprises a near-infrared light signal emitted to drainage liquid in the drainage tube by a light source emitting module in the drainage liquid real-time monitoring device and a receiving signal detected by a photoelectric detection module in the drainage liquid real-time monitoring device after passing through the drainage liquid; based on the to-be-analyzed optical signal, determining target substances in the drainage liquid as well as substance amount concentrations and substance amounts corresponding to the target substances one by one; judging whether the drainage liquid is abnormal or not based on the substance amount concentration and the substance amount; under the condition that the drainage liquid is abnormal, an alarm signal is generated, and complication prediction is carried out. The system can assist medical staff in discovering potential complications as soon as possible and preventing disease deterioration, and does not need to depend on regular observation and professional judgment of the medical staff, so that the workload of the medical staff is effectively relieved, and the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a drainage fluid monitoring method and device based on a drainage fluid real-time monitoring device. Background Art

[0002] After many surgical operations, a drainage tube is usually left in place, and the other end of the drainage tube is connected to a drainage bag to drain the patient's secretions and excretions out of the body. The drainage fluid can also be used to determine the patient's clinical manifestations, such as whether there are complications such as bleeding and infection.

[0003] At present, during the postoperative observation process, medical staff often judge possible complications by manually observing the color, appearance and properties of the drainage fluid regularly. This method is intuitive and direct, but it is very dependent on the experience and professional knowledge of medical staff. In addition, it is difficult for medical staff to monitor the drainage fluid in real time 24 hours a day, and the observation results will also be affected by personal experience, subjective judgment, fatigue and other factors, which is prone to misdiagnosis and missed diagnosis, thereby increasing the risk of postoperative complications for patients. Summary of the invention

[0004] In view of this, the present invention provides a drainage fluid monitoring method and device based on a drainage fluid real-time monitoring device to solve the problem in the prior art that it is difficult to monitor the changes in drainage fluid in real time after surgical operations, and to promptly determine possible complications based on the changes in drainage fluid.

[0005] In a first aspect, the present invention provides a drainage fluid monitoring method based on a drainage fluid real-time monitoring device, the method comprising:

[0006] Acquire the optical signal to be analyzed, the optical signal to be analyzed includes the near-infrared light signal emitted by the light source emission module in the drainage fluid real-time monitoring device to the drainage fluid in the drainage tube and the received signal detected by the photoelectric detection module in the drainage fluid real-time monitoring device after passing through the drainage fluid;

[0007] Based on the optical signal to be analyzed, determining the target substance in the drainage fluid and the corresponding substance concentration and substance amount of the target substance;

[0008] Based on the concentration and amount of the substance, determine whether the drainage fluid is abnormal;

[0009] In case of abnormalities in the drainage fluid, an alarm signal is generated and complications are predicted.

[0010] Based on the collected near-infrared light signals and received signals, the present invention automatically monitors the target substances, the amount-of-substance concentration, and the amount of substance in the drainage fluid. By monitoring these substance changes, it can assist medical staff in detecting potential complications such as infections or inflammations at an early stage, taking timely intervention measures, and preventing the deterioration of the condition. Moreover, through the automatic monitoring and analysis method, it does not rely on the regular observation and professional judgment of medical staff, effectively reducing the workload of medical staff and improving work efficiency.

[0011] In an alternative embodiment, the target substance is determined by the following steps:

[0012] Obtain an infrared spectrum database, which includes characteristic absorption peaks corresponding to substances one by one;

[0013] Determine the absorption spectrum data corresponding to the received signal in the optical signal to be analyzed;

[0014] Match the absorption spectrum data with the infrared spectrum database to determine the target substance.

[0015] In this embodiment, by obtaining the infrared spectrum database and matching and predicting the received spectrum data in the optical signal to be analyzed, the target substance can be accurately identified, potential infections or other complications can be detected at an early stage, and timely intervention can be carried out to reduce the risk of condition deterioration.

[0016] In an alternative embodiment, based on the optical signal to be analyzed, determining the amount-of-substance concentration and the amount of the target substance in the drainage fluid includes:

[0017] Obtain a preset standard curve library, which includes the absorbances corresponding to each substance at known amount-of-substance concentrations established in advance;

[0018] Based on the near-infrared light signal and the received signal, determine the absorbance at the characteristic absorption peak corresponding to the target substance;

[0019] Based on the absorbance and the preset standard curve, determine the corresponding amount-of-substance concentration of the target substance;

[0020] Determine the volume of the drainage fluid irradiated by the near-infrared light signal;

[0021] Based on the amount-of-substance concentration and the volume of the drainage fluid, determine the amount of substance.

