290 results about "Mathematics model" patented technology

According to the new energy automobile flexible assembly line multi-target dynamic intelligent scheduling optimization method coping with the uncertain disturbance influence, the flexibility of the assembly manufacturing unit and the flexibility of the assembly manufacturing process sequence are comprehensively considered, and a dynamic intelligent scheduling optimization scheme is generated for multiple optimization targets. The method comprises the following steps: firstly, constructing a multi-target dynamic intelligent scheduling optimization problem mathematical model of an automobile flexible final assembly line; furthermore, the optimization problem is converted into a Markov decision process, and states, actions, multi-target reward components and reward component aggregation thereof are defined. And then, carrying out a multi-target deep reinforcement learning training process oriented to a new energy automobile flexible final assembly line, and constructing and forming a scheduling agent. And finally, realizing self-adaptive decision making of the automobile flexible assembly line under an uncertain disturbance condition based on the scheduling intelligent agent. According to the method, the robustness and the execution efficiency of the new energy automobile flexible assembly line in a dynamic environment can be remarkably improved, and the method has good practical value and popularization prospect.

The invention relates to the technical field of computer-aided engineering, in particular to a pure cotton dark-color fabric dye-resistant area dynamic prediction method based on a dye diffusion model. The method comprises the following steps: acquiring a parameter data set representing a fabric microstructure, dye properties and a printing process; on the basis, a multi-scale coupling mathematical model is constructed, the dye diffusion range is predicted, the diffusion process is decomposed into seepage diffusion between macroscopic yarns and diffusion in microcosmic fibers through the model, and the diffusion rate difference of the dye in the fabric direction is represented through anisotropic diffusion tensors; through finite element analysis of structure perception, running a model to calculate dye space-time concentration distribution, and generating geometric data for predicting a bleeding boundary according to a preset concentration threshold value; according to the data, printing process parameters and model parameters are adjusted in a self-adaptive mode, and dynamic prediction of the dye-resistant area is achieved; according to the method, computer simulation and finite element analysis technologies are utilized, and the accuracy of dynamic prediction of the dyeing-resistant area of the pure cotton dark-color fabric is improved.

The invention provides a collaborative scheduling and income distribution method for energy storage power stations in an alliance chain, and belongs to the technical field of energy storage power stations. The method comprises the steps of constructing an energy storage capacity sharing alliance block chain taking the energy storage power stations as nodes; an intelligent contract is constructed, the intelligent contract comprises a mathematical model for solving the shared scheduling plan, and the benefit of the shared capacity of each energy storage power station under the optimal shared scheduling plan is obtained based on the intelligent contract; each energy storage power station solves the shared scheduling plan through real-time information, the smart contract compares the earnings under the shared scheduling plans to obtain the shared scheduling plan with the maximum total earnings as the optimal shared scheduling plan, and each energy storage power station executes the optimal shared scheduling plan. And the maximization of the total capacity is realized.

The invention discloses a multi-objective reinforcement learning man-machine cooperative assembly task allocation method based on neighborhood parameter migration, and the method comprises the steps: building a mathematical model which aims at minimizing the physiological fatigue accumulated value of a human operator and minimizing the maximum completion time for a multi-objective optimization problem of task allocation in a man-machine cooperative assembly system; a multi-target problem is decomposed into N standard sub-problems by adopting a weighting and decomposition strategy, and training is accelerated through a neighborhood parameter migration strategy; each sub-problem is solved based on a near-end strategy optimization algorithm of an Actor-Critic framework, Gaussian noise is added to an Actor network to simulate environment uncertainty, an action mask mechanism is introduced to process priority constraints of assembly tasks, and it is ensured that a generated task allocation scheme is always feasible. According to the method, the convergence speed and diversity of the Pareto solution set can be remarkably improved, and the assembly efficiency and operator fatigue are effectively balanced.

