Calibration method and device for liquid flow in peristaltic pump

Abstract

The invention discloses a calibration method and device for liquid flow in a peristaltic pump. The method comprises the steps that the peristaltic pump is initialized and calibrated; the actual conveying amount of the detection liquid in the current peristaltic pump is obtained through the liquid detection data, and the flow deviation is calculated according to the initial liquid flow and the actual conveying amount of the peristaltic pump; detecting the continuous flow deviation times, and if the continuous flow deviation times exceed a set threshold value, dynamically adjusting the step number of a motor in the peristaltic pump; and establishing an LSTM network prediction model according to the historical residual data, calculating a motor step number compensation factor, and superposing the compensation factor to the dynamically adjusted motor step number to obtain a target motor step number. According to the scheme, the problem of errors caused by aging and residues can be solved, accurate calibration is achieved, and meanwhile the method has the advantages of automatic adjustment and calibration and high calibration efficiency.

Description

Technical Field

[0001] The present invention relates to a peristaltic pump, and in particular to a method and device for calibrating the liquid flow rate in a peristaltic pump, a peristaltic pump calibration system and a readable storage medium. Background Art

[0002] A peristaltic pump is a pumping device with the characteristics of a constant flow pump and a hose pump. Due to its good liquid delivery accuracy, it is widely used in many application fields such as pharmaceuticals, chemicals, beverages, and scientific research. During long-term use, the delivery accuracy of a peristaltic pump decreases due to internal mechanical aging and liquid residue, which directly affects the accuracy of the test results. Traditional solutions use manual intervention or external sensors (such as pressure sensors), while POCT (point-of-care testing) equipment usually only has liquid testing functions due to volume limitations and cannot integrate additional sensors. Static calibration solutions cannot adapt to the dynamic changes in peristaltic pump performance, resulting in frequent maintenance and high costs.

[0003] In view of this, it is necessary to propose an improvement in the detection method of the delivery accuracy of the liquid flow in the peristaltic pump. Summary of the Invention

[0004] In order to solve at least one of the above technical problems, the main purpose of the present invention is to provide a method and device for calibrating the liquid flow rate in a peristaltic pump.

[0005] To achieve the above object, the present invention adopts a technical solution: providing a method for calibrating the liquid flow rate in a peristaltic pump, comprising:

[0006] Initialize and calibrate the peristaltic pump;

[0007] The actual delivery volume of the test liquid in the current peristaltic pump is obtained through the liquid test data, and the flow deviation is calculated based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume;

[0008] Detect the number of continuous flow deviations. If the number of continuous flow deviations exceeds the set threshold, the number of motor steps in the peristaltic pump will be dynamically adjusted.

[0009] An LSTM network prediction model is established based on historical residual data to calculate the motor step compensation factor, which is then superimposed on the dynamically adjusted motor steps to obtain the target motor steps.

[0010] The initialization and calibration of the peristaltic pump specifically includes:

[0011] Establish the initial flow model of the peristaltic pump,

[0012] Q=k·N·f(η,t)

[0013] Where Q is the flow rate, N is the number of motor steps, f(η,t) is the peristaltic pump attenuation model, η is the pump efficiency coefficient, t is the usage time, and k is the comprehensive coefficient;

[0014] The ideal flow is calculated based on Poiseuille's law and modified into a multi-parameter coupling model.

[0015] Q=128LπD·μΔP·η(t)·N0N·(1-αe-βt)

[0016] η(t)=η0·(1-γt)

[0017] Where D is the inner diameter of the hose, L is the length of the pressurized section, ΔP is the pressure difference, μ is the fluid viscosity, η(t) is the time decay term, which indicates the efficiency drop caused by hose aging, γ is the material fatigue coefficient, N0 is the relationship between the initial number of steps and flow rate, α is the error compensation coefficient, e is the error signal, β is the time compensation coefficient, and t is time;

[0018] Under standard viscosity liquid, the sensitivity coefficients of D, L, and μ to Q are determined separately by the controlled variable method and finally combined into a comprehensive coefficient k.

[0019] The liquid test data specifically include: reaction time, electrochemical signal and optical intensity of electrolytes, metabolites, blood gas and blood oxygen detection reagents.

[0020] The liquid test data is the reaction time difference of the electrolyte test reagent. The actual delivery volume of the test liquid in the current peristaltic pump is obtained through the liquid test data, and the flow rate deviation is calculated based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume, including:

[0021] Calibrate the reaction time of electrolyte detection reagents and determine the relationship between reagent colorimetric reaction time and flow rate.