[0022] In this embodiment, by calculating the amount-of-substance concentration and the amount of substance, it can quickly determine whether there is an abnormality in the drainage fluid, achieve non-invasive monitoring, and can also help medical staff detect potential complications in a timely manner, which is beneficial to the rapid recovery of patients.

[0023] In an alternative embodiment, the complication prediction is carried out by the following steps:

[0024] Extract the signal features corresponding to the received signal;

[0025] Input the signal features into a pre-trained complication prediction model to output the complication prediction result; among them, the complication prediction model is constructed based on the infrared spectrum data corresponding to known complications.

[0026] In this embodiment, through the real-time acquisition of infrared spectrum data and the analysis of the prediction model, the possible complications of the patient can be predicted in advance, which helps to improve the accuracy and timeliness of early complication warning.

[0027] In an alternative embodiment, the complication prediction model is established through the following steps:

[0028] Establish infrared spectrum data;

[0029] Extract the feature information in the infrared spectrum data to establish a feature information set;

[0030] Train a neural network model based on the feature information set to obtain the trained complication prediction model.

[0031] The complication prediction model established in this embodiment can accurately capture the change information related to complications, effectively improve the accuracy of complication prediction, and provide information reference for medical staff.

[0032] In a second aspect, the present invention provides a real-time monitoring device for drainage fluid, and the device includes:

[0033] A housing for fixing on the drainage tube;

[0034] A light source emission module provided on the housing for emitting a near-infrared light signal to the drainage fluid in the drainage tube;

[0035] A photoelectric detection module provided on the housing and disposed opposite to the light source emission module for detecting the received signal after the near-infrared light passes through the drainage fluid;

[0036] A control module electrically connected to the light source emission module and the photoelectric detection module respectively for executing the drainage fluid monitoring method described in any one of the above embodiments.

[0037] In an alternative embodiment, the control module includes:

[0038] An acquisition unit for acquiring the optical signal to be analyzed, and the optical signal to be analyzed includes a near-infrared light signal and a received signal;

[0039] A determination unit for determining the target substance in the drainage fluid and the amount-of-substance concentration and amount of substance corresponding to the target substance one by one based on the optical signal to be analyzed;

[0040] A judgment unit, configured to judge whether there is an abnormality in the drainage fluid based on the molar concentration and the amount of substance;

[0041] An alarm unit, configured to generate an alarm signal when there is an abnormality in the drainage fluid;

[0042] A prediction unit, configured to perform complication prediction when there is an abnormality in the drainage fluid.

[0043] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to the first aspect or any corresponding embodiment thereof.

[0044] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to the first aspect or any corresponding embodiment thereof.

[0045] In a fifth aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to the first aspect or any corresponding embodiment thereof.

[0046] It should be noted that since the drainage fluid real-time monitoring device, computer device, computer-readable storage medium, and computer program product provided by the present invention correspond to the above-mentioned drainage fluid monitoring method based on the drainage fluid real-time monitoring device. Therefore, for the beneficial effects of the drainage fluid real-time monitoring device, computer device, computer-readable storage medium, and computer program product, please refer to the description of the corresponding beneficial effects of the drainage fluid monitoring method based on the drainage fluid real-time monitoring device above, and will not be elaborated here. Description of the Drawings

[0047] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a flowchart of the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to an embodiment of the present invention;

[0049] Figure 2Schematic structural diagram of a drainage fluid real-time monitoring device according to an embodiment of the present invention;

[0050] Figure 3 Schematic infrared spectrum diagram corresponding to glutamine according to an embodiment of the present invention;

[0051] Figure 4 Block diagram of the structure of a control module according to an embodiment of the present invention;

[0052] Figure 5 Schematic hardware structure diagram of a computer device according to an embodiment of the present invention. Specific implementation manners

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] According to an embodiment of the present invention, there is provided an embodiment of a drainage fluid monitoring method based on a drainage fluid real-time monitoring device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0055] In this embodiment, there is provided a drainage fluid monitoring method based on a drainage fluid real-time monitoring device, which can be executed by devices such as a drainage fluid real-time monitoring device, other servers, terminals, and mobile terminals. Figure 1 Flowchart of a drainage fluid monitoring method based on a drainage fluid real-time monitoring device according to an embodiment of the present invention, as Figure 1 shown, the process includes the following steps:

