Method and device for calculating building-related load

Abstract

An embodiment of the present application provides a method and a device for calculating a building-related load. The method for calculating a building-related load comprises the steps of: acquiring simulation activity features of a plurality of virtual persons on the basis of simulation activity in a virtual building environment simulating an environment in a building, performed by the plurality of virtual persons simulating a crowd in the building, the simulation activity features including heat generation amounts of the plurality of virtual persons; and calculating a building-related load on the basis of at least the heat generation amounts of the plurality of virtual persons. Due to this configuration, it is possible, by a simple and efficient method, to simulate crowd activity features that are more realistic and accurate than preset fixed values, i.e., crowd simulation activity features. Given the above, it is possible, by a simple and efficient method, to simulate crowd activity features that are more realistic and accurate than preset fixed values, i.e., crowd simulation activity features, thereby improving the calculation accuracy of the building-related load on the basis of the simulation activity features.

Description

Method and apparatus for calculating building-related loads

[0001] The embodiments of this application relate to the technical field of electromechanical control.

[0002] The accuracy of building load calculations is of extremely high value for application in building management and energy management. The influencing factors of building loads include four types: the building heat transfer coefficient, meteorological parameters, equipment operating status, and personnel activity characteristics. Personnel activity characteristics include people's behavior, location, and heat generation. Normally, when calculating building loads, personnel activity characteristics are set to fixed values, but in real-world scenarios, they are influenced by the uncertainty of personnel activity and change randomly and dynamically, causing the actual building load to fluctuate accordingly. This shows that random and dynamic changes in personnel activity characteristics significantly affect the accuracy of building load calculations.

[0003] In calculating building loads, a crucial challenge is how to absorb the random, dynamic changes in personnel activity characteristics and improve the accuracy of the calculation.

[0004] Patent Document 1 discloses a technique for calculating building load by setting the amount of heat generated by a person to a fixed value.

[0005] Patent Document 2 discloses a technique for constructing an energy consumption simulation model by combining a power model, a thermodynamics model, and a random human behavior model constructed based on the Markov chain Monte Carlo method, and for calculating building loads by further collecting random human activity characteristics.

[0006] It should be noted that the above explanation of the technical background is merely intended to make the technical solutions of this application clear, easy to explain, and easy to understand for those skilled in the art. These solutions are not considered publicly known to those skilled in the art simply because they are described in the background art section of this application.

[0007] In the method for calculating building-related loads provided by the above Patent Document 1, the inventor of the present application sets all of the position, behavior, and heat generation amount of a person to fixed values. However, in an actual scene, the characteristics of personnel activities change dynamically and randomly. That is, since the actual heat generation amount, position, and behavior of a person have a certain degree of randomness, by adopting fixed values, it has been found that the heat generation amount, position, and behavior of a person in the actual environment cannot be accurately reflected.

[0008] Furthermore, in the above Patent Document 2, even when identifying a person's behavior using a mathematical probability model, the random behavior of a person that occurs is completely based on the randomness of the probability distribution. Therefore, although the randomness is high, a person's behavior is not simply random behavior but can be predicted based on certain rules. Thus, according to the prior art, although the random behavior of a person is identified to a certain extent, the accuracy of the random behavior of a person is low.

[0009] Also, since a person's heat generation amount actually has randomness due to individual differences, the state of physical activity, and the influence of the environment in which the person is placed, using a fixed heat generation amount is simple but does not conform to the actual situation.

[0010] As can be seen from this, in the above Patent Documents 1 and 2, there is still a problem that the authenticity of the data on the heat generation amount, position, and behavior of a person adopted is insufficient, and as a result, the calculation accuracy of the building load is low.

[0011] In response to at least one of the above technical problems, the embodiments of the present application provide a method and apparatus for calculating building-related loads that can improve the authenticity of crowd activity feature simulation and the calculation accuracy of building loads by a simple and efficient method.

[0012] According to an embodiment of the first aspect of the present application, there is provided a method for calculating a building-related load, including: obtaining simulation activity characteristics of a plurality of virtual characters that simulate a crowd in a building through simulation activities performed by the plurality of virtual characters that simulate the crowd in the building in a virtual building environment that simulates the environment in the building, where the simulation activity characteristics include the calorific value of the plurality of virtual characters; and calculating the building-related load based at least on the calorific value of the plurality of virtual characters.

[0013] According to an embodiment of the second aspect of the present application, there is provided a device for calculating a building-related load, including: an acquisition module that obtains simulation activity characteristics of a plurality of virtual characters that simulate a crowd in a building through simulation activities performed by the plurality of virtual characters that simulate the crowd in the building in a virtual building environment that simulates the environment in the building, where the simulation activity characteristics include the calorific value of the plurality of virtual characters; and a calculation module that calculates the building-related load based at least on the calorific value of the plurality of virtual characters.

[0014] One of the beneficial effects of the embodiment of the present application is that, according to the method for calculating a building-related load provided by the embodiment of the present application, by calculating the building-related load based on the characteristics of the simulation activities performed by a plurality of virtual characters that simulate a crowd in a virtual building environment, the cost and risk of data collection can be reduced, and the calculation steps can be optimized and the operation can be simplified by automatically setting parameters to obtain data. Therefore, a crowd activity characteristic that is more real and accurate than a preset fixed value, that is, a simulation activity characteristic of the crowd, can be simulated by a simple and efficient method. Furthermore, when the simulation activity characteristics are used for calculating the building-related load, the calculation accuracy of the building-related load can be improved.

[0015] Specific embodiments of the embodiments of this application are disclosed in detail with reference to the description and drawings set forth below, and the manner in which the principles of the embodiments of this application may be employed is clearly indicated. The embodiments of this application should be understood not to be limited in scope by these descriptions. Within the scope of the attached claims, the embodiments of this application include many changes, modifications and equivalents.

[0016] The included drawings are provided for further understanding of the embodiments of the present application, constitute part of the specification, illustrate embodiments of the present application, and explain the principles of the present application together with textual descriptions. Clearly, the drawings below are only some embodiments of the present application, and those skilled in the art can obtain other embodiments based on these drawings without requiring progressive work. The drawings are as follows: A schematic diagram of the method for calculating building-related loads according to the present application. A schematic diagram of the configuration of a virtual person in an embodiment of the present application. A flowchart for acquiring crowd activity characteristics and calculating building loads in an embodiment of the present application. A schematic diagram of a specific embodiment of S1 in the method shown in Figure 3. A schematic diagram of a specific embodiment of S2 in the method shown in Figure 3. A schematic diagram of a specific embodiment of S3 in the method shown in Figure 3. A schematic diagram of the configuration of a general-purpose AI NPC model. A schematic diagram of a specific embodiment of S4 in the method shown in Figure 3. A schematic diagram of a specific embodiment of S5 in the method shown in Figure 3. A schematic diagram of a specific embodiment of S6 in the method shown in Figure 3. Another flowchart for acquiring crowd activity characteristics and calculating building loads according to an embodiment of the present application. This is another flowchart for acquiring crowd activity characteristics and calculating building loads according to the embodiment of the present invention. This is a schematic diagram of the crowd activity characteristics simulation device according to the embodiment of the present invention. This is a schematic diagram of the building-related load calculation device according to the embodiment of the present invention. This is a flowchart for calculating building loads in the embodiment of the present invention.

[0017] Referring to the drawings, the above and other features of the embodiments of the present application will become apparent from the following specification. The specification and drawings specifically disclose certain embodiments of the present application and show some embodiments in which the principles of the embodiments of the present application can be adopted. The present application is not limited to the embodiments described herein; rather, the embodiments of the present application should be understood to include all amendments, modifications, and equivalents that fall within the scope of the appended claims.

[0018] In the embodiments of this application, terms such as “first,” “second,” etc., are used to distinguish different elements from their names, but do not indicate a spatial arrangement or temporal order of these elements, and these elements should not be limited by these terms. The “and / or” in a term includes any one or more of the terms listed in relation and all combinations thereof. Terms such as “include,” “contain,” and “have” refer to the presence of the described feature, element, component, or part, but do not exclude the presence or addition of one or more other features, elements, components, or parts.

[0019] In the embodiments of this application, unless otherwise specified in the context, singular nouns such as "one" and "the" should be understood to include the plural form and not be limited to the meaning of "one," but rather broadly to mean "one kind" or "one type." Furthermore, the term "the aforementioned" should be understood to include both singular and plural forms. Also, unless otherwise specified in the context, the term "according to" should be understood to mean "at least partially according to," and the term "based on" should be understood to mean "at least partially based on." In this application, "plural" means "two or more."

[0020] Features described and / or shown in one embodiment may be used in the same or similar manner in one or more other embodiments, combined with or substituting for features in other embodiments. The term “includes” as used herein refers to the presence of a feature, an entire component, a step, or a component, but does not exclude the presence / addition of one or more other features, an entire component, a step, or a component.

[0021] <Example relating to the first aspect> The embodiment of the present application provides a method for calculating building-related loads.

[0022] In the embodiments of the present invention, the entity performing the method may be a building-related load calculation device, and the building-related load may be automatically calculated by the building-related load calculation device. However, the embodiments of the present invention are not limited thereto, and for example, the building-related load may be automatically calculated by a cloud server.

[0023] Figure 1 is a schematic diagram of a method for calculating building-related loads according to an embodiment of the present invention, and as shown in Figure 1, the method includes a step 101 in which simulation activity is performed by a plurality of virtual people simulating a crowd inside a building in a virtual building environment simulating the environment inside the building, and the simulation activity features include the amount of heat generated by the plurality of virtual people, and a step 102 in which building-related loads are calculated based on the amount of heat generated by at least the plurality of virtual people.

