Micro-fluidic chip micro-valve monitoring and scheduling method, device and equipment and storage medium

Abstract

The invention discloses a micro-fluidic chip micro-valve monitoring and scheduling method, device and equipment and a storage medium, and the method comprises the steps: determining a plurality of target micro-fluidic elements for controlling the opening and closing states of a micro-channel, and obtaining the historical log data of each target micro-fluidic element; clustering the target micro-fluidic elements according to the historical log data to obtain a plurality of micro-valve categories, and determining a corresponding relationship between each micro-valve category and an optical fiber gas sensor category; according to the corresponding relation, the target micro-fluidic element is used as a target micro-valve to be applied to a target micro-fluidic chip of the corresponding optical fiber gas sensor; and monitoring the current signal state of the microfluid controlled by each target microvalve in the target micro-fluidic chip for material transfer through the microchannel, and performing control optimization scheduling on the target microvalve according to the current signal state. The service life of the optical fiber gas sensor is prolonged, the adjustment efficiency of the optical fiber gas sensor is improved, and the optical fiber gas sensor can be widely applied to the technical field of micro-fluidic chips.

Description

Technical Field

[0001] The present invention relates to the technical field of microfluidic chips, and in particular to a method, device, equipment and storage medium for monitoring and scheduling microvalve of a microfluidic chip. Background Art

[0002] As the craze for artificial intelligence (AI) sweeps across all walks of life, "AI chips", which are the core of artificial intelligence, have become very popular. They are essential core components for all smart devices and are specifically used to handle AI-related computing tasks.

[0003] Sensors that use the optical properties of gas to detect gas composition and concentration are called optical gas sensors. According to the specific optical principle, they are divided into infrared absorption type, visible light absorption photometric type, optical interference type, chemiluminescence type, test paper photoelectric photometric type, photoionization type, optical fiber chemical material type, etc. Gas sensors based on the infrared principle are the most common optical absorption gas sensors. Optical gas sensors can not only be used for the detection of various gases, but also for the analysis of petroleum composition and proportion, quantitative analysis of textile products, and have achieved good results in infrared thermal imaging, infrared mechanical non-destructive detection, object recognition, military infrared night vision, infrared guided navigation, infrared stealth, infrared telemetry and remote sensing. Among them, fiber optic gas sensors have been widely used in coal, chemical, petroleum and other sectors due to their advantages of fast response, corrosion resistance, no electromagnetic interference, and flexible reuse. Optical fiber has the characteristics of small size, wide bandwidth, low transmission loss, strong resistance to electromagnetic interference and large amount of information. The sensors made of it have many advantages such as anti-electromagnetic interference, electrical insulation, corrosion resistance, high sensitivity, easy reuse, easy networking, etc. It has been applied in many aspects of social life, such as online monitoring of industrial gas, analysis of harmful gases, environmental air quality monitoring and explosion gas detection, and analysis of volcanic eruption gas. Existing optical fiber gas sensors have the problems of short life and difficult adjustment.

[0004] Terminology explanation:

[0005] Microfluidics: A technology that integrates basic operating units such as sample preparation, reaction, separation, and detection in the carbon emission, biological, chemical, and medical analysis processes onto a micron-scale chip to automatically complete the entire analysis process. Summary of the invention

[0006] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0007] To this end, an object of an embodiment of the present invention is to provide a microfluidic chip microvalve monitoring and scheduling method, which improves the service life and adjustment efficiency of the optical fiber gas sensor.

[0008] Another object of an embodiment of the present invention is to provide a microfluidic chip microvalve monitoring and scheduling device.

[0009] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0010] On the one hand, an embodiment of the present invention provides a microfluidic chip microvalve monitoring and scheduling method, comprising the following steps:

[0011] Determine a plurality of target microfluidic components that control the opening and closing states of the microchannels, and obtain historical log data of each of the target microfluidic components;

[0012] Clustering the target microfluidic element according to the historical log data to obtain a plurality of microvalve categories, and determining a corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category;

[0013] According to the corresponding relationship, the target microfluidic element is applied as a target microvalve to a target microfluidic chip of a corresponding optical fiber gas sensor;

[0014] The current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel is monitored, and the target microvalve is controlled and optimized according to the current signal state.

[0015] Further, in one embodiment of the present invention, the determining of multiple target microfluidic components that control the opening and closing states of the microchannels and obtaining the historical log data of each of the target microfluidic components specifically includes:

[0016] Acquire system log data of the microfluidic system, and determine a plurality of target microfluidic elements that control the opening and closing states of the microchannels according to the system log data;

[0017] A matching query is performed in the system log data according to the component identification of each target microfluidic component to obtain the historical log data of each target microfluidic component.

