Cell culture monitoring method and system based on infrared-Raman image recovery model

Abstract

The invention relates to the technical field of bioengineering, in particular to a cell culture monitoring method and system based on an infrared-Raman image recovery model.The method comprises the steps that 1, a metabolic activity comprehensive scoring model is built, and a metabolic jump region set is built through the metabolic activity comprehensive scoring model; 2, selecting first N metabolic active regions in the metabolic active region set as selected regions, dividing the selected regions into low-change regions and key regions, and solving a compressed measurement Raman image according to the key regions; 3, constructing a data set; 4, training a pre-established infrared-Raman image recovery model by using the data set, and transmitting the trained infrared-Raman image recovery model to the equipment end; and 5, monitoring cell culture by using the infrared-Raman image recovery model on the equipment end. According to the method, the problems of insufficient global information capture and low visual and language information fusion efficiency in a traditional single-mode classification method are solved, and higher classification precision and task generalization ability are realized.

Description

Technical Field

[0001] The present invention relates to the field of bioengineering technology, and in particular to a cell culture monitoring method and system based on an infrared-Raman image restoration model. Background Art

[0002] The rapid development of the biopharmaceutical industry has placed higher demands on real-time monitoring and precise control of cell culture processes. During the cultivation of cell therapy products such as monoclonal antibodies and CAR-T cells, real-time monitoring of cell metabolic status and product quality is crucial for ensuring product quality and improving production efficiency. Traditional cell culture monitoring relies primarily on offline sampling and analysis, which is labor-intensive and subject to lags, making it difficult to meet the demands of biopharmaceutical process control.

[0003] Raman spectroscopy, due to its high sensitivity and specificity for detecting cellular metabolites, has become an effective means of monitoring cell culture. However, existing Raman spectroscopy imaging faces the following technical challenges: First, imaging is slow; a full-field Raman scan typically takes several hours, making it difficult to capture dynamic changes in cellular metabolism; second, sampling efficiency is low; traditional uniform sampling strategies fail to account for the uneven distribution of information within the sample, resulting in significant waste of resources in low-information areas; and third, alignment of infrared and Raman data is difficult, limiting the collaborative application of these two complementary spectral techniques by technical bottlenecks.

[0004] Therefore, there is an urgent need for an efficient and accurate cell culture monitoring method to realize online monitoring of the cell culture process and provide scientific support for real-time monitoring and precise control technology of biopharmaceutical processes. Summary of the Invention

[0005] The present invention provides a cell culture monitoring method and system based on an infrared-Raman image restoration model to solve the technical problems mentioned in the background technology.

[0006] To achieve the above object, the technical solution of the present invention is achieved as follows: The present invention provides a cell culture monitoring method based on an infrared-Raman image restoration model, comprising the following steps: S1. Take multiple infrared images of cell metabolic activities, then use infrared image analysis to calculate the difference map of infrared images at consecutive time points, and build a metabolic activity comprehensive scoring model based on the difference map. Use the metabolic activity comprehensive scoring model to build a metabolic jump region set. ; S2. Gather in metabolically active areas Select metabolically active The selected area is divided into a low-variation area and a key area, and then the compressed measurement Raman image is solved according to the key area; S3. Generate the range of the compressed measurement Raman image using a linear interpolation algorithm, and acquire an infrared spectrum image of the corresponding area based on the range of the compressed measurement Raman image; then acquire a complete Raman image of the corresponding area, and construct a data set using the complete Raman image, the adjusted infrared spectrum image, and the compressed measurement Raman image; S4. Using the data set to train the pre-built infrared-Raman image restoration model to obtain a trained infrared-Raman image restoration model, and transmitting the trained infrared-Raman image restoration model to the device end; S5. Monitor cell culture using the infrared-Raman image restoration model on the device side.

[0007] Another aspect of the present invention provides a cell culture monitoring system based on an infrared-Raman image restoration model, comprising a device end configured to execute a cell culture monitoring method.