[0024] According to the embodiment of the present invention, by calculating building-related loads based on the characteristics of simulation activities performed in a virtual building environment by multiple virtual individuals simulating a crowd inside a building, the cost and risk of data collection can be reduced, and the calculation steps can be optimized and operations simplified by automatically setting parameters and acquiring data. As a result, crowd activity characteristics that are more true and accurate than pre-set fixed values ​​can be simulated in a simple and efficient manner. Furthermore, when simulation activity characteristics are used to calculate building-related loads, the accuracy of the calculation of building-related loads can be improved.

[0025] In the embodiments of the present application, multiple virtual characters are used to simulate crowds within a building, and a virtual building environment is used to simulate the building environment. Here, the building may be an actually constructed building or a building represented by an architectural model. For example, the architectural model may be a BIM model, and furthermore, the architectural model may be a three-dimensional model or a two-dimensional model, and the embodiments of the present application do not specifically limit the architectural model.

[0026] Furthermore, the multiple virtual characters in the embodiments of the present application are used to simulate a crowd inside a building, and their number may be two or more. However, the virtual characters in the embodiments of the present application may be just one, for example, one virtual character in a certain area.

[0027] In step 101, the step of obtaining simulation activity characteristics of multiple virtual people by having multiple virtual people simulate a crowd inside a building in a virtual building environment that simulates the environment inside the building may include the steps of: calculating the amount of heat generated by the basal metabolism of multiple virtual people based on the static influence elements of the multiple virtual people; calculating the dynamic amount of heat generated by multiple virtual people based on the dynamic influence elements related to the simulation activities of multiple virtual people; and calculating the total amount of heat generated by multiple virtual people based on the amount of heat generated by basal metabolism and the dynamic amount of heat generated.

[0028] The simulation activities of multiple virtual characters can include various actions and activities of multiple virtual characters, and the dynamic influencing elements are related to the simulation activities of multiple virtual characters.

[0029] In the embodiments of the present invention, the multiple virtual characters that simulate the crowd inside the building and the virtual building environment that simulates the environment inside the building may be pre-generated or may be generated when the building-related load is calculated for the first time. For example, when calculating the building-related load for the first time, multiple virtual characters and a virtual building environment are generated, and multiple virtual characters are placed in the virtual building environment, or a virtual building environment is first constructed, and then multiple virtual characters are generated and placed in the virtual building environment. If it is necessary to recalculate the building-related load, the multiple virtual characters and the virtual building environment can be used directly.

[0030] In other words, the method for calculating building-related loads according to the embodiment of the present application may include a step of generating multiple virtual people and virtual building environments, or it may not include such a step. For example, the step may be included when calculating the building-related loads for the first time, but not when calculating the building-related loads again.

[0031] The following describes in detail the method for generating multiple virtual characters and virtual building environments according to the embodiment of the present application.

[0032] In the embodiment of the present invention, multiple virtual characters can be generated by a virtual character generation means based on placement data corresponding to the multiple virtual characters, and a virtual building environment can be generated based on building information and environmental information.

[0033] In the above embodiment, the virtual character generation means generates multiple virtual characters driven by artificial intelligence based on a general-purpose virtual character generation model. Here, a virtual character refers to a character that simulates a human character in a virtual environment (e.g., a game), the virtual character generation means may include virtual character generation software, and the general-purpose virtual character generation model refers to an artificial intelligence model capable of generating general-purpose virtual characters. Using the general-purpose virtual character generation model, the virtual character generation means can construct personality, simulated thinking, emotions, and cognitive processes for the virtual character based on placement data pre-input by the user or player, thereby enabling the virtual character to perform autonomous activities in the virtual environment without being operated by the user or player.

[0034] For example, these multiple virtual characters may include Non-Player Characters (NPCs) driven by Artificial Intelligence (AI), also known as AI NPCs. Traditional NPCs are typically applied to games, performing anthropomorphic activities as personified characters and interacting with player characters. AI NPCs are capable of learning and adapting, can process relationships using emotional intelligence (EQ), possess memory and recall abilities, and can autonomously activate targets, perform actions, and follow their own motivations. In short, AI NPCs have a richer range of behaviors and are more similar to real humans than traditional NPCs.

[0035] In the following description of this application, AI NPCs are used as an example to explain virtual characters driven by artificial intelligence. However, this description also applies to other types of virtual characters. Hereafter, unless otherwise specified in the context, "virtual character driven by artificial intelligence," "AI-driven virtual character," "AI NPC," and "virtual character" refer to the same concept.

[0036] In the above embodiment, the placement data includes at least one of the following: number of people, gender ratio, age distribution, job title ratio, and relationship network.

[0037] In some embodiments, the relationship network includes at least one of the following: intimacy relationships, project collaboration relationships, and organizational architecture relationships.

[0038] In the above embodiment, the intimacy relationships, project collaboration relationships, and organizational architecture relationships of multiple virtual characters influence the interaction behaviors between them in the virtual environment. For example, two virtual characters with a high level of intimacy may trigger interaction behaviors such as conversation or dating after meeting in the virtual environment. Similarly, two virtual characters with a project collaboration relationship may engage in interaction behaviors such as meetings or discussions. Different interaction behaviors also influence the virtual characters' movement patterns, the areas they occupy in the virtual environment, and their states (e.g., emotions, fatigue levels).

[0039] In other words, differences in the placement of relational networks in the configuration data generate different interactive behaviors between different virtual characters, thereby making the autonomous activities of virtual characters more similar to those of real humans.

[0040] In the above embodiment, the virtual person generation means can generate multiple virtual people driven by artificial intelligence that match the placement data by setting at least one of the placement data. Figure 2 is a schematic diagram of the configuration of a virtual person in an embodiment of the present invention. As shown in Figure 2, the virtual person 200 has a heat generation amount calculation module 201 and a state monitoring module 202. In some embodiments, the virtual person 200 may further include other modules, such as a personal information module, and specifically, prior art can be referenced, which will not be described here.

[0041] In real-world scenarios, calculating a person's heat output involves several factors, such as basal metabolic rate (BMR) and physical activity level. BMR is an estimate of the minimum energy expenditure required for the body to maintain life at rest, while an individual's physical activity level estimates total daily energy expenditure by multiplying the BMR by an activity factor corresponding to the BMR. The World Health Organization categorizes physical activity levels into several levels, such as light, moderate, and heavy physical activity.

[0042] In other words, calculating a person's heat output involves multiple factors, including both static and dynamic influencing elements.

[0043] In the above embodiment, the heat generation calculation module 201 calculates the heat generation of the virtual person 200 based on the static influence factors of the virtual person 200, calculates the dynamic heat generation of the virtual person 200 based on the dynamic influence factors of the virtual person 200, and calculates the total heat generation of the virtual person 200 (total heat generation of the virtual person 200) based on the heat generation from basal metabolism and the dynamic heat generation. However, the specific calculation method is not limited. The calculation method will be described below as an example. Furthermore, by adding the heat generation amounts of multiple virtual people 200, the heat generation amounts of multiple virtual people (total heat generation of multiple virtual people) can be obtained.

[0044] As mentioned above, the factors influencing a person's heat output include static influencing factors (sex, age, height, and weight for calculating basal metabolic rate) and dynamic influencing factors (physical activity level, environmental data, emotional data, and lifestyle rhythm data (e.g., meal times)).

[0045] For example, the total amount of heat (TEE) consumed daily by a normal adult is calculated as follows: basal metabolic rate (BEE) + diet-induced thermogenesis (DEE) + energy required for physical activity (AEE).

[0046] From an energy consumption distribution perspective, in human TEE, BEE and DEE remain relatively constant, but AEE's proportion of TEE changes the most, indicating the greatest number of influencing factors.

[0047] In the embodiment of the present invention, for example, the heat generation calculation module performs heat generation calculations in the following three main ways based on the above formula.

[0048] (i) Amount of energy consumed by basal metabolism (BEE): By obtaining personal information data of AI NPC, such as age, gender, height, and weight, basal metabolism can be calculated, for example, using the Harris-Benedict equation.

[0049] For example, the Harris-Benedict equation is as follows:

[0050] For men, BMR = 88.362 + (13.397 × weight in kg) + (4.799 × height in cm) - (5.677 × age in years). For women, BMR = 447.593 + (9.247 × weight in kg) + (3.098 × height in cm) - (4.330 × age in years).

[0051] (ii) Energy consumed by food-induced thermogenesis (DEE): Generally, food-induced thermogenesis accounts for about 10% of the total energy, and this is a relatively constant ratio.

[0052] For example, let's consider a 30-year-old man who weighs 70 kg and is 175 cm tall.

[0053] BMR = 88.362 + (13.397 x 70) + (4.799 x 175) - (5.677 x 30) = 88.362 + 937.79 + 839.825 - 170.31 ≒ 1695.67 kcal / day.

[0054] Furthermore, for example, TEE can also be calculated using the following formula.

[0055] TEE=(BEE+AEE)÷0.9.

[0056] For example, BEE = 1500 kcal, AEE = 500 kcal, TEE = (1500 + 500) / 0.9 ≈ 2222 kcal. Based on food-induced thermogenesis, DEE = TEE × 10% ≈ 222 kcal.

[0057] (iii) Amount of energy consumed by physical activity (AEE): AI NPC activity status (location, behavior, etc.) is obtained to identify energy consumption status. The World Health Organization divides physical activity levels into several levels, such as light, moderate, and heavy physical activity. Different levels of physical activity generate different energy consumption for people of different ages, genders, and weights. Specific methods for identifying energy consumption status based on activity status can be found in conventional techniques, and a detailed explanation is omitted here.

[0058] In the embodiment of the present application, the total energy consumption of the human body can be calculated by integrating the energy consumption based on (i), (ii), and (iii) above.

[0059] Furthermore, generally speaking, the total energy consumption of an adult's body is roughly equal to the amount of heat a person generates. Considering that heat dissipation from the body into the environment is also blocked by clothing, a person's heat generation is also affected by the season and temperature.

