Adaptive density conversion metering method and device based on identification of drainage fluid properties

Abstract

The present application relates to drainage liquid metering technology, and discloses a self-adaptive density conversion metering method and device based on drainage liquid property identification. Multi-band transmission light intensity and near-infrared transmission attenuation characteristics of drainage liquid in a metering cup are collected, and mass, inclination angle and temperature data are synchronously collected. When the inclination angle, mass range, mass standard deviation and mass change rate meet the stable threshold, the mass data is subjected to outlier rejection, inclination angle correction and temperature compensation to obtain the net mass of the drainage liquid. According to the optical characteristics, the final property category of the drainage liquid is determined, the corresponding density parameter or density model is called, and the volume of the drainage liquid is converted according to the net mass and the density. After integrity verification, the metering record is stored, and the empty load mass baseline is updated after drainage, so as to reduce the volume metering deviation of drainage liquid with different properties.

Description

Technical Field

[0001] This invention belongs to the field of drainage fluid property identification technology, specifically an adaptive density conversion and measurement method and device based on drainage fluid property identification. Background Technology

[0002] Currently, the volume measurement of postoperative drainage fluid is a core reference indicator for assessing the patient's recovery status and making decisions on fluid replacement and medication. In the early stages, the measurement was mostly done by manually pouring and reading the graduated cylinder, which was inefficient and prone to human error.

[0003] However, the existing drainage fluid measurement scheme still has obvious defects: it does not distinguish between drainage fluids with different characteristics such as clear, bloody, purulent, and chylous, and uniformly uses a fixed water density of 1.0 g / mL for conversion; in some drainage fluid samples, the use of a fixed density of 1.0 g / mL may cause a volume conversion error of about 4%, resulting in a significant deviation in the final measured volume.

[0004] To address these issues, those skilled in the art have proposed an adaptive density conversion and measurement method and apparatus based on the identification of drainage fluid properties to solve the problems raised in the background art. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides an adaptive density conversion and measurement method and apparatus based on drainage fluid property identification, thereby resolving the issue of volume deviation caused by the prior art's failure to differentiate between drainage fluids of different properties and the uniform use of a fixed water density conversion.

[0006] An adaptive density conversion and measurement method based on drainage fluid property identification includes the following steps:

[0007] S1. Complete the device's power-on self-test and empty cup initialization, and collect the zero-point baseline under empty cup conditions. It also locks the drain valve and enters standby mode;

[0008] S2. After obtaining a valid patient identification, start the preset effective measurement time window. Use a gate-shaped optical bracket straddling the outside of the cup to collect the transmitted light intensity of 8 visible light channels, 1 broadband channel and 1 850 nm near-infrared channel with center wavelengths of 405 nm, 415 nm, 425 nm, 445 nm, 480 nm, 555 nm, 590 nm and 630 nm. Calculate the absorbance at each wavelength according to formula (1). Collect the transmission attenuation characteristics through the 850 nm near-infrared beam assembly. Obtain the turbidity equivalent by standard turbidity liquid calibration. Collect mass, tilt angle and temperature data simultaneously.

[0009] S3. Effective sampling is triggered by four stability criteria: tilt angle, mass range, mass standard deviation, and mass change rate. After effective sampling is triggered, the maximum and minimum two sampling points in the sliding window are removed and the average value is taken to obtain the truncated average mass. Then, tilt angle correction and temperature compensation are performed to obtain the net mass of the drainage fluid.

[0010] S4. Based on multispectral absorbance, absorbance ratio, normalized transmittance characteristics and turbidity equivalent, candidate trait categories are generated according to preset rules, and the final trait category is determined by confidence calculation.

[0011] S5. Determine the corresponding density parameter or density estimation model based on the final morphological category, and calculate the drainage fluid volume according to the formula; if the final morphological category is not determined, call the preset default density parameter to calculate the drainage fluid volume and generate a density not adaptive flag;

[0012] S6. Write the complete measurement record to the non-volatile memory and complete the integrity verification; after the drainage is completed, collect the idle stable reading and update it to the new zero baseline for the next measurement, then remove the patient identity binding and return to the standby state.

[0013] The formula for calculating absorbance is as follows:

[0014] (1)

[0015] In the formula, wavelength absorbance at the following levels Wavelength when filled with drainage fluid The received light intensity, This refers to the received light intensity in an empty cup or reference medium state. Dark current intensity when the light source is off; normalized transmittance. Defined as:

[0016] (2)

[0017] In the formula, To normalize the transmittance, it is normalized using dark current and reference light intensity, which can reduce the influence of light source power, receiver sensitivity, and light source aging degree on transmittance characteristics at different wavelengths. In one embodiment, the measuring cup is made of transparent medical-grade polycarbonate material, and the cup wall thickness in the transmission detection area is a preset value, forming a fixed optical path between the transmitting end and the receiving end. During each acquisition, the received light intensity in the light source-on state and the dark current light intensity in the light source-off state are obtained to eliminate ambient light interference; and the light intensity drift caused by light source aging and optical path contamination is compensated by the periodic updating of the empty cup reference light intensity.

[0018] After a valid sample is triggered, the sliding window will be... The quality samples are sorted by numerical value, and the two largest and two smallest samples are removed before taking the remaining samples. The mean of each sampling point is used to obtain the truncated average mass. The formula is:

[0019] (3)

[0020] In the formula, The first number in the sliding window after sorting by value The original measurement quality. All within the window. The arithmetic mean mass of the mass samples for:

[0021] (3a)

[0022] Then, by adjusting the tilt angle and compensating for temperature, the range and zero-point temperature drift are corrected to obtain the net mass of the drainage fluid.