[0008] Beneficial effects of the present invention: 1. The present invention provides a cell culture monitoring method based on an infrared-Raman image restoration model. By identifying metabolically active areas through infrared pre-scanning and combining it with a priority sampling strategy, limited Raman sampling resources are concentrated on the areas with the most information value, shortening the imaging time by more than 80%, significantly improving the efficiency of Raman spectroscopy imaging, and realizing real-time monitoring of the cell culture process.

[0009] In addition, the present invention breaks through the technical bottleneck of traditional Raman spectroscopy imaging, provides an innovative solution for cell culture process monitoring, and is of great significance to promoting the development of the biopharmaceutical industry.

[0010] 2. The present invention provides a cell culture monitoring method based on an infrared-Raman image restoration model. The internal design uses an infrared-Raman image restoration model, fully utilizing the complementary characteristics of infrared and Raman images. The self-attention mechanism is used to achieve effective fusion of multimodal information, thereby significantly improving the signal-to-noise ratio of the reconstructed image and ensuring the high quality of the reconstructed Raman image.

[0011] 3. The infrared-Raman image restoration model proposed in this invention solves the problem of spatial alignment of two spectral data, realizes the precise alignment of Raman spectrum and infrared spectrum, and provides a reliable basis for multimodal spectral analysis.

[0012] 4. The present invention discloses a cell culture monitoring system based on an infrared-Raman image restoration model. The cell culture monitoring system can automatically detect and track metabolically active areas without manual intervention, greatly reducing operational complexity and human errors, while also significantly improving the automation level of cell culture monitoring.

[0013] 5. The cell culture monitoring system of the present invention can monitor the cell metabolism status and product quality in real time, detect abnormalities in a timely manner, provide a scientific basis for culture parameter optimization and product quality control, and also provide a reliable quality control method for the biopharmaceutical process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a framework diagram of the infrared-Raman image restoration model of the present invention. DETAILED DESCRIPTION

[0015] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The accompanying drawings illustrate preferred embodiments of the present invention. However, the present invention may be implemented in many other forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present disclosure.

[0016] Reference Figure 1 The present invention provides a cell culture monitoring method based on an infrared-Raman image restoration model, comprising the following steps: S1. Take multiple infrared images of cell metabolic activities, then use infrared image analysis to calculate the difference map of infrared images at consecutive time points, and build a metabolic activity comprehensive scoring model based on the difference map. Use the metabolic activity comprehensive scoring model to build a metabolic jump region set. ; S2. Gather in metabolically active areas Select metabolically active The selected area is divided into a low-variation area and a key area, and then the compressed measurement Raman image is solved according to the key area; S3. Generate the range of the compressed measurement Raman image using a linear interpolation algorithm, and acquire an infrared spectrum image of the corresponding area based on the range of the compressed measurement Raman image; then acquire a complete Raman image of the corresponding area, and construct a data set using the complete Raman image, the adjusted infrared spectrum image, and the compressed measurement Raman image; S4. Using the data set to train the pre-built infrared-Raman image restoration model to obtain a trained infrared-Raman image restoration model, and transmitting the trained infrared-Raman image restoration model to the device end; S5. Monitor cell culture using the infrared-Raman image restoration model on the device side.

[0017] The cell culture monitoring method of the present invention uses infrared pre-scanning to identify metabolically active areas. Combined with a priority sampling strategy, it concentrates limited Raman sampling resources on the areas with the most information value, shortening imaging time by more than 80%, significantly improving Raman spectroscopy imaging efficiency, and achieving real-time monitoring of the cell culture process.

[0018] In addition, the present invention breaks through the technical bottleneck of traditional Raman spectroscopy imaging, provides an innovative solution for cell culture process monitoring, and is of great significance to promoting the development of the biopharmaceutical industry.

