68 results about "Growth curve" patented technology

The invention relates to the technical field of power system operation and control, and particularly discloses a time-sharing electric quantity prediction method based on a logarithmic load density growth curve, which comprises the following steps of: firstly, performing causal detection and dynamic time-delay optimization on historical load and multivariate external data through convergence cross mapping and mutual information technologies, and constructing a causal time-delay feature set; and the problems of multi-element coupling and time-delay effect quantization are solved. Secondly, fitting a load trend by using time-frequency decomposition in cooperation with a segmented logistic model, extracting dynamic parameters representing a growth rate and a saturation capacity, and endowing the model with a sensing ability for a load evolution stage; then, causal features, growth parameters and load components are deeply fused through cross-domain modulation and a gating mechanism, the nonlinear modulation effect of an external environment on a load mode is explicitly modeled, and finally, a probability interval is generated in combination with quantile regression and residual error correction. According to the scheme, accurate and probabilistic prediction of the time-sharing electric quantity in a complex scene is realized, and the scientificity of an agent electricity purchase decision is improved.

The invention provides a resource information determination method and device, equipment and a storage medium, relates to the field of human resources, and is used for determining reserve resource information of a main body object of an institution and providing reliable data support for the institution to formulate long-term and accurate resource planning. The specific scheme is as follows; the method comprises the following steps: quantifying historical business processing data of a subject object to obtain an efficiency index, and fitting based on the quantified efficiency index to obtain a first growth rate of efficiency index growth of the subject object; a target senescence factor coefficient is introduced to correct the first growth rate to obtain a second growth rate which reflects the law of efficiency growth slowing of the subject object after the target time window; combining the first growth rate, the second growth rate and the target time window to generate an efficiency growth curve, and representing the change of the efficiency of the main object along with time; and finally, determining resource information of the mechanism in the target time window based on the efficiency growth curve of each main body object.

The invention provides a strength data processing method and system for concrete performance, and relates to the technical field of data processing.The method comprises the steps that hydration heat temperature strain data of a concrete structure of a super high-rise building foundation is obtained to generate a temperature strain data set with spatial distribution characteristics, and the strength data of the concrete performance is obtained by combining material characteristic parameters of the concrete structure; constructing a concrete three-dimensional entity by using the pre-constructed digital twin model so as to simulate a temperature gradient field, a thermal stress field and a time-space evolution rule of strength development of a concrete structure; based on the space-time evolution rule and different maintenance condition parameters, predicting a strength increase curve of the concrete structure under different maintenance conditions; according to the strength increasing curve, the strength standard probability and the risk threshold value of different curing stages are calculated, strength development simulation and risk early warning of the concrete structure are carried out based on the strength standard probability and the risk threshold value, and the intelligent monitoring and early cracking risk early warning capacity of the concrete strength development process is improved.

This invention relates to the field of mechanical property monitoring technology for mine backfill bodies, specifically to a system and method for acquiring and analyzing tensile strength data of mine backfill bodies. The system comprises a data acquisition unit, a physical information coupling unit, a tensile strength inversion unit, and a spatiotemporal evolution mapping unit. This invention acquires the waveguide flight time and energy attenuation coefficient of the backfill body using an embedded guided wave sensor array. It uses the monotonically increasing law of hydration degree as a physical constraint to train the physical information coupling network, outputting a compliant hydration degree time series. Based on a hydration degree-tensile strength mapping relationship library, it inverts and fits to generate a strength growth curve, which is then loaded into a four-dimensional digital twin model to invert the internal tensile strength gradient field. It automatically identifies local abnormal regions where the growth rate is below the safety threshold, improving the physical rationality and accuracy of the inversion results, and realizing visualization of the spatiotemporal evolution of backfill body strength and early safety warning.

This invention relates to the field of ecological environment management technology, specifically disclosing a refined method and system for visualizing regional vegetation carbon sequestration. The method includes: extracting land use types of a target area based on remote sensing data of land use types and biomass; determining forest areas based on land use types and establishing growth curves for the forest areas based on preset fitting curves; calculating the carbon sequestration amount of the forest areas based on the growth curves and determining the carbon sequestration ratio of different forest areas; allocating the total carbon sequestration amount to different sub-regions of the target area based on the carbon sequestration ratios, and visually converting the allocated target areas. This invention first selects a target area, calculates the carbon sequestration amount based on the forest areas within the target area, then redistributes the carbon sequestration to different areas according to land use types, and visualizes the redistribution results, facilitating the management of the area.