[0023] (4)

[0024] In the formula, The zero-point baseline of the empty cup; The actual measured temperature in the current environment; The reference temperature for calibration is 25℃. Zero-point temperature drift coefficient (unit: g / ℃); This is the range temperature drift coefficient (unit: 1 / ℃ or ppm / ℃). Empty cup baseline. It is preferable to collect data at the same or similar tilt angle as the effective measurement. When the difference between the tilt angle of the empty cup baseline and the tilt angle of the effective measurement exceeds the preset tilt angle difference threshold, the attitude of the empty cup baseline is corrected first. Take the average or median of the tilt angle data within the effective sliding sampling window. The tilt angle correction model can be obtained by calibration using multiple known standard weights at different tilt angles; in cases where the model does not conform to cosine, the calibrated polynomial correction function is used. replace.

[0025] The volume conversion formula is:

[0026] (5)

[0027] In the formula, The volume of the drainage fluid is in mL. Net mass of drainage fluid (in grams); For trait categories At temperature The density (in g / mL) is given below. If the density parameter is calibrated at 25℃, then the temperature correction is performed according to formula (6):

[0028] (6)

[0029] In the formula, This is the density and temperature correction factor for the corresponding drainage fluid.

[0030] Broad spectral channel transmittance Defined as:

[0031] (7)

[0032] In the formula, The measured received light intensity for the wide-spectrum channel. For broadband channel reference light intensity, This represents the intensity of dark current light in a wide-spectrum channel.

[0033] An adaptive density conversion metering device based on drainage fluid property identification, employing the aforementioned metering method, includes: a metering cup, a weighing sensor, a tilt sensor, a temperature sensor, a multi-band light source and light receiver, a near-infrared detection component, a pipeline fluid flow detection component, an identification acquisition component, a clock component, a communication component, a valve actuation component, a secure storage unit, a processor, a non-volatile memory, and a drain valve. The metering cup is supported on the bearing end of the weighing sensor; the multi-band light source and light receiver are respectively mounted on gate-shaped optical supports on opposite sides of the transmission detection area of ​​the metering cup; the weighing sensor, tilt sensor, temperature sensor, multi-band light source, light receiver, near-infrared detection component, pipeline fluid flow detection component, identification acquisition component, clock component, communication component, secure storage unit, non-volatile memory, and valve actuation component are all electrically connected to the processor; the pipeline fluid flow detection component is located at the drainage pipeline inlet and is used to collect near-infrared transmission attenuation characteristics in standby mode.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This invention identifies seven common clinical drainage fluid properties by combining multispectral absorbance and turbidity equivalent, and matches the corresponding density parameters to complete adaptive volume conversion, which can reduce the volume deviation caused by fixed water density conversion.

[0036] 2. The present invention adds a pre-judgment step for the inflow of drainage fluid, which can shorten the effective volume output delay after the drainage fluid reaches the measuring cup; it also adds a dynamic bubble interference removal mechanism, which, combined with an adaptively extended sliding sampling window and a bubble confidence weighted filtering algorithm, can reduce the weighing and measurement error caused by bubble interference.

[0037] 3. The measurement records of the present invention adopt an append-type structure and chain-type summary integrity verification, which fully covers the core parameters of the entire process such as device number, timestamp, property label, and converted volume. If correction is required, only a new record with a void / correction mark and pointing to the original record number can be appended, while the original record is retained, thereby improving the continuity and integrity of the measurement records.

[0038] 4. This invention supports an automatic cyclic metering process that automatically initializes and acquires the zero-point baseline upon startup, automatically starts metering after obtaining the patient's identification, and automatically updates and resets the zero-point baseline after drainage is completed. This reduces manual zero-point calibration and parameter adjustment operations, supports continuous metering for multiple patients, and reduces the operational burden on medical staff. Attached Figure Description

[0039] Figure 1 This is a flowchart of the adaptive density conversion and measurement method based on drainage fluid property identification of the present invention;

[0040] Figure 2 This is a block diagram of the adaptive density conversion metering device based on the identification of drainage fluid properties according to the present invention.

[0041] Explanation of reference numerals in the attached diagram: 100-Measuring cup, 200-Weighing sensor, 300-Gate-shaped optical support, 310-Multi-band light source, 320-Optical receiver, 330-Near-infrared detection component, 400-Tilt sensor, 500-Temperature sensor, 600-Processor, 700-Non-volatile memory, 800-Drain valve, 810-Valve actuation component, 900-Identification acquisition component, 910-Communication component, 920-Pipeline flow detection component, 930-Clock component, 940-Secure storage unit. Detailed Implementation

[0042] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0043] Example 1

[0044] This invention provides an adaptive density conversion and measurement method based on the identification of drainage fluid properties, such as... Figure 1 As shown, it includes the following steps:

[0045] S1. Complete the device's power-on self-test and empty cup initialization, and collect the zero-point baseline under empty cup conditions. It also locks the drain valve and enters standby mode;

[0046] S2. After obtaining a valid patient identification, start the preset effective measurement time window and simultaneously collect multi-channel absorbance data and turbidity equivalent data of the drainage fluid.

[0047] S3. Effective sampling is triggered by four stability criteria: tilt angle, mass range, mass standard deviation, and mass change rate. The original mass data obtained from the sampling is corrected for tilt angle and compensated for temperature to obtain the net mass of the drainage fluid.

[0048] S4. Determine the drainage fluid properties based on the collected multispectral absorbance and turbidity equivalent, match the corresponding density parameters, and substitute them into the conversion formula to obtain the drainage fluid volume.

[0049] S5. Write the complete measurement record containing patient identification, phenotype, density parameters used, original mass, and converted volume into a non-volatile memory, and output the drainage enable signal after completing the integrity check.

[0050] S6. After the drainage is completed, a stable reading is collected under the current unloaded state of the cup. This reading is automatically updated as the new zero-point baseline for the next measurement. The current patient's identity is unbound, the valve is relocked and returned to standby state, supporting the cyclic measurement of the next patient.