[0018] Further, in one embodiment of the present invention, clustering the target microfluidic element according to the historical log data to obtain a plurality of microvalve categories, and determining the correspondence between each of the microvalve categories and the optical fiber gas sensor category, specifically includes:

[0019] Performing cluster analysis on the historical log data by using a K-means clustering algorithm to obtain multiple target clusters;

[0020] Dividing the target microfluidic element into a plurality of microvalve categories according to the target clustering clusters;

[0021] The driving force category, driving form and action force category corresponding to each of the microvalve categories are determined, and the corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category is determined according to the driving force category, the driving form and the action force category.

[0022] Furthermore, in one embodiment of the present invention, the historical log data is clustered and analyzed by a K-means clustering algorithm to obtain a plurality of target clusters, which specifically include:

[0023] Using the historical log data of each target microfluidic element as a data set sample of each target microfluidic element;

[0024] Selecting a preset number of current cluster centers from the data set samples;

[0025] Determine the sample distance from each remaining data set sample to each current cluster center, and divide each remaining data set sample into the nearest current cluster center according to the sample distance to obtain multiple current cluster clusters;

[0026] Determine the sample mean of each of the current clustering clusters, update the current cluster center according to the sample mean, and return to determine the sample distance from each remaining data set sample to each current cluster center, until the update number of the current cluster center reaches a preset first threshold, or the update distance of the current cluster center is less than or equal to a preset second threshold, to obtain multiple target clustering clusters.

[0027] Furthermore, in one embodiment of the present invention, the step of selecting a preset number of current cluster centers from the data set samples specifically includes:

[0028] Randomly select a data set sample as the current cluster center, and determine the sample distance between each remaining data set sample and the current cluster center;

[0029] Select the remaining data set sample with the largest sample distance from the current cluster center as the new cluster center, and add the new cluster center to the current cluster center;

[0030] Determine the minimum sample distance between each remaining data set sample and multiple current cluster centers;

[0031] Select the remaining data set sample with the largest minimum sample distance to multiple current cluster centers as the new cluster center, add the new cluster center to the current cluster center, and return to determine the minimum sample distance between each remaining data set sample and multiple current cluster centers until the number of current cluster centers reaches the preset number.

[0032] Further, in one embodiment of the present invention, the control optimization scheduling of the target microvalve according to the current signal state specifically includes:

[0033] Determine an initial population of microvalve control parameters according to the current signal state, wherein an individual in the population represents a set of control parameter vectors, and a gene of the individual represents a control parameter;

[0034] Through mutation and crossover, experimental individuals corresponding to each current individual in the current population are obtained;

[0035] Determine the cost value of the current individual and the corresponding experimental individual, select the individual with the smaller cost value as the current individual of the next round, and return the experimental individuals corresponding to each current individual in the current population obtained through mutation and crossover until the number of population iterations reaches a preset third threshold, and determine the optimal control parameter group according to the optimal individual in the current population;

[0036] The target microvalve is controlled according to the optimal control parameter group.

[0037] Furthermore, in some optional embodiments, obtaining experimental individuals corresponding to each current individual in the current population through mutation and crossover specifically includes:

[0038] For each current individual in the current population, randomly select two other current individuals, calculate the difference vector of the two other current individuals, and add the difference vector to the current individual to obtain the mutant individual;

[0039] Randomly exchange genes between the mutant individual and the current individual to obtain the experimental individual corresponding to the current individual.

[0040] On the other hand, an embodiment of the present invention provides a microfluidic chip microvalve monitoring and scheduling device, comprising:

[0041] A data acquisition module, used to determine a plurality of target microfluidic components that control the opening and closing states of the microchannels, and to acquire historical log data of each of the target microfluidic components;

[0042] A clustering module, used for clustering the target microfluidic elements according to the historical log data to obtain a plurality of microvalve categories, and determining a corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category;

[0043] A microvalve application module, used for applying the target microfluidic element as a target microvalve to a target microfluidic chip of a corresponding optical fiber gas sensor according to the corresponding relationship;

[0044] The microvalve control module is used to monitor the current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel, and to control and optimize the scheduling of the target microvalve according to the current signal state.

[0045] On the other hand, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the microfluidic chip microvalve monitoring and scheduling method as described above is realized.

[0046] On the other hand, an embodiment of the present invention further provides a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the microfluidic chip microvalve monitoring and scheduling method as described above.

