121 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 relates to a multi-dimensional space long-span bridge track vertical deviation temperature-sensitive component extraction method. The method comprises the steps of integrating long-span bridge track vertical deviation three-dimensional historical data added with an environment temperature label; adopting empirical wavelet transform to construct an MRA component four-dimensional data set containing a temperature label and a wavelength label; after mileage dimension resampling, projecting data to an amplitude-wavelength space, drawing an accumulated amplitude growth curve, judging the type of the amplitude-wavelength curve, and determining a boundary wavelength of bridge deformation and track irregularity; projecting the data to an amplitude-temperature space, and judging an amplitude curve and temperature sensitivity; and in combination with the boundary wavelength and the temperature sensitivity, extracting a temperature sensitive component of track irregularity and a construction deviation component in actual bridge deformation. According to the method, synchronous identification and extraction of the temperature sensitive component in the track vertical deviation and the bridge construction deviation component can be realized, and a quantitative basis is provided for accurate maintenance and repair of a long-span railway bridge.

The invention discloses an intelligent henhouse management system and method based on the Internet of Things, and the method comprises the steps: S1, obtaining the individual parameters of each broiler chicken and the environmental parameters of each column, dividing the columns with similar feeding time into one group, and constructing a digital twinborn model; the method comprises the following steps: constructing a database containing abnormal broiler chicken caused by environment and non-environment correlation through abnormal cases in broiler chicken breeding in combination with a machine learning interpolation method; according to the collected data, constructing a normal growth curve prediction model, and outputting an expected normal growth curve of each broiler chicken in the current environment; monitoring broiler growth conditions in real time, comparing actual growth data with an expected normal growth curve, marking an abnormal column, and positioning abnormal individual broilers; according to the image data of the abnormal individual broiler chicken, the digital twinborn model and the abnormal database are combined, abnormal reasons are analyzed, abnormal types are judged, and a targeted solution is pushed. According to the method, the accuracy of anomaly detection is improved, and full-closed-loop management of dynamic sensing, accurate diagnosis and self-adaptive regulation and control is also realized.

The invention discloses a forest carbon reserve prediction method coupling a Logistic growth curve and meteorological data. The method comprises the following steps: 1) constructing a relation of a forest dynamic Logistic growth curve; 2) fitting a theoretical carbon reserve value based on a Logistic growth curve; 3) a growth period monthly meteorological data calculation model; 4) constructing an actual carbon reserve prediction model coupling the meteorological data and the theoretical carbon reserve; and 5) predicting the future forest carbon reserve by using the multi-scene meteorological data. According to the method, the influence of long-time-span monthly-scale meteorological data on forest carbon reserve value prediction is considered, the forest carbon reserve prediction method coupling the Logistic growth curve and the meteorological data is constructed for forest carbon reserve prediction, and a brand new method is provided for accurate carbon reserve prediction.

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.

The invention provides a culture method and preparation of bifidobacterium longum subsp.infantis B8762 based on carbon source regulation and control, and belongs to the technical field of biology. The method comprises the following steps: inoculating an activated strain into a carbon-source-free improved MRS culture medium, adding a specific carbon source, monitoring a growth curve in real time through an on-line living cell sensor, and analyzing and regulating the growth stage and activity of thalli in combination with metabolites. By precisely regulating and controlling a substrate, the cell activity is remarkably improved, the proportion of living cells is increased, the sugar uptake ability and the key metabolic enzyme activity are enhanced, a growth curve is optimized, and the cell yield coefficient and the substrate conversion efficiency are improved. A bacteriostatic preparation, a viable bacterium preparation and a metabolite preparation can be prepared and respectively have specific metabolite concentration, living cell proportion and enzyme activity. The culture method and preparation of the bifidobacterium longum subsp.infantis B8762 based on carbon source regulation and control are simple to operate and easy to industrialize, the production cost can be reduced, the production efficiency can be improved, and the requirements of different application scenes can be met.

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 present application provides a plant control method and device based on bubble clay, relating to the technical field of plant control technology. The key points of its technical solution are as follows: obtaining plant species information; based on the plant species information, matching the type, ratio, and replacement cycle of the bubble clay; monitoring the actual growth state of the plant and calculating the deviation data between the actual growth curve and the standard growth curve; if the deviation data exceeds the preset threshold, generating a bubble clay regulation plan; performing bubble clay adjustment or replacement operations to guide the user to perform environmental collaborative regulation; collecting the plant state after the operation, evaluating the control effect, and dynamically adjusting the subsequent control strategy to make the plant growth state approach the standard growth curve. The plant control method and device based on bubble clay provided by the present application have the advantages of matching the bubble clay configuration with the plant requirements, having more targeted bubble clay selection, reducing fertilizer loss and environmental pollution, more accurate judgment of the timing of bubble clay replacement and adjustment, and more effective plant control.

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.