[0051] Specifically, the acquisition of drainage fluid characteristics in step S2 involves: using a portal-shaped optical support straddling the outside of the cup to acquire the transmitted light intensity of eight visible light channels, one broadband channel, and one 850 nm near-infrared channel with center wavelengths of 405 nm, 415 nm, 425 nm, 445 nm, 480 nm, 555 nm, 590 nm, and 630 nm. The absorbance at each wavelength is calculated using formula (1), and the normalized transmittance is obtained using formula (2). The near-infrared transmission attenuation characteristics are acquired using an 850 nm infrared beam-beam assembly, and the turbidity equivalent is obtained by calibration with a standard turbidity solution. This acquisition scheme can complete multi-dimensional optical data acquisition without contacting the drainage fluid, avoiding liquid contamination of the optical assembly and simultaneously acquiring the transmitted light intensity data of eight visible light channels, one broadband channel, and one near-infrared channel, providing a multi-dimensional basis for subsequent characteristic classification.

[0052] Specifically, the four stability criteria in step S3 are as follows: a sliding sampling window with a duration of 2.0 s and a sampling rate of 10 times / s is set, and all four criteria must be met simultaneously to trigger valid sampling:

[0053] ①Real-time tilt angle of the cup body ;

[0054] ② The quality data range within the sliding window is extremely poor. ;

[0055] ③Standard deviation of quality data within the sliding window ;

[0056] ④ Real-time change rate of quality data .

[0057] in, The preferred implementation method is to take the average or median of the tilt angle data within the effective sliding sampling window. Take 0.5° Take 0.8 g, Take 0.3 g, Take 0.5 g / s. The threshold value can be selected within the following ranges depending on different weighing ranges, sensor accuracies, and operating environments: The range is 0.2° to 2.0°. The dosage is 0.2–2.0 g. The content is 0.1–1.0 g. The value ranges from 0.1 to 1.0 g / s. This quadruple stability criterion simultaneously verifies four dimensions: cup tilt, data fluctuation, dispersion, and rate of change. It can filter out dynamic fluctuation data when the drainage fluid is first injected into the cup and avoid invalid sampling triggers caused by temporary shaking and instantaneous jumps in the weighing unit.

[0058] Specifically, the data correction algorithm in step S3 is as follows: after effective sampling is triggered, remove the two largest and two smallest sampling points within the sliding window, and take the remaining points. (i.e., truncated average mass) is used as the representative mass of the window, i.e., formula (3); the aforementioned (i.e., all windows) The arithmetic mean of the samples is used to calculate the standard deviation of the mass, i.e., formula (3a); then, the tilt angle correction and the bidirectional correction of the range and zero-point temperature drift are completed according to formula (4), and finally the net mass of the drainage fluid is calculated. The data correction algorithm first removes extreme points to filter out accidental jumps and offset data caused by instantaneous disturbances. Then, it averages the remaining sampling points to reduce random fluctuations. Finally, it eliminates the effects of placement tilt and temperature drift through tilt correction and temperature compensation, filtering out interference errors layer by layer in multiple dimensions.

[0059] Specifically, the rule for determining the drainage fluid characteristics in step S4 first involves detecting the effectiveness of the optical signal: when the received light intensity is below the lower limit of the dynamic range, the channel is marked as a low-signal channel; when the received light intensity is above the upper limit of the dynamic range, the channel is marked as a saturated channel. The absorbance ratio of low-signal or saturated channels is not directly calculated; instead, other non-low-signal, non-saturated channels, turbidity equivalent characteristics, and a preset default density model are used for processing. Only when... , Time calculation ,when , The absorbance ratio is then marked as an invalid feature. Next, candidate trait category determination is performed, with the specific rules as follows:

[0060] (1) When the entire wavelength band exhibits high transmission attenuation and low selective absorption characteristics, and the normalized transmittance of each visible light channel meets the preset white domain flatness condition, it is determined to be chyle-like drainage fluid.

[0061] (2) When turbidity equivalent and At that time, it was determined to be purulent drainage fluid;

[0062] (3) When And wide spectral channel transmittance At that time, it was determined to be bloody drainage fluid;

[0063] (4) When At that time, it was determined to be a lightly bloody drainage fluid;

[0064] (5) When and At that time, it was determined to be bile-like drainage fluid;

[0065] (6) When Furthermore, if the turbidity equivalent is <50 NTU, it is classified as urine;

[0066] (7) When the transmittance of the wide spectral channel , And turbidity equivalent At that time, it was determined to be a clear serous drainage fluid.

[0067] in, , , , , , All thresholds are calibrated and determined. In this embodiment... Take 1.2, Take 0.8, Take 0.1, The value is set to 0.05. "High transmission attenuation and low selective absorption characteristics" can be determined by the transmittance of the wide spectral channel being lower than a preset threshold; "White domain flatness condition" is determined by the normalized transmittance dispersion of the visible light channel.

[0068] (8)

[0069] In the formula, This represents the normalized transmittance of the visible light channel. It is the set of wavelengths of visible light channels. The number of visible light channels. This represents the normalized transmittance dispersion of the visible light channel. The white domain dispersion threshold is defined as follows: when the normalized transmittance dispersion of the visible light channel is lower than the white domain threshold, the white domain flatness condition is satisfied.

[0070] When a candidate class is generated, output that candidate class; when at least two candidate classes are generated, calculate the confidence score of each candidate class using the class center distance method.

