Anomaly detection device, method, and program
The abnormality detection device uses a microfluidic chip to create an anomaly detection model for early identification of contamination or genetic mutations in microalgae culture, addressing inefficiencies and costs in conventional methods by detecting abnormalities under varied conditions.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- NIPPON TELEGRAPH & TELEPHONE CORP
- Filing Date
- 2022-09-22
- Publication Date
- 2026-07-07
AI Technical Summary
Conventional methods for detecting abnormalities during large-scale microalgae cultivation, such as contamination or genetic mutations, are inefficient and costly, and fail to detect abnormalities early due to the rapid change in culture conditions, leading to irrecoverable equipment damage and reduced efficiency.
An abnormality detection device and method that utilizes a microfluidic chip to culture a sample under varied conditions, creating an anomaly detection model to identify deviations from normal culture states by comparing culture data against predefined thresholds, enabling early detection of contamination or genetic mutations.
The system effectively detects abnormalities in microalgae culture, reducing costs and preventing further contamination or genetic mutations by identifying issues early, thus maintaining culture efficiency and reducing equipment damage.
Smart Images
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Abstract
Description
Technical Field
[0001] Embodiments of the present invention relate to an abnormality detection apparatus, method, and program.
Background Art
[0002] Cultured objects, such as microalgae, are utilized in various applications such as food, environmental purification, and production of biofuels (see, for example, Patent Document 1 and Non-Patent Document 1). In the mass culture of microalgae (sometimes referred to as large-scale culture), it is necessary to avoid problems such as contamination organisms mixing into the culture apparatus and thus not obtaining microalgae with desired properties.
Prior Art Documents
Patent Documents
[0003]
Patent Document 1
Non-Patent Documents
[0004]
Non-Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0005] Conventional methods detect abnormalities during large-scale microalgae cultivation by directly acquiring cultivation data of microalgae in real time using large-scale cultivation equipment, or by utilizing past cultivation data. However, problems arise when abnormalities are detected, such as the large-scale cultivation equipment already being significantly affected, for example, by contamination that is irrecoverable or a decrease in cultivation efficiency due to genetic mutations, or the high cost of acquiring cultivation data.
[0006] During the large-scale culture described above, contamination or genetic mutation may occur in strains that exhibit superior characteristics under specific culture conditions. However, conventional methods have problems such as the short time required to meet specific culture conditions in a large-scale culture device, which prevents the relative proportion of mutant strains from increasing and thus makes it impossible to detect the contamination or genetic mutation.
[0007] This invention was made in view of the above circumstances, and its purpose is to provide an abnormality detection device, method, and program that can appropriately detect abnormalities in the culture state of a culture target during mass cultivation. [Means for solving the problem]
[0008] An abnormality detection device according to one aspect of the present invention detects the culture state of a sample of a culture target being cultured in large quantities by a large-scale culture device, which is cultured by being delivered to a microfluidic chip. The culture conditions differ from those of the aforementioned mass culture apparatus. A measurement unit that measures under set culture conditions, and a determination criterion for abnormal culture state that indicates that the culture state of the sample of the culture target is different from normal. , different from the culture conditions in the aforementioned mass culture apparatus, Anomaly detection models defined for each of multiple culture conditions and , before The culture state of the sample measured by the measurement unit and The culture conditions set above Under The system includes a detection unit that detects abnormalities in the culture state of a sample of the culture target by comparison.