[0060] Specifically, the clothing worn by people differs depending on the season, and it is generally assumed that 70% to 90% of TEE is converted into the heat load of the building environment. For example, the heat dissipation efficiency in summer is about 90%, and in winter it is about 70%. Furthermore, in the embodiment of this application, based on data such as the age, height, weight, and position of the AI ​​NPC, it is possible to estimate the types of clothing that different AI NPCs can wear in different seasons, different preferences, different physical characteristics, etc. For example, there are positions that require wearing thin uniforms even in winter, and some AI NPCs can wear down jackets even in winter, and the type of clothing worn by AI NPCs is also used in the calculation of radiation. According to this calculation method, the heat dissipation efficiency of each AI NPC can be calculated by narrowing down the target, and this improves the accuracy of heat generation calculation by not using a method that sets the heat dissipation rate as a fixed coefficient. In some embodiments, static influencing elements may include at least one of the gender, age, height, and weight of multiple virtual characters, and / or dynamic influencing elements may include at least one of the physical activity levels, environmental data, state data, and lifestyle rhythm data of multiple virtual characters.

[0061] In some embodiments, the dynamic influence factors of multiple virtual characters may be automatically acquired in real time during the simulation activities of the multiple virtual characters.

[0062] In some embodiments, as shown in Figure 2, the virtual person further includes a state monitoring module 202 that monitors the state of the virtual person 200.

[0063] Here, the state of the virtual person 200 can include at least one of fatigue level, health level, comfort level, and emotion. In other words, the state data of the virtual person can be identified based on at least one of fatigue level, health level, comfort level, and emotion, where health level is related to diseases that may affect the person's fever level.

[0064] In some embodiments, diseases that can affect a person's fever have at least one of the following characteristics.

[0065] Effects on the thermoregulatory center: Many diseases affect the thermoregulatory center in the hypothalamus, thereby altering the body temperature setpoint.

[0066] Changes in metabolic rate: These directly affect the basal metabolic rate; for example, thyroid disease directly regulates the metabolic activity of cells throughout the body.

[0067] Inflammatory response: Infections and autoimmune diseases affect the fever process through inflammatory mediators (e.g., interleukins, tumor necrosis factors).

[0068] Abnormalities in autonomic nervous system function: These affect the balance between heat dissipation and heat generation by influencing vasoconstriction and dilation and sweat gland activity.

[0069] Alteration of energy metabolic pathways: This directly interferes with the production and utilization of ATP, affecting heat generation efficiency.

[0070] The following are some examples of diseases that can affect a person's fever.

[0071] For example, thyroid dysfunction includes the following:

[0072] Hyperthyroidism: Significantly increases the basal metabolic rate, leading to increased fever, constant feeling of heat, and excessive sweating. Hypothyroidism: Decreases the basal metabolic rate, resulting in decreased fever, and constant feeling of cold.

[0073] For example, infectious diseases, including various bacterial and viral infections, cause fever, and pyrogens act on the hypothalamic thermoregulatory center, leading to an increase in body temperature and fever.

[0074] For example, autoimmune diseases include the following:

[0075] Rheumatoid arthritis, systemic lupus erythematosus, and other conditions can cause persistent low-grade fever, and in some cases, abnormalities in thermoregulation may occur.

[0076] For example, neoplastic diseases include the following:

[0077] Certain cancers (e.g., lymphoma) can cause tumor fever, and cachexia can affect the overall metabolic rate.

[0078] For example, endocrine disorders include the following:

[0079] Pheochromocytoma: Releases large amounts of catecholamines, causing increased metabolic rate and fever. Diabetes mellitus: In severe cases, it can cause metabolic disorders and affect energy utilization and fever.

[0080] In some embodiments, dynamic influencing factors include, for example, physical activity level influencing heat generation through energy consumption during physical activity, environmental data influencing heat generation through changes in body temperature, state data influencing heat generation through metabolic efficiency, and lifestyle rhythm data influencing heat generation through the regulation and control of metabolic rhythms by the biological clock.

[0081] For example, regarding temperature and humidity in environmental data, this includes the following:

[0082] High temperature and high humidity: When the ambient temperature is >32°C and humidity is >60%, the efficiency of sweat evaporation decreases, inhibiting heat dissipation. This causes the body to increase cardiac output and respiratory rate to promote heat dissipation and significantly improve the metabolic rate. At this time, the ratio of heat energy consumption in TEE can increase by 20% to 30%. Low temperature: In cold environments, fever due to skeletal muscle shivering (non-shivering fever) increases, and BMR rises by 5% to 15% to maintain core body temperature.

[0083] For example, regarding air quality in environmental data, high concentrations of CO2 ２ Alternatively, this may include inducing hypoxic stress through formaldehyde, stimulating the sympathetic nervous system, and increasing the resting metabolic rate, or inducing airway inflammation through fine particulate matter (PM2.5), leading to the consumption of excess energy during the repair process.

[0084] For example, regarding fatigue level and health level in status data, this includes the following:

[0085] Chronic fatigue syndrome (CFS): Patients experience impaired mitochondrial function, leading to reduced ATP synthesis efficiency and a 10-15% decrease in BMR. Individuals with lower psychological flexibility experience more severe fatigue symptoms, reduced activity levels, and a further decrease in TEE. Acute illness (e.g., infectious fever): For every 1°C increase in body temperature, BMR increases by approximately 13% due to energy consumption for immune response and repair.

[0086] For example, regarding emotion and comfort levels in state data, this includes the following:

[0087] Emotional fluctuations: Excessive fatigue can lead to elevated cortisol and serotonin imbalance, causing metabolic disruption. Anxiety can cause muscles to remain tense, increasing resting energy expenditure, while depression can reduce activity levels and potentially lower TEE. Decreased comfort: An unpleasant environment (e.g., noise > 65 dB, excessive light) activates the HPA axis, and the release of cortisol promotes gluconeogenesis, improving metabolism in the short term but leading to decreased metabolic efficiency in the long term.

[0088] For example, meal times in lifestyle rhythm data include the following:

[0089] Irregular feeding disrupts biological clock genes (e.g., CLOCK, BMAL1) and reduces TEF efficiency. Studies have shown that individuals who feed at regular intervals have 8% to 12% higher TEF than those who feed randomly, and that nocturnal feeding causes the thermogenic effect of food to clash with the metabolic lows during sleep, making it easier for energy to be stored as fat.

[0090] For example, regarding sleep interruptions in lifestyle rhythm data, decreased sleep duration (e.g., frequent awakenings) includes reducing the secretion of growth hormone during deep sleep, which lowers the daytime BMR. For every hour of reduced deep sleep, the BMR decreases by approximately 5% the following day.

[0091] In some embodiments, the state of the virtual person 200 can be generated by AI, where states such as fatigue level, health level, and comfort level may be set as evaluation values. For example, the range of evaluation values ​​for fatigue level, health level, and comfort level may be 1 to 10 or 1 to 100, or the evaluation of fatigue level, health level, and comfort level may be set by specific descriptions. For example, fatigue level may include helplessness and drowsiness, health level may include normal and illness, and emotions may include anger, joy, sadness, excitement, etc., but this invention is not limited thereto.

[0092] In some examples, the range of influence of metabolic rate on the amount of heat generated was ±25%, for example, the amount of heat generated for a male engineer weighing 70 kg was the standard value of heat generated × 1.15, and the amount of heat generated for a female designer weighing 55 kg was the standard value of heat generated × 0.9. In some examples, the range of influence of age on the amount of heat generated was -30% (elderly), for example, when the building is an activity center for the elderly, the corrected amount of heat generated was the standard value of heat generated × 0.7.

[0093] In some examples, the effect of body fat percentage on calorific value is -15% (in obese individuals), for example, the sensible heat of the group with a BMI > 30 decreases: corrected calorific value = standard value of calorific value × 0.85. For example, the standard value of calorific value is 100W, and the calorific value of obese individuals is approximately 85W. BMI (body mass index) refers to the body mass index.

[0094] In some examples, the influence of hormone levels on fever was +20% (in pregnant women). For example, if the building is an obstetric clinic, the corrected fever amount = standard fever amount × 1.2.

[0095] In some embodiments, regarding the effect of activity levels on heat generation, for example, in the case of static office work, the corresponding metabolic rate (MET) is any value in the range of 1.0 to 1.2, and the corrected heat generation = standard value of heat generation × any value in (1.0 to 1.2). In the case of a conference room, if the corresponding metabolic rate is, for example, 1.3 MET, then the corrected heat generation = standard value of heat generation × 1.3.

[0096] In some embodiments, the influence of posture and clothing on heat generation was determined as follows: For example, for clothing (1.0 clo) + seated posture, thermal resistance (1.2 clo), the corrected heat generation was equal to the standard value of heat generation × 0.9; for example, for short sleeves (0.5 clo) + standing and walking, thermal resistance (0.6 clo), the corrected heat generation was equal to the standard value of heat generation × 1.15. The clo value is a unit of measurement used to evaluate the heat retention performance of clothing. Table 1 shows examples of specific correspondences between physical activity and energy consumption according to embodiments of this application.

[0097] The scenes and specific calculation methods shown in Table 1 above are merely examples, and the embodiments of this application are not limited to these examples.

[0098] In some embodiments, the elements that affect the amount of heat generated may include essential elements (also called necessary elements) and non-essential elements (also called unnecessary elements).

[0099] The essential elements refer to the indispensable physiological elements that constitute the basic heat-generating capacity of the human body. Without these elements, the human body cannot maintain basic life activities and heat generation functions.