The invention discloses a design method of a barrier function rehabilitation robot safety tracking control system, and relates to the technical field of medical robots. The method comprises the steps that on the basis of an existing double-joint single-arm robot mathematical model, a mathematical model with uncertain model parameters, uncertain external disturbance and uncertain external force borne by the tail end is established, and the established model is closer to a real model of a rehabilitation robot; the uncertainty in the model is classified into one class, and a disturbance observer is used for estimating and compensating the uncertainty; a logarithmic obstacle Lyapunov function is adopted to constrain the joint angle; and designing an adaptive trajectory tracking controller based on the disturbance observer and the obstacle Lyapunov function method, and tracking a set trajectory. The high-precision tracking of the target trajectory is ensured, the actual motion trajectory always moves within a certain range on the two sides of the target trajectory, and the secondary injury of the patient in the rehabilitation process is avoided.

The invention provides a self-adaptive fixed time tracking control method based on event triggering, which is used for solving the technical problem that a virtual control law is easy to generate singularity in the traditional backstepping method design. The method comprises the following steps: establishing a mathematical model of a non-strict feedback nonlinear system with input time delay, and introducing an auxiliary system to counteract delay influence; an error dynamic system is obtained through coordinate transformation; according to system fixed time convergence, designing a piecewise function, introducing a backstepping method to design a controller and an adaptive law, using an RBFNN to approach an unknown function therein, designing a virtual control signal corresponding to each error signal, and obtaining an actual controller and an adaptive law; according to the actual self-adaptive controller, event triggering conditions are designed, when the event triggering moment is reached, the event triggering conditions containing input are triggered, and information transmission and response are carried out. An event triggering mechanism is designed, the number of times of control instruction transmission is reduced, communication resources are saved, and rapid convergence and safe operation of the system state are achieved.

The invention provides a collaborative collision and obstacle avoidance strategy of an underactuated unmanned ship considering input and state quantification. The collaborative collision and obstacle avoidance strategy comprises the following steps: establishing a mathematical model of the underactuated unmanned ship; a distributed guidance rule is constructed in the USV kinematics subsystem by using an auxiliary variable method, and a repulsive force function of an improved artificial potential field method is introduced to reconstruct the guidance rule, so that collaborative collision avoidance and obstacle avoidance at the kinematics level and expected trajectory tracking of the underactuated USV are realized; designing an extended state observer to solve the influence of unknown items in the system and quantitative state variables in the communication process on the formation control system; a linear time-varying model is adopted to describe a quantizer, so that a bottom-layer quantization controller does not need to predict specific information of quantization parameters; and proving the stability of the constructed USV formation tracking control system according to an input-state stability theory. According to the invention, starting from two aspects of input quantization and state quantization, the problem of USV formation tracking control in the marine environment with limited communication bandwidth and complex quantization environment is solved.

The invention relates to the field of intelligent temperature control, and provides a refrigeration house PID control parameter optimization method and device, equipment and a storage medium. The method comprises the steps of collecting temperature data and energy consumption data of the refrigeration house at a constant-temperature stage; based on the temperature data and the energy consumption data, a mathematical model representing the mapping relation between the temperature and the energy consumption of the refrigeration house is established; constructing a deep reinforcement learning model based on a preset reward function and a mathematical model giving consideration to the temperature stability and energy consumption optimization of the refrigeration house; and through interactive learning of the intelligent agent of the deep reinforcement learning model and the refrigeration house environment, determining optimization parameters used for adjusting refrigeration house PID control. According to the refrigeration house PID control parameter optimization method, device and equipment and the storage medium provided by the invention, the mathematical model of the temperature and energy consumption mapping relation of the refrigeration house can be effectively established, the deep reinforcement learning model is constructed through the reward function and the mathematical model, and the optimization parameters of refrigeration house PID control are adjusted through deep reinforcement learning.