[0071] (9)

[0072] (10)

[0073] In the formula, For the first One optical characteristic value, For the first Type of drainage fluid The calibration center value of each optical feature, For the corresponding discrete scale and , The non-negative feature weights The total number of candidate trait categories. For the first Confidence level of a class trait; when the highest confidence level is reached... The corresponding trait category is output when the trait is identified; otherwise, "unidentified trait" is output. The optical feature vector... At least including:

[0074] (11)

[0075] More specifically, in step S5, the pre-stored density values ​​corresponding to each trait category are an example parameter set: clear serous 1.007 g / mL, lightly bloody 1.018 g / mL, bloody 1.040 g / mL, purulent 1.025 g / mL, bile-like 1.026 g / mL, chylous 1.008 g / mL, urine 1.012 g / mL, with a default density of 1.010 g / mL, and density temperature correction can be performed according to formula (6). The example parameter set is stored in non-volatile memory and can be updated according to the calibration results of different sample groups.

[0076] Density parameter calibration example: Multiple calibration samples were obtained for each trait category. At least two repeated measurements were performed on each calibration sample using a hydrometer (accuracy ±0.0005 g / mL) under constant temperature conditions of 25℃. The average value was taken as the pre-stored density value for that trait category, and the standard deviation and density range were recorded. Density temperature correction coefficients were obtained by measuring the density at multiple temperature points (e.g., 15℃, 20℃, 25℃, 30℃, 35℃) and fitting the data. .

[0077] More specifically, the density of the bloody drainage fluid is further estimated and corrected using the equivalent volume fraction of blood components, calculated as follows:

[0078] (12)

[0079] (13)

[0080] In the formula, This is an equivalent volume fraction used for density estimation and is not considered as a result of patient hematocrit testing. The turbidity equivalent value is obtained by calibration based on the near-infrared transmission attenuation characteristics; The coefficients are obtained by calibration using samples with known volume fractions. , For the lower and upper limits of the equivalent volume fraction (e.g.) =0、 =1), This is the plasma equivalent density (approximately 1.026 g / mL). The red blood cell equivalent density is approximately 1.096 g / mL. Indicates when < When L is taken, when L≤ ≤ Time to take ,when > Time to take When the non-blood diluting components in the bloody drainage fluid are within a preset range, the above-mentioned two-component equivalent density model is used; when they exceed the preset range, a bloody drainage fluid density regression model calibrated by the sample is used.

[0081] Specifically, the measurement record data structure in step S5 is an append-only structure, with specific fields including device number, record number, patient identification, tubing identification, operator identification, timestamp, original mass, density used, converted volume, original received values ​​and corresponding reference values ​​for each optical channel, turbidity equivalent, property label, tilt angle value, stability criterion parameter, density adaptive flag, upload status, and check code. If correction is needed, only a new record with a "void / correction" flag pointing to the original record number can be added, and the original record is retained. Generate an integrity check code according to the chained summary formula (14):

[0082] (14)

[0083] In the formula, For the nth measurement record, This is a summary of the previous record. The timestamp is generated by the clock component. The device key is stored in a secure storage unit, which restricts the device key from being read externally; when the first metering record is generated, the digest of the previous record is... An initial device digest is pre-written into the secure storage unit, or generated based on the device number and device key. This structure is used to verify the continuity and integrity of measurement records, and the communication interface can be used for data exchange with external medical information systems.

[0084] Application Example 1 (Calculation Example)

[0085] This application example is a calculation example based on preset working parameters, used to illustrate the implementation process and calculation method of the method. It is not intended as actual performance verification data and should be replaced with actual test records before formal submission. Five sets of calculation samples (numbered P01 to P05) are constructed according to preset density parameters. The exemplary measurement conversion is completed using the method described in Embodiment 1 of this invention. The operation process is as follows:

[0086] 1) Initialization: The device performs a self-test upon power-on, acquires a zero-point baseline in the empty cup state, locks the drain valve, and enters standby mode;

[0087] 2) Sample binding: Read the sample number to obtain the identity identifier and start the effective measurement time window (preset duration 4 hours);

[0088] 3) Data acquisition: The device automatically acquires multi-channel absorbance and turbidity equivalent data of the drainage fluid, and uses a quadruple stability criterion (tilt angle ≤ 0.5°, range ≤ 0.8 g, standard deviation ≤ 0.3 g, rate of change ≤ 0.5 g / s) to trigger effective sampling;

[0089] 4) Correction and Conversion: The original mass data is corrected for tilt angle and temperature (ambient temperature 22-26℃) to obtain the net mass; at the same time, the density parameters are automatically matched according to the spectral characteristics (clear serous fluid is matched with 1.007 g / mL, light bloody fluid with 1.018 g / mL, bloody fluid with 1.040 g / mL and corrected with equivalent volume fraction, purulent fluid with 1.025 g / mL, default match 1.010 g / mL) to convert the volume;

[0090] 5) Recording and Drainage: Complete measurement records are written to non-volatile memory. After integrity verification, a drainage enable signal is output. After drainage is completed, the zero baseline is automatically updated, the binding is released, and the next cycle begins.

[0091] Each sample group was measured using two methods:

[0092] Fixed density conversion method: Weigh the mass using the device and forcibly divide by 1.0 g / mL to convert the volume;

[0093] The method of this invention: The device automatically completes morphological identification and adaptive density conversion, and outputs volume.

[0094] With standard reference volume The true value is calculated as (standard reference mass divided by measured density). Example calculation data is as follows:

[0095] Sample number Characteristics of drainage fluid Measured density (g / mL) Mass (g) Standard reference volume (mL) Volume conversion (mL) for fixed density Volume (mL) of the method of this invention Fixed density conversion error (%) Error (%) of this invention P01 Clear slurry 1.0068 512.3 508.840 512.3 508.8 +0.68 -0.01 P02 pale blood 1.0175 386.2 379.558 386.2 379.6 +1.75 +0.01 P03 Blood 1.0392 273.5 263.183 273.5 263.2 +3.92 +0.01 P04 purulent 1.0248 451.0 440.086 451.0 440.1 +2.48 +0.00 P05 Unidentified traits 1.0103 604.7 598.535 604.7 598.7 +1.03 +0.03

[0096] Note: The specific values ​​mentioned above in this application example are calculation examples based on preset parameters, used to illustrate the method implementation process; actual performance verification should be based on the original detection records. Because sample P05 contained trace amounts of fat particles, the device failed to match the known trait category, automatically adopted the default density of 1.010 g / mL and marked it as "density not adaptive".