[0100] For example, essential elements include the following:

[0101] Basic body composition (e.g., body weight): Metabolic active tissues such as muscles are the main source of heat, and the basal metabolic rate accounts for 60-70% of the total daily heat expenditure of the human body. Age factor: Due to the demands of growth, the basal metabolic rate is high in young people and gradually decreases as age increases. Gender difference: The basal metabolic rate differs between men and women due to differences in body composition and hormone levels, and is an essential factor that affects the basic heat-generating capacity of the human body. Genetic factor: Genetic background regulates energy consumption in the body and includes the basal metabolic rate, the heat-generating effect of food, and non-autonomous movement related to heat generation. Basal thermoregulatory mechanism: The physiological mechanism by which the human body maintains its core body temperature is the basis of heat generation, and body temperature itself is an important factor that affects the basal metabolic rate. Basal level of thyroid hormones: Thyroid hormones are essential hormones that regulate the metabolic activity of cells throughout the body, and their basal level directly affects the basal metabolic rate.

[0102] Non-essential factors are those that may affect the actual amount of heat generated, but are not necessary for maintaining the body's basic heat-generating function. In the basal metabolic state, the influence of these factors is eliminated.

[0103] For example, non-essential factors include the following:

[0104] Ambient temperature: Basal metabolic rate is defined as "energy metabolism when not affected by ambient temperature," and changes in ambient temperature trigger thermoregulatory responses, but are not necessary for basic heat generation. Muscle activity: Heat generation increases with any form of body movement, but the influence of "muscle activity" is specifically excluded when measuring basal metabolic rate. Food intake: Food-induced thermogenesis (TEF) occurs after eating, but the influence of "food" is clearly excluded when measuring basal metabolic rate. Mental stress and short-term emotions: Psychological state affects the actual amount of heat generated, but basal metabolic rate is defined as "energy metabolism when not affected by mental stress, etc."

[0105] As mentioned above, calculating a person's heat output involves multiple factors, and an artificial intelligence-driven heat output calculation module for a virtual person can calculate the corresponding heat output based on different static and dynamic influencing factors corresponding to the virtual person.

[0106] According to the above embodiment of the present application, by having a virtual person 200 driven by AI simulate the activities of personnel, the distribution and state of personnel can be made closer to a real scene, and dynamic influencing elements can also be made closer to the actual situation, thereby increasing the truthfulness and accuracy of the acquired heat output of people.

[0107] In the embodiment of the present invention, the AI-driven virtual character simulates and acts like a real person in a real-world scene within a virtual building environment, thereby acquiring the simulated activity characteristics of a crowd.

[0108] As described above, a virtual building environment can be generated based on architectural and environmental information.

[0109] In some embodiments, the building information includes at least one of building drawings, building models, and building heat transfer coefficients, and in some embodiments, the building model is a three-dimensional or two-dimensional building model, for example, the building model is a BIM model.

[0110] In some embodiments, the environmental information includes building equipment information and / or weather information. In the above embodiments, the equipment information may include equipment identification information (e.g., equipment type, equipment manufacturing date, equipment power, etc.) and equipment operation data (e.g., on / off status, operating power, set temperature, set wind speed, set airflow, set wind direction, set brightness, energy consumption information, etc.). The equipment type may be, for example, an air conditioner, a display, lighting equipment, etc. Weather information may include, but is not limited to, outdoor temperature, outdoor humidity, outdoor ultraviolet intensity, indoor temperature, indoor air humidity, etc.

[0111] In some embodiments, environmental information is updated based on information in the actual environment. For example, if equipment information in the building environment changes in the actual environment, the equipment information in the virtual building environment can be updated manually. Alternatively, weather information in the virtual building environment can be automatically updated in real time by acquiring weather information in the actual environment in real time, but this invention is not limited to these examples. This makes the virtual building environment more similar to the actual environment, allowing users to perform activity simulations and data collection in the virtual building environment, thereby reducing the difficulty and cost of data collection.

[0112] For example, a building may be modeled using a three-dimensional digital twin model to obtain an architectural information model, or a virtual building environment may be constructed using CAD architectural drawings as architectural information. The heat transfer coefficient of the building may be set in advance when generating the virtual building environment, and this invention is not limited thereto.

[0113] In some embodiments, the simulation activity features may further include the moisture release amounts of multiple virtual characters. Based on the moisture release amounts of multiple virtual characters, the moisture load of the building can be calculated.

[0114] In some embodiments, the simulation activity features may further include the actions and / or locations of multiple virtual characters.

[0115] In some embodiments, the actions and locations of multiple virtual characters differ depending on the building information, environmental information, and weather information set when the virtual building environment is constructed. For example, the actions of multiple virtual characters differ depending on the building type of the virtual building environment. Here, actions may include sitting, standing, walking, running, etc. If the virtual building environment is an office area, the actions of the virtual characters may be sitting, standing, or walking. If the virtual building environment is a fitness place, the actions of the virtual characters may be running, training, etc.

[0116] In the above embodiment, equipment information is updated based on the actions and / or locations of multiple virtual characters. For example, by acquiring the actions and / or location information of multiple virtual characters in real time, interaction information between multiple virtual characters and equipment can be further acquired. That is, for example, when a virtual character operates equipment in a virtual building environment, the current state of the equipment can be changed. For example, when one virtual character leaves a room, equipment such as doors, windows, lamps, air conditioners, and computers in that room are closed.

[0117] In some embodiments, the scenes of a virtual building environment may be associated with the settings of multiple virtual characters. For example, if the scene types of the virtual building environment are a stadium, a shopping mall, and an office building, the number and distribution of personnel within them will differ, and the actions, locations, physical activity levels, and states of multiple AI-driven virtual characters in different scenes will also differ. For example, if the virtual building environment is set to a shopping mall, the distribution of personnel within it may be relatively dense in certain locations, but the activity level of the personnel may be relatively low. Conversely, if the virtual building environment is set to a stadium, the distribution of personnel within it may be relatively dispersed, but the activity level of the personnel may be relatively high. Because the scene types of the virtual building environment are set, virtual characters can perform activities corresponding to the characteristics of the scene type, thus increasing the realism of activities in the corresponding scenes and improving the diversity and realism of the acquired data.

[0118] In the above embodiment of the present invention, the amount of heat generated by a person was calculated from two aspects: static influencing elements and dynamic influencing elements. Simulated activity characteristics were obtained by collecting the actions and locations of personnel activities.

[0119] After obtaining the simulation activity characteristics of multiple virtual characters, in step 102, the building-related load is calculated based on the heat generated by at least multiple virtual characters.

[0120] For example, building-related loads are calculated based on equipment information, weather information, and heat generation in a virtual building environment. Alternatively, building-related loads are calculated based on equipment information, weather information, and heat generation and moisture release in a virtual building environment.

[0121] For example, equipment information, weather information, and heat and / or moisture emissions in a virtual building environment are input into algorithm-based software (e.g., a building load calculator) to calculate the loads related to the building. In some embodiments, the building-related loads include at least one of the following: the total building load (also called the building load), heat load, cold load, and wet load.

[0122] In some embodiments, the total load of a building (also called the building load) includes the building's thermal load, cold load, and wet load.

[0123] For example, the total load of a building (also called the building load) refers to the amount of heat and moisture that must be removed from (or replenished into) the building space by the heating, ventilation, and air conditioning system within a unit of time in order to maintain a temperature, humidity, and air quality environment that meets specific, comfortable, or process requirements within the building.

[0124] For example, the cooling load refers to the total amount of heat that needs to be removed from the indoor space per unit time in order to maintain the indoor temperature at a set value at a given time.

[0125] For example, heat load refers to the total amount of heat that needs to be supplied to the indoor space per unit time in order to maintain the indoor temperature at a set value at a given time.

[0126] For example, humidity load refers to the amount (or mass) of water vapor that needs to be removed from the indoor space per unit time in order to maintain the indoor humidity at a set value at a given time.

[0127] The following provides a illustrative explanation of how to calculate total load, thermal load, cold load, and wet load.

[0128] For example, the algorithm used is the steady-state load day method.

[0129] Here, the input parameter is the building area (m²). ２ ), surrounding protective structure heat transfer coefficient (W / m ２・K), indoor design temperature (°C), outdoor average temperature (°C), heat generation amount of a single virtual person (W), equipment power (W), lighting power (W), fresh air volume (m ３ / h), indoor air humidity and outdoor air humidity (g / kg).

[0130] The output parameters include heat load (kW), cooling load (kW), moisture load (kg / h), and annual heating / cooling total load (kWh).

[0131] First, the calculation of the heat load will be specifically described.

[0132] For example, the heat load can be calculated by the following formula.

[0133] Q 熱 = U·A·(T 室外 - T 室内 ) + Q 内部の熱 + Q 新気 Here, · indicates the multiplication symbol, U is the surrounding protection structure heat transfer coefficient (W / m ２ ·K), A is the building area or the area of a certain area within the building (m ２ ), Q 周囲保護 = U·A·(T 室内 - T 室外 ), Q 内部の熱 = the total heat generation amount of personnel, equipment, and lighting, fresh air load Q 新気 = V 新気 ·ρ·cP·(T 室外 - T 室内 ), V = building area * height (m), ρ is the air density (kg / m ３ ), and cp is the specific heat capacity (J / kg·K). In the embodiments of the present application, by simulating the crowd and the building environment, a numerical value closer to the actual Q 内部の熱 can be simulated.

[0134] For all people and equipment in the building, for example, the integration of the heat generation amount of the following formula is performed.

[0135] Q 内部の熱 = Q 人員発熱量 + Q 設備発熱量 + Q 照明発熱量 For example, an office has a building area of 50 m ２ , a heat transfer coefficient U = 0.5 W / m ２K, indoor design temperature 24°C, outdoor average temperature 30°C (actual outdoor temperature may also be acceptable), Q 人員発熱量 This is the heat generated by the personnel (for example, the area corresponding to the building area includes staff A with a heat generation of 95W, staff B with a heat generation of 100W, staff C with a heat generation of 105W, staff D with a heat generation of 110W, staff E with a heat generation of 120W, staff F with a heat generation of 95W, staff G with a heat generation of 100W, and staff H with a heat generation of 105W), Q 設備発熱量 This is the heat generated by the equipment (for example, the area corresponding to the building area includes equipment A with a heat generated of 150W, equipment B with a heat generated of 160W, equipment C with a heat generated of 160W, equipment D with a heat generated of 170W, equipment E with a heat generated of 180W, equipment F with a heat generated of 180W, equipment G with a heat generated of 180W, equipment H with a heat generated of 180W, and equipment I with a heat generated of 0W), Q 照明発熱量 This is the amount of heat generated by lighting (for example, an area corresponding to the building area includes 10 lighting fixtures, and the heat generated by a single fixture is 50W), and furthermore, the new air volume V 新気 = 0.5m ３ / H・m ２ That is the case.