[0019] In some embodiments, the step S1 specifically includes the following steps: S11. Capture multiple infrared images of cellular metabolic activity. Based on the heat changes and pH changes generated by cellular metabolic activity, these changes will appear in the infrared images as temperature gradients and changes in absorption intensity in specific bands. S12. Extract the temperature gradient map in each infrared image through the built-in radiation-to-temperature conversion algorithm of the shooting system , and in the temperature gradient diagram Identify the area where the local temperature is higher than the environment, obtain the local physical characteristics, and use them as physical properties to subsequently determine the metabolic activity score of each pixel in the infrared image; S13, identify the characteristics of the set metabolites (such as lactic acid) in the infrared image, obtain the local biological characteristics, and then target the absorption bands of the local biological characteristics (such as region), infrared absorption intensity map of the local biological characteristic absorption band; used as a biological characteristic to subsequently determine the metabolic activity score of each pixel in the infrared image; S14, and calculating a difference image of the infrared images at adjacent time points based on the time series change rate analysis; S15: Segment the difference image using the first threshold value and extract the significant areas where the degree of change reaches the set degree. , significant areas That is, the area with significant changes, which usually represents high metabolic activity and can be used to further calculate the comprehensive metabolic activity score; then the time series change rate curve of the area with significant changes C is calculated; S16. In the extracted salient area Incorporating temperature gradient maps including local physical characteristics , the infrared absorption intensity map of the local biological characteristic absorption band and the time series change rate curve are used to construct a metabolic activity comprehensive scoring model, which can quantify the metabolic activity level of each pixel; the metabolic activity comprehensive scoring model is used to calculate the metabolic activity score of each pixel in the infrared image, and a metabolic activity score map is constructed based on this; S17, using a second threshold to segment the metabolic activity score map, where the second threshold is preferably 20%; that is, the top 20% of all metabolic activity scores are used as regions of interest, and the metabolic activity score map is segmented using the regions of interest; Morphological operations (such as opening and closing operations) are used to denoise the segmentation results and connect the broken areas. The obtained areas are then statistically analyzed and a metabolic jump area set is constructed based on the statistical results. , , where n represents the total number of obtained regions.

[0020] In some embodiments, the calculation formula of the difference map in S14 is specifically as follows: ; in, Represents the difference image of infrared images at adjacent time points; Indicates the The average value of all bands of the infrared image at the moment; Indicates the The infrared image at each moment is targeted at the absorption band of local biological features. 、 Represent the position of the infrared image on the x-axis and y-axis respectively.

[0021] In some embodiments, the significant change area in S15 The expression is as follows: ; in, Indicates the first threshold.

[0022] In some embodiments, the expression of the metabolic activity comprehensive scoring model in S16 is specifically as follows: ; in, represents the metabolic activity score; 、 、 Represents the temperature gradient diagram , the weight coefficient of the infrared absorption intensity map of the local biological characteristic absorption band and the difference map of the infrared images at adjacent time points; Infrared absorption intensity map showing local biogenic characteristic absorption bands.

[0023] In some embodiments, the step S2 specifically includes the following steps: S21. Gather in metabolically active areas Select metabolically active regions as selected regions; preferably, The value of is 5; S22. For each selected area, calculate the mean and variance of all pixels in the selected area, define the selected area with a variance less than a first set percentage of the mean as a low-variance area, and define the selected area with a variance greater than or equal to the first set percentage of the mean as a key scanning area. , , where m represents the number of key scanning areas; the first set percentage is preferably 10%; Then randomly select The position of each pixel is scanned at a single point, and the calculation The average value of the Raman signal of the single-point scan represents the characteristic curve of the low-variation area (i.e., the Raman spectrum curve), which is used for subsequent analysis of the material composition of the low-variation area; preferably, The value is 10. For low-variability areas, a few random sampling points can represent the characteristics of the area without extensive scanning and imaging. This characteristic can then be used in chemical and physical measurements to determine the substance and corresponding composition by analyzing the Raman spectrum curve.

[0024] For each key area , establish current key areas The maximum circumscribed rectangle is used as the area to be finely imaged, and then a global sampling scan is performed in the imaging device. During the global sampling scan, a sampling matrix is ​​generated using a random function, and a second set percentage of pixels are randomly sampled within the sampling matrix as a compressed measurement Raman image. , compressed measurement Raman image This is used for subsequent reconstruction, significantly shortening the Raman scan time in key areas. The second set percentage is preferably 20%.