[0009] An abnormality detection method according to one aspect of the present invention is performed by an abnormality detection device. circleA method wherein the measurement unit of the abnormality detection device determines the culture state of a sample of a culture target being cultured in large quantities by a large-scale culture device and which is delivered to a microfluidic chip for culture, The culture conditions differ from those of the aforementioned mass culture apparatus. The measurement is performed under the set culture conditions, and the detection unit of the abnormality detection device indicates that the culture state of the sample of the culture target is different from normal, and the criteria for determining the abnormality of the culture state are , different from the culture conditions in the aforementioned mass culture apparatus, Anomaly detection models defined for each of multiple culture conditions and , before The culture state of the sample measured by the measurement unit and The culture conditions set above Under The system includes detecting abnormalities in the culture state of the sample of the culture target by comparison. [Effects of the Invention]
[0010] According to the present invention, abnormalities in the culture state of a culture target during large-scale culture can be appropriately detected. [Brief explanation of the drawing]
[0011] [Figure 1] Figure 1 shows an example of an application of an anomaly detection system according to one embodiment of the present invention. [Figure 2] Figure 2 shows an example of a microfluidic system configuration. [Figure 3] Figure 3 is a block diagram showing an example of the functional configuration of the control server. [Figure 4] Figure 4 is a block diagram showing an example of the functional configuration of an anomaly detection server. [Figure 5] Figure 5 is a flowchart showing an example of the procedure for creating an anomaly detection model. [Figure 6] Figure 6 is a diagram showing an example of an anomaly detection model in tabular format. [Figure 7] Figure 7 is a flowchart showing an example of a procedure for large-scale culture and anomaly detection. [Figure 8] Figure 8 is a table showing an example of an abnormality detection result. [Figure 9] FIG. 9 is a diagram showing an example of a notification screen for abnormality detection. [Figure 10] FIG. 10 is a block diagram showing an example of the hardware configuration of the abnormality detection server according to an embodiment of the present invention. MODE FOR CARRYING OUT THE INVENTION
[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a diagram showing an application example of an abnormality detection system according to an embodiment of the present invention. As shown in FIG. 1, the abnormality detection system according to the present embodiment includes a large-scale culture device 100, a microchannel system 200, an abnormality detection server 300, and a monitoring server 400. The abnormality detection server 300 and the monitoring server 400 can be connected via a network, for example.
[0013] Next, creation of an abnormality detection model by the abnormality detection system will be described. In the present embodiment, the abnormality detection system creates an abnormality detection model, which is a model for detecting abnormalities during culture, before starting the large-scale culture of the culture target. Hereinafter, an application example when the culture target is microalgae will be described, but the present invention is not limited thereto, and it can also be applied when the culture target is, for example, microorganisms, cells, bacteria, or yeast.
[0014] First, in the microchannel system 200, an ideal culture strain that is a standard for abnormality determination during culture, that is, a culture strain without contamination or genetic mutation, is cultured under various culture conditions that are environmental conditions to be considered during culture, and culture data related to this culture is acquired and transmitted to the abnormality detection server 300.
[0015] Based on this culture data, the abnormality detection server 300 creates an abnormality detection model.
[0016] Next, large-scale culture and abnormality detection by the abnormality detection system will be described. First, the large-scale cultivation of microalgae strains is started using the large-scale cultivation device 100. The large-scale culture apparatus 100 delivers a small sample of the culture medium containing the target strain being cultured to the microfluidic system 200 (indicated as a in Figure 1). In the microfluidic system 200, the above-mentioned trace sample is cultured under various culture conditions, and culture data related to this culture is generated and transmitted to the anomaly detection server 300 (indicated as b in Figure 1).
[0017] The anomaly detection server 300 detects anomalies during the mass cultivation of microalgae, namely contamination or changes in cultivation efficiency due to gene mutations, based on the culture data and the anomaly detection model created in advance as described above.
[0018] When the anomaly detection server 300 detects an anomaly, it takes action to respond to the anomaly. Examples of this response include (1) notification from the anomaly detection server 300 to the monitoring server 400 (indicated by c in Figure 1) and (2) transmission from the anomaly detection server 300 to the control server 209 of a valve control command for selecting a sample at the time of anomaly detection.
[0019] Figure 2 shows an example of a microfluidic system configuration. As shown in Figure 2, the microfluidic system 200 includes an automatic push liquid delivery device (for fluid culture) 201, a reservoir (for samples) 202a, a reservoir (for buffers) 202b, a reservoir (for culture medium) 202c, a reservoir (for diluents) 202d, an automatic pull liquid delivery device (for sample collection) 203, a fluid channel chip (microfluidic chip) 204, a measuring device (microscope system, etc.) 205, an environmental control device (temperature, illuminance, etc.) 206, a valve control device 207, a reservoir (for waste liquid) 208a, a reservoir (for liquid containing samples in case of abnormality detection) 208b, and a control server 209. The automatic push-type fluid dispenser 201 and the automatic pull-type fluid dispenser 203 may include a pump, a flow path regulator, and a flow sensor, among other things.