[0136] In one example scenario, staff member A feels the ambient temperature is low and therefore turns off equipment I. Equipment I is affected by human action, and its power consumption at this time is 0W.

[0137] For example, regarding the calculation of the heat transfer load of the surrounding protective structure, Q 周囲保護 =U.A.(T 室外 -T 室内 ) = 0.5 * 50 * (30 - 24) = 150W, For example, regarding the calculation of the internal heat load: Q 内部 = Q 人 +Q 設備 +Q 照明 = (95 + 100 + 105 + 110 + 120 + 95 + 110 + 105) + (150 + 160 + 160 + 170 + 180 + 180 + 180 + 180 + 0) + (50 * 10) = 2700W. For example, regarding the calculation of fresh air load, total fresh air volume = 0.5 m³ ３ / h・m ２× 50m ２ = 25m ３ / h Unit conversion: 25m ３ / h=3600 / 25m ３ / s ≈ 0.00694 m ３ / s New air load calculation formula: Q 新気 = V 新気 ρ cp (T 室外 -T 室内 ) Substituting example values, Q 新気 = 0.00694 * 1.2 * 1005 * (30 - 24) ≈ 50W, For example, regarding the calculation of total heat load, Q 総 = Q 周囲保護 +Q 内部 +Q 新気 = 150 + 2700 + 50 = 2900W For example, air density ρ = 1.2 kg / m³ ３ Therefore, the specific heat capacity CP = 1005 J / KG·K.

[0138] Next, we will explain in detail how to calculate the wet load. For example, the wet load can be calculated according to the following formula.

[0139] W 湿 =N.W. 人員 +V 新気 ・(D 室外 -D 室内 Here, N is the number of personnel, W is the amount of moisture released per person (humidification rate) (G / H), and D is the humidity of the air (G / KG). This formula is based on the law of conservation of mass and is consistent with the method for calculating humidity load in the "Design Guidelines for Heating, Ventilation and Air Conditioning in Private Buildings" (GB50736-2012).

[0140] Next, we will explain in detail how to calculate the annual load. For example, the annual load can be calculated based on the following formula.

[0141] E 年間 =∫ 年間 (Q 熱負荷 +W 湿 )dt Here, dt is a correction factor that is dynamically adjusted according to climate data (e.g., degree days).

[0142] For example, the basic definition of degree days is the difference between the average outdoor temperature T on a given day and the heating standard temperature T (for example, 18°C). If T ≥ T, the "degree days" for that day are set to 0, and if T < T, then HDD = T - T (unit: °C・days).

[0143] For example, the degree-days metric evaluates "how cold it was outdoors during the day and how long the cold lasted," and a higher number indicates that more heat needs to be replenished that day.

[0144] Adding up all positive HDDs for a year yields "Annual Heating Degree Days ΣHDD," which is used as a macro-indicator to measure the degree of winter cold in a region.

[0145] For example, the simplified algorithm for building energy consumption (ASHRAE's degree-day method) assumes that the instantaneous heat load of a building ∝ (indoor set temperature - outdoor temperature).

[0146] Then, the annual cumulative heat consumption Q ∝ Σ HDD.

[0147] Specifically, the process begins by calculating the "maximum heat load Q on the design day" (corresponding to the non-design temperature T) using design software or specifications. Then, by expanding or contracting Q using ΣHDD as a "weighting coefficient," the cumulative heat consumption for the entire heating season is obtained.

[0148] Q ≈ Q × (ΣHDD) / (T - T) This way, even without performing hourly simulations, it is possible to reflect differences between different climate years and different regions.

[0149] Furthermore, dt is essentially a minimum period, and can be, for example, one hour or one day, depending on the resolution of the acquired weather data.

[0150] Furthermore, the statement that "the correction factor is dynamically adjusted based on climate data (e.g., degree days)" means that, when using a simplified method, it is not actually integrated, but rather the year is divided into several "time zones," and the value of dt is corrected as a "weight" using degree days (or degree hours CDH) for each time zone.

[0151] In some embodiments, the method may further include the step of calculating the load and / or energy consumption of equipment within a building based on building-related loads, the equipment including an air conditioning system.

[0152] For example, the total load of an air conditioning system typically consists of two parts: sensible heat load (corresponding to cooling / heating load) and latent heat load (primarily due to humidity load). Cooling / heating load focuses on temperature control and corresponds to the removal or replenishment of sensible heat. Humidity load focuses on humidity control and corresponds to the removal of water vapor (latent heat).

[0153] For example, the power consumption of an air conditioning system = building load = cooling / heating load + humidity load.

[0154] Calculating equipment energy consumption based on load can be applied to detailed simulations or high-precision scenarios and serves as the basis for equipment type selection and energy consumption simulation.

[0155] For specific calculation methods, please refer to the examples described later.

[0156] In some embodiments, the method may further include the step of calculating at least one of the following based on building-related loads: load in at least one area within a building, load in the building during a predetermined time period, and real-time load of the building.

[0157] In some embodiments, calculating the load of at least one area within a building based on building-related loads may include, for the building's thermal load, generally dividing the area by rooms or into smaller areas based on the air conditioning group in the building design (e.g., dividing one open office area into five smaller areas), and then calculating the load based on the data for each area. The calculated building load results represent data for each area in any time dimension (e.g., hourly, minutely, secondly) within the corresponding time period, and include personnel location, personnel activity, area thermal load, air conditioning energy consumption, equipment energy consumption, etc. Here, virtual persons in each area can be identified based on personnel location, and the area load can be calculated based on the heat generated by the virtual persons in the area.

[0158] In some embodiments, it is possible to set time periods during which the load needs to be calculated according to actual requirements, for example, to calculate the annual load on a building. For example, relevant data for AI NPCs in a virtual building environment, such as the number of people, gender ratio, age ratio, relationship network, and job title, is placed based on placement data, the simulation time is set to one year, multiple generated AI NPCs are placed in the virtual building environment constructed in S1, the AI ​​NPCs are made to perform autonomous activities, and the building information, environmental information, equipment information, and simulation activity features obtained using the autonomous activities of the AI ​​NPCs in the virtual building environment are input into a building load calculator to calculate and obtain the annual building load.

[0159] In some embodiments, the AI ​​NPC can autonomously operate within the virtual building environment information by driving each module of the AI ​​NPC, thereby generating simulation activity features (e.g., AI NPC behavior, location, heat generation, etc.) in real time. Furthermore, during the AI ​​NPC's autonomous activity period, changes in the AI ​​NPC's state, such as changes in emotion or fatigue level, can be monitored in real time. Based on the real-time simulation activity features and real-time state changes of multiple AI NPCs, the real-time load on the building can be calculated.

[0160] For specific calculation methods of area load, load during a predetermined time period, and real-time load, please refer to the examples described later.

[0161] In some embodiments, the following applications can also be made based on the building-related loads.

[0162] (1) The selection of ventilation and air conditioning system types includes the following:

[0163] Fresh air volume calculation (ASHRAE 62.1, GB 50736): Personnel density directly determines the minimum fresh air volume, but body heat dissipation (sensible heat + latent heat) affects the total cooling load and further affects the unit type selection (e.g., fan coil, AHU capacity). Overall airflow of the air conditioning system: When a variable airflow (VAV) system is adopted, the airflow must be adjusted according to the dynamic load of personnel, and body heat is a core variable.

[0164] (2) Energy consumption simulations and carbon emission assessments include the following: Annual dynamic energy consumption model (ENERGYPLUS, DESIGNBUILDER): The amount of heat generated by the human body directly affects the annual distribution of cooling and heating loads as one of the factors influencing internal heat over time, and furthermore - HVAC system (heating, ventilation and air conditioning system) energy consumption (kWh / M ２ A) - Carbon emission factors (e.g., KG CO2) especially in high-density spaces (e.g., theaters, gymnasiums) ２ / M ２ It will affect A).

[0165] (3) Thermal comfort and IEQ (Indoor Environmental Quality) analysis include the following:

[0166] PMV-PPD index (ISO 7730): The human metabolic rate (MET value) is the core parameter for calculating PMV, and the output data includes the thermal comfort achievement rate (e.g., the percentage of spaces with PPD < 10%). Local discomfort analysis: For example, in high-temperature environments in summer, radiation may become asymmetrical due to insufficient heat dissipation by the human body (e.g., when close to a chilled beam).

[0167] (4) Urban microclimate / outdoor heat island simulations include the following:

[0168] In models such as ENVI-MET, CFD-URBAN, and SOLWEIG, pedestrian heat dissipation is a local heat source term (W / m²). ２ ) can be set to output indicators such as air temperature at pedestrian height, PET, and UTCI.

[0169] (5) A design laboratory or cleanroom includes the following:

[0170] In ISO 14644 and GMP-certified pharmaceutical plants, heat dissipation from personnel serves a dual role as both an internal heat source and a source of contamination, and therefore needs to be included in the calculation of load and ventilation rate.

[0171] The simulation activity characteristics obtained by the method in the above embodiment are closer to the crowd activity characteristics in real-world scenes. Thus, by calculating building-related loads based on the characteristics of simulation activities performed by multiple virtual individuals simulating a crowd inside a building in a virtual building environment, the cost and risk of data collection can be reduced, and the calculation steps can be optimized and operations simplified by automatically setting parameters and acquiring data. Therefore, it is possible to simulate crowd activity characteristics that are more truthful and accurate than pre-set fixed values, i.e., simulated crowd activity characteristics, using a simple and efficient method. Furthermore, when simulation activity characteristics are used to calculate building-related loads, the accuracy of the calculation of building-related loads can be improved.