[0047] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0048] The embodiment of the present invention determines multiple target microfluidic elements that control the opening and closing states of microchannels, obtains historical log data of each target microfluidic element, clusters the target microfluidic elements according to the historical log data, obtains multiple microvalve categories, and determines the correspondence between each microvalve category and the fiber optic gas sensor category. According to the correspondence, the target microfluidic element is applied as a target microvalve to the target microfluidic chip of the corresponding fiber optic gas sensor, monitors the current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer matter through the microchannel, and controls the target microvalve for optimal scheduling according to the current signal state. The embodiment of the present invention clusters multiple target microfluidic elements that can control the opening and closing states of microchannels according to historical log data, obtains multiple microvalve categories, and determines the correspondence between each microvalve category and the fiber optic gas sensor category, so that the target microfluidic element can be used as a microvalve in the microfluidic chip of the corresponding fiber optic gas sensor, thereby improving the service life, detection accuracy and detection efficiency of the fiber optic gas sensor; by monitoring the current signal state of the microvalve controlling the microfluid to transfer matter through the microchannel, and controlling and optimizing the microvalve according to the current signal state, the accuracy and real-time performance of the microvalve control are improved, thereby improving the calibration efficiency of the fiber optic gas sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0050] Figure 1 A flow chart of the steps of the microfluidic chip microvalve monitoring and scheduling method provided by an embodiment of the present invention;

[0051] Figure 2 A step flow chart of step S101 provided in an embodiment of the present invention;

[0052] Figure 3 A step flow chart of step S102 provided in an embodiment of the present invention;

[0053] Figure 4 A step flow chart of step S1021 provided in an embodiment of the present invention;

[0054] Figure 5 A step flow chart of step S10212 provided in an embodiment of the present invention;

[0055] Figure 6 A step flow chart of step S103 provided in an embodiment of the present invention;

[0056] Figure 7 A step flow chart of step S1032 provided in an embodiment of the present invention;

[0057] Figure 8 A schematic diagram of the structure of a microfluidic chip microvalve monitoring and scheduling device provided in an embodiment of the present invention;

[0058] Fig. 9 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention;

[0059] Fig.10 A schematic diagram of the structure of a storage medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limitations on the present application. It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order from the module division in the system schematic diagram or the flow chart. For the step numbers in the following embodiments, they are only set for the convenience of explanation, and the order between the steps is not limited in any way. The execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0061] In the description of the present invention, the meaning of "a plurality" is two or more. If there is a description of the first or the second, it is only for the purpose of distinguishing the technical features, and it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this document are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0062] The microfluidic chip microvalve monitoring and scheduling method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a set-top box, etc.; the server side can be configured as an independent physical server, or a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the microfluidic chip microvalve monitoring and scheduling method, but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with the relevant laws, regulations, and standards of the relevant countries and regions. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0065] like Figure 1 FIG. 1 is a flow chart showing a method for monitoring and scheduling a microvalve on a microfluidic chip according to an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a microfluidic chip microvalve monitoring and scheduling method, which specifically includes the following steps:

[0066] S101, determining multiple target microfluidic components that control the opening and closing states of microchannels, and obtaining historical log data of each target microfluidic component;

[0067] S102, clustering the target microfluidic components according to the historical log data to obtain multiple microvalve categories, and determining the corresponding relationship between each microvalve category and the optical fiber gas sensor category;

[0068] S103, applying the target microfluidic element as a target microvalve to the target microfluidic chip of the corresponding optical fiber gas sensor according to the corresponding relationship;

[0069] S104, monitoring the current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel, and performing control optimization and scheduling on the target microvalve according to the current signal state.

[0070] Specifically, the microfluidic chip of the micro-analysis and reaction system is constructed by integrating functional components such as microchannels, microvalves, micropumps, micro-liquid storage units, micro-mixers, and micro-detection units on the chip through micro-electromechanical processing technology. The microvalve drives and controls the microfluidic to flow through each unit through the microchannel to transfer substances. This is the basic feature that distinguishes the microfluidic chip from the matrix chip. Usually, in the microfluidic system, the switch is realized by the valve to control the flow direction of the fluid and play a role of controlled flow limitation. The fluid is driven by the pump. The pump plays the role of transmitting and distributing the liquid flow. It is the premise and basis for realizing microfluidic control. The flow and mixing of the fluid can also be controlled by the design of the channel.

[0071] The embodiment of the present invention clusters multiple target microfluidic elements that can control the opening and closing states of microchannels according to historical log data, obtains multiple microvalve categories, and determines the correspondence between each microvalve category and the fiber optic gas sensor category, so that the target microfluidic element can be used as a microvalve in the microfluidic chip of the corresponding fiber optic gas sensor, thereby improving the service life, detection accuracy and detection efficiency of the fiber optic gas sensor; by monitoring the current signal state of the microvalve controlling the microfluid to transfer matter through the microchannel, and controlling and optimizing the microvalve according to the current signal state, the accuracy and real-time performance of the microvalve control are improved, thereby improving the calibration efficiency of the fiber optic gas sensor.