[0025] In some embodiments, S3 specifically includes the following steps: S31. For each key area Positioning will be performed to obtain the infrared spectrum images collected by the infrared spectrometer corresponding to each area. ; Infrared spectral image The dimensions are length H, width W, and number of bands B; S32, use linear interpolation algorithm to convert infrared spectrum image Band mapping and compression of measured Raman images The bands are the same size ; Get the adjusted infrared spectrum image , adjusted infrared spectrum image The dimensions are length H, width W, number of bands ;

[0026] Step S32 in the present invention solves the problem of spatial alignment of two spectral data, achieves precise alignment of Raman spectrum and infrared spectrum, and provides a reliable basis for multimodal spectral analysis.

[0027] Acquire a complete Raman image within each area , complete Raman image The dimensions are length H, width W, and the number of bands is ; Using the complete Raman image , the corresponding adjusted infrared spectrum image And the corresponding compressed measurement Raman image Build a dataset.

[0028] In some embodiments, the infrared-Raman image restoration model includes a near-infrared image encoder, a Raman image encoder, an image interaction attention model, and a reconstruction decoder; Among them, the near-infrared image encoder and the Raman image encoder are connected in parallel, and the output ends of both are connected to the image interaction attention model and the reconstruction decoder in turn.

[0029] The infrared-Raman image restoration model in the present invention makes full use of the complementary characteristics of infrared and Raman images, and realizes the effective fusion of multimodal information through the self-attention mechanism, which improves the signal-to-noise ratio of the reconstructed image by more than 40% and ensures the high quality of the reconstructed Raman image.

[0030] In some embodiments, the S4 specifically includes the following steps: S41. Select complete Raman image from the data set , the corresponding adjusted infrared spectrum image And the corresponding compressed measurement Raman image Input into a pre-built infrared-Raman image restoration model; S42, the adjusted infrared spectrum image , compressed measurement Raman image Input them into the near infrared image encoder and Raman image encoder respectively to obtain the near infrared features and Raman characteristics , expressed by the formula, as follows: ; ; in, represents the feature extraction operation of the near infrared image encoder; Represents the feature extraction operation of the Raman image encoder; near infrared features and Raman characteristics The dimensions are all long ,Width , Band Number ; S43, adjust the near infrared characteristics separately and Raman characteristics The order of the spatial dimension (i.e., length and width) and the number of bands is then expanded in the dimension of the number of bands to obtain the eigenvectorized Raman signal and infrared spectral signals ; At this time, the dimension of the eigenvectorized Raman signal and infrared spectrum signal is ;Spatial dimensions refer to the length and width of the dimensions; S44. Generate token vector using feature vectorized infrared spectrum signal and sequence vector , using the eigenvectorized Raman signal to generate a value vector , expressed by the formula, as follows: ; ; ; in, 、 、 Represents token vectors , sequence vector and the value vector The weight of S45, then based on the token vector and sequence vector Calculate attention weights and apply to the value vector , and get the output features , expressed by the formula, as follows: ; ; in, represents the attention weight; Indicates that according to the square root The numerical size of is used to scale the dot product to prevent the gradient from disappearing; Represents a token vector The length of the last dimension, Normalized exponential function; at this time, the output feature The dimension is ; S46. Output features The spatial dimension and number of bands are reconverted to make the output features Convert vector features into image features and obtain the converted output features , the output features after conversion The dimension is ; and convert the output features Input to the reconstruction decoder, the reconstruction decoder remaps the features back to the original dimensions (H, W, B) to obtain the reconstructed Raman image ; S47. Using the reconstructed Raman image and the corresponding complete Raman image Construct the total loss function, which is as follows: ; in, represents the total loss value, Indicates the number of batches in the training process; represents Fourier transform; represents the square of the L2 norm, represents the L1 norm, and Represents the loss weight distribution coefficient in frequency and spatial domains; represents the i-th complete Raman image; Represents the reconstructed i-th Raman image ; S48. Loop S41 to S47 to minimize the total loss function until the total loss function converges, and update the weights of the infrared-Raman image restoration model to obtain the trained infrared-Raman image restoration model.