[0056] Step S101, obtain an optical signal to be analyzed. The optical signal to be analyzed includes a near-infrared optical signal emitted by a light source emission module in the drainage fluid real-time monitoring device to the drainage fluid in the drainage tube and a received signal detected by a photoelectric detection module in the drainage fluid real-time monitoring device after passing through the drainage fluid. The drainage fluid real-time monitoring device can be referred to Figure 2 as shown. The light source emission module and the photoelectric detection module are arranged on both sides of the drainage tube. The light source emission module emits near-infrared light to the drainage fluid in the drainage tube, and after passing through the drainage fluid, it is detected by the photoelectric detection module to obtain a received signal.

[0057] Step S102: Based on the optical signal to be analyzed, determine the target substances in the drainage fluid, as well as the corresponding molar concentrations and amounts of substances of the target substances. The target substances can be determined by near-infrared spectroscopy analysis technology. That is, the target substances in the drainage fluid will have characteristic absorptions at certain infrared wavelengths, namely characteristic absorption peaks. By comparing the characteristic absorption peaks with the infrared spectrum database of known substances, it is possible to accurately judge whether a certain specific target component exists in the drainage fluid; then, according to the optical signal to be analyzed, the absorbance of the drainage fluid at a specific wavelength can be determined, and based on the Lambert-Beer law, the molar concentration of the target substance can be determined; finally, according to the molar concentration and the solution volume, the amount of substance can be determined, thus completing the qualitative analysis of the drainage fluid.

[0058] Step S103: Based on the molar concentration and the amount of substance, judge whether the drainage fluid is abnormal. Specifically, the characteristic absorption peaks, molar concentrations and alarm signal ranges of the amounts of substances in the wavelength range of the target substances in the drainage fluid can be set in advance. When the characteristic absorption peaks, molar concentrations and amounts of substances of different target substances in the detected drainage fluid reach the values corresponding to the occurrence of complications, that is, within the corresponding alarm signal ranges, it is judged that the drainage fluid is abnormal. If some substances, such as the number of white blood cells and the molar concentration of protein, exceed the normal range, it may indicate that some pathological processes are taking place, such as inflammation, infection, bleeding, etc. If the molar concentration of a certain substance in the drainage fluid, such as lactic acid, increases significantly, it is judged that it may be a sign of hypoxia, infection or other acute pathological processes.

[0059] In addition, the position, shape, intensity, etc. of the characteristic absorption peaks can also reflect the molecular structure information and the interactions between molecules. For example, in the infrared spectrum of proteins, different peptide bond vibration modes will produce absorption peaks at specific wavelengths. By analyzing the changes in these absorption peaks, the changes in the secondary structure of proteins (such as α-helix, β-sheet, etc.) can be understood. In the analysis of the components of the drainage fluid, this helps to deeply understand the molecular state of certain components and whether there are interactions between them and other components. In this embodiment, it is possible to judge whether the drainage fluid is abnormal through the absorption characteristic peaks. Through a multi-dimensional strategy, even if the absorption peaks overlap, accurate identification can still be achieved by combining chemical information, statistical models and biological characteristics.

[0060] Step S104: When the drainage fluid is abnormal, generate an alarm signal and perform complication prediction. The alarm signal can include warning information and complication prediction result information. The alarm signal can be sent to the terminal device for warning prompts, and at the same time, the predicted complications, such as symptoms of infection, bleeding, etc., are sent together during the warning to remind medical staff to take corresponding treatment measures; or directly give warning prompts through the beeping sound, light, etc. of the drainage fluid real-time monitoring device, which is more intuitive.

[0061] In this embodiment, by collecting the near-infrared light signal and the received signal, the target substance, the amount-of-substance concentration, and the amount of substance in the drainage fluid are automatically monitored. By monitoring these substance changes, it is possible to assist medical staff in detecting potential complications such as infections or inflammations at an early stage, taking timely intervention measures, and preventing the deterioration of the condition. Moreover, through the automatic monitoring and analysis method, it is not necessary to rely on the continuous observation and professional judgment of medical staff, effectively reducing the workload of medical staff and improving work efficiency.