This invention provides a method, apparatus, equipment, and medium for constructing an oil and gas field production growth curve model, belonging to the field of oil and gas field production evolution technology. The method collects multiple sets of geological reserves, first and second rate coefficients, and cumulative production data for oil and gas fields. Then, it applies the oil and gas field growth differential equation and obtains a growth model for cumulative production through mathematical solution. After the model is converted to a first-order linear form, iterative calculations are used to find at least two sets of data to ensure a linear relationship between the independent and dependent variables. These data are used to determine the slope and intercept of the linear equation, thereby estimating the final recoverable reserves and model constants of the oil and gas field. Finally, combining the rate coefficients and estimated parameters, a growth curve model for predicting the cumulative production of the oil and gas field is constructed. This invention improves the accuracy of oil production prediction by constructing an accurate oil and gas field production growth curve model.

The invention relates to the technical field of agricultural engineering, and discloses a grain machinery operation area accurate extraction method based on visual remote sensing and AI, and the method comprises the following steps: constructing a multi-source spatio-temporal data set of land parcel level alignment; reconstructing a normalized vegetation index time sequence for each plot by adopting an interpolation method and a filtering method, and generating a plot scale crop growth curve; generating a hourly improved vegetation index image through calculation, and extracting texture features in combination with a crop growth curve; constructing a change atlas based on the normalized vegetation index time sequence; inputting the remote sensing image and the crop growth curve into a semantic segmentation model; performing fusion classification on the texture features and the change atlas by using a random forest algorithm; and converting the probability graph and a binary mask result into a vector boundary, and completing precision verification by calculating an intersection-to-union ratio. Accurate data support is provided for agricultural production monitoring, agricultural machinery scheduling and the like.

The invention discloses an idiopathic knee joint deformity correction method based on combination of growth curve prediction and absorbable material time gradient regulation and control, and is applied to the technical field of biomedical materials. Comprising the following steps: acquiring lower limb bone length data of a normal child, and drawing a growth curve; constructing and fitting a lower limb bone length model by using the growth curve data, and constructing a mathematical model for correcting idiopathic knee varus and valgus; constructing a prediction model to calculate the time of implanting the child patient with idiopathic genu varum into the steel plate for fixation during the semi-epiphyseal fixation; the absorbable zinc-magnesium alloy internal fixation nail plate with different in-vivo absorption time gradients is developed; the zinc-magnesium alloy internal fixation nail plate with the corresponding absorption time is selected for correction according to the time required by different child patients to be implanted into the steel plate for fixation. After a child patient receives a semi-epiphyseal block operation to achieve the correction effect, the internal fixation steel plate can be automatically absorbed and broken, the operation of taking out internal fixation again is omitted, the operation risk and pain are reduced, and the treatment cost is reduced.

This invention belongs to the field of granulation, specifically disclosing an automated water suspension granulation control method based on online particle size monitoring, comprising: Step 1, during the granulation process, using an online particle size analyzer to monitor the particle size in real time and plotting a real-time particle size growth curve over time; Step 2, based on the morphological characteristics such as the slope change points of the real-time particle size growth curve, automatically dividing the granulation process into three characteristic stages; Step 3, for each stage divided in Step 2, establishing a quantitative model between particle size growth and key process parameters; Step 4, based on the model's predictive and feedback control, executing closed-loop control in a new granulation batch. This invention also discloses an automated water suspension granulation control system. This method and system, through real-time feedback and dynamic adjustment, ensure that the granulation process of each batch follows the optimal growth trajectory, significantly reducing product quality differences between batches.

The embodiment of the invention discloses a sample analysis system and a control method thereof, and the sample analysis system comprises a blood culture module, a display module and a control module. The blood culture module comprises an incubation assembly and a detection assembly, the incubation assembly is used for loading at least one sample and incubating the sample, and the detection assembly is used for detecting microorganism growth parameters of the sample in the incubation assembly to obtain corresponding detection data; the control module is used for acquiring target detection data of the target sample, generating a growth curve of the target sample according to the target detection data, and displaying the growth curve of the target sample in the first display area through the display module; the growth curve of the target sample comprises a historical growth curve before the target sample is reloaded and an extended growth curve after the target sample is reloaded, and the historical growth curve and the extended growth curve are displayed in different styles. On the basis, the sample analysis system provided by the embodiment of the invention can improve the interaction experience of the user after the sample is reloaded.