A measurement method and system for nutrient solution components of a hydroponic plant, which method and system relate to the technical field of plant nutrition management and hydroponic cultivation. The measurement method comprises: setting growth node time periods for a plant, performing, multiple times at set time points, nutrient component measurement on a nutrient solution where the plant is located, and recording water consumption amounts; using a neural network to establish a detection model by means of the acquired water consumption amounts and data of a nutrient component growth curve, training the detection model, and storing the optimal model; and measuring nutrient components at different time points in real time, and when the difference between an actual measured value and a predicted value of the optimal model is greater than a set error threshold value, triggering a replenishment and adjustment mechanism to ensure that components of the nutrient solution are always maintained within the optimal range. Thus, the growth speed and quality of plants are improved, the frequency and difficulty of manual intervention are reduced, measurement is accurately performed on the basis of a corresponding plant growth period, and a nutrient solution ratio is adjusted on the basis of a measurement result, so as to meet the growth requirements of the plants.

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 computer-based pig feed formula data optimization processing method and a computer-based pig feed formula data optimization processing system, and relates to the technical field of breeding management. According to the method, by constructing a growth curve expansion algorithm model and combining individual data of pork pigs and microbial metabolic activity data, the weight gain of the pork pigs can be predicted; and further calculating the nutritional requirement Dn of each growth stage. Compared with a traditional method for formulating a feed formula depending on experience or a static model, the method has the advantages that the daily nutritional requirements are calculated in real time through the growth curve extension algorithm model, so that the feed formula is adjusted more accurately. And when the nutritional demand Dn exceeds a preset nutritional demand threshold value, the system can automatically trigger a feed formula optimization mechanism and output the adjusted feed demand Fi, so that the growth demand of the pork pigs is met. Through the dynamic adjustment, the pork pigs can obtain the nutrition proportion most suitable for the current growth stage, and the problem that health and growth speed are affected due to overnutrition or insufficient nutrition is avoided.

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 method for constructing a growth model of a brain in a life cycle, and the method comprises the steps: obtaining brain MRI images of individuals of different age groups, and dividing the cerebral cortex into n regions; constructing a morphological feature network for sMRI of the brain MRI image of the individual, and performing morphological feature similarity degree analysis to obtain a first phenotype; constructing a functional network for the fMRI of the brain MRI image of the individual; constructing a structure-function coupling matrix for the morphological feature network and the function network, and calculating to obtain a second phenotype; selecting the first phenotype and the second phenotype of each individual as dependent variables and the age of each individual as a smooth item to carry out GAMLSS modeling, and selecting JSU distribution to respectively fit the change of the first phenotype and the second phenotype along with the age to generate a reference growth curve. The method can be used for studying the growth mode of the brain network in the whole life cycle, studying life cycle association between morphological characteristics and functional characteristics of different areas and the like.

An embodiment of the present invention provides a method for distributing bio-fermented feed, comprising: obtaining historical growth data, historical environmental data, and historical feeding data of the population to which the feeding target belongs, the historical feeding data including the historical feeding amount of the bio-fermented feed; calculating the growth curve of the population to which the feeding target belongs based on the historical growth data, the historical environmental data, and the historical feeding data; generating a feeding strategy for the feeding target based on the growth curve, and distributing feeding to the feeding target based on the feeding strategy. By using the historical growth data, historical environmental data, and historical feeding data of the population to which the feeding target belongs, the growth situation of the population to which the target belongs is analyzed from multiple dimensions to obtain a growth curve of the population to which the feeding target belongs, and a feeding strategy for the feeding target is generated through the growth curve. Adopting the feeding strategy of the feeding target can improve the accuracy of the feeding amount in the distribution of bio-fermented feed.

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 planting cabins, and discloses an intelligent ecological planting cabin system which comprises a planting square cabin, an equipment room and a working island, multi-point acquisition data analysis is adopted, environment data control is more accurate, and environment adjusting equipment for temperature and humidity, fresh air, illumination, energy recovery and the like is arranged in the square cabin, and growth curve influence factors of the environment on crops are promoted; the growth stage of crops is monitored through a camera, environment control equipment is intelligently adjusted, and precise planting is achieved; an internet-of-things big data platform is introduced, the growth condition of plants in the intelligent cabin is remotely monitored through the platform, various data are accurately recorded, intelligent analysis and processing are performed on the data, an optimal growth environment parameter curve of various crops is organized in combination with agricultural experts, a full-automatic control system is adopted, manual operation is not needed, and an intelligent mode is set after starting up. Normal work can be achieved by selecting planting species, the Internet of Things platform automatically follows up the growth state of crops, and the equipment automatically adjusts environmental parameters.

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 present invention discloses a method and device for spatiotemporal prediction of medium- and long-term capacity demand of distributed energy storage in a distribution network. The method comprises: selecting a logistic regression growth curve as an expected function to fit the distributed energy storage capacity demand curve of a target area to obtain a first curve; obtaining a composite loss function according to the original regression loss and the anti-disturbance loss; obtaining a robust optimization function of the first curve according to the composite loss function in combination with the curve smoothing principle; optimizing the first curve according to the robust optimization function in combination with parameter constraints to obtain a second curve; processing time periods according to preset rigid time nodes and flexible conditions to obtain a time segment set; correcting the second curve segments in each time interval in the time segment set according to preset parameters to obtain a third curve; the preset parameters include an influencing factor set, a distribution variable threshold for heavy overload of the distribution network, and a unit capacity cost threshold for the energy storage battery; and obtaining the medium- and long-term capacity demand of the distributed energy storage in the target area according to the third curve.