[0097] The calculation results show that, based on the standard reference volume, the fixed density conversion method ignores the density differences of different properties, and the maximum relative error is 3.92% (bloody drainage fluid); the maximum absolute relative error of the method of the present invention under the above calculation example conditions is 0.03%.

[0098] Example 2

[0099] This embodiment is basically the same as the previous embodiment, except that before the effective metering time window is activated in step S2, a pre-judgment step for predicting the inflow of drainage fluid is added: In the standby state of the device, the transmission attenuation characteristics of the near-infrared 850 nm through-beam component at the inlet of the drainage tube are collected at a sampling frequency of 2 times / s, and substituted into the fluid flow triggering judgment formula:

[0100] (15)

[0101] in, The near-infrared light intensity at the current moment. The reference received light intensity under dry conditions in an empty pipeline. The dark current received value of the pipeline fluid flow detection component when the near-infrared light source is turned off. This is the normalized value of pipeline transmission attenuation. This is the liquid flow trigger threshold. When three consecutive samples simultaneously meet the above judgment formula, it is determined that the drainage fluid has started to flow in, automatically waking up the optical acquisition module to complete the exposure gain self-calibration and synchronously triggering the weighing unit to complete the zero-point pre-drift compensation.

[0102] The flow prediction step continuously collects the transmission attenuation characteristics of the 850 nm near-infrared photoelectric component in low-power standby mode, and identifies it in real time through the flow trigger judgment formula. If the condition is met after three consecutive samplings, it is predicted that the drainage fluid will start to flow in, and the optical acquisition module is woken up in advance and the zero-point pre-drift compensation of the weighing unit is completed, so as to avoid the response lag caused by starting the full module initialization only after the drainage fluid has flowed in.

[0103] Application Example 2 (Calculation Example)

[0104] This application example is a calculation example used to illustrate the impact of the inflow prediction step on the working sequence and does not represent actual product performance test results. The drainage fluid triggering process is simulated according to preset parameters:

[0105] Group A (including pre-judgment): Activate all processes of Example 2. In standby mode, collect the transmission attenuation characteristics of the 850 nm near-infrared photoelectric component at the inlet of the drainage tube at a frequency of 2 times / s. After satisfying the judgment formula for 3 consecutive times, wake up the optical acquisition module in advance to complete the exposure gain self-calibration, and simultaneously trigger the weighing unit to complete the zero-point pre-drift compensation before opening the effective measurement time window.

[0106] Group B (control group): The pre-judgment optical path was removed, and the "start" button was manually pressed only after the drainage fluid entered the measuring cup. The rest of the algorithm (quadruple criteria, density matching, integrity verification, etc.) was completely consistent with Group A.

[0107] The following three indicators were statistically analyzed: module preparation delay (the time from the near-infrared liquid flow trigger condition being met to the optical acquisition module and weighing unit entering the ready state); effective volume output delay (the time from the arrival of the drainage fluid in the measuring cup to the first output of the effective volume, including the stable sampling window); and total delay (the time from the start of the drainage fluid flowing into the pipeline to the first output of the effective volume). The following is a calculation example based on preset operating parameters:

[0108] index Group A (including preliminary predictions) Group B (No prediction) Module preparation delay (s) 0.6 4.1 Effective volume output delay (s) 2.2 6.3 Total delay (s) 4.2 8.3

[0109] Note: The module preparation delay mainly consists of exposure gain self-calibration (approximately 0.4 s) and zero-point pre-drift compensation (approximately 0.2 s); the effective volume output delay includes a 2.0 s stable sampling window. The values ​​in this application example are calculation examples and are not intended as actual performance verification data.

[0110] Based on the preset parameters, Group A has completed module preparation when the drainage fluid reaches the measuring cup, and therefore can theoretically output an effective volume in approximately 2.2 seconds. Group B needs to complete module preparation and stable sampling sequentially after startup, with a theoretical output delay of approximately 6.3 seconds. Under the test conditions of the above calculation example, the delay from the arrival of the drainage fluid in the measuring cup to the first output of an effective volume is reduced from 6.3 seconds to 2.2 seconds, a reduction of approximately 65.1%. This calculation example is only used to illustrate the impact of the inflow prediction step on the working sequence and does not represent the actual product performance test results.

[0111] Example 3

[0112] This embodiment is basically the same as the previous embodiment, except that a dynamic bubble interference removal step is added to the effective sampling triggering process in step S3: real-time synchronous analysis of the temporal fluctuation characteristics of the multi-channel transmitted light intensity, and determination of the bubble state according to the bubble characteristic index:

[0113] (16)

[0114] in, This represents the short-time dispersion of multi-channel transmitted light intensity. It is high-frequency fluctuation energy. This is an indicator of the inconsistency between the normalized transmittance changes of different optical channels. , , These are the weighting coefficients. One way to calculate the above three indicators is as follows:

[0115] (17)

[0116] (18)

[0117] (19)

[0118] In the formula, The number of optical channels involved in bubble detection. The time-domain window length for bubble detection; multi-channel average normalized transmittance. for The spectrum obtained by discrete Fourier transform within the window. The preset frequency band corresponding to the bubble disturbance. To prevent positive numbers with a denominator of zero, Indicates standard deviation, Indicates to Calculate the standard deviation.

[0119] When continuous Secondary satisfaction If bubbles are detected, the sliding sampling window duration of 2.0 s is dynamically and adaptively extended to 4–8 s. Simultaneously, a bubble confidence-weighted filtering algorithm is introduced to generate a filtered sequence from the original quality data.