[0072] like Figure 2 FIG. 1 is a flowchart of step S101 provided in an embodiment of the present invention, referring to FIG. Figure 2 As an optional implementation, multiple target microfluidic components that control the opening and closing states of the microchannels are determined, and historical log data of each target microfluidic component is obtained, which specifically includes:

[0073] S1011, obtaining system log data of the microfluidic system, and determining a plurality of target microfluidic elements that control the opening and closing states of the microchannels according to the system log data;

[0074] S1012: Perform a matching query in the system log data according to the component identification of each target microfluidic component to obtain the historical log data of each target microfluidic component.

[0075] Specifically, the microfluidic elements that control the closed and open states of the microchannel can all be used as microvalves in the microfluidic chip. An ideal microvalve should have the characteristics of low leakage, low power consumption, fast speed, wide linear range, and wide adaptability. According to different braking mechanisms, microvalves mainly include the following types: pneumatic micro (pump) valves, phase change valves, shape memory alloys, electrostatic valves, electric valves, solenoid valves, optical capture micro (pump) valves, and torque control; according to the structure of the microvalve, the microvalves can be divided into active valves, passive valves, or active microvalves and passive microvalves according to whether there is a driving force.

[0076] like Figure 3 FIG. 1 is a flow chart of step S102 provided in an embodiment of the present invention, referring to FIG. Figure 3 As an optional implementation, the target microfluidic components are clustered according to the historical log data to obtain multiple microvalve categories, and the corresponding relationship between each microvalve category and the optical fiber gas sensor category is determined, which specifically includes:

[0077] S1021. Perform cluster analysis on historical log data by using a K-means clustering algorithm to obtain multiple target clusters;

[0078] S1022, dividing the target microfluidic element into a plurality of microvalve categories according to the target clustering clusters;

[0079] S1023, determining the driving force category, driving form and action force category corresponding to each microvalve category, and determining the correspondence between each microvalve category and the optical fiber gas sensor category according to the driving force category, driving form and action force category.

[0080] Specifically, the historical log data reported by the UE is defined as a data set sample as follows:

[0081] X (i) ={x 1 (i) , x 2 (i) , ..., x n (i)}, where i = 1, 2, ..., mm represents the number of data set samples, and n represents the number of data features in the data set samples.

[0082] The K-means clustering algorithm is used to perform cluster analysis on the data set samples to obtain multiple target clusters. The target microfluidic elements are divided into multiple microvalve categories according to the target clusters to which they belong. Then, the corresponding relationship between the microvalve categories and the fiber optic gas sensor categories is determined according to the driving force categories, driving forms and force categories corresponding to each microvalve category.

[0083] After determining the corresponding relationship between each microvalve category and the fiber optic gas sensor category, the target microfluidic element can be used as a microvalve and applied to the microfluidic chip of the corresponding fiber optic gas sensor.

[0084] The basic principle of fiber optic sensor is to send the light emitted by the light source into the modulation area through the optical fiber. In the modulation area, the external measured parameter interacts with the light entering the modulation area, causing certain properties of the light such as light intensity, wavelength, frequency, phase, polarization state, etc. to change and become signal light, which is then sent to the optical detector and demodulator through the optical fiber to obtain the measured parameter.

[0085] Fiber optic gas sensing technology is an important application branch of fiber optic sensing technology, which is mainly based on optical phenomena or characteristics related to the physical or chemical properties of gas. In recent years, its application in environmental monitoring, power systems, and safety protection of oil fields, mines, and radiation zones has shown its unique superiority. At present, fiber optic gas sensors mainly include spectral absorption fiber optic gas sensors, fluorescence fiber optic gas sensors, evanescent field fiber optic gas sensors, refractive index change fiber optic gas sensors, etc.

[0086] Spectral absorption optical fiber gas sensors are the most studied and the closest to practical use. The spectral method detects the gas concentration by detecting the changes in the transmitted light intensity or reflected light intensity of the sample gas. Each gas molecule has its own absorption spectrum characteristics. The emission spectrum of the light source only absorbs in the part that overlaps with the gas absorption spectrum, and the light intensity changes after absorption. According to Beer-Lambert's law, when monochromatic light with a wavelength of λ propagates a distance L in a gas chamber filled with the gas to be measured, its light intensity after passing through the sensor is

[0087] I(λ)=I 0 (λ)exp(α λ CL)λ

[0088] In the formula, I 0 (λ) represents the light intensity of monochromatic light with wavelength λ passing through a gas chamber without the gas to be measured; C is the concentration of the absorbing gas; α λ is the absorption coefficient of light passing through the medium.