[0020] As shown in Figure 2, a small sample containing microalgae cultures and culture medium from the large-scale culture device 100 is delivered to the reservoir (for samples) 202a. The control server 209 controls the delivery of liquid from the reservoir (for samples) 202a, the reservoir (for buffer) 202b, the reservoir (for culture medium) 202c, and the reservoir (for diluent) 202d to the flow channel tip 204 using the automatic push liquid delivery device (for flow channel culture) 201.
[0021] The automatic pull-and-delivery device 203, in accordance with control commands from the control server 209, collects a small sample from the reservoir (for samples) 202a before delivering it to the microfluidic system 200. The measuring device 205, including, for example, a microscope system, can measure the specific growth rate of a small sample delivered to the fluid channel chip 204, i.e., the increase in cell volume per unit time, multiple times in accordance with control commands from the control server 209. The object of this measurement may be, for example, the specific substrate consumption rate, the specific product production rate, or the specific oxygen consumption rate. It is assumed that the results of these multiple measurements are distributed according to a normal distribution.
[0022] The environmental control device 206 controls, in accordance with control commands from the control server 209, the temperature of the trace sample flowing through the channel in the channel chip 204 and the illuminance of the light irradiating the trace sample, which are types of culture conditions for the trace sample flowing through the channel chip 204, via air conditioning and lighting devices (not shown). The control server 209 also controls the delivery of liquid from various reservoirs to the microfluidic system 200 by the automatic push liquid delivery device 201 in order to control pH (hydrogen ion concentration index), pCO2 (partial pressure of carbon dioxide), and nutrient concentration, which are types of culture conditions for the trace sample flowing through the channel chip 204. The above nutrient concentrations may include concentrations of multiple types of substances, such as phosphorus, nitrogen, metal ions, and silicic acid. A metal ion is, for example, iron ions.
[0023] The valve control device 207 controls a valve (not shown) located in the flow path between the outlet side of the flow path tip 204 and the reservoirs 208a and 208b, in accordance with control commands from the control server 209, thereby switching the destination of the trace sample from the flow path tip 204 between the reservoirs 208a and 208b.
[0024] Figure 3 is a block diagram showing an example of the functional configuration of a control server. As shown in Figure 3, the control server 209 includes a storage device 221, a control unit 222, an acquisition unit 223, a data generation unit 224, and a communication unit 225. The storage device 221 is provided with, for example, a working memory related to processing by the data generation unit 224. The functions of each unit will be described later.
[0025] Figure 4 is a block diagram showing an example of the functional configuration of an anomaly detection server. As shown in Figure 4, the anomaly detection server 300 includes a storage device 301, an acquisition unit 302, a model creation unit 303, an anomaly determination unit 304, a notification unit 305, and a display processing unit 306. The storage device 301 is provided with a culture condition DB (database) 301a and an anomaly detection model DB 301b, as well as a work memory related to processing by, for example, the model creation unit 303 and the anomaly determination unit 304. The functions of each unit will be described later.
[0026] Next, we will explain how to create an anomaly detection model. Figure 5 is a flowchart showing an example of the procedure for creating an anomaly detection model. First, a culture medium containing the target strain of microalgae, which will be used as the standard for abnormality detection, is delivered from the mass culture device 100 to the microfluidic system 200 (S11). In the microfluidic system 200, the culture strains contained in the culture medium delivered in S11 are cultured under various culture conditions using the fluid channel chip 204. The control unit 222 of the control server 209 controls multiple measurements of the culture strains (sometimes referred to as culture results) being cultured by the fluid channel chip 204 using the measuring device 205, and also controls the setting of culture conditions by the environmental control device 206.
[0027] The acquisition unit 223 of the control server 209 is connected to the measuring device 205 of Obtain measurement results using a microscope system. The data generation unit 224 of the control server 209 calculates the specific growth rate of the microalgae culture strain sample multiple times based on the acquired measurement results. The data generation unit 224 then generates culture data that includes the results of multiple calculations of the specific growth rate according to a normal distribution for each of the multiple types of culture conditions controlled by the environmental control device 206, etc., and culture data that includes the specific growth rate calculated by the data generation unit 224 (S12).