The present invention relates to the use of online biomass capacitance monitoring in cultures as a way to control the growth of cells through the use of a temperature control loop. In certain embodiments, a biomass capacitance probe is used to measure the cell density, and a predetermined growth curve is used to adjust the temperature in the culture.

This application discloses a method for association analysis based on high-throughput dynamic phenotypes and genetic effects, relating to the field of agricultural bioinformatics. By fitting growth curves to high-throughput dynamic phenotype data from multiple time points, the dynamic changes in the growth cycle of a biological population are obtained. A joint likelihood function algorithm is then constructed to associate this dynamic vector with genetic effects, revealing genetic markers related to growth and development trends and processes. Compared to existing GWAS methods, which can only analyze static, single-time-point phenotype data and cannot uncover genetic loci regulating biological growth and development, this application, by performing association analysis on data collected from multiple consecutive time points, can more comprehensively reveal the evolutionary patterns of phenotypes over time, thereby achieving accurate analysis of genetic markers.

The invention discloses a sewage disposal robot homeward voyage decision-making method and system based on real-time loading capacity prediction, and relates to the technical field of sewage disposal robots, and the method comprises the following four steps: firstly, collecting operation state, environment perception and lake partition garbage density data through a multi-source sensing assembly; constructing a model to predict a loading capacity growth curve and saturation time in the future 10 minutes; then, in combination with multi-dimensional verification return flight conditions such as the residual electric quantity and the loading capacity, delay or instant return flight is decided; and finally, planning an optimal path to the to-be-replaced area as required and allocating tasks, or planning a homeward voyage path giving consideration to smooth cleaning. Through multi-dimensional pre-judgment, the homeward voyage decision accuracy is improved, the path and cleaning operation are coordinated to maximize resource utilization, the task matching is accurate, the efficiency is optimized, the landscape lake scene is adapted, the cleaning efficiency and the ecological value are considered, the equipment cost is reduced, and the timeliness and the stability of water area cleaning are improved.

The invention relates to a training course pushing method and device, electronic equipment and a storage medium. The method comprises the steps of collecting multi-dimensional information data of employees; constructing a correlation model, outputting a fusion feature set, and identifying key influence factors based on the fusion feature set; inputting the fused feature set into a trained intelligent decision model, and outputting training decision data information; wherein the intelligent decision-making model is configured with a composite reward function adjusted based on a key influence factor; and generating a training course push instruction according to the training decision data information, and optimizing the intelligent decision model. By collecting multi-dimensional information data of employees and capturing space-time correlation between an employee ability growth curve and a post demand evolution trajectory, a recommendation strategy can respond to ability updating, post iteration and strategic adjustment in real time, and the hysteresis problem of static planning is thoroughly solved. And meanwhile, based on dynamic adjustment and optimization of a composite reward function in the intelligent decision model, deep binding of training management and organization strategy is realized.

The invention relates to the technical field of artificial intelligence, in particular to an artificial intelligence-based error analysis and growth feedback system, which comprises a knowledge graph construction module, an error cause source prediction module, a growth feedback prediction module, a generative AI module and a multi-role feedback module, and the knowledge graph construction module is used for constructing a knowledge base supporting error analysis and feedback, and realizing structured association and dynamic iteration of all-disciplinary and cross-grade knowledge of middle and primary schools. According to the method, an interdisciplinary and cross-grade dynamic knowledge graph is provided, root cause analysis can be accurately carried out in combination with AI and causal inference, a growth curve can be output through a time sequence model, then a risk index is ascertained, and growth feedback is provided; and personalized practice and multi-role feedback of students, teachers and parents can be generated, and the problems of inaccurate root cause analysis and no growth feedback of a traditional APP are solved.