[0172] The following describes, with reference to specific examples, a method and steps for simulating crowd activity characteristics using multiple virtual individuals and calculating building-related loads.

[0173] <Example 1> In Example 1, AI NPCs, which are multiple virtual characters driven by AI, are made to simulate personnel activities in a virtual building environment, and building loads are calculated based on the simulation activity characteristics obtained by this method.

[0174] Figure 3 is a flowchart for acquiring simulation activity characteristics and calculating building load according to an embodiment of the present invention, and as shown in Figure 3, the simulation method includes: step S1, constructing a virtual building environment based on building information and environmental information; step S2, arranging AI NPC-related data in the virtual building environment based on placement data, such as the number of people, gender ratio, age ratio, relationship network, job title, etc., for example, the crowd activity characteristics simulation device 1300, described later, receives placement data input by the operator and arranges the AI ​​NPC-related data in the virtual building environment; step S3, generating multiple individualized AI NPCs based on the related data in S2, for example, the object generation module 1301 in the crowd activity characteristics simulation device 1300, described later, generates multiple AI NPCs based on placement data; step S4, placing the generated multiple AI NPCs in the virtual building environment constructed in S1 (for example, the generated multiple AI NPCs are input into the virtual building environment constructed in S1), and causing the AI ​​NPCs to perform autonomous activities. Step S5 includes inputting building information, environmental information, equipment information, and simulation activity characteristics obtained using the autonomous activities of AI NPCs in a virtual building environment into a building load calculator and calculating the load, and step S6 includes the building load calculator outputting the calculated building load.

[0175] In the above embodiment, the simulation time length can also be set (for example, the crowd activity feature simulation device 1300, described later, sets the simulation time length by receiving time length data input by the operator). For example, the simulation time length may be one day, one week, or one year, and this simulation time length indicates the length of time that the crowd is active in the virtual building environment. Depending on the differences in the simulation time length and the input weather information, environmental information, etc., simulation activity features can be obtained in different building scenes, different seasons, and different time ranges, and the corresponding building load can be calculated.

[0176] In the above embodiment, the autonomous activities of the AI ​​NPC in the virtual building environment are driven by AI, and these autonomous activities can include the AI ​​NPC's personal activities such as movement, lunch breaks, and meals, interactions between AI NPCs, and interactions between AI NPCs and equipment placed in the building environment. This generates simulation activity features of the AI ​​NPC (e.g., behavior, location, heat generation) in real time, and at the same time, during the interaction between the AI ​​NPC and the equipment, it also affects the equipment state in the environment, for example, by performing operations such as turning lights on and off or adjusting air conditioners, thereby generating simulation operation data for the equipment. For example, the simulation activity features obtained in different building scenes (e.g., schools, office buildings, etc.) and different seasons (e.g., summer, winter) will all be different.

[0177] In the above embodiment, the building information, environmental information, and AI NPC placement data of the virtual building environment can be pre-configured as needed.

[0178] By using the above method, it is possible to obtain simulated crowd activity characteristics by autonomously arranging relevant information for the virtual building environment and relevant information for the AI ​​NPC. This allows users to perform simulations in different virtual building environments that are more similar to the actual environment as needed, resulting in greater diversity in the virtual building environments used for simulations, and since the autonomous activities of the AI ​​NPCs are more similar to those of actual humans, the diversity and truthfulness of the collected simulation activity characteristics can be improved.

[0179] The following describes in detail specific examples of each of the above steps S1 to S6.

[0180] Figure 4 is a schematic diagram of a specific embodiment of S1 in the method shown in Figure 3. As shown in Figure 4, a specific embodiment of S1 includes: step S11 in which a system program for project modeling is started and a new project is created or opened in the system program; step S12 in which an architectural model is introduced into the system program, the system program reads the architectural model information and the architectural model information can be displayed on a display screen controlled by the system program; step S13 in which data is extracted, for example, the extracted data includes at least one of the following: architectural scene type, architectural heat transfer coefficient, number of users; and step S14 in which each area of ​​the architectural model is marked based on the extracted data, for example, the marks may be name marks, equipment attribution marks, etc.

[0181] In the above embodiment, the system program for project modeling may be provided in the crowd activity feature simulation device 1300, which will be described later. For example, the system program for project modeling may be executed in the crowd activity feature simulation device 1300.

[0182] In the above embodiment, the model introduced in S12 may be a BIM (Building Information Modeling) model or a regular building model, and the system program can automatically read the model information and display the building model in the system program.

[0183] In some possible embodiments, a virtual building environment may be constructed using architectural drawings (e.g., CAD drawings) as architectural information, and this application is not limited thereto.

[0184] In some embodiments, the information placed in the virtual building environment includes various types of data that need to be considered when calculating building loads. These types of data include building structure, materials, size, heat transfer coefficient of the building structure, building scene type (e.g., office building, hospital, factory), number of people using the building, building usage time (e.g., the time of the occupant's daily rhythm can be considered the building usage time), geographical location of the building (e.g., latitude and longitude), and indoor equipment information. The indoor equipment information includes equipment identification information (equipment type, equipment manufacturing date, equipment power, etc.) and equipment operation data (on / off status, operating power, set temperature, set wind speed, set airflow, set wind direction, set brightness, etc.). In addition, environmental information such as outdoor temperature, outdoor humidity, outdoor UV intensity, indoor temperature, indoor air humidity, and weather data for the geographical location (e.g., real-time data, future forecast data, and historical data) can be selected, either automatically or manually.

[0185] In the above embodiment, if the introduced model is a BIM model, data that needs to be considered when calculating the building load can be automatically extracted, such as building structure, materials, size, building structure heat transfer coefficient, building scene type, number of occupants, usage time, geographical location of the building, indoor equipment information, and weather information.

[0186] For standard building models, data that needs to be considered when calculating building loads may be entered into the system program using different methods, such as a placement interface, a table, or a document format. Furthermore, other parameters that cannot be automatically extracted from the BIM model may be introduced in the form of a placement interface, a table, or a document.

[0187] For example, weather information generally includes hourly annual average temperature, weather, humidity, atmospheric pressure, precipitation, etc., at the geographical location of a building, and may also include, but is not limited to, radiant temperature, indoor temperature and humidity, particulate matter concentration, pollutant concentration, etc.

[0188] Because this information can change dynamically in real-world scenes, it can also change dynamically in virtual architectural environments.

[0189] In the above embodiment, the AI ​​NPC can sense environmental changes in real time and adjust its activity state in real time according to the environment. Therefore, the more detailed the building information, environmental information, and weather information provided when constructing the virtual building environment, the more similar the dynamic changes between the virtual building environment and the real-world scene will be, meaning that the accuracy of the simulation activity feature data collected based on the environment will also be higher.

[0190] Figure 5 is a schematic diagram of a specific embodiment of S2 in the method shown in Figure 3. As shown in Figure 5, a specific embodiment of S2 includes, in a system program for project modeling, step S21 of receiving placement data input by the operator and placing the relevant AI NPC data in the virtual building environment using a crowd activity feature simulation device 1300, which will be described later, and step S22 of setting the simulation time and simulation speed.

[0191] In the above embodiment, the input placement data is pre-set based on the building scene type, and the placement data includes the number of people, gender ratio, age range, relationship network (closeness, organizational architecture relationship, project relationship, etc.), and job title (title, personnel ratio).

[0192] Among the placement data entered by the operator, the number of people is essential information, and the more completely all other information is filled in, the more accurate the data obtained through simulation will be. The placement data may be automatically placed by the system, for example, based on default values.

[0193] Furthermore, in the above embodiment, the simulation duration and simulation speed can be set by receiving duration data and speed data input by the operator using the crowd activity characteristics simulation device 1300, which will be described later. For example, the set simulation duration may include one day, one week, one year, etc., or the simulation may be freely performed without limiting the duration, and may also be stopped manually. The set simulation speed may include a 5x simulation speed, a 10x speed, or a freely performed simulation.

[0194] By setting the simulation duration, it is possible to acquire crowd activity data within different time ranges, such as one month, one quarter, or one year. By setting the simulation speed, it is possible to acquire data within a long simulation duration in a short amount of real time. For example, if a 30-day activity simulation is performed at a 10x speed, data can be acquired in just three days of real time.

[0195] Figure 6 is a schematic diagram of a specific embodiment of S3 in the method shown in Figure 3. As shown in Figure 6, a specific embodiment of S3 includes the steps of establishing a general-purpose AI NPC model in step S31, and the general-purpose AI NPC model receiving AI NPC placement data input by an operator and generating a plurality of AI NPCs based on the placement data in step S32.

[0196] In the above embodiment, the general-purpose AI NPC model refers to the general-purpose configuration structure of an AI NPC, and the object generation module 1301 can generate AI NPCs in bulk based on the general-purpose AI NPC model. Similar to a copy-and-paste method, the general-purpose AI NPC is copied and pasted to multiple AI NPCs, and although the basic configuration modules of the multiple AI NPCs are the same, the placement data of each differs, thereby forming diversified virtual humans.

[0197] Figure 7 is a schematic diagram of the configuration of the general-purpose AI NPC model 700. As shown in Figure 7, the general-purpose AI NPC model provided by the embodiment of the present invention has six modules: a motion module 701, a state monitoring module 702, a heat generation calculation module 703, a personal information module 704, a social interaction module 705, and an environment sensing module 706. In the above embodiment, the present invention provides the above configuration as just one example of the general-purpose AI NPC model 700, and in actual operation, the modules in the general-purpose AI NPC model 700 may be increased or decreased as needed, and the present invention is not limited thereto.

