Indoor environment dynamic prediction method, system, medium and equipment

Abstract

The invention discloses an indoor environment dynamic prediction method and system, a medium and equipment, and relates to the technical field of building intelligence. The method comprises the following steps: constructing a plurality of mechanism models for indoor environment prediction, inputting indoor environment related data into the plurality of mechanism models, and obtaining a plurality of first moment prediction values of the plurality of mechanism models; taking the first moment predicted value as the input of a neural network, carrying out nonlinear capture training on the neural network, obtaining a nonlinear fusion device, and obtaining a first moment indoor physical parameter predicted value; determining a residual error between the indoor physical parameter predicted value and the indoor physical parameter true value at the first moment, and inputting the residual error into a depth layered fuzzy model for training to obtain a data compensation model; inputting the indoor environment related data into the data compensation model to obtain a data compensation amount; and adding the residual error and the data compensation amount to obtain a second moment physical parameter prediction value related to the indoor environment. According to the scheme, the environment parameters of the complex indoor environment can be accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent buildings, and in particular to a method, system, medium and equipment for dynamic prediction of indoor environment. Background Art

[0002] With the continuous development of society, people spend more and more time indoors, and their activities become more and more diverse, which makes the indoor environment of buildings constantly change. Since good control of the indoor environment helps to improve the building performance and reduce the energy consumption of buildings, a technology for predicting the indoor environment is needed to better control the various parameters of the indoor environment.

[0003] The existing technology mainly predicts the indoor environment through a single model. The model has poor learning generalization ability and limited use of environmental information, which leads to the problem of being unable to accurately predict the physical parameters of complex indoor environments. Summary of the invention

[0004] Based on this, it is necessary to provide a method, system, medium and equipment for dynamic prediction of indoor environment in response to the above technical problems.

[0005] The present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for dynamic prediction of an indoor environment, the method comprising:

[0007] Constructing a plurality of mechanism models for indoor environment prediction, and inputting data related to indoor environment prediction into the plurality of mechanism models to obtain a plurality of first-moment prediction values ​​of the plurality of mechanism models;

[0008] The predicted value at the first moment is used as the input of the neural network, the neural network is trained to capture the nonlinear relationship, a nonlinear fuser is obtained, and the predicted value of the indoor physical parameter at the first moment is obtained;

[0009] Determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model;

[0010] Inputting the data related to indoor environment prediction into the data compensation model to obtain a data compensation amount;

[0011] The residual is added to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

[0012] Furthermore, before constructing the multiple mechanism models for indoor environment prediction, the method further includes:

[0013] Acquire the heat source distribution of the room within a preset time period, and determine a first number of people in the room within the preset time period according to the heat source distribution;

[0014] Determine a second number of people in the room during the preset time period based on the monitoring images of the room during the preset time period;

[0015] During the preset time period, the persons in the room are located by using ultra wideband (UWB) technology to obtain the number of third persons in the room during the preset time period;

[0016] Acquire a fourth number of people who enter or leave the room within the preset time period;

[0017] The first number of people, the second number of people, the third number of people, and the fourth number of people are used as inputs of a deep learning network model for training to obtain a personnel distribution model for predicting the number of people in the room within the preset time period, where the number of people in the room is one of the data related to the indoor environment.

[0018] Furthermore, the data related to indoor environment prediction specifically includes:

[0019] The parameter vector of the indoor environment, the parameter vector of the outdoor environment, the number of people in the room, the physical parameter vector of the room in the room, and the terminal start / stop status and parameter settings of the indoor air conditioning and fresh air system.

[0020] Furthermore, the construction of multiple mechanism models for indoor environment prediction specifically includes:

[0021] Constructing a building thermal dynamic model based on the obtained indoor structural information, constituent material information, external environment information, heating information, ventilation information, operating status of the air conditioning system, and start / stop status of the fresh air system;

[0022] The obtained indoor ventilation information, personnel activity information, indoor air flow rate information, and carbon dioxide generation and emission information are used as boundary conditions of the constraint equation to construct a carbon dioxide concentration diffusion model;

[0023] According to the collected indoor three-dimensional architectural information, window position information, and light blocking object information, simulation parameters about the indoor environment are defined, and the simulation parameters are input into a lighting simulation software to obtain an illumination model.

[0024] Further, the determining a residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter specifically includes:

[0025] Calculating the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter;

[0026] When the residual is greater than a preset threshold, the predicted values ​​of the multiple mechanism models are input into the nonlinear fuser again for training until the residual is less than the preset threshold.