[0089]

[0090] By detecting the change in light intensity before and after ventilation, the concentration of the gas to be measured can be measured. The absorption type fiber optic gas sensor is made by using the property that the medium absorbs light and causes light to attenuate. The light emitted by the light source is sent into the gas chamber by the optical fiber, and after being absorbed by the gas, it is transmitted to the photoelectric detector by the output optical fiber. The obtained signal light is sent to the computer for signal processing to obtain the gas concentration. The spectral absorption type fiber optic gas sensor has high measurement sensitivity, strong anti-interference ability, good gas identification ability, fast response speed, high temperature resistance, moisture resistance, long life, and easy to assemble into a network. However, there are still problems such as the ideal light source technology has not yet been broken through, the weak signal detection equipment is complex, and the cost is high.

[0091] The principle of the fluorescence fiber optic gas sensor is that when the light source emits a laser signal of a certain wavelength to illuminate the fluorescent material, the fluorescent material will emit fluorescence of a specific wavelength. The fluorescence reacts with the gas to be measured, and the gas molecules change the fluorescence intensity or lifespan. By measuring the change in fluorescence intensity or lifespan, the concentration of the gas to be measured can be inferred. It can be described by the Stern-Volmer equation:

[0092]

[0093] In the formula, I 0 , T 0 are the fluorescence intensity and lifetime when there is no gas to be measured; I and T are the fluorescence intensity and lifetime when there is gas to be measured; x(air) is the concentration of the gas to be measured; K is a constant. The measured fluorescence lifetime or intensity change can be used to calculate the gas concentration value. Fluorescence fiber optic gas sensors have outstanding advantages such as simple sensing, strong anti-interference ability, high system accuracy, and fast response speed. However, due to the high measurement accuracy requirements of weak signal detection systems and the high cost of measurement systems, its wide application is limited.

[0094] Evanescent field fiber optic gas sensor: In recent years, evanescent field fiber optic gas sensors have received widespread attention and rapid development. The principle is that light propagating in the optical fiber does not undergo total reflection, but forms an exponentially decaying evanescent field around the fiber core. If there is a gas to be measured around the evanescent field, the light signal will be reduced due to the absorption energy of the gas to be measured. By measuring the attenuation of light intensity, the concentration information of the gas can be obtained. The evanescent field fiber optic gas sensor has the advantages of high sensitivity, the ability to realize distributed measurement and cross-analysis, and good repeatability, but it is easily affected by ambient temperature, humidity and cleanliness, and surface contamination cannot be well resolved.

[0095] like Figure 4 FIG. 1 is a flow chart of step S1021 provided in an embodiment of the present invention, referring to FIG. Figure 4As an optional implementation, the historical log data is clustered by using a K-means clustering algorithm to obtain multiple target clusters, which specifically include:

[0096] S10211, using the historical log data of each target microfluidic element as a data set sample of each target microfluidic element;

[0097] S10212, selecting a preset number of current cluster centers from the data set samples;

[0098] S10213, determining the sample distances from each remaining data set sample to each current cluster center, and dividing each remaining data set sample into the nearest current cluster center according to the sample distances, to obtain multiple current cluster clusters;

[0099] S10214. Determine the sample mean of each current clustering cluster, update the current clustering center according to the sample mean, and return to determine the sample distance from each remaining data set sample to each current clustering center, until the update number of the current clustering center reaches a preset first threshold, or the update distance of the current clustering center is less than or equal to a preset second threshold, to obtain multiple target clustering clusters.

[0100] Specifically, the first step of implementing clustering by the K-means clustering algorithm is to set clustering parameters. In the embodiment of the present invention, the microvalve data is classified into three categories, so the clustering parameter is set to K=3; three data set samples are selected from the data set samples as the current clustering centers, and then the Euclidean distance formula is applied to calculate the sample distances of each remaining data set sample to the three current clustering centers in turn, and each remaining data set sample is assigned to the current clustering cluster to which the current clustering center closest to the remaining data set belongs; after all the sample data are assigned, the clustering centers of the three types of data need to be recalculated, that is, the current clustering centers are updated based on the sample means of each current clustering cluster, and then the remaining data set samples are reclassified to obtain the updated current clustering cluster; in order to ensure the classification quality, the data under the K-means clustering algorithm need to be sent for multiple generations before the clustering center will not change. Whether to stop iteration can be determined by specifying the number of iterations and setting the class center variation range. When the number of iterations is specified, it stops when the number reaches the specified value. When the cluster center variation range is set, the update stops when the distance between the new clustering center and the old clustering center is less than or equal to the specified value.