[0031] On the other hand, the present invention further provides a cell culture monitoring system based on an infrared-Raman image restoration model, comprising a device end configured to execute the above-mentioned cell culture monitoring method.

[0032] The cell culture monitoring system can automatically detect and track metabolically active areas without manual intervention, greatly reducing operational complexity and human errors, while also significantly improving the level of automation in cell culture monitoring.

[0033] The cell culture monitoring system of the present invention can monitor the cell metabolism status and product quality in real time, detect abnormalities in a timely manner, provide a scientific basis for culture parameter optimization and product quality control, and also provide a reliable quality control method for the biopharmaceutical process.

[0034] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. In addition, the technical solutions between the various embodiments of the present invention can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A cell culture monitoring method based on infrared-Raman image restoration model, characterized in that: The steps include: S1. Take multiple infrared images of cell metabolic activities, then use infrared image analysis to calculate the difference map of infrared images at consecutive time points, and build a metabolic activity comprehensive scoring model based on the difference map. Use the metabolic activity comprehensive scoring model to build a metabolic jump region set. ; S2. Gather in metabolically active areas Select metabolically active The selected area is divided into a low-variation area and a key area, and then the compressed measurement Raman image is solved according to the key area; S3. Generate a range of a compressed measurement Raman image using a linear interpolation algorithm, and acquire an infrared spectrum image of a corresponding area based on the range of the compressed measurement Raman image; Then, the corresponding area is collected to obtain a complete Raman image, and a data set is constructed using the complete Raman image, the adjusted infrared spectrum image, and the compressed measurement Raman image; S4. Using the data set to train the pre-built infrared-Raman image restoration model to obtain a trained infrared-Raman image restoration model, and transmitting the trained infrared-Raman image restoration model to the device end; S5. Monitor cell culture using the infrared-Raman image restoration model on the device side.

2. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Take multiple infrared images of cell metabolic activities; S12. Extract the temperature gradient map in each infrared image through the built-in radiation-to-temperature conversion algorithm of the shooting system , and in the temperature gradient diagram Identify the areas where the local temperature is higher than the environment and obtain the local physical characteristics; S13. In the infrared image, for the absorption band of the local biological characteristics, calculate and obtain an infrared absorption intensity map of the absorption band of the local biological characteristics; S14, and calculating a difference image of the infrared images at adjacent time points based on the time series change rate analysis; S15: Segment the difference image using the first threshold value and extract the significant areas where the degree of change reaches the set degree. , and calculate the significant change area Time series change rate curve; S16. In the extracted salient area Incorporating temperature gradient maps including local physical characteristics , construct a metabolic activity comprehensive scoring model based on the infrared absorption intensity map of the local biological characteristic absorption band and the time series change rate curve, use the metabolic activity comprehensive scoring model to calculate the metabolic activity score of each pixel in the infrared image, and construct a metabolic activity score map based on this; S17, using the second threshold to segment the metabolic activity score map, using morphological operations to denoise the segmentation results and connect the broken areas, then statistically analyze the obtained areas, and construct a metabolic jump area set based on the statistical results. , , where n represents the total number of obtained regions.

3. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 2, characterized in that: The calculation formula of the difference map in S14 is as follows: ； in, Represents the difference image of infrared images at adjacent time points; Indicates the The average value of all bands of the infrared image at the moment; Indicates the The infrared image at each moment is targeted at the absorption band of local biological features. 、 Represent the position of the infrared image on the x-axis and y-axis respectively.

4. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 3, characterized in that: The significant changes in S15 The expression is as follows: ； in, Indicates the first threshold.

5. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 4, characterized in that: The expression of the metabolic activity comprehensive scoring model in S16 is as follows: ； in, represents the metabolic activity score; 、 、 Represents the temperature gradient diagram , the weight coefficient of the infrared absorption intensity map of the local biological characteristic absorption band and the difference map of the infrared images at adjacent time points; Infrared absorption intensity map showing local biogenic characteristic absorption bands.

6. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 5, characterized in that: The S2 specifically includes the following steps: S21. Gather in metabolically active areas Select metabolically active areas as selected areas; S22. For each selected area, calculate the mean and variance of all pixels in the selected area, define the selected area with a variance less than a first set percentage of the mean as a low-variance area, and define the selected area with a variance greater than or equal to the first set percentage of the mean as a key scanning area. , , where m represents the number of key scanning areas; S23. For each key area , establish current key areas The maximum circumscribed rectangle is used as the area to be finely imaged, and then a global sampling scan is performed in the imaging device. During the global sampling scan, a sampling matrix is ​​generated using a random function, and a second set percentage of pixels are randomly sampled within the sampling matrix as a compressed measurement Raman image. .

7. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 6, characterized in that: The S3 specifically includes the following steps: S31. For each key area Positioning will be performed to obtain the infrared spectrum images collected by the infrared spectrometer corresponding to each area. ; Infrared spectral image The dimensions are length H, width W, and number of bands B; S32, use linear interpolation algorithm to convert infrared spectrum image Band mapping and compression of measured Raman images The bands are the same size ; Get the adjusted infrared spectrum image , adjusted infrared spectrum image The dimensions are length H, width W, number of bands ; S33, collect complete Raman images in each area , complete Raman image The dimensions are length H, width W, and the number of bands is ; Using the complete Raman image , the corresponding adjusted infrared spectrum image X HK And the corresponding compressed measurement Raman image Build a dataset.

8. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 7, characterized in that: The infrared-Raman image restoration model includes a near-infrared image encoder, a Raman image encoder, an image interaction attention model and a reconstruction decoder; Among them, the near-infrared image encoder and the Raman image encoder are connected in parallel, and the output ends of both are connected to the image interaction attention model and the reconstruction decoder in turn.

9. The cell culture monitoring method based on the infrared-Raman image restoration model according to claim 8, characterized in that: The S4 specifically includes the following steps: S41. Select complete Raman image from the data set , the corresponding adjusted infrared spectrum image And the corresponding compressed measurement Raman image Input into a pre-built infrared-Raman image restoration model; S42, the adjusted infrared spectrum image , compressed measurement Raman image Input them into the near infrared image encoder and Raman image encoder respectively to obtain the near infrared features and Raman characteristics , expressed by the formula, as follows: ； ； in, represents the feature extraction operation of the near infrared image encoder; represents the feature extraction operation of the Raman image encoder; S43, adjust the near infrared characteristics separately and Raman characteristics The order of the spatial dimension and the number of bands is then expanded in the dimension of the number of bands to obtain the eigenvectorized Raman signal and infrared spectral signals ;Spatial dimensions refer to the length and width of the dimensions; S44. Generate token vector using feature vectorized infrared spectrum signal and sequence vector , using the eigenvectorized Raman signal to generate a value vector , expressed by the formula, as follows: ； ； ； in, 、 、 Represents token vectors , sequence vector and the value vector The weight of S45, then based on the token vector and sequence vector Calculate attention weights and apply to the value vector , and get the output features , expressed by the formula, as follows: ； ； in, represents the attention weight; Indicates that according to the square root The numerical size of is used to scale the dot product to prevent the gradient from disappearing; Represents a token vector The length of the last dimension, Normalized exponential function; S46. Output features The spatial dimension and number of bands are reconverted to make the output features Convert vector features into image features and obtain the converted output features , and the converted output features Input to the reconstruction decoder, the reconstruction decoder remaps the features back to the original dimension to obtain the reconstructed Raman image ; S47. Using the reconstructed Raman image and the corresponding complete Raman image Construct the total loss function, which is as follows: ； in, represents the total loss value, Indicates the number of batches in the training process; represents Fourier transform; represents the square of the L2 norm, represents the L1 norm, and Represents the loss weight distribution coefficient in frequency and spatial domains; Indicates the A complete Raman image; Reconstruction Zhang Raman image ; S48. Loop S41 to S47 to minimize the total loss function until the total loss function converges, and update the weights of the infrared-Raman image restoration model to obtain the trained infrared-Raman image restoration model.

10. A cell culture monitoring system based on infrared-Raman image restoration model, characterized in that: The device comprises a device end configured to execute the cell culture monitoring method according to any one of claims 1 to 9.

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