[0027] Furthermore, after constructing the multiple mechanism models for indoor environment prediction, the method further includes:

[0028] Comparing the predicted values ​​output by the multiple mechanism models with the collected true values ​​corresponding to the multiple mechanism models to obtain a comparison result;

[0029] According to the comparison results, the parameters of the multiple mechanism models are adjusted.

[0030] Furthermore, after inputting the predicted value at the first moment into the nonlinear fuser to obtain the predicted value of the indoor physical parameter at the first moment, the method further includes:

[0031] Calculating an indoor energy consumption value associated with the predicted value of the indoor physical parameter at the first moment;

[0032] According to the predicted value of the indoor physical parameter at the first moment and the indoor energy consumption value, the real value of the indoor physical parameter is adjusted to reduce the indoor energy consumption value.

[0033] In a second aspect, the present invention provides a system for dynamic prediction of indoor environment, comprising:

[0034] A first acquisition module is used to: construct a plurality of mechanism models for indoor environment prediction, and input data related to indoor environment prediction into the plurality of mechanism models to obtain a plurality of first moment prediction values ​​of the plurality of mechanism models;

[0035] A training module, used to: use the predicted value at the first moment as an input of a neural network, perform capture training on the neural network for nonlinear relationships, obtain a nonlinear fuser, and obtain a predicted value of the indoor physical parameter at the first moment;

[0036] A determination module, used to: determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model;

[0037] A second acquisition module is used to: input the data related to the indoor environment prediction into the data compensation model to obtain a data compensation amount;

[0038] The calculation module is used to add the residual to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

[0039] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the indoor environment dynamic prediction method is implemented.

[0040] The present invention provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the indoor environment dynamic prediction method when executing the program.

[0041] The at least one technical solution adopted by the present invention can achieve the following beneficial effects: first, multiple mechanism models for indoor environment prediction are constructed, and indoor environment related data are input into the multiple mechanism models to obtain multiple first-moment prediction values ​​of the multiple mechanism models; then, the first-moment prediction values ​​are used as inputs of a neural network, and nonlinear capture training is performed on the neural network to obtain a nonlinear fusion device and obtain the first-moment prediction values ​​of indoor physical parameters; then, the residual between the first-moment prediction values ​​of the indoor physical parameters and the true values ​​of the indoor physical parameters is determined, and the residual is input into a deep hierarchical fuzzy model for training to obtain a data compensation model; the indoor environment related data is input into the data compensation model to obtain a data compensation amount; finally, the residual is added to the data compensation amount to obtain the second-moment prediction values ​​of the physical parameters related to the indoor environment. Through the above scheme, multiple mechanism models are combined with the data compensation model to dynamically predict indoor physical parameters. The different forms of expression and characterization information of different models can be comprehensively utilized to dynamically predict the strongly coupled, nonlinear and highly uncertain indoor environment during operation. This effectively solves the problems of poor generalization ability of a single model for data learning and low utilization of external environmental information. It can therefore accurately predict the parameters of the complex indoor environment at a certain moment or several moments in the future, and achieve more accurate dynamic prediction of the indoor environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0043] Figure 1 A schematic diagram of a flow chart of a method for dynamic prediction of indoor environment provided by the present invention;

[0044] Figure 2 A schematic diagram of dynamic prediction modeling of building indoor environment provided by the present invention;

[0045] Figure 3A flow chart of a multi-model fusion method provided by the present invention;

[0046] Figure 4 A schematic flow chart of a method for establishing a thermal dynamic model provided by the present invention;

[0047] Figure 5 A carbon dioxide concentration diffusion model diagram provided by the present invention;

[0048] Figure 6 A schematic diagram of an indoor environment dynamic prediction system provided by the present invention;

[0049] Figure 7 A schematic diagram of a computer device for implementing a method for dynamic prediction of indoor environment provided by the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] The server mentioned in the present invention can be a server set on a business platform, or a device such as a desktop computer, a laptop computer, etc. that can execute the solution of the present invention. For the convenience of description, the following description is only based on the server as the execution subject. The following is a detailed description of the technical solutions provided by various embodiments of the present invention in conjunction with the accompanying drawings.

[0052] Figure 1 The present invention is a schematic flow chart of a method for dynamic prediction of indoor environment, which specifically includes the following steps:

[0053] S10: construct multiple mechanism models for indoor environment prediction, and input data related to indoor environment prediction into the multiple mechanism models to obtain multiple first-moment prediction values ​​of the multiple mechanism models.