[0101] like Figure 5 FIG. 1 is a flow chart of step S10212 provided in an embodiment of the present invention, referring to FIG. Figure 5 As an optional implementation, a preset number of current cluster centers are selected from the data set samples, which specifically includes:

[0102] S102121. Randomly select a data set sample as the current cluster center, and determine the sample distance between each remaining data set sample and the current cluster center;

[0103] S102122, selecting the remaining data set sample with the largest sample distance from the current cluster center as the new cluster center, and adding the new cluster center to the current cluster center;

[0104] S102123, determining the minimum sample distance between each remaining data set sample and multiple current cluster centers;

[0105] S102124. Select the remaining data set sample with the largest minimum sample distance to multiple current cluster centers as the new cluster center, add the new cluster center to the current cluster center, and return to determine the minimum sample distance between each remaining data set sample and multiple current cluster centers until the number of current cluster centers reaches a preset number.

[0106] Specifically, an embodiment of the present invention randomly designates a data set sample in the data set samples as the first cluster center, then calculates the distances from the remaining data set samples to the cluster center, selects the data set sample with the farthest distance as the second cluster center, then calculates the minimum sample distance from the remaining data set samples to the two cluster centers (i.e., the smallest of the sample distances to multiple cluster centers), selects the remaining data set samples with the largest minimum sample distance as the third cluster center, and so on, until the number of cluster centers reaches a preset number.

[0107] like Figure 6 FIG. 1 is a flowchart of step S104 provided in an embodiment of the present invention, referring to FIG. Figure 6 As an optional implementation, the target microvalve is controlled and optimized according to the current signal state, which specifically includes:

[0108] S1041, determining an initial population of microvalve control parameters according to the current signal state, where an individual in the population represents a set of control parameter vectors, and a gene of the individual represents a control parameter;

[0109] S1042. Obtain experimental individuals corresponding to each current individual in the current population through mutation and crossover;

[0110] S1043, determining the cost values ​​of the current individual and the corresponding experimental individual, selecting the individual with the smaller cost value as the current individual of the next round, and returning the experimental individuals corresponding to each current individual in the current population obtained through mutation and crossover, until the number of population iterations reaches a preset third threshold, and determining the optimal control parameter group according to the optimal individual in the current population;

[0111] S1044. Control the target microvalve according to the optimal control parameter group.

[0112] like Figure 7 FIG. 1 is a flow chart of step S1042 provided in an embodiment of the present invention, referring to FIG. Figure 7 As an optional implementation, experimental individuals corresponding to each current individual in the current population are obtained through mutation and crossover, which specifically includes:

[0113] S10421. For each current individual in the current population, randomly select two other current individuals, calculate the difference vectors of the two other current individuals, and add the difference vector to the current individual to obtain a mutant individual;

[0114] S10422. Randomly exchange genes between the mutant individual and the current individual to obtain the experimental individual corresponding to the current individual.

[0115] Specifically, the embodiment of the present invention uses a cell variation algorithm to monitor the signal state of microvalve drive and control the flow of microfluids through each unit in the microchannel and the transfer of substances. It is the first to perform sensor monitoring of AI chips in microfluidic chips. In the microfluidic system, valves are used to realize switching to control the flow direction of the fluid, thereby playing a controlled flow limiting role.

[0116] The embodiment of the present invention determines the initial population of microvalve control parameters according to the current signal state, the individuals in the population represent a set of control parameter vectors, and the gene of the individual represents a control parameter; the experimental individuals corresponding to each current individual in the current population are obtained through mutation and crossover; the cost values ​​of the current individual and the corresponding experimental individuals are determined, the individual with the smaller cost value is selected as the current individual of the next round, and the experimental individuals corresponding to each current individual in the current population obtained through mutation and crossover are returned until the number of population iterations reaches a preset third threshold, and the optimal control parameter group is determined according to the optimal individual in the current population; the target microvalve is controlled according to the optimal control parameter group.

[0117] The mutation operation refers to the generation of new individuals, which is obtained by linear operations on multiple individual individuals in a population of size M. In the embodiment of the present invention, each control parameter vector undergoes mutation to expand the search space. In the gth iteration, three individuals X are randomly selected from the population. p1 (g), X p2 (g), X p3 (g), and p 1 ≠p 2 ≠p 3 ≠I, then the generated mutant individuals are as follows:

[0118] X i (g) = X p1 (g)+F·(X p2(g)-X p3 (g))

[0119] Where, X p2 (g)-X p3 (g) represents the difference vector, and F represents the scaling factor, which determines the size of the difference step of the population individuals. A smaller F will affect the differences between the population individuals, causing the algorithm results to fall into the local optimum. A larger F will enhance the global search ability of the algorithm, which is conducive to the search for the optimal solution, but will affect the convergence speed of the algorithm.