[0028] This culture data may include (1) the date and time the culture result of the trace sample was measured by the measuring device 205, (2) a sample number that identifies the trace sample, (3) the date and time the above trace sample was acquired by the automatic pull liquid delivery device 203, and (4) a culture result number that identifies the culture result associated with (1) to (3) above. The above specific growth rate may be calculated by the control server 209 based on the measurement results from the microscope system of the measuring device 205.
[0029] The communication unit 225 of the control server 209 of the microfluidic system 200 transmits the culture data for each of the multiple types of culture conditions acquired in S12 to the anomaly detection server 300 (S13).
[0030] The acquisition unit 302 of the anomaly detection server 300 acquires culture data for each of the multiple types of culture conditions transmitted in S13 and stores it in the culture condition DB 301a of the storage device 301. The model creation unit 303 of the anomaly detection server 300 selects culture data relating to one of the multiple types of culture conditions from the stored culture data. The model creation unit 303 then calculates the mean and variance of the individual specific growth rates in the culture data under this selected condition, for example, based on Hotelling's T2 method, and calculates the degree of anomaly of the specific growth rate based on these mean and variance.
[0031] The model creation unit 303 then calculates an anomaly threshold, which is the threshold at which this anomaly becomes an outlier, and performs this calculation for each of the other culture conditions, thereby creating an anomaly detection model in which an anomaly threshold is defined for each culture condition, and stores this anomaly detection model in the anomaly detection model DB301b of the storage device 301 (S14).
[0032] Figure 6 is a diagram showing an example of an anomaly detection model in tabular format. As shown in Figure 6, the anomaly detection model defines anomaly detection model parameters for each of the multiple culture condition patterns provided by the microfluidic system 200. The above culture condition patterns include a pattern number, which is the identification number of each culture condition pattern, and the content of the culture conditions. These culture conditions include the temperature and illuminance related to the channel tip 204 of the microfluidic system 200, as well as the pH, pCO2, and concentrations of multiple types of nutrients of the trace sample flowing through the channel of the channel tip 204. Note that "nutrient a concentration" and "nutrient b concentration" shown in Figure 6 refer to the concentrations of the first and second types of nutrients, respectively. The anomaly detection model parameters described above include the mean, variance, and anomaly threshold of the specific growth rate.
[0033] For example, Figure 6 The first row of the table shown indicates that an anomaly detection model with an anomaly threshold "a1" was created under culture conditions including temperature "T1" and illuminance "l1" related to culture condition pattern No. "X1". 6 The second row of the table shown indicates that an anomaly detection model with an anomaly threshold "a2" was created under culture conditions including temperature "T2" and illuminance "l2" related to culture condition pattern No. "X2".
[0034] Figure 7 is a flowchart showing an example of a procedure for large-scale culture and anomaly detection. Here, we assume that mass cultivation using the mass culture device 100 has started and is continuing, and that the anomaly detection model has already been created by the anomaly detection server 300.
[0035] First, a sample, i.e., a culture medium containing the target strain being cultured in large quantities, is delivered from the large-scale culture apparatus 100 to the reservoir 202a of the microfluidic system 200 (S21).
[0036] The microfluidic system 200 cultures the sample delivered in S21 under various culture conditions using the fluid channel tip 204. The control unit 222 of the control server 209 controls the measurement by the measuring device 205 and also controls the culture conditions via the environmental control device 206 and the like. The measuring device 205 calculates the specific growth rate of the sample based on the measurement results from the microscope system.
[0037] The acquisition unit 223 of the control server 209 acquires culture data, including the specific growth rate calculated by the measuring device 205, for each of the multiple types of culture conditions set by the environmental control device 206 (S22).
[0038] The communication unit 225 of the control server 209 transmits the culture data acquired in S22 to the anomaly detection server 300 (S23). The acquisition unit 302 of the anomaly detection server 300 acquires the culture data transmitted in S23 (S24). The abnormality determination unit 304 of the abnormality detection server 300 selects culture data relating to one of several types of culture conditions from the culture data acquired in S24. The abnormality determination unit 304 then calculates the degree of abnormality of each specific growth rate shown in the selected culture data. The abnormality determination unit 304 reads the degree of abnormality threshold relating to the same culture condition as the selected culture condition from the abnormality detection model stored in the abnormality detection model DB 301b of the storage device 301. The abnormality determination unit 304 compares this read-out degree of abnormality threshold with the calculated degree of abnormality to perform an abnormality determination for each of the multiple culture conditions, which is the determination of whether or not there is an abnormality during mass culture (S25).