[0120] (20)

[0121] (twenty one)

[0122] in, For the first The original quality values ​​of each sampling point To correspond to the bubble-free confidence level, This is the weighting adjustment coefficient. Number of sampling points in the bubble-weighted filter window. Bubble-free confidence score. Based on bubble characteristic indicators calculate:

[0123] (twenty two)

[0124] In the formula, This represents the upper limit of the bubble-free state indicator. This is the lower limit of the strong bubble state index, and ; The minimum confidence level for bubble-free conditions is given, and 0 < 0. <1; when ≤ hour =1, when ≥ hour = After detecting bubbles, the filtered quality sample value is used. Replace the original quality sample value This is used for subsequent calculations of quality range, quality standard deviation, and quality change rate; after the bubbles dissipate, within the extended sliding sampling window, extreme values ​​are removed from the filtered quality samples, and the truncated average is calculated. When continuous... Secondary satisfaction If the bubble disappears, the quadruple stability criterion verification of tilt angle, range, standard deviation, and rate of change is restarted. If the bubble-free condition is still not met after the window is extended to the maximum duration, the output of the formal measurement result is paused, the current conversion result is temporarily stored, and a low confidence prompt of "bubble interference not eliminated" is output.

[0125] This dynamic bubble interference removal step identifies the bubble state on the surface of the drainage fluid by analyzing the temporal fluctuation characteristics of the multi-channel transmitted light intensity in real time. When a bubble is detected, the fixed sliding sampling window is adaptively extended, and the confidence weight of no bubble is dynamically adjusted according to the bubble disturbance characteristics corresponding to the sampling point. The weight of sampling points with strong bubble interference is reduced. After there are no visible bubbles on the liquid surface, the quadruple stability criterion verification is restarted. The interference of bubble symmetry is weakened from two dimensions: sampling time sequence and data filtering.

[0126] Application Example 3 (Calculation Example)

[0127] This application example is a calculation example based on preset working parameters, used to illustrate the impact of the dynamic bubble interference removal step on volumetric measurement error. It is not intended as actual performance verification data and should be replaced with actual test records before formal submission. Calculation scenarios with different bubble levels are constructed according to preset parameters:

[0128] (1) No bubbles (baseline, volume fraction < 0.1%); (2) Low bubbles (volume fraction approximately 1.5%); (3) High bubbles (volume fraction approximately 4.0%).

[0129] Two modes are run at each level:

[0130] Group A (using the algorithm of Example 3): Real-time analysis of the time-domain fluctuation characteristics of multi-channel transmitted light intensity, adaptive extension of the sliding sampling window from 2.0 s to 6.0 s after detecting bubbles, and introduction of bubble confidence weighted filtering. After optical determination that there are no visible bubbles, the quadruple stability criterion is restarted.

[0131] Group B (control group): The bubble removal logic was disabled, the window was fixed at 2.0 s, no confidence filtering was performed, and the other weighing and density conversion algorithms were exactly the same.

[0132] Each group was individually measured 10 times at each bubble level (approximately 500 g of drainage fluid per measurement). Based on the theoretical volume (converted to a calibrated density of 1.0180 g / mL), the results are summarized below:

[0133] bubble level Group Average volume measured (mL) Theoretical volume (mL) Mean absolute error (mL) Relative error (%) No bubbles Group A 491.2 490.0 1.2 0.24 No bubbles Group B 491.0 490.0 1.0 0.20 Low bubble Group A 489.8 490.0 0.8 0.16 Low bubble Group B 504.3 490.0 14.3 2.92 High bubble Group A 490.5 490.0 0.9 0.18 High bubble Group B 510.8 490.0 20.8 4.24

[0134] Note: Theoretical volume = weighed mass of the precision balance (preset value approximately 498.8 g) / calibrated density 1.0180 g / mL, approximately 490.0 mL. The mean absolute error is the arithmetic mean of 10 single-measurement absolute errors (|single measurement volume − theoretical volume|); relative error = mean absolute error / theoretical volume × 100%. The above values ​​are calculation examples and are not intended as actual performance verification data.

[0135] Under the conditions of the above calculation example, the maximum relative error in the bubble interference scenario can be reduced from 4.61% to 0.27%.

[0136] Device Implementation

[0137] An adaptive density conversion metering device based on drainage fluid property identification, such as Figure 2 As shown, the above-mentioned measurement method is used. A measuring cup 100 is supported on the bearing end of a weighing sensor 200 and is used to contain drainage fluid. A portal-shaped optical support 300 straddles the outside of the measuring cup 100. A multi-band light source 310 and a light receiver 320 are respectively disposed on opposite sides of the transmission detection area of ​​the measuring cup 100. A near-infrared detection component 330 is used to collect near-infrared transmission attenuation characteristics. A tilt sensor 400 is used to collect tilt angle data of the measuring cup. A temperature sensor 500 is used to collect temperature data. A processor 600 is used to execute each step in the above measurement method. A non-volatile memory 700 is used to store measurement records. A drain valve 800 is used to drain drainage fluid after measurement and is connected to a valve drive component 810. An identification acquisition component 900 is used to acquire patient identification. A communication component 910 is used to upload measurement records. A tubing flow detection component 920 is disposed at the drainage tubing inlet and is used to collect near-infrared transmission attenuation characteristics in standby mode. A clock component 930 is used to generate a timestamp for the measurement record. A secure storage unit 940 is used to store the device key used to generate an integrity check code. Weighing sensor 200, tilt sensor 400, temperature sensor 500, multi-band light source 310, light receiver 320, near-infrared detection component 330, pipeline fluid flow detection component 920, identification acquisition component 900, clock component 930, communication component 910, secure storage unit 940, non-volatile memory 700, and valve drive component 810 are all electrically connected to processor 600. This device implements the entire process metering logic into a hardware-executable closed-loop architecture, achieving modular control from power-on initialization, multi-dimensional morphological data acquisition, anti-interference effective sampling and data correction, adaptive density conversion, integrity verification to automatic reset after metering completion.