[0054] In this embodiment, schematically, Figure 2 A schematic diagram of dynamic prediction modeling of a building indoor environment provided by the present invention.

[0055] refer to Figure 2The multiple mechanism models refer to mathematical models based on basic principles of physics, chemistry, biology, etc., which simulate the dynamic behavior and performance of the system by establishing mathematical equations, algorithms or logical relationships, reveal the internal laws of the system, and provide a basis for predicting and controlling the behavior of the system. The multiple mechanism models include but are not limited to: building thermal dynamic model, carbon dioxide concentration diffusion model and illumination model. The first moment can be the kth moment, where the kth moment can be any second in any hour. The data related to indoor environment prediction include but are not limited to: indoor environment parameter vector W i (k), outdoor environmental parameters W o (k), room physical parameter vector P, air conditioning and fresh air terminal start and stop status u c (k) and parameter setting y c (k) The data related to indoor environment prediction is obtained by using sensors deployed during the operation of buildings and parks.

[0056] S11: using the predicted value at the first moment as an input of a neural network, performing training on the neural network to capture nonlinear relationships, obtaining a nonlinear fuser, and obtaining the predicted value of the indoor physical parameter at the first moment.

[0057] In this embodiment, before using the predicted value at the first moment as the input of the neural network, the predicted value of the mechanism model associated with the first moment needs to be normalized.

[0058] Optionally, in this embodiment, the normalization method may adopt a maximum-minimum normalization method, which can normalize data such as temperature, carbon dioxide concentration, and light intensity to the interval [0,1], as shown in formula (1):

[0059]

[0060] Among them, X n represents the nth data obtained by normalizing X in the data set, X represents the data to be processed, and X min represents the minimum value in the data set, X max Represents the maximum value in the data set.

[0061] refer to Figure 2 , the predicted value of the indoor physical parameter at the first moment is Figure 2 The predicted value of the medium mechanism model, Figure 2The real value in is the indoor physical parameter at the first moment, and the real value is collected by pre-arranged sensors, including but not limited to: carbon dioxide (CO2) concentration sensor, light illumination sensor, infrared sensor, humidity sensor and temperature sensor. The residual refers to the error between the predicted value of the mechanism model and the collected real value of the indoor environment, which is used to measure the prediction error of the mechanism model.

[0062] In this embodiment, the normalized predicted value of the mechanism model associated with the first moment is input into a multilayer perceptron (MLP) for training to obtain an optimized predicted value of the predicted value of the mechanism model associated with the first moment.

[0063] Indicative, Figure 3 This is a flow chart of the multi-model fusion method provided by the present invention.

[0064] In this embodiment, the normalized predicted value of the mechanism model associated with the first moment is input into a neural network to train a nonlinear fuser, such as Figure 3 The model fuser in . This nonlinear fuser can adaptively fuse the outputs according to the complex relationship between multi-dimensional data.

[0065] Optionally, the process of building a model fusion and data compensation model requires sufficient measured data and prediction data of the mechanism model for training and verification. At the same time, model selection and parameter adjustment are also key steps, and the appropriate model type and training algorithm can be selected according to the specific situation.

[0066] refer to Figure 3 , Figure 3 The neural network used in the paper is a multi-layer perceptron. The objective function of the neural network is f(x, θ), where x is the set of input variables, θ is the set of unknown parameters, and θ = {w1, w2, w3, b}. Among them, w1, w2 and w3 are the weight parameters of the building thermal dynamic model, carbon dioxide concentration diffusion model and illumination model, respectively, and b is the bias term of the neural network. The purpose of the neural network is to train the parameter set so that the predictions of the three models are closest to the actual values ​​of the collected environment. If the residual is greater than the set value, it needs to be trained again. Finally, the residual is added to the output of the data compensation model to obtain the final prediction output.

[0067] S12: Determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model.

[0068] In this embodiment, reference Figure 3 , Figure 3 The predicted value of the mechanism model shown in is the predicted value of the indoor physical parameter at the first moment. Figure 3 The data model shown in is the data compensation model.

[0069] In this embodiment, for data containing time series, the data compensation model can predict and compensate for environmental parameters at future moments according to the historical trends of various data.

[0070] In this embodiment, a deep neural network is designed as a feature extractor, and a fully connected neural network can be used as a feature extractor, which can identify the data type and change trend of data such as temperature, carbon dioxide concentration, and light intensity.