The invention relates to the cross technical field of molecular biology and parasitology, and particularly discloses a giardia living body marking method based on fluorescent protein UnaG, which comprises the following steps: firstly, optimizing and synthesizing a UnaG gene sequence according to the codon use preference of giardia; then, constructing a marker plasmid pGT-UnaG-neo on a double expression framework containing an alpha2-tubulin promoter and a gdh promoter, and introducing the plasmid into the giardia giardia trophozoite through electroporation. And carrying out resistance screening in a culture medium containing geneticin to obtain an insect strain capable of stably integrating and expressing UnaG. Through bilirubin induction, efficient maturation and green fluorescence emission of the UnaG protein in giardia cytoplasm are realized. The stably transferred strain obtained by the method has no significant difference from a wild strain in the aspects of growth curve and survival rate, and UnaG and bilirubin have no obvious toxic effect. The limitation that traditional fluorescent protein is inactivated under the anaerobic condition is broken through, and a reliable tool is provided for dynamic imaging, functional research and drug screening of giardia and other anaerobic protozoa.

According to the isolated culture identification and key marker detection method for the down producing goat hair follicle stem cells, trypsin and EDTA are used for jointly digesting skin tissue, and high-activity cells are efficiently obtained; a culture medium containing various growth factors is adopted for culture, and cell proliferation and stemness maintenance are promoted. And identifying and integrating morphological observation, growth curve determination and clone formation experiment to accurately judge the characteristics of the cells. Immunofluorescent staining and real-time fluorescent quantitative PCR are applied to key marker detection, and expression of markers such as CK15, CD34 and beta1 integrin is detected with high sensitivity. The method is high in separation efficiency, accurate in identification and sensitive in marker detection, can provide sufficient high-quality cells for the research on the hair follicle stem cells of the cashmere goat, assists in deeply understanding the biological characteristics and the regulation mechanism of the cells, and is of great significance for improving the yield and quality of cashmere and promoting the development of related biotechnologies.

The invention relates to an automatic multi-temperature-control microplate reader based on integration of a mechanical arm and artificial intelligence (AI), and aims to improve automation and accuracy of microbe growth monitoring and analysis. Operation such as culture dish taking and placing, culture solution adding and timing sampling can be automatically completed through the mechanical arm. The AI algorithm automatically adjusts parameters such as culture temperature, humidity, illumination and the like according to real-time data and environmental conditions so as to optimize the growth environment of microorganisms. Meanwhile, key indexes such as a light absorption value and cell density are monitored, a microorganism growth curve is drawn in combination with AI analysis, and important parameters such as the growth rate and the maximum growth amount are calculated. The AI can also mine potential laws in the data and provide valuable research information. The instrument improves the efficiency, accuracy and reliability of microbiological research, is widely applied to the fields of microbiological growth law research, resource development, disease diagnosis and the like, and promotes related technical progress.

The invention discloses a multi-objective decision support system for enterprise human resource configuration, and relates to the technical field of human resource management and intelligent decision. The system comprises a data acquisition module, an employee ability evolution prediction module, a team collaborative effect evaluation module, a matching degree calculation module, a hierarchical collaborative optimization module, a real-time feedback and adaptive update module and a result output module. According to the system, an employee ability growth curve prediction model is established, the cooperative effect between employees is quantified, the competency matching degree, the team cooperation degree, the cost and the ability growth potential are comprehensively considered, a hierarchical collaborative optimization algorithm combining differential evolution and improved ant colony is adopted, and an optimal personnel allocation scheme is generated under multi-target constraints. According to the invention, a new matching degree formula and an optimization algorithm are provided on the basis of considering employee ability evolution and team cooperation, and the system also has a real-time feedback and adaptive updating mechanism, can continuously improve the man-post matching accuracy in a dynamic environment, and achieves the intelligentization and prospect of enterprise human resource management.

The invention discloses a stabilized soil scouring protection scheme decision-making method. The method comprises the steps of S1, determining key design parameters of a stabilized soil protection scheme; s2, an equation that the pile foundation solidified soil protection structure reaches the scouring balance state is established; s3, the relation between the solidified soil strength and the erosion protection measure failure probability is established; s4, establishing a relationship between the curing range and the erosion protection measure failure probability; s5, determining a curve that the economic cost of the stabilized soil protection scheme is increased along with the stabilized soil strength and the solidification range; and S6, determining a failure probability value of the solidified soil protection scheme, and further determining an optimal scheme of solidified soil protection. The method can effectively guide the optimization design of the anti-scouring scheme of the solidified soil.