[0022] T ref =QV chamber

[0023] Among them, T ref is the reaction time, Q is the flow rate, V chamber is the volume of the reaction chamber,

[0024] Calculate the actual reaction time and determine the actual flow rate,

[0025] Q actual =T actual V chamber

[0026] Among them, Q actual is the actual flow rate, T actual is the actual reaction time, V chamber is the volume of the reaction chamber,

[0027] calculating flow deviation,

[0028] ΔQ=Q t theoretical flow actua l=k·N·f(μ,t)-T actual V chamber

[0029] Wherein, ΔQ is flow deviation, Q theory is theoretical flow, N is motor step number.

[0030] Wherein, the set threshold of detecting continuous flow deviation times is three times.

[0031] Wherein, the dynamic adjustment of motor step number in peristaltic pump comprises: updating the peristaltic pump efficiency coefficient by using recursive least square method, and recalculating the motor step number,

[0032] N new =k v ·η^(t)·f(μ,t)Q target

[0033] Wherein, k v is a comprehensive correction coefficient, η^(t) is the peristaltic pump efficiency coefficient updated online by recursive least square method, f(μ,t) is motor-flow transfer function, μ is motor type, t is time parameter, Q target is target flow.

[0034] Wherein, the LSTM network prediction model is established according to historical residual data, the motor step number compensation factor is calculated, and the compensation factor is superimposed on the dynamically adjusted motor step number, comprising:

[0035] Obtain the historical conveying step sequence of peristaltic pump and the corresponding measured residual sequence and train the LSTM network prediction model;

[0036] The residual amount is predicted by the LSTM network prediction model according to the current conveying step sequence of the peristaltic pump;

[0037] Calculate the motor step number compensation factor,

[0038] C residual =Q per_step R^t·ξ

[0039] Wherein, C residual is the motor step number compensation factor, Q per_step is single-step theoretical conveying flow, R^t is residual amount, and ξ is safety factor.

[0040] To achieve the above purpose, another technical scheme adopted by the present application is to provide a calibration device for liquid flow in peristaltic pump, comprising:

[0041] an initialization module configured to initialize the calibration peristaltic pump;

[0042] a calculation module configured to acquire an actual delivery amount of the detection liquid in the peristaltic pump through the liquid detection data, and calculate a flow deviation according to the initial liquid flow of the peristaltic pump and the actual delivery amount;

[0043] an adjustment module configured to detect a continuous flow deviation number, and dynamically adjust the motor step number in the peristaltic pump if the continuous flow deviation number exceeds a set threshold value;

[0044] a compensation module configured to establish an LSTM network prediction model according to historical residual data, calculate a motor step number compensation factor, and superimpose the compensation factor on the dynamically adjusted motor step number to obtain a target motor step number.

[0045] To achieve the above object, the present application employs another technical scheme, which provides a peristaltic pump calibration system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the above method when executing the computer program.

[0046] To achieve the above object, the present application employs another technical scheme, which provides a readable storage medium having a computer program stored thereon, wherein the computer program is executable by a processor to implement the steps in the above method.

[0047] The technical scheme of the present application first initializes the calibration peristaltic pump, then acquires an actual delivery amount of the detection liquid in the peristaltic pump through the liquid detection data, and calculates a flow deviation according to the initial liquid flow of the peristaltic pump and the actual delivery amount, detects a continuous flow deviation number, dynamically adjusts the motor step number in the peristaltic pump if the continuous flow deviation number exceeds a set threshold value, and finally establishes an LSTM network prediction model according to historical residual data, calculates a motor step number compensation factor, and superimposes the compensation factor on the dynamically adjusted motor step number to obtain a target motor step number, which can accurately calibrate the liquid delivery amount of the peristaltic pump in real time, solve the error problem caused by aging and liquid residue of the peristaltic pump, and realize automatic adjustment without manual operation, thereby improving the calibration efficiency. In summary, the present application can calibrate the liquid delivery flow of the peristaltic pump without external sensors, can correct the motor step number in real time through the liquid detection data, solve the error problem caused by aging and residue, and has the advantages of automatic adjustment and high calibration efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0049] Figure 1 Schematic diagram of a flow chart of a method for calibrating liquid flow in a peristaltic pump according to one embodiment of the present invention;

[0050] Figure 2 1 is a block diagram of a device for calibrating liquid flow in a peristaltic pump according to an embodiment of the present invention;

[0051] Figure 3 This is a module block diagram of a peristaltic pump calibration system according to an embodiment of the present invention.