[0071] In this embodiment, a fuzzy system is used to handle the uncertainty of physical quantities in a complex environment, and decisions are made on physical quantities in the environment through fuzzy sets and fuzzy rules, and fuzzy logic is embedded in a deep neural network. Among them, the weights of fuzzy rules can be dynamically updated through the training process of the neural network to form an adaptive fuzzy reasoning system and a hybrid model.

[0072] In this embodiment, the deep hierarchical fuzzy model expands the traditional fuzzy system into a multi-layer structure, and each layer can be regarded as a sub-fuzzy system. Through layer-by-layer reasoning, higher-level and more abstract features or decision information are gradually extracted. Each layer in the deep hierarchical fuzzy model can be fuzzified and reasoned through fuzzy rules, and the output is used as the input of the next layer, layer by layer. The deep hierarchical fuzzy model includes an input layer, a hidden layer, and an output layer. The input layer converts numerical values ​​into membership functions through fuzzification. The fuzzy rules in the hidden layer describe the relationship between the input variables. The output of each layer can be regarded as the input of the next layer. The output layer includes a defuzzification process to convert the fuzzy output into a clear value.

[0073] S13: Inputting the data related to the indoor environment prediction into the data compensation model to obtain a data compensation amount.

[0074] In this embodiment, reference Figure 2 , the data related to the indoor environment prediction is input into the data compensation model, and the output of the data compensation model is used to perform data compensation on the residual.

[0075] S14: Add the residual to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

[0076] In this embodiment, reference Figure 2, adding the residual to the data compensation amount output by the data compensation model to obtain the predicted value of the physical parameter at the second moment related to the indoor environment. The second moment refers to the moment after the first moment, such as Figure 2 The K+1 to K+L sampling periods.

[0077] based on Figure 1 A method for dynamic prediction of indoor environment shown in the embodiment first constructs multiple mechanism models for indoor environment prediction, and inputs data related to indoor environment prediction into the multiple mechanism models to obtain multiple first-time prediction values ​​of the multiple mechanism models. Then the first-time prediction value is used as the input of the neural network, and the neural network is trained to capture nonlinear relationships to obtain a nonlinear fusion device, and the first-time indoor physical parameter prediction value is obtained. Then the residual between the first-time indoor physical parameter prediction value and the collected indoor physical parameter true value is determined, and the residual is input into the deep hierarchical fuzzy model for training to obtain a data compensation model. Finally, the data related to indoor environment prediction is input into the data compensation model to obtain the data compensation amount, and the residual is added to the data compensation amount to obtain the second-time physical parameter prediction value related to the indoor environment. Through the technical solution of this embodiment, the problem of poor generalization ability of a single model for data learning and low utilization rate of external environment information is effectively solved, so that the parameters of the complex indoor environment at a certain moment or several moments in the future can be accurately predicted, so that the building manager can better manage the building according to the predicted environmental parameters, and the energy loss of the building can be reduced, so that the working environment of indoor personnel becomes better.

[0078] When applying the indoor environment dynamic prediction method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0079] In addition, in one or more embodiments of the present invention, before constructing a plurality of mechanism models for indoor environment prediction, the method further includes:

[0080] S001: Obtain the heat source distribution in the room within a preset time period, and determine a first number of people in the room within the preset time period according to the heat source distribution.

[0081] In this embodiment, the method of acquiring the heat source distribution includes but is not limited to: acquiring the heat source distribution in the room within the preset time period by using an infrared sensor or a temperature sensor.

[0082] S002: Determine a second number of people in the room within the preset time period based on monitoring images of the room within the preset time period.

[0083] In this embodiment, the method of determining the second number of people in the room includes but is not limited to: determining the second number of people in the room by analyzing the number of head portraits of people in the monitoring screen; or determining the second number of people in the room by analyzing the number of palms and / or soles of feet of people in the monitoring screen.

[0084] S003: Within the preset time period, the persons in the room are located by using ultra-wideband technology to obtain a third number of persons in the room within the preset time period.

[0085] In this embodiment, the ultra-wideband technology can be used to track the positions of indoor people in real time, and by analyzing data at different time points and spatial positions, the estimation of the number of people indoors can be corrected and optimized.

[0086] S004: Obtain the fourth number of people who enter or leave the room within the preset time period.

[0087] In this embodiment, the method of obtaining the fourth number of people who enter or leave the room within the preset time period includes but is not limited to: counting the number of people who enter and leave the room through a sensor installed at the door.

[0088] S005: The first number of people, the second number of people, the third number of people, and the fourth number of people are used as inputs of a deep learning network model for training to obtain a personnel distribution model for predicting the number of people in the room within the preset time period, where the number of people in the room is one of the data related to the indoor environment.