[0039] In the determination in S25, if the degree of abnormality based on the specific growth rate shown in the culture data exceeds the abnormality threshold under certain culture conditions, the abnormality determination unit 304 determines that the result of the abnormality determination is "abnormal," that is, that an abnormality occurred during mass culture under these culture conditions ("abnormality" in S26).
[0040] When this determination is made, an abnormality response is performed (S27). This abnormality response includes, for example, an abnormality notification from the notification unit 305 of the abnormality detection server 300 to the monitoring server 400, and a notification from the abnormality detection server 300 to the control server 209 of the microfluidic system 200 that the sample delivered to the fluid channel chip 204 at the time of the abnormality determination is a sample in which an abnormality was observed during mass culture, and that the sample is then sent to the reservoir 208b. of Liquid delivery of Examples include the output of valve control commands.
[0041] On the other hand, if the degree of abnormality does not exceed the threshold for abnormality, the abnormality determination unit 304 determines that the result of the abnormality determination is "normal," that is, that no abnormality has occurred during the mass cultivation of the cultured strain ("normal" in S26). At this determination or after the response to the abnormality, the abnormality determination unit 304 stores the series of abnormality determination results in, for example, a storage device 301, and the process ends.
[0042] Figure 8 is a table showing an example of an abnormality detection result. In the example shown in Figure 8, an anomaly detection result is generated for each of the culture condition patterns in the culture data acquired in S24 from the control server 209 of the microfluidic system 200.
[0043] Furthermore, the abnormality determination result includes (1) an abnormality determination flag indicating the presence or absence of an abnormality, (2) the date and time of the determination of the presence or absence of an abnormality, (3) the degree of abnormality of the specific growth rate calculated in the determination of the presence or absence of an abnormality, (4) reservoir information that identifies the reservoir in which the sample delivered to the flow channel tip 204 is held when an abnormality is determined, and (5) an abnormality determination No. that identifies the abnormality determination result associated with (1) to (4) above.
[0044] Furthermore, the example shown in Figure 8 shows the culture results in the microfluidic system 200 under each culture condition, and these culture results include the culture result No., date and time of measurement of the culture result, specific growth rate, sample No., and date and time of sample acquisition. For example, the abnormality judgment result for abnormality judgment No. "ad1" in the first row of the table shown in Figure 8 indicates that the abnormality judgment flag at the abnormality judgment date and time "tad1" under the culture condition pattern "X1" was "True," meaning that an abnormality was observed during mass culture.
[0045] Furthermore, the abnormality judgment result for abnormality judgment No. "ad2" in the second row of the table shown in Figure 8 indicates that the abnormality judgment flag at the abnormality judgment date and time "tad2" under the culture condition pattern "X2" is "False," meaning that no abnormalities were observed during mass culture.
[0046] Figure 9 shows an example of a notification screen for an anomaly detection. The screen G1 shown in Figure 9 is displayed on an external display device (not shown) by the display processing unit 306 of the abnormality detection server 300 when the determination result from the abnormality detection server 300 indicates that an abnormality was observed during mass cultivation.
[0047] This screen displays the message, "The following culture conditions may result in a culture state different from the normal state," along with the culture conditions, the date and time of the abnormality detection, the date and time of sample acquisition, the specific growth rate at the time of the abnormality detection, the threshold for the degree of abnormality of the specific growth rate (specific growth rate at the abnormality detection threshold), the sample number, and the reservoir in which the sample is held when the abnormality is detected.
[0048] In the embodiments described above, a small sample containing the target strain and culture medium being cultured in a large-scale culture apparatus is delivered to a microfluidic system, culture data is obtained by culturing under various culture conditions in this microfluidic system, and abnormalities during large-scale culture are identified. of Detect. This microfluidic system allows for the cultivation of small samples, thus reducing the cost of data acquisition. Furthermore, since samples can be cultured and culture data obtained using a microfluidic system under conditions different from those of a mass culture device, abnormalities during mass culture can be detected early.