The invention discloses a robot optimal path planning method based on an improved Hemma optimization algorithm. A plurality of improvement measures are provided for problems possibly existing in path planning of a Hemma optimization algorithm. Firstly, a Circle chaotic mapping strategy is adopted to replace random initialization, so that the quality and distribution uniformity of an initial population are improved, and the global exploration capability is enhanced. Secondly, mathematical models of three core behavior stages of a standard HO algorithm are deeply optimized, and adaptive dynamic weight, a dynamic weighting strategy based on a current optimal solution and individual fitness and guided Levy flight direction adjustment are introduced; and finally, introducing a dynamic stage conversion and adaptive exploration intensity mechanism DPTAEI, and dynamically adjusting the participation proportion of the individual in different behavior stages and the intensity of the exploration behavior according to the real-time search progress of the algorithm. Through the improvements, the optimization precision, the convergence speed and the global search capability of the IHO algorithm in the aspect of robot path planning are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle management and control, and discloses a low slow small unmanned aerial vehicle countering method and system, which eliminates space-time deviation and redundant information of different sensor data by acquiring multi-source data and performing fusion processing. Furthermore, the target tracking trajectory of the low-slow small unmanned aerial vehicle is processed and generated through an adaptive Kalman filtering algorithm, and real-time smooth tracking of the trajectory is realized. Furthermore, probabilistic region prediction is performed by combining a Monte Carlo method and a kernel density estimation method, and a threat region prediction result of the low-slow small unmanned aerial vehicle is generated, so that the possible motion range of the target is comprehensively covered, and the motion intention of the target is accurately captured. Furthermore, in combination with a preset constraint condition set, a target tracking trajectory and a threat area prediction result, the interception success rate mathematical model is solved by using an improved particle swarm algorithm, and a collaborative countering strategy is obtained, so that the limitation of an existing countering method is avoided, and the accuracy and efficiency of countering are improved.

A storage device stores a medical knowledge graph including nodes corresponding to medical care events, and edges indicative of a relationship between the nodes. A graph feature of the medical knowledge graph is expressed by a mathematical model characterized by patient background information. A processing circuitry obtains patient background information relating to a background factor of one or more target patients, computes variation of the graph feature relating to the target patient, based on the patient background information of the target patient and the mathematical model, and displays the variation of the graph feature on a display device.

The invention relates to the field of vehicle route planning, in particular to a dynamic multi-target vehicle route planning method and system based on prediction and recombination, and the method comprises the steps: building a mathematical model of dynamic multi-target vehicle route planning, initializing a population through real number coding, adjusting the population to meet a constraint, carrying out the iteration through a genetic algorithm, and obtaining a candidate solution set; a Pareto front set is obtained and clustered, and a clustering center is recorded as a special individual; and when environment change is detected, predicting individuals in a new environment by using a gray model or a least square support vector machine, and constructing a new population until an optimal solution set is output. According to the method, the convergence speed of the algorithm is high, the response to the environment change is more sensitive and timely, the dynamic multi-target vehicle routing problem is effectively solved, and the application effect and the economic benefit of the vehicle routing in the actual life are effectively promoted.

The invention provides a sepsis multi-task prediction method and system, and belongs to the technical field of disease prediction based on a mathematical model, and the method comprises the steps: obtaining a symptom data record of a to-be-detected patient; and processing the obtained symptom data records by using a pre-trained classification model to obtain a final task result. According to the method, a multivariable time sequence in medical health record data of a patient is utilized, time representation containing sepsis pathological characteristics is obtained through a time mode of modeling data based on a self-attention encoder, and the trend of the condition of the patient changing along with time is obtained; correlating different medical features according to features including patient condition change trends, so that each feature can perceive other features mutually, and mining global feature representation of the patient; and inputting the global feature representation into a task-aware hybrid expert system, and routing the unified global feature representation into different expert systems, thereby obtaining task-level representation and realizing multi-task prediction.