Claims

1. An adaptive density-volume measurement method based on drainage fluid property identification, characterized in that, include: S1. Collect the empty mass baseline of the measuring cup, and collect the transmitted light intensity and near-infrared transmission attenuation characteristics of the drainage liquid in the measuring cup at multiple preset wavelengths, or the turbidity equivalent obtained by calibration of the near-infrared transmission attenuation characteristics. S2. Simultaneously collect the mass data, tilt angle data and temperature data of the measuring cup, and determine the valid mass sampling data when the tilt angle, mass range, mass standard deviation and mass change rate within the preset sliding sampling window meet the corresponding stable thresholds respectively. S3. Remove outliers from the effective mass sampling data and correct it based on the empty mass baseline, tilt angle data and temperature data to obtain the net mass of the drainage fluid. S4. Generate candidate trait categories based on the absorbance characteristics, absorbance ratio, normalized transmittance characteristics, and turbidity equivalent at the multiple preset wavelengths. When at least two candidate trait categories are generated, the trait confidence level corresponding to each candidate trait category is calculated, and the final trait category is determined based on the candidate trait categories and the trait confidence levels. When a candidate trait category is generated, that candidate trait category is determined as the final trait category; S5. Determine the corresponding density parameter or density estimation model based on the final trait category, and calculate the drainage fluid volume based on the net mass of the drainage fluid and the density parameter or density estimation model; when the final trait category is not determined, call the preset default density parameter to calculate the drainage fluid volume and generate a density not adaptive flag.

2. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: Step S1 specifically involves: using a portal-shaped optical support straddling the outside of the measuring cup, collecting the transmitted light intensity of eight visible light channels, one broadband channel, and one 850 nm near-infrared channel with center wavelengths of 405 nm, 415 nm, 425 nm, 445 nm, 480 nm, 555 nm, 590 nm, and 630 nm, respectively, and calculating the absorbance at each wavelength using the following formula: in, wavelength absorbance at the following levels Wavelength when filled with drainage fluid The received light intensity, This refers to the received light intensity in an empty cup or reference medium state. Dark current intensity when the light source is off; normalized transmittance. Calculate using the following formula: Near-infrared transmission attenuation characteristics were collected using an 850 nm near-infrared beam transducer, and a calibration relationship between transmission attenuation characteristics and turbidity equivalent was established using a standard turbidity solution.

3. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: The stability threshold determination in step S2 is specifically as follows: a sliding sampling window with a duration of 2.0 s and a sampling rate of 10 times / s is set, and the following four criteria must be met simultaneously to determine valid quality sampling data: in, To represent the average or median of the tilt angle data within the effective sliding sampling window. The tilt angle threshold, The quality within the window is extremely poor. The standard deviation of quality within the window. The rate of change of quality within the window. For the first Each quality sample value, This represents the number of sampling points within the window. The sampling interval is denoted as .

4. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: The data correction in step S3 specifically involves: adjusting the data within the sliding window... The quality samples are sorted by numerical value, and the two largest and two smallest samples are removed before taking the remaining samples. The truncated mean is obtained by taking the average of each sampling point. Calculate the net mass of the drainage fluid using the following formula: in, For the net mass of the drainage fluid, The zero-point baseline of the empty cup. To represent the average or median of the tilt angle data within the effective sliding sampling window. For actual measured temperature, To calibrate the reference temperature, This is the zero-point temperature drift coefficient. The temperature drift coefficient of the measuring range; the zero-point baseline of the empty cup. The attitude is obtained under the same attitude as the effective quality sampling data or the tilt angle difference is less than the preset tilt angle difference threshold. When the tilt angle difference is not less than the preset tilt angle difference threshold, the attitude of the empty cup zero point baseline is corrected first.

5. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: The trait classification rules in step S4 are used to generate candidate trait categories. First, optical signal validity is detected (including low-signal channel and saturation channel detection), then the following rules are applied for determination; only when… , Time calculation ,when , The absorbance ratio is then marked as an invalid feature, and other non-low signal, non-saturated channel, turbidity equivalent, and preset default density model are used for processing. (1) When the entire wavelength band exhibits high transmission attenuation and low selective absorption characteristics, and the normalized transmittance of each visible light channel meets the preset white domain flatness condition, it is determined to be chyle-like drainage fluid. (2) When turbidity equivalent and At that time, it was determined to be purulent drainage fluid; (3) When And wide spectral channel transmittance At that time, it was determined to be bloody drainage fluid; (4) When At that time, it was determined to be a lightly bloody drainage fluid; (5) When and At that time, it was determined to be bile-like drainage fluid; (6) When Furthermore, if the turbidity equivalent is <50 NTU, it is classified as urine; (7) When the transmittance of the wide spectral channel , And turbidity equivalent At that time, it was determined to be a clear, serous drainage fluid; When a candidate class is generated, output that candidate class; when at least two candidate classes are generated, calculate the confidence score of each candidate class using the class center distance method. in, For the first One optical characteristic value, For the first Type of drainage fluid The calibration center value of each optical feature, For the corresponding discrete scale and , The non-negative feature weights The total number of candidate trait categories. For the first Confidence level of a class trait; when the highest confidence level is reached... Output the corresponding trait category when the condition is met; otherwise, output "Unidentified trait". If no candidate category is generated, use the preset default density parameter and mark it with the "Density not adaptive" flag. Normalized transmittance , , , , , All of these are threshold values ​​that have been calibrated and determined.

6. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 5, characterized in that: In step S5, the density parameter includes pre-stored density values ​​corresponding to clear serous, slightly bloody, bloody, purulent, bile-like, chylous, and urine, respectively, and is temperature-corrected according to the following formula: in, For trait categories Calibration density at 25°C The density is adjusted using a density-temperature correction factor; the density of the bloody drainage fluid is further corrected by estimating the equivalent volume fraction of blood components. in, This is an equivalent volume fraction used for density estimation and is not considered as a result of patient hematocrit testing. The turbidity equivalent value is obtained by calibration based on the near-infrared transmission attenuation characteristics; The coefficients are obtained by calibration using samples with known volume fractions. , These are the lower and upper limits of the equivalent volume fraction. For plasma equivalent density, Equivalent density of red blood cells; Indicates when < When L is taken, when L≤ ≤ Time to take ,when > Time to take .

7. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: It also includes a step to predict the inflow of drainage fluid: In standby mode, the transmission attenuation characteristics of the near-infrared 850nm photoelectric sensor at the inlet of the drainage tube are collected at a frequency of 2 times / s, and then substituted into the fluid flow triggering determination formula: in, The near-infrared light intensity at the current moment. The reference received light intensity under dry conditions in an empty pipeline. The dark current received value of the pipeline fluid flow detection component when the near-infrared light source is turned off. This is the normalized value of pipeline transmission attenuation. The threshold for triggering the liquid flow is defined as follows: when three consecutive samples meet the given criteria, it is determined that the drainage fluid has started to flow in, the optical acquisition module is automatically woken up to complete the exposure gain self-calibration, and the weighing unit is triggered to complete the zero-point pre-drift compensation.

8. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that, Step S3 also includes a dynamic bubble interference removal step: real-time analysis of the temporal fluctuation characteristics of the multi-channel transmitted light intensity, and determination of the bubble state according to the following bubble characteristic indicators: in, This represents the short-time dispersion of multi-channel transmitted light intensity. It is high-frequency fluctuation energy. This is an indicator of the inconsistency between the normalized transmittance changes of different optical channels. , , These are weighting coefficients; when continuous Secondary satisfaction ≥ If bubbles are detected, the sliding sampling window duration is adaptively extended to 4–8 s, and a bubble confidence-weighted filtering algorithm is used to generate a filtered sequence from the original quality data. in, For the first The original quality values ​​of each sampling point To correspond to the bubble-free confidence level, This is the weighting adjustment coefficient. The number of sampling points in the bubble-weighted filtering window; after a bubble is detected, the filtered quality sample value is used. Replace the original quality sample value This is used for subsequent calculations of quality range, quality standard deviation, and quality change rate; after the bubbles dissipate, extreme values ​​are removed from the filtered quality samples within an extended sliding sampling window, and the truncated average is calculated; bubble-free confidence level. Based on bubble characteristic indicators The value range is calculated using the following formula. ,and : in, This represents the upper limit of the bubble-free state indicator. This is the lower limit of the strong bubble state index, and ; The minimum confidence level for bubble-free operation; when continuous Secondary satisfaction If the bubble disappears, restart the quadruple stability criterion verification; if the bubble-free condition is still not met after the window is extended to the maximum duration, pause the output of the formal measurement result, temporarily save the current conversion result and output a low confidence prompt of "bubble interference not eliminated".

9. The adaptive density conversion and measurement method based on drainage fluid property identification as described in claim 1, characterized in that: It also includes a metrology record integrity verification step: The metrology record, containing device number, record number, patient identification, tubing identification, operator identification, timestamp, original mass, density used, converted volume, original received values ​​and corresponding reference values ​​for each optical channel, turbidity equivalent, property label, tilt angle value, stability criterion parameter, density adaptation flag, upload status, and check code, is written to non-volatile memory, and an integrity check code is generated according to the chained digest formula. in, For the first One measurement record, This is a summary of the previous record. The timestamp is generated by the clock component. The device key is stored in a secure storage unit, which restricts the device key from being read externally; when the first metering record is generated, the digest of the previous record is... The device initial digest is pre-written into the secure storage unit, or generated based on the device number and device key; the measurement record adopts an append-only structure, and the chained digest is used to verify the continuity and integrity of the measurement record; the method further includes: after obtaining a valid patient identity identifier, starting a preset effective measurement time window; after the drainage is completed, collecting a stable reading in the current unloaded state of the measuring cup, automatically updating it to the new zero-point baseline for the next measurement, unbinding the current patient identity, and relocking the valve to return to the standby state to support the cyclic measurement of the next patient.

10. An adaptive density conversion and metering device based on drainage fluid property identification, characterized in that, include: Measuring cup, used to hold drainage fluid; A weighing sensor is used to collect the mass data of the measuring cup; An inclination sensor is used to collect the inclination data of the measuring cup; Temperature sensor, used to collect temperature data; A multi-band light source and a light receiver are used to collect the transmitted light intensity of the drainage fluid in the measuring cup at multiple preset wavelengths. Near-infrared detection component, used to collect near-infrared transmission attenuation characteristics; A pipeline fluid flow detection component is installed at the inlet of the drainage pipeline; The identity identification component is used to obtain patient identity information. Clock component, used to generate timestamps for metering records; Communication components are used to upload metering records; Valve actuation assembly, connected to the drain valve; A secure storage unit is used to store the device key used to generate the integrity check code; A processor for performing the metering method as described in any one of claims 1 to 9; Non-volatile memory used to store measurement records; The drain valve is used to drain the drainage fluid after metering is completed. The measuring cup is supported on the load-bearing end of the weighing sensor; the multi-band light source and the light receiver are respectively disposed on the gate-shaped optical brackets on opposite sides of the transmission detection area of ​​the measuring cup; the weighing sensor, tilt sensor, temperature sensor, multi-band light source, light receiver, near-infrared detection component, pipeline fluid flow detection component, identification acquisition component, clock component, communication component, secure storage unit, non-volatile memory and valve drive component are respectively electrically connected to the processor.

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