[0052] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] It should be noted that the descriptions of "first", "second", etc. in the present invention are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0055] Unlike existing peristaltic pump technologies that address the problem of errors in liquid delivery due to aging and residual liquid, this solution proposes a method for calibrating the liquid flow rate within a peristaltic pump, aiming to address both these issues. The specific steps of this peristaltic pump flow rate calibration method are described in the following examples.

[0056] Please refer to Figure 1 , Figure 11 is a flow chart of a method for calibrating the liquid flow rate in a peristaltic pump according to an embodiment of the present invention. In an embodiment of the present invention, the method for calibrating the liquid flow rate in a peristaltic pump comprises the following steps:

[0057] S110, initialize and calibrate the peristaltic pump. When the peristaltic pump is in operation, the liquid is transported by controlling the internal motor step count. As the peristaltic pump operates for a long time, it is prone to aging, resulting in a gradual decrease in the accuracy of the motor step count control. In order to facilitate dynamic adjustment of the peristaltic pump motor step count, it is necessary to initialize and calibrate the peristaltic pump and establish a corresponding peristaltic pump flow model.

[0058] S120. Obtain the actual delivery volume of the test liquid in the current peristaltic pump through the liquid detection data, and calculate the flow deviation based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume. The liquid detection data corresponds to the liquid flow rate of the liquid in the peristaltic pump, that is, the actual delivery volume of the peristaltic pump. In this step, the liquid detection results are used as the only input source to realize closed-loop calibration, breaking through the limitations of traditional reliance on sensors. Specifically, in equipment with only liquid detection function (no flow / pressure sensor), the actual flow deviation (ΔQ) is inferred through indirect data such as reagent reaction time difference and optical signal change rate, breaking through the traditional calibration dependence on hardware. In this process, it is necessary to establish a high-precision mapping model between the liquid detection signal and the flow rate, for example, through experimental calibration k reaction coefficient.

[0059] S130: Detect the number of continuous flow deviations. If the number of continuous flow deviations exceeds a set threshold, dynamically adjust the number of motor steps in the peristaltic pump. If the number of continuous flow deviations detected is less than or equal to the set threshold, there is no need to adjust the number of motor steps in the peristaltic pump. At this point, the system automatically re-detects the number of continuous flow deviations. If the number of continuous flow deviations exceeds the set threshold, dynamically adjust the number of motor steps in the peristaltic pump to overcome the problem of peristaltic pump aging affecting its delivery capacity.

[0060] It is understood that the number of consecutive flow deviations can be selected based on actual requirements, such as the average number over a period of time, such as when the number of flow deviations is too large several times in a short period of time. The threshold can be flexibly selected based on actual design requirements, such as four times, five times, etc., and is not limited here.

[0061] S140, an LSTM network prediction model is established according to historical residual data, a motor step compensation factor is calculated, and the compensation factor is superimposed on the dynamically adjusted motor step to obtain a target motor step. According to the historical residual data of the peristaltic pump, an LSTM (Long Short-Term Memory) network prediction model is established to predict the liquid residual flow, and then the motor step compensation factor is calculated and superimposed on the adjusted motor step to obtain the target motor step. The influence of the accumulation of liquid history residual on the liquid delivery amount of the peristaltic pump can be overcome. In addition, the memory occupation of the LSTM network algorithm is compressed to less than 50KB, which is suitable for low-power processors (such as Cortex-M4) and ensures real-time operation in POCT devices.

[0062] In summary, the present scheme can solve the calibration misalignment problem caused by multi-factor coupling through aging compensation and residual compensation of the peristaltic pump.

[0063] In a specific embodiment, the initialization calibration peristaltic pump specifically includes:

[0064] establishing an initial flow model of the peristaltic pump,

[0065] Q=k·N·f(η,t)

[0066] wherein Q is the flow, N is the motor step, f(η,t) is the peristaltic pump attenuation model, η is the pump efficiency coefficient, t is the use time, and k is the comprehensive coefficient;

[0067] calculating the ideal flow based on the Poiseuille law and correcting it to a multi-parameter coupling model,

[0068] Q=128LπD·μΔP·η(t)·N0N·(1-αe-βt)

[0069] η(t)=η0·(1-γt)

[0070] wherein D is the inner diameter of the hose, L is the length of the pressure receiving section, ΔP is the pressure difference, μ is the fluid viscosity, η(t) is the time attenuation term, which represents the efficiency decline caused by hose aging, γ is the material fatigue coefficient, N0 is the initial step number and flow corresponding relationship, α is the error compensation coefficient, e is the error signal, β is the time compensation coefficient, and t is the time;

[0071] In a standard viscosity liquid (such as physiological saline), the sensitivity coefficients of D, L and μ to Q are determined by the control variable method, and finally combined into the comprehensive coefficient k.