[0062] In addition, during the actual detection and analysis process, a filter screen can also be set in the drainage tube before it flows through the housing. By selecting a filter screen with a small pore size, impurities in the drainage fluid can be effectively removed, further reducing the turbidity of the drainage fluid, thereby further ensuring the detection accuracy. Further, both the drainage bag and the drainage tube can be designed to be transparent.

[0063] In some alternative embodiments, the target substance is determined through the following steps:

[0064] Obtain an infrared spectrum database, which includes characteristic absorption peaks corresponding to substances one by one.

[0065] Determine the absorption spectrum data corresponding to the received signal in the optical signal to be analyzed.

[0066] Match the absorption spectrum data with the infrared spectrum database to determine the target substance.

[0067] In this embodiment, the near-infrared spectroscopy technology is used to determine the possible target substances in the drainage fluid. The near-infrared spectroscopy technology obtains the chemical information of substances by measuring the absorption, reflection, and scattering characteristics of substances to near-infrared light. The color of the drainage fluid is essentially the presentation of the absorption, scattering, and reflection characteristics of the components to light of different wavelengths. The infrared spectroscopy technology is based on the absorption characteristics of substances to infrared light of different wavelengths, and the target substances in the drainage fluid will have characteristic absorption peaks at certain infrared wavelengths. By comparing with the infrared spectrum database of known substances, it is possible to accurately determine whether a certain specific target substance exists in the drainage fluid. Further, by measuring the absorbance of the drainage fluid at a specific wavelength and according to the Lambert-Beer law, the concentration of the target component can be calculated to complete the qualitative analysis of the drainage fluid.

[0068] For example, as shown in Table 1, it is the infrared spectrum characteristic information corresponding to typical substances in the drainage fluid stored in the infrared spectrum database. The infrared spectrum diagram corresponding to glutamine is referred to Figure 3 as shown.

[0069] Table 1

[0070]

[0071] That is, when the characteristic absorption peak is in the range of 3300 - 3500 cm -1 , 1700 - 1750 cm -1 , 1600 - 1700 cm -1 , 1500 - 1600 cm -1 , the corresponding target substance is glutamine.

[0072] In addition, in this embodiment, before matching the absorption spectrum data with the infrared spectrum database, the absorption spectrum data is also processed. Specifically, after converting the optical signal into an electrical signal, the electrical signal is digitized and a near-infrared spectrum diagram is generated. Further, data processing is performed on the near-infrared spectrum diagram. For example, using second derivative / deconvolution analysis method can enhance the separation degree of overlapping peaks and identify hidden shoulder peaks. For example: in the range of 1650 - 1680 cm -1 (amide I band), using second derivative processing can distinguish the structural differences between α-helix (1650 cm -1 ) and β-sheet (1630 cm -1 ), and assist in judging the protein conformation. Then, chemometric methods are used, including: principal component analysis (PCA), partial least squares regression (PLSR), etc., to analyze the data of the spectrum diagram, effectively extract information from multi-wavelength data and match it with the infrared spectrum database, support the dimensionality reduction of high-dimensional data, and improve the model prediction ability.

[0073] Specifically, it includes: using principal component analysis (PCA) to extract the features with the largest variance in the spectrum to reduce the dimension. Using partial least squares discriminant analysis (PLS-DA) to establish a classification model, for example: distinguishing Gram-negative bacteria / positive bacteria, etc., and being able to determine the features related to the target substance in multi-variable spectrum data. Using support vector machine (SVM) to process non-linear spectrum data (such as anaerobic bacteria and aerobic bacteria classification). For example: using the full-spectrum data of 900 - 1800 cm -1 to train the PLS-DA model to distinguish Aspergillus (polysaccharide C-O-C peak) from bacteria (lipopolysaccharide P=O peak). In the prediction of spectrum data, SVM can help find the decision boundary among different substances, so as to predict the target substance.

[0074] In this embodiment, by obtaining the infrared spectrum database and matching and predicting the received spectrum data in the optical signal to be analyzed, the target substance can be accurately identified, potential infections or other complications can be detected early, so as to carry out timely intervention and reduce the risk of disease deterioration.