The invention relates to a drug sensitivity analysis and judgment system and method based on deep learning and a microbial growth curve. The core of the method is as follows: fitting is carried out by adopting parameterized models such as Gompertz or Logistic to extract kinetic parameters such as the maximum growth rate and the hysteresis period; then calculating a residual sequence between a growth curve observation value and a model fitting value; jointly inputting the kinetic parameters, the residual sequence and the metadata into a mixed deep learning model (1D-CNN-BiLSTM-Attention) so as to estimate a drug sensitivity category posterior probability at each time point; and finally, dynamically calculating the optimal stop time based on the posterior probability through a set of sequential decision-making module, and outputting a drug sensitivity judgment result in advance before the microorganisms grow completely when preset confidence and stability conditions are met. According to the method, the defects that a traditional endpoint method is long in period and an existing rapid method depends on single-point imaging are overcome, rapid, accurate and interpretable drug sensitivity analysis can be achieved within 6-8 hours, and timely guidance is provided for clinical anti-infection treatment.

The present application relates to a long-span bridge track vertical deviation temperature-sensitive component extraction method in multi-dimensional space, comprising: integrating the long-span bridge track vertical deviation three-dimensional historical data with additional environmental temperature labels; using empirical wavelet transform to construct a four-dimensional data set containing temperature labels and wavelength labels; after resampling in the mileage dimension, projecting the data to the amplitude-wavelength space, drawing the accumulated amplitude growth curve, identifying the amplitude-wavelength curve type and determining the boundary wavelength of the bridge deformation and track irregularity; then projecting the data to the amplitude-temperature space to identify the amplitude curve and temperature sensitivity; combining the boundary wavelength and temperature sensitivity to extract the temperature-sensitive component of the track irregularity and the construction deviation component in the actual bridge deformation. The method of the present application can realize the synchronous identification and extraction of the temperature-sensitive component in the track vertical deviation and the bridge construction deviation component, and provide quantitative basis for the accurate maintenance and repair of long-span railway bridges.

The invention provides an interaction network construction method and device for stacking fermentation microorganisms, equipment and a medium, and the method comprises the steps: constructing a quasi-dynamic differential equation, and decoupling the quasi-dynamic differential equation into a plurality of independent regression equations, the quasi-dynamic differential equation comprises a natural growth curve of microorganisms and a dependent growth curve of the influence of other microorganisms on the quasi-dynamic differential equation; solving the regression equation by using an add group lasso algorithm to obtain an independent expression quantity of each microorganism and at least one dependency expression quantity between the microorganisms and other microorganisms, and screening out significant variables in the microorganisms; and constructing a dynamic interaction network according to the independent expression quantity of the significant variable and the at least one dependency expression quantity. By implementing the method for constructing the interaction network of the stacking fermentation microorganisms, the problem that the interaction relationship among the stacking fermentation microorganisms is difficult to effectively analyze by a traditional method can be improved.

The invention relates to the technical field of data extraction, and discloses a lake phytoplankton phenological parameter extraction method and system based on multi-scale parameter migration and a dynamic threshold, and the method comprises the steps: obtaining remote sensing image data of a target region, and constructing image data of a multi-scale time sequence; the method comprises the following steps: dividing a growth cycle of a plant, constructing a growth curve reference model and a parameter mapping model to obtain phenological morphological parameters of the plant in the growth cycle, and identifying and extracting key phenological parameters of the growth cycle through a preset dynamic threshold value. According to the method, the fitting stability of the high-noise sparse time sequence is improved, the fitting parameter mapping model is established, direct fitting of high-noise data is avoided, and the model robustness is improved; the dynamic self-adaptive adjustment of the phenological threshold is realized, so that the threshold is self-adaptively changed along with the algal bloom intensity, and the adaptability to lakes with different nutrition states and different years is improved; and long-time-sequence lake algal bloom phenology monitoring in different climate regions and different nutrition levels is supported.

The invention discloses a seedling phenotype high-throughput analysis method and system based on a time sequence growth image, particularly relates to the technical field of seedling cultivation and phenotype analysis, and is used for solving the problems of growth curve distortion and inaccurate evaluation and screening caused by instantaneous disturbance noise introduced by mobile imaging operation in existing high-throughput analysis. The method comprises the following steps: acquiring time sequence images and extracting phenotypic parameters in a seedling growth period, respectively constructing group and individual time sequences, carrying out time-frequency transformation on the group and individual time sequences to separate high-frequency disturbance components and generate an energy proportion time sequence, and judging an operation disturbance occurrence moment by identifying synchronous peak fluctuation in the group and individual time sequences. And carrying out interpolation correction on the numerical value of the polluted phenotype parameter at the moment. According to the method, the operation disturbance noise can be effectively identified and eliminated, and the seedling phenotype data quality and the subsequent analysis reliability are improved.