The invention discloses an ultrasonic guidance navigation system based on multi-modal detection and a CNB ejection mechanism, and belongs to the technical field of ultrasonic auxiliary diagnosis, multi-modal detection loads a directional bounding box model and a segmentation model when the system is initialized, and the directional bounding box model performs directional detection of a puncture needle on an input frame; in the result extraction stage, puncture needle information is extracted from a directional bounding box model detection result, a focus mask is extracted from a segmentation result, and the focus mask is adjusted to be the same as an input frame in size; when the focus mask is extracted, the validity of a segmentation result is verified, then first effective mask data are extracted from multiple types of segmentation results, the mask data are transmitted to a CPU from a GPU memory, a floating point probability value is converted into an integer binary mask, and puncture needle direction detection and segmentation of multiple types of anatomical structures are achieved. According to the CNB ejection mechanism, a mathematical model of the ejection process is established through an ejection trajectory calculation function, and a reliable mathematical basis is provided for CNB ejection operation diagnosis.

The invention relates to a resource-constrained unmanned aerial vehicle cluster network data privacy protection method, which comprises the following steps of: establishing a mathematical model, and describing by using an undirected graph; the method comprises the following steps of: randomly generating two different system security parameter bit big prime numbers by each unmanned aerial vehicle in a cluster according to a preset system security parameter; calculating a public key and a private key according to a Paillier key generation algorithm, and encrypting the public key and the private key; and the unmanned aerial vehicles calculate homomorphic products of the encrypted random numbers sent by all neighbors, decrypt the homomorphic products by using own private keys, calculate offset initial position coordinates and control input according to decryption results, and repeatedly execute until all the unmanned aerial vehicles in the cluster converge to the same position. The problem that an unmanned aerial vehicle cluster network data privacy protection method is difficult to effectively balance in the aspects of resource efficiency, privacy security and accurate convergence is solved, the privacy of the initial position of the unmanned aerial vehicle is effectively protected, and the accuracy of task execution and the strong anti-eavesdropping capability are ensured.

The invention discloses a hypersonic flight vehicle attitude intelligent control method based on global quadratic heuristic programming, and relates to the technical field of hypersonic flight vehicle attitude control, and the method comprises the steps: obtaining aerodynamic data of a target hypersonic flight vehicle; establishing a mathematical model of the hypersonic aircraft according to the pneumatic data of the target hypersonic aircraft; adopting a barrier conversion function to convert the hypersonic aircraft mathematical model into a hypersonic aircraft conversion system in an unconstrained form, and determining a tracking error and system dynamic information of the hypersonic aircraft conversion system; and performing attitude control on the hypersonic flight vehicle conversion system by adopting a global secondary heuristic dynamic programming algorithm based on an incremental model according to the attitude tracking error and the system dynamic information of the hypersonic flight vehicle conversion system. According to the invention, the attitude control precision of the hypersonic aircraft can be improved.

The invention relates to a nuclear accident fault diagnosis method and equipment combining physical constraints and a large language model, and the method comprises the steps: constructing a mathematical model of fluid flow in a nuclear energy pipeline, and describing the behavior of fluid in the pipeline; converting related information of the nuclear energy pipeline fluid model established in the step into lexical elements tokens which can be understood by a large language model LLM; the physical constraint information is embedded into a large language model LLM; after the physical constraint information is embedded, performing fine tuning training on the large language model LLM to enable the large language model LLM to adapt to a nuclear energy pipeline fault diagnosis task; the large language model LLM after fine tuning training is used for carrying out fault category classification on the new nuclear energy pipeline operation data. According to the fault diagnosis text adaptation mechanism based on nuclear energy related lexical element embedding, nuclear energy operation parameter information presented in a numerical form in a work order is converted into language information containing rich nuclear energy operation features, and then the advantages of a large model in the aspect of feature extraction are fully played.