[0075] In some alternative embodiments, based on the optical signal to be analyzed, determining the amount-of-substance concentration and the amount of substance of the target substance in the drainage fluid includes:

[0076] Obtain a preset standard curve library, which includes the absorbance corresponding to each substance at a known amount-of-substance concentration established in advance. A series of standard solutions of the target component of the drainage fluid with known amount-of-substance concentrations can be prepared, the absorbance of each standard solution can be measured, and then a standard curve can be plotted with the amount-of-substance concentration as the abscissa and the absorbance as the ordinate.

[0077] Based on the near-infrared light signal and the received signal, determine the absorbance at the characteristic absorption peak corresponding to the target substance. This can be done through the formula: Calculate the absorbance of the drainage fluid at the selected wavelength. Among them, A is the absorbance, which is used to measure the degree of light absorption by the substance; I O is the intensity of the near-infrared light signal; I is the intensity of the received signal.

[0078] Based on the absorbance and the preset standard curve, determine the corresponding amount-of-substance concentration of the target substance. The amount-of-substance concentration can be calculated through the Lambert-Beer law. The Lambert-Beer law describes the relationship between the degree of light absorption by a substance, the amount-of-substance concentration, and the optical path length. The formula is: A = εbc; where ε is the molar absorptivity, with the unit L / (mol·cm), which is usually known and is related to factors such as the properties of the light-absorbing substance and the wavelength of the incident light, and is a characteristic constant of the substance; b is the optical path length, which is usually known, that is, the distance that light travels in the sample, with the unit cm; c is the amount-of-substance concentration, with the unit mol / L. That is, when a beam of parallel monochromatic light passes perpendicularly through a certain homogeneous non-scattering light-absorbing substance, its absorbance A is proportional to the amount-of-substance concentration c of the light-absorbing substance and the optical path length b. According to the determined absorbance, find the corresponding amount-of-substance concentration on the standard curve, which is the amount-of-substance concentration of the target substance in the drainage fluid.

[0079] Determine the volume of the drainage fluid irradiated by the near-infrared light signal. Since the relative positions of the light source emission module, the drainage tube, and the photoelectric detection module are fixed, the volume of the drainage fluid irradiated by the near-infrared light signal can be accurately determined.

[0080] Based on the amount-of-substance concentration and the volume of the drainage fluid, determine the amount of substance.

[0081] Specifically, according to the volume information of the drainage fluid, calculate the amount of the target substance, that is, n = c×V. Where n is the amount of substance, with the unit mole (mol); c is the amount-of-substance concentration, with the unit mole per liter (mol / L); V is the volume of the solution, with the unit liter (L).

[0082] Regarding the conversion between the amount of substance and the mass, if the amount of substance n (unit: mol) of the target substance is known and its mass m (unit: g) is to be calculated, the formula m = n×M can be used, where M is the molar mass of the target substance (unit: g / mol), and M is usually a known coefficient.

[0083] In this embodiment, by calculating the molar concentration and the amount of substance, it is possible to quickly determine whether there is an abnormality in the drainage fluid, achieve intelligent real-time monitoring, and also help medical staff to detect potential complications in a timely manner, which is beneficial to the rapid recovery of patients.

[0084] In some alternative embodiments, the complication prediction is performed through the following steps:

[0085] Extract the signal features corresponding to the received signal. These include absorption peaks in the infrared spectrum, spectral bandwidth features, and statistical features, etc. Among them, the absorption peaks in the infrared spectrum can reflect the characteristics of substances in the drainage fluid. By analyzing these absorption peaks, substances or changes related to complications can be identified. By observing the different bandwidths of the spectrum (such as the intensity change within the wavelength range), details related to complications can be captured. In addition, the mean, standard deviation, skewness, and kurtosis of the signal can also be calculated, and these statistical features can effectively summarize the basic properties of the signal.

[0086] Input the signal features into a pre-trained complication prediction model to output the complication prediction result; among them, the complication prediction model is constructed based on the infrared spectrum data corresponding to known complications.

[0087] For example, when the target substance is glutamine and the extracted signal feature is that the glutamine value increases significantly, the predicted complications are: impaired liver function (cirrhosis, hepatic coma) or urea cycle disorder, or the body is in a stress state; when the glutamine value decreases significantly, the predicted complications are: malnutrition or infection, inflammation.

[0088] In this embodiment, through the real-time collection of infrared spectrum data and the analysis of the prediction model, it is possible to predict in advance the possible complications of patients, which helps to improve the accuracy and timeliness of early complication warning.