[0198] It can be understood that multiple AI NPCs generated based on the general-purpose AI NPC model 700 may have one or more of the same modules as the general-purpose AI NPC model, namely, a motion module, a state monitoring module, a heat generation calculation module, a personal information module, a social interaction module, and an environmental sensing module.

[0199] In the above embodiment, the exercise module 701 can control the AI ​​NPC to perform exercises (e.g., sitting, standing, walking, running, etc.) in the virtual building environment; the state monitoring module 702 monitors the state data of the AI ​​NPC (e.g., emotions, state, fatigue level, health level, etc.) in real time; the heat generation calculation module 703 can calculate the basal metabolic rate and heat generation of the corresponding AI NPC; the personal information module 704 is used to store and display basic information that the AI ​​NPC has when it is created, such as gender, age, job title, etc.; the social interaction module 705 can control the interaction between AI NPCs; and the environment sensing module 706 controls the interaction between the AI ​​NPC and the virtual building environment, such as physical collisions and changes in equipment status.

[0200] In the above embodiment, the heat generation calculation module 703 calculates the heat generation amount corresponding to each different AI NPC. For the specific calculation method, please refer to the relevant part of the above embodiment, and a detailed explanation will be omitted here. Figure 8 is a schematic diagram of a specific embodiment of S4 in the method shown in Figure 3, and as shown in Figure 8, the specific embodiment of S4 includes: step S41 in which a plurality of generated AI NPCs are input into the virtual building environment and the plurality of AI NPCs perform autonomous activities according to the placement data set at the time of their generation; step S42 in which the state monitoring module 702 monitors the state changes of the AI ​​NPCs according to the different activities of the AI ​​NPCs; and step S43 in which the environment sensing module 706 acquires data changes that occur in the environment information when the AI ​​NPC interacts with the virtual building environment.

[0201] In the above embodiment, the AI ​​NPC can autonomously operate within the virtual building environment information by driving each module of the AI ​​NPC, thereby generating simulation activity features (e.g., AI NPC behavior, position, heat generation, etc.) in real time. At the same time, the AI ​​NPC can interact with the virtual building environment and control equipment (e.g., air conditioners, ventilation, lamps, doors, windows, etc.).

[0202] In the above embodiment, if the virtual building environment is set to an office and the AI ​​NPC's role is set to programmer, the AI ​​NPC will act according to the general lifestyle and work habits of a group of programmers. If the AI ​​NPC's role is set to a personnel administrator, the AI ​​NPC will perform activity simulations according to the lifestyle and behavioral habits of an administrator.

[0203] Furthermore, the intimacy relationships, project collaboration relationships, and organizational architecture relationships of multiple virtual characters influence the interactive behavior between multiple virtual characters in the virtual environment. In the same virtual building environment, a social interaction module may be triggered when different AI NPCs meet at close range, potentially interrupting their current activities and allowing for temporary interaction. For example, when two virtual characters with a high level of intimacy meet in the virtual environment, interaction behaviors such as conversation or dating may be triggered. Similarly, two virtual characters with a project collaboration relationship may engage in interaction behaviors such as meetings or discussions. Different interaction behaviors also influence the virtual characters' movement patterns, the areas they occupy in the virtual environment, and their states (e.g., emotions, fatigue levels).

[0204] During the AI ​​NPC's autonomous activities, the state monitoring module can also monitor changes in the AI ​​NPC's state in real time, such as changes in emotion or fatigue level. Different emotional, fatigue, and health states affect the AI ​​NPC's movement position and activity intensity, which in turn affects its heat output. For example, when the AI ​​NPC is focused on work, its metabolic rate increases, leading to higher heat output, while increased fatigue levels result in a decrease in metabolic rate, leading to lower heat output. In short, changes in the AI ​​NPC's state alter its metabolic rate, which in turn alters its heat output.

[0205] In some embodiments, changes in environmental information due to interaction between the AI ​​NPC and the environment also affect the amount of heat generated by the AI ​​NPC. For example, when the AI ​​NPC leaves the station, the computer and display may enter sleep mode, which reduces the amount of heat generated by the equipment. When the AI ​​NPC goes to get water, the water dispenser enters operation mode, and after the amount of heat generated by the AI ​​NPC increases, actions to change the environment, such as lowering the temperature of the air conditioner or opening a window, may be taken. Under different environmental conditions, the amount of heat generated by the AI ​​NPC will also differ, and the amount of heat generated by different equipment under different conditions will also change, which affects the load on the building.

[0206] In some embodiments, the crowd activity feature simulation device 1300, described later, may further include a visualization module (not shown) for displaying information related to the virtual building environment and AI NPCs. For example, the visualization module may display information such as AI NPC simulation activity features (location, behavior, heat output), state data (emotions, fatigue levels, etc.), equipment energy consumption, equipment status (on / off status, set temperature, airflow rate, etc.), space usage (vacant, occupied), heat change processes in each area, environmental data, and weather data.

[0207] Figure 9 is a schematic diagram of a specific embodiment of S5 in the method shown in Figure 3. As shown in Figure 9, a specific embodiment of S5 includes the following steps: the AI ​​NPC simulation activity is stopped, the crowd activity feature simulation device 1300 (described later) extracts data necessary for calculating building load in step S51, and the crowd activity feature simulation device 1300 inputs the extracted data into a building load calculator and performs calculation in step S52.

[0208] In the above embodiment, the AI ​​NPC simulation activity may be stopped manually by the operator, or it may be stopped automatically when the simulation time set in advance by the operator is reached.

[0209] At this time, the crowd activity characteristics simulation device 1300, which will be described later, can extract data necessary for calculating building load from the current virtual building environment, such as environmental information, weather information, equipment information, and building information. Here, it may further include equipment status change data extracted during the activity simulation, such as the on / off status, power changes, and energy consumption status of various electrical equipment such as air conditioners and lamps, as well as the activity status of occupants indicated by the AI ​​NPC.

[0210] Furthermore, the building-related load calculation device 1400, described later, may also be calculated by inputting the data extracted above into a building load calculator.

[0211] In the above embodiment, the building load calculator may be provided in the building-related load calculation device 1400, which will be described later. For example, the building load calculator may be executed in the building load calculation device 1400.

[0212] In the above embodiment, the calculation method used in the building load calculator can be based on prior art in this field, and therefore a detailed explanation is omitted here.

[0213] The following explains how to calculate the heat load, air conditioning energy consumption, and equipment energy consumption of the building environment.

[0214] 1. Regarding the thermal load of a building, generally, the area is divided by rooms, or by sub-areas based on the air conditioning group in the building design (for example, dividing one open office area into five sub-areas), and then the load is calculated based on the data for each area, and the load calculation results for each area are added together to obtain the total building load.

[0215] 2. Calculation logic for air conditioner energy consumption After obtaining the building's heat load using the method described above, the air conditioners in the corresponding areas need to cool (or heat) these heat loads. Generally, the amount of heat load to be handled and the amount of cooling provided by the air conditioner must be equal, so the following logic can be obtained.

[0216] Qt = Q1 + Q2 + Q3, where Qt represents the total cooling amount (kW), Q1 represents the indoor equipment load, Q2 represents the environmental heat load, and Q3 represents the human heat dissipation load, of which Q3 can be calculated using the heat generation calculation module of the above embodiment.

[0217] After obtaining the cooling capacity, the energy consumption of the air conditioner is calculated.

[0218] The formula for calculating the power consumption of an air conditioner is: Power Consumption = Cooling Amount * Operating Time / 1000.

[0219] 3. Equipment Energy Consumption: In the embodiment of this invention, the virtual building environment is obtained using a BIM model or a conventional building model. Therefore, the power of the equipment within it can be obtained from the BIM model and manually placed equipment information. Then, the energy consumption of the equipment can be calculated from the environmental information changes obtained by the AI ​​NPC influencing the environment, and the operating time of the equipment. Here, equipment energy consumption = equipment power * operating time.

[0220] From the building load obtained using the above calculation logic, power consumption and equipment energy consumption can be calculated further according to the same calculation logic.

[0221] In some embodiments, the building load may further include a humidity load in addition to the cold (heat) load. The cold (heat) load corresponds to the amount of heat generated by a person, and the humidity load corresponds to the amount of moisture released by a person. Similarly, to calculate the humidity load, the AI ​​NPC may simulate the amount of moisture released by a person. For example, as shown in Figure 7, the general-purpose AI NPC model 700 is further provided with a humidity release calculation module 707 for acquiring activity-related data of the AI ​​NPC in a virtual building environment and for calculating the amount of moisture released by the AI ​​NPC according to the calculation logic described above.

[0222] In some embodiments, the building load calculator may further output at least one of the building's cold load, building's heat load, building's wet load, equipment energy consumption, and power consumption, in accordance with the operator's actual requirements, and this application is not limited thereto.

[0223] In the above embodiment, the fresh air system can adjust the humidity in the room, and in application scenarios where it is necessary to calculate the humidity load, the data from the fresh air system may be used for calculation.

[0224] Figure 10 is a schematic diagram of a specific embodiment of S6 in the method shown in Figure 3. As shown in Figure 10, the specific embodiment of S6 includes step S61 in which, after the calculation is completed, the building load calculator outputs the load data status of the virtual building environment.

[0225] In the above embodiment, the calculated building load results represent data for each area within the corresponding time period in any time dimension (e.g., hourly, minutely, secondly), and include personnel location, personnel activity, area heat load, air conditioning energy consumption status, equipment energy consumption status, etc. In other words, the output result data includes the hourly or minutely location coordinates of personnel in each area of ​​the virtual building environment, personnel activity, heat load of different areas, air conditioning energy consumption status, and equipment energy consumption status, etc.

[0226] In some embodiments, the method for calculating building loads can be selected as needed. Specific calculation methods are described in reference to prior art and are omitted here for further explanation.

[0227] In some embodiments, the output results may be displayed as needed, for example, by displaying only the calculation results for the selected area or only the calculation results within the selected time range.