[0089] In this embodiment, based on the first number of people, the second number of people, the third number of people, and the fourth number of people, the personnel distribution model is obtained by training a deep learning model through big data.

[0090] Through the technical solution of this embodiment, the indoor heat source distribution, the number of people in the monitoring video screen, the real-time location information of the indoor people and the number of people entering or leaving the room can be comprehensively evaluated, so as to more accurately estimate the number of people in the room.

[0091] In addition, in one or more embodiments of the present invention, the data related to indoor environment prediction specifically includes:

[0092] The parameter vector of the indoor environment, the parameter vector of the outdoor environment, the number of people in the room, the physical parameter vector of the room in the room, and the terminal start / stop status and parameter settings of the indoor air conditioning and fresh air system.

[0093] In this embodiment, the indoor environmental parameter vector includes but is not limited to: indoor temperature, indoor humidity, indoor volatile organic compounds (Volatile Organic Compounds, VOCs), indoor carbon dioxide concentration, indoor light intensity and indoor air pressure, and the outdoor environmental parameter vector includes but is not limited to: outdoor temperature, outdoor humidity, outdoor, outdoor carbon dioxide concentration, outdoor light intensity and outdoor air pressure. The room physical parameter vector includes but is not limited to: indoor space pressure change and indoor air flow rate. The terminal start-stop state and parameter setting of the air conditioning and fresh air system include but are not limited to: the operating state of the air conditioning and fresh air system, air volume change, energy consumption information and terminal start-stop state.

[0094] In addition, in one or more embodiments of the present invention, the construction of multiple mechanism models for indoor environment prediction specifically includes:

[0095] S101: Construct a building thermal dynamic model based on the acquired indoor structural information, constituent material information, external environment information, heating information, ventilation information, operating status of the air-conditioning system, and start / stop status of the fresh air system.

[0096] In this embodiment, the building thermal dynamic model refers to the use of building simulation software (such as EnergyPlus, TRNSYS, etc.) to establish a building thermal dynamic model to describe the processes of heat transfer, heat storage and heat radiation inside the building. The model can take into account the structure, materials, external environmental conditions of the building, and the operation of heating, ventilation, air conditioning and other systems to predict the temperature changes inside the building.

[0097] Indicative, Figure 4 A schematic flow chart of a method for establishing a thermal dynamic model provided by the present invention includes:

[0098] S20: Determine the simulation objectives and scope.

[0099] S21: Collect building-related data.

[0100] S22: Create a geometric model to define building partitions.

[0101] S23: Input material and component properties.

[0102] S24: Set internal load.

[0103] S25: Input environmental and weather data and set simulation parameters.

[0104] S26: Run the simulation and analyze the results.

[0105] In this embodiment, a possible implementation scheme is to use EnergyPlus software to establish a thermal dynamic model of the building. First, it is necessary to determine the modeling target, collect detailed information about the building, draw the building geometry model, divide different thermal zones, and define the building shell and building materials. Secondly, set the internal load, define the parameters of the heating, ventilation and air conditioning (HVAC) system, set the meteorological data, and define the operation schedule. Finally, perform the simulation calculation. The simulation engine calculates the temperature, humidity, energy consumption and other parameters of the building in each time step by numerically solving the dynamic equations (such as the finite difference method or the finite element method).

[0106] The building thermal dynamic model is usually based on the heat balance equation to describe the heat transfer and storage process in the building. For a building's thermal zone, the heat balance equation is shown in formula (2):

[0107]

[0108] Where C is the heat capacity of the building, T(t) is the temperature of the hot zone at time t, ∑Q i (t) is the total heat entering the hot zone, ∑Q o (t) is the total heat flowing out of the hot zone. The heat entering and flowing out of the hot zone includes the heat through conduction, convection, radiation and mechanical ventilation. The conduction heat transfer formula is shown in formula (3):

[0109]

[0110] Among them, Q cond is the conduction heat, U is the heat transfer coefficient, A is the heat transfer area, R is the thermal resistance, T out (t) and T in (t) are the outdoor and indoor temperatures respectively. The convective heat transfer formula is shown in formula (4):

[0111] Q conv (t) = mc p [T air (t)-T in (t)] (4)

[0112] Where Q conv is the convective heat, m is the air mass flow rate, C p is the specific heat capacity of air, T air (t) is the incoming air temperature. The radiation heat transfer formula is shown in formula (5):

[0113] Q rad (t) = A g S g (t)τ g(5)

[0114] Where Q rad is the radiation conduction heat, A g is the window area, S g (t) is the solar radiation intensity, τ g is the projection coefficient of the window.