[0049] During cultivation, contamination or genetic mutations may occur in strains that exhibit superior cultivation efficiency under specific culture conditions, potentially reducing the overall culture efficiency. However, in this embodiment, since samples can be cultured under various conditions in a microfluidic system and culture data can be acquired, the above-mentioned contamination or genetic mutations can be appropriately detected.
[0050] Furthermore, while outdoor raceway ponds are sometimes used for large-scale cultivation of microalgae, this method is prone to contamination by harmful organisms, and the timing of contamination is difficult to predict. An example of a harmful organism is a ciliate protozoan that is dormant when it is introduced but awakens under specific temperature conditions, affecting the cultivation process. In this embodiment, even in such cases, the contaminating organism is placed under specific temperature conditions in the microfluidic system before it actually awakens from dormancy in the large-scale cultivation apparatus, making it possible to detect changes in cultivation efficiency due to predation by awakened nematodes.
[0051] Figure 10 is a block diagram showing an example of the hardware configuration of an anomaly detection server 300 according to one embodiment of the present invention. In the example shown in Figure 10, the anomaly detection server 300 according to the above embodiment is composed of, for example, a server computer or a personal computer, and has a hardware processor 511A such as a CPU. A program memory 511B, a data memory 512, an input / output interface 513, and a communication interface 514 are connected to this hardware processor 511A via a bus 515. In the following description, the anomaly detection server 300 will be used as an example, but the same applies to the control server 209 and the monitoring server 400 within the microfluidic system 200.
[0052] The communication interface 514 includes, for example, one or more wireless communication interface units, enabling the transmission and reception of information with the communication network (network) NW. As the wireless interface, an interface employing a low-power wireless data communication standard, such as a wireless LAN (Local Area Network), is used.
[0053] The input / output interface 513 is connected to an input device 600 and an output device 700, which are attached to the anomaly detection server 300 and used by users and others. The input / output interface 513 receives operation data entered by a user or other party through an input device 600 such as a keyboard, touch panel, touchpad, or mouse, and outputs output data to an output device 700, including a display device using liquid crystal or organic EL (electroluminescence), for display. The input device 600 and output device 700 may be devices built into the anomaly detection server 300, or they may be input and output devices of other information terminals that can communicate with the anomaly detection server 300 via a network NW.
[0054] The program memory 511B is a non-temporary tangible storage medium in which a non-volatile memory that can be written to and read at any time, such as an HDD (Hard Disk Drive) or SSD (Solid State Drive), is used in combination with another non-volatile memory such as ROM (Read Only Memory), and stores programs necessary for executing various control processes according to one embodiment.
[0055] The data memory 512 is a tangible storage medium that, for example, uses a combination of the above-mentioned non-volatile memory and volatile memory such as RAM (Random Access Memory), and is used to store various data acquired and created during the process of various operations.
[0056] An anomaly detection server 300 according to one embodiment of the present invention may be configured as a data processing device having a software-based processing function unit. The storage device 301 used as work memory by the anomaly detection server 300 may be configured using the data memory 512 shown in Figure 10. However, these storage areas are not essential to the anomaly detection server 300; for example, they may be external storage media such as USB (Universal Serial Bus) memory, or areas provided in storage devices such as database servers located in the cloud.
[0057] The processing function described above can be implemented by having the hardware processor 511A read and execute a program stored in the program memory 511B. This processing function may also be implemented in various other forms, including integrated circuits such as Application Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs).
[0058] Furthermore, the methods described in each embodiment can be stored as programs (software means) that can be executed by a computer, such as magnetic disks (floppy disks, hard disks, etc.), optical disks (CD-ROMs, DVDs, MOs, etc.), and semiconductor memories (ROMs, RAMs, flash memory, etc.), and can also be transmitted and distributed via communication media. The programs stored on the media also include configuration programs that configure the computer with software means (including not only the execution program but also tables and data structures) to be executed by the computer. The computer implementing this device reads the program recorded on the recording medium and, if necessary, constructs the software means using the configuration program, and executes the above-described processes by controlling the operation of this software means. The recording medium referred to in this specification is not limited to distribution media, but also includes storage media such as magnetic disks and semiconductor memories provided inside the computer or in devices connected via a network.