[0120] The present invention adaptively adjusts the scaling factor F as follows:

[0121] Sort the three individuals randomly selected from the mutation operator from best to worst, and get X b , X m , X w And the corresponding fitness f b , f m , f w The mutation operator is rewritten as:

[0122] V i =X b +F i (X m -X w )

[0123] Among them, the value of F changes adaptively according to the two individuals that generate the difference vector:

[0124]

[0125] In the above formula, F l =0.1; F u =0.9.

[0126] For each current individual in the current population, after obtaining the corresponding mutant individual, the mutant individual and the current individual are randomly exchanged for genes to obtain the experimental individual corresponding to the current individual, and subsequent generation value comparison and individual screening can be performed until the number of population iterations reaches the preset third threshold, and the optimal control parameter group is determined according to the optimal individual in the current population; the target microvalve is controlled according to the optimal control parameter group.

[0127] The above is an explanation of the method steps of the embodiment of the present invention. It can be recognized that the embodiment of the present invention clusters multiple target microfluidic elements that can control the opening and closing states of microchannels according to historical log data, obtains multiple microvalve categories, and determines the correspondence between each microvalve category and the fiber optic gas sensor category, so that the target microfluidic element can be used as a microvalve in the microfluidic chip of the corresponding fiber optic gas sensor, thereby improving the service life, detection accuracy and detection efficiency of the fiber optic gas sensor; by monitoring the current signal state of the microvalve controlling the microfluid to transfer substances through the microchannel, and controlling and optimizing the microvalve according to the current signal state, the accuracy and real-time performance of the microvalve control are improved, thereby improving the calibration efficiency of the fiber optic gas sensor.

[0128] like Figure 8 FIG. 1 is a schematic diagram of the structure of a microfluidic chip microvalve monitoring and scheduling device provided by an embodiment of the present invention, referring to FIG. Figure 8 The embodiment of the present invention provides a microfluidic chip microvalve monitoring and scheduling device, comprising:

[0129] A data acquisition module, used to determine multiple target microfluidic components that control the opening and closing states of the microchannels, and to acquire historical log data of each target microfluidic component;

[0130] A clustering module is used to cluster the target microfluidic components according to the historical log data, obtain multiple microvalve categories, and determine the corresponding relationship between each microvalve category and the optical fiber gas sensor category;

[0131] A microvalve application module, used for applying the target microfluidic element as a target microvalve to a target microfluidic chip of a corresponding optical fiber gas sensor according to the corresponding relationship;

[0132] The microvalve control module is used to monitor the current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel, and to control and optimize the scheduling of the target microvalve according to the current signal state.

[0133] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0134] The embodiment of the present invention also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and when the program is executed by the processor, the above-mentioned microfluidic chip microvalve monitoring and scheduling method is realized. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0135] like Fig. 9 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention, referring to FIG. Fig. 9 , an embodiment of the present invention provides an electronic device, including:

[0136] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0137] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the microfluidic chip microvalve monitoring and scheduling method of the embodiment of the present invention;

[0138] Input / output interface 903, used to implement information input and output;

[0139] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0140] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0141] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0142] like Fig.10 FIG. 1 is a schematic diagram of a storage medium according to an embodiment of the present invention. Fig.10 The embodiment of the present invention also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs 1001, and the one or more programs 1001 can be executed by one or more processors to implement the above-mentioned microfluidic chip microvalve monitoring and scheduling method.

[0143] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0145] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0146] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0147] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0148] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0149] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0150] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0152] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0153] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A microfluidic chip microvalve monitoring and scheduling method, characterized in that: The following steps are involved: Determine a plurality of target microfluidic components that control the opening and closing states of the microchannels, and obtain historical log data of each of the target microfluidic components; Clustering the target microfluidic element according to the historical log data to obtain a plurality of microvalve categories, and determining a corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category; According to the corresponding relationship, the target microfluidic element is applied as a target microvalve to a target microfluidic chip of a corresponding optical fiber gas sensor; The current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel is monitored, and the target microvalve is controlled and optimized according to the current signal state.

2. A microfluidic chip microvalve monitoring and scheduling method according to claim 1, characterized in that: The step of determining a plurality of target microfluidic components that control the opening and closing states of the microchannels and obtaining historical log data of each of the target microfluidic components specifically includes: Acquire system log data of the microfluidic system, and determine a plurality of target microfluidic elements that control the opening and closing states of the microchannels according to the system log data; A matching query is performed in the system log data according to the component identification of each target microfluidic component to obtain the historical log data of each target microfluidic component.