[0115] S102: Using the obtained indoor ventilation information, personnel activity information, indoor air flow rate information, and carbon dioxide generation and emission information as boundary conditions of the constraint equation to construct a carbon dioxide concentration diffusion model.

[0116] In this embodiment, schematically, Figure 5 A diagram schematically shows a carbon dioxide concentration diffusion model.

[0117] like Figure 5 As shown in the figure, the model first transmits the coordinate position information to the neural network to obtain the velocity value u(x, θ) and air pressure value p(x, θ) of this coordinate position. Then it is multiplied by the constraint equation of the boundary condition and further added to the special solution of the equation to obtain u H (x, θ) and p H (x, θ), and the above results are automatically differentiated to predict the velocity of this point. The above operation is performed for each point to obtain the velocity field u of the entire area. The value of the velocity field is substituted into the diffusion equation to obtain the change in carbon dioxide concentration. The boundary conditions in the carbon dioxide diffusion model shown in this embodiment take into account the influence of the ventilation system, and the diffusion coefficient D takes into account factors such as human activities, indoor air flow, and the generation and emission of carbon dioxide, which can more accurately predict the change in indoor carbon dioxide concentration.

[0118] Among them, the carbon dioxide concentration diffusion model can use the algorithm combining knowledge and neural network to establish a turbulence model for prediction, combined with the Navier-Stokes equation and the mass transfer equation (convection-diffusion equation) to describe. Most low-speed flows can be described by the Navier-Stokes equation, as shown in formula (6):

[0119]

[0120] Among them, u is the vector of the velocity field, ρ is the fluid density, here it is the density of carbon dioxide in the air, p is the pressure, μ is the dynamic viscosity coefficient, F is the external force (such as gravity), To ensure the conservation of mass during air flow, the fluid is incompressible. The change in carbon dioxide concentration is described by formula (7):

[0121]

[0122] Where C is the CO2 concentration, D is the diffusion coefficient, and u is the velocity field derived from the Navier-Stokes equations. Fluid motion requires boundary conditions to be set, which determine how the gas interacts with the external environment. For CO2, the boundary can be specified as concentration or flux.

[0123] S103: defining simulation parameters for the interior of the room according to the collected three-dimensional architectural information, window position information, and light blocking object information, and inputting the simulation parameters into a lighting simulation software to obtain an illumination model.

[0124] In this embodiment, the illumination model can use illumination simulation software (such as Radiance) or illumination calculation algorithm to establish an illumination model for predicting the illumination level in the building, that is, the light intensity distribution. To establish an illumination model, it is first necessary to determine accurate building information based on the data set collected by the sensor, determine the lighting requirements of the building, and use modeling tools to create a three-dimensional model of the building. Secondly, define optical properties, set simulation parameters, and run the illumination simulation program. Radiance will use the ray tracing method based on physical optics to solve the radiance equation to determine the energy exchange between different surfaces, thereby calculating the illumination value of each point. The radiance equation is usually called the radiation transfer equation or the photometric equation, as shown in formula (8):

[0125]

[0126] Among them, L o (x,w o ) is the direction w at point x o The radiant brightness, L e (x,w o ) is the direction w at point x o The spontaneous radiation brightness (i.e. the light emitted by the light source), f r (x,w i , w0) is the bidirectional reflectance distribution function (BRDF) at point x, which describes the reflection of light from the incident direction w i To the outgoing direction w o The reflectance ratio. L i (x,w i ) is the direction w at point x i The incident radiance, w i n is the dot product of the incident direction and the surface normal direction, which determines the effect of light on the energy transfer of the surface, and Ω is the hemispherical space of all possible incident directions. The model can take into account factors such as the window position of the building, the lighting conditions of the building's external environment, and the influence of obstructions.

[0127] This embodiment can fully utilize various complex parameters of the indoor environment and improve the prediction accuracy of the indoor environment by constructing a mechanism model including a thermal dynamic model, a carbon dioxide concentration diffusion model and an illumination model.

[0128] In addition, in one or more embodiments of the present invention, determining the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter specifically includes:

[0129] S131, calculating the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter.

[0130] S132, when the residual is greater than a preset threshold, inputting the predicted values ​​of the multiple mechanism models into the nonlinear fuser for training again until the residual is less than the preset threshold.