[0089] In some alternative embodiments, the complication prediction model is established through the following steps:

[0090] Establish infrared spectrum data;

[0091] Extract the characteristic information in the infrared spectrum data to establish a characteristic information set;

[0092] Train a neural network model based on the characteristic information set to obtain the trained complication prediction model.

[0093] Specifically, draw on the existing infrared spectroscopy data in the database, and compare the spectral information such as the wavelength and transmittance corresponding to the measured characteristic absorption peaks with the values in the infrared spectroscopy database. Compare the spectral characteristics of the measured data with the literature data, and use the principal component analysis method to reduce the data dimension and extract the main features. According to the data characteristics, use calculation models such as multiple linear regression and artificial neural networks, and use methods such as cross-validation to determine the optimal parameters of the model. Divide the data after preprocessing and feature extraction into a training set and a validation set, train the model with the training set, and continuously adjust the parameters to optimize the model performance. Evaluate the performance of the model on the validation set using indicators such as mean square error and coefficient of determination. Adjust the model according to the evaluation results, such as adding features and adjusting parameters. If the model is still not good, consider changing the model or further analyzing the reasons for the data differences. Finally, use an independent test set or new experimental data to verify the generalization ability of the model to ensure the reliability of the complication prediction model.

[0094] The complication prediction model established in this embodiment can accurately capture the change information related to complications, effectively improve the accuracy of complication prediction, and provide information reference for medical staff.

[0095] In this embodiment, a real-time monitoring device based on drainage fluid is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0096] This embodiment provides a real-time monitoring device based on drainage fluid, such as Figure 2 shown, the device includes:

[0097] A housing 1, which is used to be fixed on the drainage tube through a fixing clip 5;

[0098] A light source emission module 2, which is arranged on the housing and is used to emit a near-infrared light signal to the drainage fluid in the drainage tube;

[0099] A photoelectric detection module 3, which is arranged on the housing and is arranged opposite to the light source emission module, and is used to detect the received signal after the near-infrared light passes through the drainage fluid;

[0100] A control module 4, which is electrically connected to the light source emission module and the photoelectric detection module respectively, and is used to execute the drainage fluid monitoring method of any one of the above implementation manners.

[0101] In some alternative implementation manners, the control module 4 includes:

[0102] An acquisition unit 401, which is used to acquire the optical signal to be analyzed, and the optical signal to be analyzed includes a near-infrared light signal and a received signal;

[0103] A determination unit 402, configured to determine a target substance in the drainage fluid, and the amount-of-substance concentration and the amount of substance corresponding one-to-one to the target substance, based on the optical signal to be analyzed.

[0104] A judgment unit 403, configured to judge whether the drainage fluid is abnormal based on the amount-of-substance concentration and the amount of substance.

[0105] An alarm unit 404, configured to generate an alarm signal when the drainage fluid is abnormal.

[0106] A prediction unit 405, configured to perform complication prediction when the drainage fluid is abnormal.

[0107] The target substance is determined through the following steps:

[0108] Obtain an infrared spectrum database, where the infrared spectrum database includes characteristic absorption peaks corresponding one-to-one to substances.

[0109] Determine the absorption spectrum data corresponding to the received signal in the optical signal to be analyzed.

[0110] Match the absorption spectrum data with the infrared spectrum database to determine the target substance.

[0111] The control module 4 further includes:

[0112] A target substance determination unit, configured to obtain a preset standard curve library, where the preset standard curve library includes the absorbances corresponding one-to-one to each substance at known amount-of-substance concentrations; determine the absorbance at the characteristic absorption peak corresponding to the target substance based on the near-infrared optical signal and the received signal; determine the amount-of-substance concentration corresponding one-to-one to the target substance based on the absorbance and the preset standard curve; determine the volume of the drainage fluid irradiated by the near-infrared optical signal; and determine the amount of substance based on the amount-of-substance concentration and the volume of the drainage fluid.

[0113] A complication prediction unit, configured to extract the signal characteristics corresponding to the received signal; input the signal characteristics into a pre-trained complication prediction model, and output a complication prediction result; wherein, the complication prediction model is constructed based on the infrared spectrum data corresponding to known complications.

[0114] A complication prediction model establishment unit, configured to establish infrared spectrum data; extract the characteristic information in the infrared spectrum data to establish a characteristic information set; and train a neural network model based on the characteristic information set to obtain the trained complication prediction model.