[0072] In this step, by introducing the peristaltic pump efficiency attenuation model f(η, t), the aging of the peristaltic pump can be addressed to prevent the impact of aging on the accuracy of its liquid delivery volume. The sensitivity coefficients of D, L, and μ to Q are measured separately using the control variable method. Specifically, the sensitivity coefficients of Q can be tested by selecting different hose inner diameters D, different pressure section lengths L, and different fluid viscosities u. The sensitivity coefficients of Q are finally combined into a comprehensive coefficient k.

[0073] In a specific embodiment, the types of fluid test data include: electrolytes, metabolites, reaction time of blood gas and blood oxygen detection reagents, electrochemical signals, and optical intensity. It is understood that the fluid test data can be selected based on the mapping relationship between other data and flow rate, and this is not limited here.

[0074] Specifically, the liquid test data is the reaction time difference of the electrolyte test reagent. The actual delivery volume of the test liquid in the current peristaltic pump is obtained through the liquid test data, and the flow rate deviation is calculated based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume, including:

[0075] Reaction time T of calibration electrolyte detection reagent ref , determine the reagent colorimetric reaction time T ref Relationship with flow Q,

[0076] T ref =QV chamber

[0077] Among them, T ref is the reaction time, Q is the flow rate, V chamber is the volume of the reaction chamber,

[0078] Calculate the actual reaction time T actual , and determine the actual flow rate Q actual ,

[0079] Q actual =T actual V chamber

[0080] Among them, Q actual is the actual flow rate, T actual is the actual reaction time, V chamber is the volume of the reaction chamber,

[0081] Calculate the flow deviation,

[0082] ΔQ=Q theory -Q actual =k·N·f(μ,t)-T actual V chamber

[0083] Among them, ΔQ is the flow deviation, Q theory is the theoretical flow rate, and N is the number of motor steps.

[0084] In this step, the reagent colorimetric reaction time T ref The flow rate Q can be calibrated by experiment reaction The coefficient is used to determine the high-precision mapping relationship between the liquid test data and the flow rate, and then combined with the flow model of the peristaltic pump constructed previously, the flow deviation can be calculated.

[0085] Specifically, the threshold value for detecting the number of consecutive flow deviations is set to three times. The threshold value can be flexibly selected according to actual requirements, that is, the threshold value can be set to more than three times or less than three times.

[0086] In a specific embodiment, the dynamic adjustment of the number of motor steps in the peristaltic pump includes: updating the efficiency coefficient of the peristaltic pump using a recursive least squares method and recalculating the number of motor steps.

[0087] N new =k v ·η^(t)·f(μ,t)Q target

[0088] Among them, k v is a comprehensive correction coefficient, including the motor step angle error (e.g., 0.045% step-1) and the gearbox transmission ratio fluctuation, η^(t) is the peristaltic pump efficiency coefficient updated online by the recursive least squares method, f(μ,t) is the motor-flow transfer function, μ is the motor type, t is the time parameter, Q target The target flow.

[0089] Among the above parameters, the iterative formula of η^(t) is as follows:

[0090] η^(t)=η^(t-1)+λ+φ(t)TP(t-1)φ(t)P(t-1)·φ(t)·ΔQ

[0091] Where φ(t) is the observation vector (a linear combination of N, μ, and t), λ is the forgetting factor (0.95-0.99 is recommended), T is the row vector coefficient, and TP(t-1) is the inverse matrix of the error covariance matrix.

[0092] In an update example, if the current η0 = 0.92 and ΔQ = +8% is detected, then after the update,

[0093] η new =0.92×(1-0.08 / 1.08)≈0.85,

[0094] The number of motor steps N (the number of motor steps of the flow model) can be corrected in reverse.

[0095] The step can solve the problem of the influence of the aging of the peristaltic pump on the accuracy of the liquid delivery amount by updating the peristaltic pump efficiency coefficient by the recursive least square method and recalculating the motor step number.