[0131] In this embodiment, when the residual is greater than the preset threshold, the predicted values ​​of the multiple mechanism models are input into the nonlinear fusion device again for training until the residual is less than the preset threshold. Figure 3 , Figure 3 The set value shown in is the preset threshold, and the preset threshold can be adjusted according to different indoor environments. Figure 3 The neural network shown in is a multi-layer perceptron. The objective function of the neural network is f(x, θ), where x is the set of input variables, θ is the set of unknown parameters, and θ = {w1, w2, w3, b}. Among them, w1, w2 and w3 are the weight parameters of the building thermal dynamic model, carbon dioxide concentration diffusion model and illumination model, respectively, and b is the bias term of the neural network.

[0132] Through the scheme described in this embodiment, the optimal weight parameters and bias can be found to reduce the errors between the predicted values ​​calculated by the three models and the collected true values ​​of the environment.

[0133] In addition, after constructing multiple mechanism models for indoor environment prediction, it also includes:

[0134] Comparing the predicted values ​​output by the multiple mechanism models with the collected true values ​​corresponding to the multiple mechanism models to obtain a comparison result;

[0135] According to the comparison results, the parameters of the multiple mechanism models are adjusted.

[0136] In this embodiment, the predicted values ​​output by the multiple mechanism models are compared with the collected true values ​​corresponding to the multiple mechanism models. If the difference between the predicted values ​​output by the multiple mechanism models and the collected true values ​​corresponding to the multiple mechanism models is greater than a preset difference limit, it is necessary to adjust the parameters of the multiple mechanism models so that the error between the predicted values ​​output by the multiple mechanism models and the true values ​​is reduced.

[0137] In addition, after inputting the predicted value at the first moment into the nonlinear fusion device to obtain the predicted value of the indoor physical parameter at the first moment, the method further includes:

[0138] Calculating an indoor energy consumption value associated with the predicted value of the indoor physical parameter at the first moment;

[0139] According to the predicted value of the indoor physical parameter at the first moment and the indoor energy consumption value, the real value of the indoor physical parameter is adjusted to reduce the indoor energy consumption value.

[0140] In this embodiment, the indoor energy consumption value refers to the electric energy consumed by all the electric devices in the room. After obtaining the indoor physical parameter prediction value and the indoor energy consumption value, the indoor physical parameter real value can be adjusted according to the real feeling of the indoor occupants.

[0141] Through the scheme shown in this embodiment, the real values ​​of indoor physical parameters can be reasonably adjusted according to the predicted values ​​of indoor physical parameters and the indoor energy consumption, thereby saving energy consumption and improving the comfort of the indoor environment.

[0142] The above is a method for dynamic prediction of indoor environment provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding dynamic prediction system for indoor environment, such as Figure 6 shown.

[0143] Figure 6 A schematic diagram of an indoor environment dynamic prediction system provided by the present invention includes:

[0144] A first acquisition module is used to: construct a plurality of mechanism models for indoor environment prediction, and input data related to indoor environment prediction into the plurality of mechanism models to obtain a plurality of first moment prediction values ​​of the plurality of mechanism models;

[0145] A training module, used to: use the predicted value at the first moment as an input of a neural network, perform capture training on the neural network for nonlinear relationships, obtain a nonlinear fuser, and obtain a predicted value of the indoor physical parameter at the first moment;

[0146] A determination module, used to: determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model;

[0147] A second acquisition module is used to: input the data related to the indoor environment prediction into the data compensation model to obtain a data compensation amount;

[0148] The calculation module is used to add the residual to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

[0149] For the specific definition of a dynamic prediction system for an indoor environment, please refer to the definition of a dynamic prediction method for an indoor environment above, which will not be repeated here. Each module in the dynamic prediction system for an indoor environment can be implemented in whole or in part by software, hardware, and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0150] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the Figure 1 A dynamic prediction method for indoor environment is provided.

[0151] The present invention also provides Figure 7 A schematic diagram of a computer device for realizing a method for dynamic prediction of indoor environment is shown in FIG. Figure 7 As shown, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the Figure 1 A dynamic prediction method for indoor environment is provided.

[0152] Those skilled in the art can understand that all or part of the processes in the embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0153] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the present invention.