[0115] The control module in this embodiment is presented in the form of functional units, where the unit here refers to an ASIC circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0116] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be repeated here.

[0117] An embodiment of the present invention further provides a computer device having the above-mentioned Figure 4 shown control module.

[0118] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As shown in Figure 5 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 5 Here, one processor 10 is taken as an example.

[0119] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0120] Among them, the memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0121] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0122] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0123] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0124] The embodiments of the present invention further provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or non-transitory machine-readable storage medium and to be stored in a local storage medium downloaded through a network, so that the method described herein can be stored in such software processed on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0125] A part of the present invention can be applied as a computer program product, for example, computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0126] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A drainage fluid monitoring method based on a real-time monitoring device for drainage fluid, characterized in that, The method includes: Obtaining an optical signal to be analyzed, where the optical signal to be analyzed includes a near-infrared optical signal emitted by a light source emission module in the drainage fluid real-time monitoring device to the drainage fluid in the drainage tube and a received signal detected by a photoelectric detection module in the drainage fluid real-time monitoring device after passing through the drainage fluid; Based on the optical signal to be analyzed, determining the target substance in the drainage fluid, the corresponding amount-of-substance concentration and amount of substance of the target substance; Based on the amount-of-substance concentration and the amount of substance, determining whether the drainage fluid is abnormal; When the drainage fluid is abnormal, generating an alarm signal and performing complication prediction.

2. The method according to claim 1, wherein The target substance is determined through the following steps: Obtaining an infrared spectrum database, where the infrared spectrum database includes characteristic absorption peaks corresponding to substances one by one; Determining the absorption spectrum data corresponding to the received signal in the optical signal to be analyzed; Matching the absorption spectrum data with the infrared spectrum database to determine the target substance.

3. The method according to claim 2, characterized in that The determining the amount-of-substance concentration and amount of substance of the target substance in the drainage fluid based on the optical signal to be analyzed includes: Obtaining a preset standard curve library, where the preset standard curve library includes the absorbance corresponding to each substance at a known amount-of-substance concentration established in advance; Based on the near-infrared optical signal and the received signal, determining the absorbance at the characteristic absorption peak corresponding to the target substance; Based on the absorbance and the preset standard curve, determining the amount-of-substance concentration corresponding to the target substance one by one; Determining the volume of the drainage fluid irradiated by the near-infrared optical signal; Based on the amount-of-substance concentration and the volume of the drainage fluid, determining the amount of substance.

4. The method according to claim 1, wherein Complication prediction is performed through the following steps: Extracting the signal features corresponding to the received signal; Inputting the signal features into a pre-trained complication prediction model to output a complication prediction result; where the complication prediction model is constructed based on the infrared spectrum data corresponding to known complications.

5. The method according to claim 4, characterized in that, The complication prediction model is established through the following steps: Establishing infrared spectrum data; Extracting the characteristic information in the infrared spectrum data to establish a characteristic information set; Training a neural network model based on the characteristic information set to obtain the trained complication prediction model.

6. A real-time monitoring device for drainage fluid, characterized in that, The device includes: A housing for fixing on the drainage tube; A light source emission module provided on the housing for emitting a near-infrared optical signal to the drainage fluid in the drainage tube; A photoelectric detection module provided on the housing and disposed opposite to the light source emission module for detecting the received signal after the near-infrared light passes through the drainage fluid; A control module electrically connected to the light source emission module and the photoelectric detection module respectively for executing the drainage fluid monitoring method according to any one of claims 1-5 above.

7. The device according to claim 6, characterized in that, The control module includes: An acquisition unit for acquiring an optical signal to be analyzed, where the optical signal to be analyzed includes the near-infrared optical signal and the received signal; A determination unit for determining the target substance in the drainage fluid, the corresponding amount-of-substance concentration and amount of substance of the target substance based on the optical signal to be analyzed; A judgment unit, configured to judge whether the drainage fluid is abnormal based on the amount-of-substance concentration and the amount of substance; An alarm unit, configured to generate an alarm signal when the drainage fluid is abnormal; A prediction unit, configured to perform complication prediction when the drainage fluid is abnormal.

8. A computer device, characterized in that, Comprising: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to any one of claims 1-5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to any one of claims 1-5.

10. A computer program product, characterized in that, Including computer instructions for causing a computer to execute the drainage fluid monitoring method based on the drainage fluid real-time monitoring device according to any one of claims 1-5.

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