[0096] In a specific embodiment, the LSTM network prediction model is established according to historical residual data, the motor step number compensation factor is calculated, and the compensation factor is superimposed on the dynamically adjusted motor step number, which comprises the following steps:

[0097] The historical delivery step number sequence of the peristaltic pump and the corresponding measured residual sequence are obtained, and the LSTM network prediction model is trained;

[0098] The residual amount is predicted by the LSTM network prediction model according to the current delivery step number sequence of the peristaltic pump;

[0099] The motor step number compensation factor is calculated,

[0100] C residual =Q per_step R^t·ξ

[0101] Wherein, C residual is the motor step number compensation factor, Q per_step is the single-step theoretical delivery amount, R^t is the residual amount, and ξ is the safety factor.

[0102] In this step, the historical delivery step number sequence {N1, N2,..., Nt-1} of the motor of the peristaltic pump is collected by data acquisition, and the measured residual sequence {R1, R2,..., Rt-1} is obtained. The residual sequence can be trained by a weighing sensor or an electric conductivity detection LSTM network prediction model. The LSTM network prediction model comprises an input layer, a hidden layer and an output layer. The input layer has a step number sequence (sliding window) of 10 time steps, the hidden layer has 2 layers of LSTM (32 units per layer), Dropout=0.2, and the output layer can predict the residual amount R^t. The residual amount is predicted by the LSTM network prediction model according to the current delivery step number sequence of the peristaltic pump, and the motor step number compensation factor is calculated. The compensation factor and the motor step number adjusted by the aging of the peristaltic pump are superimposed, and the target motor step number can be obtained. The compensation factor can further improve the calibration accuracy of the peristaltic pump as a compensation for the cumulative history residual.

[0103] Please refer to Figure 2 , Figure 2 is a module block diagram of the peristaltic pump liquid flow calibration device in an embodiment of the present application. In the embodiment of the present application, the peristaltic pump liquid flow calibration device comprises:

[0104] The initialization module 110 is configured to initialize the calibration peristaltic pump;

[0105] The computing module 120 is configured to acquire the actual delivery amount of the detection liquid in the peristaltic pump through the liquid detection data, and calculate the flow deviation according to the initial liquid flow of the peristaltic pump and the actual delivery amount;

[0106] The adjusting module 130 is configured to detect the number of continuous flow deviations, and dynamically adjust the motor step number in the peristaltic pump if the number of continuous flow deviations exceeds a set threshold value.

[0107] The compensation module 140 is configured to establish an LSTM network prediction model according to historical residual data, calculate a motor step compensation factor, and superimpose the compensation factor on the dynamically adjusted motor step number to obtain a target motor step number.

[0108] Please refer to Figure 3 , Figure 3 The figure is a module block diagram of a peristaltic pump calibration system according to an embodiment of the present application. The peristaltic pump calibration system can be used to implement the calibration method of the liquid flow in the peristaltic pump in the foregoing embodiments. As shown in the figure, the peristaltic pump calibration system includes a memory 301, a processor 302, a bus 303, and a computer program stored in the memory 301 and executable on the processor 302, and the memory 301 and the processor 302 are connected through the bus 303. When the processor 302 executes the computer program, the calibration method of the liquid flow in the peristaltic pump in the foregoing embodiments is implemented. The number of processors can be one or more. Figure 3

[0109] The memory 301 can be a high-speed random access memory (RAM) or a non-volatile memory such as a disk memory. The memory 301 is used to store executable program codes, and the processor 302 is coupled with the memory 301.

[0110] Further, the present application also provides a computer readable storage medium, which can be arranged in the peristaltic pump calibration system in the foregoing embodiments, and the computer readable storage medium can be the memory in the foregoing embodiments. Figure 3

[0111] The computer readable storage medium stores a computer program, and the program is executed by the processor to implement the calibration method of the liquid flow in the peristaltic pump in the foregoing embodiments. Further, the computer readable storage medium can also be a U disk, a mobile hard disk, a read-only memory (ROM), a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0112] ​​In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0113] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the purpose of this embodiment based on actual needs.

[0114] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0115] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a readable storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0116] It should be noted that for the aforementioned method embodiments, for ease of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0117] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0118] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for calibrating the liquid flow rate in a peristaltic pump, characterized in that: The calibration method of the liquid flow in the peristaltic pump includes: Initialize and calibrate the peristaltic pump; The actual delivery volume of the test liquid in the current peristaltic pump is obtained through the liquid test data, and the flow deviation is calculated based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume; Detect the number of continuous flow deviations. If the number of continuous flow deviations exceeds the set threshold, the number of motor steps in the peristaltic pump will be dynamically adjusted. An LSTM network prediction model is established based on historical residual data to calculate the motor step compensation factor, which is then superimposed on the dynamically adjusted motor steps to obtain the target motor steps.

2. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 1, wherein: The initialization and calibration of the peristaltic pump specifically includes: Establish the initial flow model of the peristaltic pump, Q=k·N·f(η,t) Where Q is the flow rate, N is the number of motor steps, f(η,t) is the peristaltic pump attenuation model, η is the pump efficiency coefficient, t is the usage time, and k is the comprehensive coefficient; The ideal flow is calculated based on Poiseuille's law and modified into a multi-parameter coupling model. Q=128LπD·μΔP·η(t)·N0N·(1-αe-βt) η(t)=η0·(1-γt) Where D is the inner diameter of the hose, L is the length of the pressurized section, ΔP is the pressure difference, μ is the fluid viscosity, η(t) is the time decay term, which indicates the efficiency drop caused by hose aging, γ is the material fatigue coefficient, N0 is the relationship between the initial number of steps and flow rate, α is the error compensation coefficient, e is the error signal, β is the time compensation coefficient, and t is time; Under standard viscosity liquid, the sensitivity coefficients of D, L, and μ to Q are determined separately by the controlled variable method and finally combined into a comprehensive coefficient k.

3. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 1, wherein: The liquid test data specifically includes: reaction time, electrochemical signal and optical intensity of electrolytes, metabolites, blood gas and blood oxygen detection reagents.

4. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 3, wherein: The liquid test data is the reaction time difference of the electrolyte test reagent. The actual delivery volume of the test liquid in the current peristaltic pump is obtained through the liquid test data, and the flow rate deviation is calculated based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume, including: Calibrate the reaction time of electrolyte detection reagents and determine the relationship between the reagent colorimetric reaction time and flow rate, T ref =QV chamber Among them, T ref is the reaction time, Q is the flow rate, V chamber is the volume of the reaction chamber, Calculate the actual reaction time and determine the actual flow rate, Q actua l=T actual V chamber Among them, Q actual is the actual flow rate, T actual is the actual reaction time, V chamber is the volume of the reaction chamber, Calculate the flow deviation, ΔQ=Q theory -Q actual =k·N·f(μ,t)-T actual V chamber Among them, ΔQ is the flow deviation, Q theory is the theoretical flow rate, and N is the number of motor steps.

5. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 1, wherein: The set threshold for detecting the number of consecutive flow deviations is three times.

6. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 5, wherein: The dynamic adjustment of the number of motor steps in the peristaltic pump includes: using a recursive least squares method to update the efficiency coefficient of the peristaltic pump and recalculating the number of motor steps. N new =k v ·η^(t)·f(μ,t)Q target Among them, k v is the comprehensive correction coefficient, η^(t) is the peristaltic pump efficiency coefficient updated online by the recursive least squares method, f(μ,t) is the motor-flow transfer function, μ is the motor type, t is the time parameter, Q target The target flow.

7. The method for calibrating the liquid flow rate in a peristaltic pump according to claim 1, wherein: The method of establishing an LSTM network prediction model based on historical residual data, calculating a motor step compensation factor, and adding the compensation factor to the dynamically adjusted motor step number includes: Obtain the historical delivery step sequence of the peristaltic pump and the corresponding measured residual sequence and train the LSTM network prediction model; The residual volume is predicted based on the current delivery step sequence of the peristaltic pump using the LSTM network prediction model; Calculate the motor step compensation factor, C residual =Q per_step R^t·ξ Among them, C residual is the motor step compensation factor, Q per_step is the theoretical single-step delivery flow rate, R^t is the residual volume, and ξ is the safety factor.

8. A device for calibrating the flow rate of liquid in a peristaltic pump, characterized in that: The calibration device for the liquid flow in the peristaltic pump comprises: an initialization module, configured to initialize and calibrate the peristaltic pump; a calculation module configured to obtain the actual delivery volume of the test liquid in the current peristaltic pump through the liquid test data, and calculate the flow rate deviation based on the initial liquid flow rate of the peristaltic pump and the actual delivery volume; an adjustment module configured to detect the number of consecutive flow deviations and dynamically adjust the number of motor steps in the peristaltic pump if the number of consecutive flow deviations exceeds a set threshold; The compensation module is configured to establish an LSTM network prediction model based on historical residual data, calculate a motor step compensation factor, and superimpose the compensation factor on the dynamically adjusted motor step number to obtain a target motor step number.

9. A peristaltic pump calibration system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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