Claims

1. A method for dynamic prediction of indoor environment, characterized in that: include: Constructing a plurality of mechanism models for indoor environment prediction, and inputting data related to indoor environment prediction into the plurality of mechanism models to obtain a plurality of first-moment prediction values ​​of the plurality of mechanism models; The predicted value at the first moment is used as the input of the neural network, the neural network is trained to capture the nonlinear relationship, a nonlinear fuser is obtained, and the predicted value of the indoor physical parameter at the first moment is obtained; Determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model; Inputting the data related to indoor environment prediction into the data compensation model to obtain a data compensation amount; The residual is added to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

2. The method for dynamic prediction of indoor environment according to claim 1, characterized in that: Before constructing the multiple mechanism models for indoor environment prediction, the method further includes: Acquire the heat source distribution of the room within a preset time period, and determine a first number of people in the room within the preset time period according to the heat source distribution; Determine a second number of people in the room during the preset time period based on the monitoring images of the room during the preset time period; During the preset time period, the persons in the room are located by using ultra-wideband technology to obtain a third number of persons in the room within the preset time period; Acquire a fourth number of people who enter or leave the room within the preset time period; The first number of people, the second number of people, the third number of people, and the fourth number of people are used as inputs of a deep learning network model for training to obtain a personnel distribution model for predicting the number of people in the room within the preset time period, where the number of people in the room is one of the data related to the indoor environment.

3. The method for dynamic prediction of indoor environment according to claim 1, characterized in that: The data related to indoor environment prediction specifically includes: The parameter vector of the indoor environment, the parameter vector of the outdoor environment, the number of people in the room, the physical parameter vector of the room in the room, and the terminal start / stop status and parameter settings of the indoor air conditioning and fresh air system.

4. The method for dynamic prediction of indoor environment according to claim 1, characterized in that: The multiple mechanism models for indoor environment prediction are constructed, specifically including: Constructing a building thermal dynamic model based on the obtained indoor structural information, constituent material information, external environment information, heating information, ventilation information, operating status of the air conditioning system, and start / stop status of the fresh air system; The obtained indoor ventilation information, personnel activity information, indoor air flow rate information, and carbon dioxide generation and emission information are used as boundary conditions of the constraint equation to construct a carbon dioxide concentration diffusion model; According to the collected indoor three-dimensional architectural information, window position information, and light blocking object information, simulation parameters about the indoor environment are defined, and the simulation parameters are input into a lighting simulation software to obtain an illumination model.

5. The method for dynamic prediction of indoor environment according to claim 1, characterized in that: The determining of the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter specifically includes: Calculating the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter; When the residual is greater than a preset threshold, the predicted values ​​of the multiple mechanism models are input into the nonlinear fuser again for training until the residual is less than the preset threshold.

6. The method for dynamic prediction of indoor environment according to claim 1, characterized in that: After constructing the multiple mechanism models for indoor environment prediction, the method further includes: Comparing the predicted values ​​output by the multiple mechanism models with the collected true values ​​corresponding to the multiple mechanism models to obtain a comparison result; According to the comparison results, the parameters of the multiple mechanism models are adjusted.

7. The method for dynamic prediction of indoor environment according to any one of claims 1 to 7, characterized in that: After inputting the predicted value at the first moment into the nonlinear fusion device to obtain the predicted value of the indoor physical parameter at the first moment, the method further includes: Calculating an indoor energy consumption value associated with the predicted value of the indoor physical parameter at the first moment; According to the predicted value of the indoor physical parameter at the first moment and the indoor energy consumption value, the real value of the indoor physical parameter is adjusted to reduce the indoor energy consumption value.

8. A dynamic prediction system for indoor environment, characterized in that: include: A first acquisition module is used to: construct a plurality of mechanism models for indoor environment prediction, and input data related to indoor environment prediction into the plurality of mechanism models to obtain a plurality of first moment prediction values ​​of the plurality of mechanism models; A training module, used to: use the predicted value at the first moment as an input of a neural network, perform capture training on the neural network for nonlinear relationships, obtain a nonlinear fuser, and obtain a predicted value of the indoor physical parameter at the first moment; A determination module, used to: determine the residual between the predicted value of the indoor physical parameter at the first moment and the collected true value of the indoor physical parameter, and input the residual into the deep hierarchical fuzzy model for training to obtain a data compensation model; A second acquisition module is used to: input the data related to the indoor environment prediction into the data compensation model to obtain a data compensation amount; The calculation module is used to add the residual to the data compensation amount to obtain a predicted value of a physical parameter at a second moment related to the indoor environment.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method for dynamic prediction of an indoor environment according to any one of claims 1 to 7 is implemented.

10. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for dynamic prediction of indoor environment as claimed in any one of claims 1 to 7 is implemented.

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