The invention discloses a yield prediction method and system based on a crop growth model, and the method comprises the steps: obtaining a whole growth curve of a target crop through a pre-trained vegetation index growth prediction model based on the remote sensing data of a production area where the target crop is located from a planting time point to a current time point; generating a heterogeneity development curve and a heterogeneity guide curve of the target crop by using a preset spatial heterogeneity analysis mechanism, performing differential analysis with a preset double-reference curve, and extracting a target deviation interval; and based on the target deviation interval, extracting an NDVI multivariate feature vector of the whole growth curve of the target crop, inputting the NDVI multivariate feature vector into a pre-trained crop yield prediction model, and outputting a predicted yield value of the target crop. Therefore, the accuracy and pertinence of crop yield prediction are comprehensively improved.

The present application belongs to the technical field of automatic growth performance determination of pigs, and discloses a kind of sow growth performance determination data correction method, system, equipment and terminal, obtain determination data and are summarized to the determination data table with every pig unit, the determination data table of every pig is matched with the birth information of sow population, obtain the day age, sex, variety information corresponding to every pig;The most suitable intersection between the set formed by calculating each pig during the measurement period (MP) is determined, to determine the most individuals in the whole sow population Correction measurement period;For the missing value of daily weight data and daily feed intake data of each pig in the correction measurement period, fill in the correction, calculate the average daily gain (ADG) and feed conversion ratio (FCR) of each pig in the correction measurement period, calculate the change of body weight of each pig with increasing age in the correction measurement period, and draw the growth curve graph of each pig in the correction measurement period.

The invention relates to a method for increasing the efficiency of a CVD reactor (1) and to a coating method carried out or CVD reactor produced using a result of said method, wherein the CVD reactor has a gas inlet element (5) having a plurality of gas inlet zones (6, 7, 8, 9) arranged one above the other, and the height (H1) of the lowest gas inlet zone (6) influences the growth rate of a layer deposited on a substrate in the CVD reactor (1). The aim of the invention is to maximise the growth rate using an optimisation method. For this purpose, a descending branch (15'') of a growth curve should be as linear as possible and have the greatest values.

The invention discloses a method for dynamically predicting the daily ration crude protein demand amount in the fattening stage of Ningxiang pigs, and particularly relates to the technical field of daily ration crude protein demand amount prediction. Comprising the following steps: S1, fitting a growth curve of Ningxiang pigs in a fattening stage; s2, predicting the protein deposition efficiency of the Ningxiang pigs in the fattening stage; and S3, establishing a Ningxiang pig daily ration crude protein intake prediction model based on a factorial method. The Ningxiang pig daily ration crude protein demand quantity dynamic prediction model is established based on the factorial method, and the daily ration crude protein demand quantity can be accurately predicted according to the actual production levels of different farms, so that personalized feed formula preparation is guided. The method can predict the daily ration crude protein demand amount of different Ningxiang pig farms, provides an important technical basis for precise breeding, improves the comprehensive benefits of the farms, and reduces the environmental pollution.

The invention relates to the technical field of hippocampus culture management, and discloses a hippocampus growth curve driven precise feeding dynamic optimization system and method, and the system comprises a hippocampus culture feed feeding ratio evaluation module, a hippocampus culture feed feeding amount calculation module, and a hippocampus culture feed feeding management module. The growth weight information of the hippocampus in the breeding process is intelligently evaluated in real time by dynamically monitoring the growth volume information of the hippocampus, and meanwhile, the feed feeding amount of hippocampus breeding is dynamically and accurately measured and calculated on the basis of the hippocampus breeding feed feeding ratio, the average growth weight parameters of the hippocampus and the total number of the hippocampus in the breeding environment. The feed feeding amount of the hippocampus is scientifically and accurately planned based on the hippocampus culture environment and the growth state, scientificity and intelligence of hippocampus culture feed feeding management are achieved, and rationality and economical efficiency of hippocampus culture management are improved.