[0059] It should be noted that the present invention is not limited to the embodiments described above, and can be modified in various ways during implementation without departing from its essence. Furthermore, each embodiment may be combined as appropriate, and in that case, the combined effects can be obtained. Moreover, the above embodiments include various inventions, and various inventions can be extracted by selecting combinations from the multiple constituent elements disclosed. For example, if the problem can be solved and effects obtained even if some constituent elements are deleted from all the constituent elements shown in the embodiment, then the configuration with these deleted constituent elements can be extracted as an invention. [Explanation of symbols]
[0060] 100…Mass culture equipment 200... Microfluidic systems 201...Automatic push-type liquid dispenser 202a, 202b, 202c, 202d, 208a, 208b... Reservoir 203...Automatic pull-type liquid delivery device 204…Flow channel chip 205... Measuring device 206…Environmental control devices 207… Valve control device 209... Control Server 300... Anomaly detection server 400... Monitoring Server
Claims
1. A measurement unit measures the culture state of a sample of a culture target being cultured in large quantities by a large-scale culture device, which is cultured by being delivered to a microfluidic chip, under set culture conditions different from the culture conditions of the large-scale culture device. An abnormality detection model defined for each of multiple culture conditions, in which the criteria for determining the abnormality of the culture state, which indicates that the culture state of the sample of the substance to be cultured is different from normal, differs from the culture conditions of the mass culture device; and a detection unit that detects an abnormality in the culture state of the sample of the substance to be cultured by comparing the culture state measured by the measurement unit for the sample with the set culture conditions. An anomaly detection device equipped with the following features.
2. The aforementioned measuring unit is Under each of the multiple culture conditions set, the culture state of the sample of the culture target that is delivered to the microfluidic chip and cultured is measured. The system further includes a model creation unit that creates an abnormality detection model based on the culture state measured by the measurement unit, in which criteria for determining abnormalities in the culture state of the sample of the culture target are defined for each of the multiple types of culture conditions. An anomaly detection device according to claim 1.
3. The aforementioned culture conditions are: The microfluidic chip includes at least one of the temperature of the sample flow path, the illuminance of the flow path, the hydrogen ion concentration index of the sample, the partial pressure of carbon dioxide in the sample, and the nutrient concentration of the sample. An anomaly detection device according to claim 1.
4. The culture status of the aforementioned sample is: The specific growth rate, specific substrate consumption rate, specific product production rate, and specific oxygen consumption rate of the sample of the culture target delivered to the microfluidic chip are included in the above. An anomaly detection device according to claim 1.
5. A method performed by an anomaly detection device, The measurement unit of the abnormality detection device measures the culture state of a sample of a culture target being cultured in large quantities by a large-scale culture device and cultured by being delivered to a microfluidic chip, under set culture conditions different from those of the large-scale culture device. The detection unit of the abnormality detection device detects abnormalities in the culture state of the sample of the culture target by comparing an abnormality detection model, defined for each of multiple culture conditions, where the criteria for determining the abnormality of the culture state differ from the culture conditions of the mass culture device, with the culture state measured by the measurement unit for the sample under the set culture conditions, thereby detecting abnormalities in the culture state of the sample of the culture target. An anomaly detection method comprising the following features.
6. The aforementioned measuring unit is Under each of the multiple culture conditions set, the culture state of the sample of the culture target that is delivered to the microfluidic chip and cultured is measured. The anomaly detection device further comprises the creation of an anomaly detection model in which, based on the culture state measured by the measurement unit, criteria for determining abnormalities in the culture state of the sample of the culture target are defined for each of the multiple types of culture conditions. The anomaly detection method according to claim 5.
7. The aforementioned culture conditions are: The microfluidic chip includes at least one of the temperature of the sample flow path, the illuminance of the flow path, the hydrogen ion concentration index of the sample, the partial pressure of carbon dioxide in the sample, and the nutrient concentration of the sample. The anomaly detection method according to claim 5.
8. An anomaly detection processing program that causes a processor to function as one of the components of the apparatus according to any one of claims 1 to 4.