The invention relates to a cable-stayed bridge stay cable force data processing method and system based on an influence matrix. The method comprises the following steps: constructing a bridge model, and extracting a corresponding influence matrix through finite element software; taking the sum of squares of the tower deviation value, the linear difference value and the cable force difference value as a judgment function; a cable force influence matrix equation is obtained based on the superposition principle, the cable force adjustment amount of each stay cable, the initial tension vector of the stay cable, the tower deviation and the line shape of the main tower are obtained through solving, and then the cable force adjustment value vector of each stay cable is obtained through solving; establishing a mathematical model based on an optimization theory, and calculating to obtain an optimal cable force; wherein the difference value between the adjusted main tower deviation and the target tower deviation, the difference between the adjusted bridge surface line shape and the target line shape, the difference between the adjusted cable force and the mechanical cable force and the upstream and downstream deviation are used as constraint conditions, and the cable force variance is used as an optimization target function. According to the method, the uniformity of the bridge deck line shape, the offset of tower deflection and the cable force of the stay cable in the construction process can be effectively controlled.

The invention discloses a high-speed maglev train suspension constraint control method for coping with long-wave oscillation of a track beam, and belongs to the field of maglev track traffic. The method comprises the following steps: dynamically expanding high-speed maglev train model prediction suspension control under asymmetric preset performance constraints, and coping with safety constraint control of accurate suspension of the high-speed maglev train with long-wave oscillation of a track beam: establishing a track model of the long-wave oscillation of the track beam; designing the precise suspension dynamic expansion asymmetric preset performance constraint of the high-speed maglev train according to the long-wave oscillation information of the track beam; designing a high-speed maglev train suspension air gap mathematical model containing track beam long-wave oscillation information; obtaining a state space expression based on a reconstruction error according to the dynamic expansion asymmetric preset performance constraint and the conversion error; a prediction model is obtained through the expression, an optimal control problem is designed, and an optimal solution is solved; on the premise that the track beam oscillation reference air gap tracking precision is improved, suspension deadlock of the high-speed maglev train under long-wave oscillation of the track beam is avoided, and it is ensured that the high-speed maglev train runs safely in a small suspension gap with low power consumption under long-wave oscillation of the track beam.

A method for determining the optimal pull-out strength of anchor bolts in reinforced slopes, belonging to the field of slope reinforcement technology, includes: establishing a mathematical model of the anchor bolt-reinforced slope; obtaining curves showing the relationship between the slope safety factor and the pull-out strength of anchor bolts under different numbers and bond lengths; obtaining curves showing the variation of slope failure probability and anchor bolt pull-out strength under different numbers and bond lengths; and obtaining the optimal design value of the pull-out strength of anchor bolts in the reinforced slope. This method introduces the failure probability into anchor bolt support engineering, reflecting the reliability of the slope anchor bolt support engineering through the failure probability; and effectively obtains the relationship between pull-out strength and the number of anchor bolts, bond strength and slope stability coefficient. In slope reinforcement engineering, the pull-out characteristics of anchor bolts are fully utilized. The optimal pull-out strength corresponds to the number of anchor bolts and the bond strength, avoiding waste of engineering materials and improving the economy of the support engineering while ensuring slope safety.

The present application relates to the field of photovoltaic technology, and particularly relates to a photovoltaic cell model parameter extraction method and related products, the method comprising: establishing a photovoltaic cell mathematical model, and determining model parameters to be optimized in the mathematical model; constructing a fitness function of the photovoltaic cell mathematical model; constructing a parameter optimization model based on a whale optimization algorithm and a differential evolution operator, and taking the fitness function as an objective function; executing the parameter optimization model, and obtaining an optimal parameter set corresponding to the model parameters to be optimized in the mathematical model; obtaining a final photovoltaic cell mathematical model through the optimal parameter set, and verifying errors of the final photovoltaic cell mathematical model; the present application firstly constructs a mathematical model of a photovoltaic cell, and then determines parameters to be optimized in the mathematical model, and then defines a fitness function based on multiple error indicators and physical parameter constraints, and then forms a parameter optimization model suitable for multimodal optimization by combining a whale optimization algorithm and a differential evolution operator, and finally obtains an optimal parameter set of the photovoltaic cell model.