3. A microfluidic chip microvalve monitoring and scheduling method according to claim 1, characterized in that: The step of clustering the target microfluidic elements according to the historical log data to obtain a plurality of microvalve categories, and determining the corresponding relationship between each microvalve category and an optical fiber gas sensor category specifically includes: Performing cluster analysis on the historical log data by using a K-means clustering algorithm to obtain multiple target clusters; Dividing the target microfluidic element into a plurality of microvalve categories according to the target clustering clusters; The driving force category, driving form and action force category corresponding to each of the microvalve categories are determined, and the corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category is determined according to the driving force category, the driving form and the action force category.

4. A microfluidic chip microvalve monitoring and scheduling method according to claim 3, characterized in that: The historical log data is clustered and analyzed by the K-means clustering algorithm to obtain multiple target clusters, which specifically include: Using the historical log data of each target microfluidic element as a data set sample of each target microfluidic element; Selecting a preset number of current cluster centers from the data set samples; Determine the sample distance from each remaining data set sample to each current cluster center, and divide each remaining data set sample into the nearest current cluster center according to the sample distance to obtain multiple current cluster clusters; Determine the sample mean of each of the current clustering clusters, update the current cluster center according to the sample mean, and return to determine the sample distance from each remaining data set sample to each current cluster center, until the update number of the current cluster center reaches a preset first threshold, or the update distance of the current cluster center is less than or equal to a preset second threshold, to obtain multiple target clustering clusters.

5. A microfluidic chip microvalve monitoring and scheduling method according to claim 4, characterized in that: The step of selecting a preset number of current cluster centers from the data set samples specifically includes: Randomly select a data set sample as the current cluster center, and determine the sample distance between each remaining data set sample and the current cluster center; Select the remaining data set sample with the largest sample distance from the current cluster center as the new cluster center, and add the new cluster center to the current cluster center; Determine the minimum sample distance between each remaining data set sample and multiple current cluster centers; Select the remaining data set sample with the largest minimum sample distance to multiple current cluster centers as the new cluster center, add the new cluster center to the current cluster center, and return to determine the minimum sample distance between each remaining data set sample and multiple current cluster centers until the number of current cluster centers reaches the preset number.

6. A microfluidic chip microvalve monitoring and scheduling method according to claim 1, characterized in that: The controlling and optimizing scheduling of the target microvalve according to the current signal state specifically includes: Determine an initial population of microvalve control parameters according to the current signal state, wherein an individual in the population represents a set of control parameter vectors, and a gene of the individual represents a control parameter; Through mutation and crossover, experimental individuals corresponding to each current individual in the current population are obtained; Determine the cost value of the current individual and the corresponding experimental individual, select the individual with the smaller cost value as the current individual of the next round, and return the experimental individuals corresponding to each current individual in the current population obtained through mutation and crossover until the number of population iterations reaches a preset third threshold, and determine the optimal control parameter group according to the optimal individual in the current population; The target microvalve is controlled according to the optimal control parameter group.

7. A microfluidic chip microvalve monitoring and scheduling method according to claim 6, characterized in that: The step of obtaining experimental individuals corresponding to each current individual in the current population through mutation and crossover specifically includes: For each current individual in the current population, randomly select two other current individuals, calculate the difference vector of the two other current individuals, and add the difference vector to the current individual to obtain the mutant individual; Randomly exchange genes between the mutant individual and the current individual to obtain the experimental individual corresponding to the current individual.

8. A microfluidic chip microvalve monitoring and scheduling device, characterized in that: include: A data acquisition module, used to determine a plurality of target microfluidic components that control the opening and closing states of the microchannels, and to acquire historical log data of each of the target microfluidic components; A clustering module, used for clustering the target microfluidic elements according to the historical log data to obtain a plurality of microvalve categories, and determining a corresponding relationship between each of the microvalve categories and the optical fiber gas sensor category; A microvalve application module, used for applying the target microfluidic element as a target microvalve to a target microfluidic chip of a corresponding optical fiber gas sensor according to the corresponding relationship; The microvalve control module is used to monitor the current signal state of each target microvalve in the target microfluidic chip controlling the microfluid to transfer substances through the microchannel, and to control and optimize the scheduling of the target microvalve according to the current signal state.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the microfluidic chip microvalve monitoring and scheduling method as described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the microfluidic chip microvalve monitoring and scheduling method as described in any one of claims 1 to 7.

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