According to the monitoring method for the medical health-care double-layer output coupling network under the multiple interference factors, a mathematical model suitable for the medical health-care double-layer output coupling network under the non-ideal environment is established, the mathematical model has multi-dimensional output variables, the mathematical model is expanded to obtain a driving end network and a response end network, and the driving end network and the response end network are connected with the response end network. Based on the driving end network and the response end network, constructing an error data expression representing user node health information in the medical health and health care double-layer output coupling network, solving the error data expression, and based on a solving result, performing stability monitoring and anomaly detection on the medical health and health care double-layer output coupling network. The unknown topological structure matrix of another network is monitored and identified through the network with the known topological structure matrix, and the problem of how to monitor the network topological structure of the medical health care complex dynamic network in the non-ideal environment where multiple interference factors such as coupling time delay and incomplete measurement coexist is solved.

The invention discloses a two-dimensional conversion method for a multi-dimensional spatial relationship between GNSS ambiguity vectors, and the method comprises the steps: constructing a GNSS double-difference mathematical model, and obtaining a multi-dimensional ambiguity vector floating solution and a variance-covariance matrix through a least square method; according to the ambiguity vector floating solution and the variance-covariance matrix thereof, obtaining an optimal integer solution and a suboptimal integer solution of the ambiguity vector by means of a least square ambiguity decorrelation adjustment method; by taking the ambiguity vector floating solution, the optimal integer solution and the suboptimal integer solution as end points, calculating the ellipse norm side length of a side formed by any two end points and an A-inner product included angle between any two sides; constructing a two-dimensional Euclidean triangle based on an elliptical norm side length and an A-inner product included angle, and converting a complex and abstract multi-dimensional space relationship among an ambiguity vector floating point solution, an optimal integer solution and a suboptimal integer solution into a simple and visual two-dimensional triangle relationship; the method provides important method support for analyzing the GNSS ambiguity confirmation reliability influence mechanism.

The present invention discloses a collaborative formation control method based on a distributed filtering synchronization estimator, comprising the following steps: constructing the state space equation of an intelligent agent according to its mathematical model; defining a global instruction system and generating a global reference instruction; establishing a communication network for a multi-agent system and modeling the channel perturbation of the global reference instruction; constructing a distributed filtering synchronization estimator based on a disturbed output for the global reference instruction affected by the channel perturbation; solving the regulator equation to obtain a feedforward gain matrix of a deterministic equivalent output feedback controller; designing a deterministic equivalent output feedback controller to generate the control input of the intelligent agent, thereby achieving the desired formation control effect. The present invention proposes a type of distributed filtering synchronization estimator based on a disturbed output and designs a distributed deterministic equivalent controller to achieve closed-loop system stability and the intelligent agent motion trajectory approaches the set trajectory, which has broad application prospects.

The present application relates to the technical field of electrified railway control, and discloses a phase control closing method and a heavy haul locomotive circuit breaker, the method comprising: step one, establishing equivalent mathematical models for the heavy haul locomotive, traction substation, AT substation and traction network; step two, establishing a vehicle-network-station simulation model based on a MATALB / Simulink simulation platform; step three, obtaining the influence of different line loads on phase selection closing according to the vehicle-network-station simulation model; and step four, determining a phase control closing scheme and conducting test verification. The phase control closing and opening method of the heavy haul locomotive circuit breaker with the above structure can establish equivalent mathematical models for the heavy haul locomotive, traction substation, AT substation and traction network, and then establish a vehicle-network-station simulation model, analyze the influence of different line loads on phase selection closing, and determine a phase selection closing scheme according to the breakdown characteristics of the circuit breaker and the analysis results. The phase selection closing scheme determined through test verification can achieve the purpose of reducing the overvoltage of the closing system.

The invention provides a power distribution system island division and fault reconstruction method and system. The method comprises the steps of obtaining a topological structure, load characteristic data and renewable energy output characteristic data of a power distribution system, and generating a mathematical model of the power distribution system; performing optimization calculation based on a mixed meta-heuristic algorithm framework and an adaptive step length gradient descent method; performing fuzzy binning processing on the optimization result to obtain a calibrated reconstruction optimization result; and finally, carrying out island division and fault reconstruction based on a calibration result. According to the method, the uncertainty influence of renewable energy sources can be effectively processed, and the accuracy and reliability of island division and fault reconstruction of the power distribution system are improved.

The present application relates to the technical field of generating resource scheduling, and particularly relates to a raw material resource scheduling method for plywood production based on big data, comprising the following steps: S1, obtaining raw material characteristics and order requirements, and generating dynamic scheduling instructions; S2, constructing a probabilistic transition network model; S3, generating abnormal transmission events caused by non-standard logistics paths; S4, dynamically reconstructing the topological structure of the probabilistic transition network model, and recalculating the raw material source tracing results of finished products; statistical tracing under dynamic scheduling is realized by constructing a probabilistic transition network model, and non-standard logistics in actual production is intelligently detected and fused based on the principle of material balance, so that the problem of broken tracing chain caused by scattered and reorganized scheduling in plywood production, and the problem of invalid mathematical model caused by abnormal paths such as manual carrying are effectively solved without adding new hardware, and production efficiency, tracing accuracy and implementation cost are considered.

This invention provides a method, apparatus, device, and readable storage medium for predicting the value of banking business, relating to the field of computer technology. The method includes: acquiring historical product data and demand analysis, whereby the demand analysis includes demand names, directories, associated systems, and demand rules; calculating a demand data set based on the historical product data and demand analysis, where each element in the demand data set represents the number of transaction demand modifications, the set of demand rules, and the transaction credit value corresponding to a transaction; establishing a mathematical model of product value based on the historical product data, using the demand data set as input, and solving the mathematical model to obtain a product value set, where each element in the product value set represents the number of product demand modifications, the proportion of new business rules, and the product credit value corresponding to a product; and calculating the product value prediction result based on the product value set. By establishing a value chain relationship between products and demands, the method quantitatively reflects the value attributes of a product, displays the direction of business changes and the quality of demands in real time, assesses the direction of business changes, thereby improving customer experience, reducing operating costs, and forming a positive cycle.

The present invention provides a therapeutic effect prediction system and method that make it possible to improve user-friendliness. A therapeutic effect prediction system 1 comprises: a machine learning unit 132 that outputs a prescribed parameter from patient data which has been input; and a mathematical model unit 131 that calculates a therapeutic effect from prescription data and the prescribed parameter which has been output by the machine learning unit. The machine learning unit is trained to reduce the difference between treatment history data and the therapeutic effect which has been calculated by the mathematical model unit. Thus, it is possible to predict a therapeutic effect by making combined use of the machine learning unit and the mathematical model unit without preparing a data set which is a correct answer.

The invention relates to a technique for implementing, controlling and automating a task or process on a blockchain such as, but not limited to, the Bitcoin blockchain. The invention is particularly suited for, but not limited to, automated execution of contracts such as smart contracts for financial agreements. However, other types of tasks and non-financial contracts can be implemented. The invention can be viewed as the implementation or incarnation of a state machine or DFA on a blockchain by using the unspent outputs of blockchain Transactions to represents the states of the machine, and spending of those outputs as the transition of the machine from one state to another. The invention provides a technical realisation and implementation of a mathematical model of computation conceived as an abstract machine that can be in one of a finite set of states, and can change from one state to another (transition) when a triggering event of a finite set (called input) occurs. The invention comprises compilation and codification techniques for the DFA implementation.