[0228] According to the embodiment of the present invention, by calculating building-related loads based on the characteristics of simulation activities performed in a virtual building environment by multiple virtual individuals simulating a crowd inside a building, the cost and risk of data collection can be reduced, and the calculation steps can be optimized and operations simplified by automatically setting parameters and acquiring data. As a result, crowd activity characteristics that are more true and accurate than pre-set fixed values ​​can be simulated in a simple and efficient manner. Furthermore, when simulation activity characteristics are used to calculate building-related loads, the accuracy of the calculation of building-related loads can be improved.

[0229] <Example 2> In Example 2, the simulation time was set to one year to obtain the annual building load and energy consumption.

[0230] Figure 11 is another flowchart that acquires simulation activity characteristics and calculates building loads according to an embodiment of the present invention. As shown in Figure 11, the method includes: step S10, constructing a virtual building environment based on building information and environmental information; step S20, placing AI NPC-related data in the virtual building environment based on placement data, such as the number of people, gender ratio, age ratio, relationship network, job title, etc., and setting the simulation time to one year, for example, the crowd activity feature simulation device 1300, described later, receives placement data input by the operator and places the AI ​​NPC-related data in the virtual building environment; step S30, based on the related data in S20, generating multiple individualized AI NPCs using the object generation module 1301 in the crowd activity feature simulation device 1300, described later; step S40, placing the generated multiple AI NPCs in the virtual building environment constructed in S1 (for example, the generated multiple AI NPCs are input into the virtual building environment constructed in S10), and allowing the AI ​​NPCs to perform autonomous activities; and building information, environmental information, equipment information, and AI in the virtual building environment. The process includes step S50, in which simulation activity characteristics obtained using the autonomous activities of NPCs are input into a building load calculator for calculation, and step S60, in which the building load calculator outputs the calculated annual building load.

[0231] In the above embodiment, the hourly building load throughout the year can be accumulated, and the annual building load can be obtained. Application scenarios for the annual building load include energy planning, building design and optimization, and HVAC system operation management. Conventional calculations of the annual building load generally use fixed values ​​for human activity characteristics, which is not very accurate and therefore deviates from the actual annual building load. The technical effect of this embodiment is that it calculates the annual building load based on more accurate crowd activity characteristics, resulting in more accurate results.

[0232] <Example 3> In Example 3, by inputting actual environmental information, the building load in the actual environment is simulated and calculated in a virtual building environment.

[0233] Figure 12 is another flowchart that acquires simulation activity characteristics and calculates building loads according to an embodiment of the present invention. As shown in Figure 12, the method comprises the steps of: constructing a virtual building environment based on building information and environmental information of a real scene in step S100; placing relevant data for AI NPCs in the virtual building environment, such as the number of people, gender ratio, age ratio, relationship network, and job title, based on placement data in the real scene, and setting the simulation time to predict only one hour, one day, or one week in prediction mode, for example, the crowd activity feature simulation device 1300, described later, receives placement data input by the operator and places relevant data for AI NPCs in the virtual building environment in step S200; generating multiple individualized AI NPCs using the object generation module 1301 in the crowd activity feature simulation device 1300, described later, based on the relevant data in S200; and placing the generated multiple AI NPCs in the virtual building environment constructed in S1 (for example, the generated multiple AI NPCs are input into the virtual building environment constructed in S100), and AI The process includes: step S400, which involves having the NPC perform autonomous activities; step S500, which involves inputting the building information, environmental information, equipment information, and simulation activity characteristics obtained using the autonomous activities of the AI ​​NPC in the virtual building environment into a building load calculator and calculating the load; and step S600, which involves the building load calculator outputting the building load within the predicted time range.

[0234] In this embodiment, by constructing a virtual building environment based on information from a real-world scene, crowd activity characteristics in the real-world scene can be obtained as basic parameters for building load prediction. This makes it possible to accurately obtain building loads within a certain time period in the future, and can be used in scenes that require high accuracy for building loads and building energy consumption, such as power grid demand response.

[0235] Furthermore, in the above embodiment, if the method is used to predict air conditioner energy consumption, the calculation module 1402 in the building-related load calculation device 1400, described later, may subsequently perform the steps of obtaining the cooling amount based on the building load and calculating the air conditioner energy consumption in conjunction with the air conditioner model number in step S700, and the calculation module 1402 outputting the predicted air conditioner energy consumption status in step S800.

[0236] In the above embodiment, the calculation module 1402 in the building-related load calculation device 1400, which will be described later, may further perform calculations of equipment energy consumption and power consumption.

[0237] In the above embodiment, when set to prediction mode, the method can be used to predict equipment energy consumption, for example, the energy consumption of the air conditioner. Specifically, in prediction mode, a virtual building environment can be constructed based on the building information and environmental information of the actual scene, and the energy consumption of the air conditioner can be predicted in combination with the equipment energy consumption calculation logic described in Embodiment 1 based on the actual model number of the air conditioner. In the above embodiment, the actual model number and quantity of the air conditioner are included in the building information and environmental information of the actual scene.

[0238] As can be seen from the above embodiments, the method provided by the embodiments of the present invention can further be used to predict building loads and air conditioning energy consumption, and building loads and air conditioning energy consumption within different time ranges can be obtained by changing the set mode and simulation time length. This makes it possible to make building design in real-world scenarios more rational and energy-efficient.

[0239] The above describes only the steps or procedures relevant to the present application, but the application is not limited thereto. The method may further include other steps or procedures, and the specific details of these steps or procedures can be found in the prior art.

[0240] The above embodiments are merely illustrative examples of the embodiments of the present application, and the present application is not limited thereto. Furthermore, appropriate modifications may be made based on the above embodiments. For example, each of the above embodiments may be used individually, or one or more of the above embodiments may be combined.

[0241] The simulation method for activity features provided by the above embodiment of this application, and the method for calculating building-related loads based on the simulation activity features obtained by the simulation method, allow an AI NPC to perform autonomous activities in a virtual building environment, and the simulation activity features obtained thereby are closer to the simulation activity features in a real-world scene. Therefore, it is possible to simulate crowd activity features that are more true and accurate than pre-set fixed values, i.e., simulated crowd activity features, using a simple and efficient method, thereby reducing the cost and risk of data collection, optimizing the calculation step and simplifying operations by automatically setting parameters and acquiring data, and further improving the accuracy of the building-related load calculated based on the simulation activity features obtained by this method.

[0242] <Examples relating to the second embodiment> The embodiment relating to the second embodiment relates to a crowd activity feature simulation device corresponding to step 101 in the method relating to the first embodiment. Figure 13 is a schematic diagram of a crowd activity feature simulation device according to an embodiment of the present invention. As shown in Figure 13, the device 1300 includes an object generation module 1301 that generates a plurality of virtual people driven by artificial intelligence, and a simulation activity feature generation module 1302 that acquires simulation activity features of a plurality of virtual people through simulation activities performed by the plurality of virtual people in a virtual building environment.

[0243] For an explanation of controlling each module in the human crowd activity characteristics simulation device 1300, refer to the explanation of the relevant steps in the embodiment according to the first aspect.

[0244] Although only the components or modules related to this application have been described above, this application is not limited thereto. The above-mentioned device may further include other components or modules, and the specific details of these components or modules can be found by referring to related technologies.

[0245] For simplicity, Figure 13 only illustrates the connection relationships or signal flows between each component or module; however, it will be clear to those skilled in the art that various related technologies, such as bus connections, can be employed. Each of the above components or modules can be implemented by hardware such as a processor or memory, for example. This application is not limited to these.

[0246] The above embodiments are merely illustrative examples of the embodiments of the present application, and the present application is not limited thereto. Furthermore, appropriate modifications may be made based on the above embodiments. For example, each of the above embodiments may be used individually, or one or more of the above embodiments may be combined.

[0247] <Example relating to the third embodiment> The embodiment relating to the third embodiment relates to a building-related load calculation device corresponding to the method relating to the first embodiment. Figure 14 is a schematic diagram of a building-related load calculation device according to an embodiment of the present invention. As shown in Figure 14, the device 1400 includes an acquisition module 1401 which acquires simulation activity features of a plurality of virtual people by simulation activities performed by a plurality of virtual people simulating a crowd inside a building in a virtual building environment simulating the environment inside the building, wherein the simulation activity features include the amount of heat generated by the plurality of virtual people, the plurality of virtual people are used to simulate a crowd inside a building, and the virtual building environment is used to simulate the environment inside the building, and a calculation module 1402 which calculates a building-related load based on the amount of heat generated by at least a plurality of virtual people.

[0248] For an explanation regarding the control of each module in the building-related load calculation device 1400, refer to the explanation of the relevant steps in the embodiment according to the first aspect.

[0249] Figure 15 is a flowchart for calculating building loads according to an embodiment of the present invention, and corresponds to the flowchart in Figure 3. As shown in Figure 15, the flow for calculating building load is as follows: Step S1: Start a crowd activity characteristics simulation device, introduce a building model, and the operator automatically or manually places building information and environmental information, wherein the building information includes a building model which is a BIM model or a normal building model, and parameters such as the heat transfer coefficient of the building, and the environmental information includes weather information and the status of equipment inside the building (e.g., status of doors, windows, lamps, air conditioners), etc. Step S2: The operator places relevant data of virtual people in the virtual building environment, such as the number of people, gender ratio, age ratio, relationship network, job title, etc., into the building-related load calculation device. Step S3: Based on the relevant data in S2, generate multiple individualized virtual people using an object generation module, that is, input parameters such as the number of people, gender ratio, age ratio, relationship network, job title, etc., into the object generation module to generate multiple virtual people, wherein the virtual people include a motion module, a state monitoring module, a social interaction module, a personal information module, an environmental sensing module, and a heat generation amount calculation module. Step S4 includes: placing multiple virtual characters generated in S3 into a virtual building environment constructed in S1, allowing them to act autonomously, for example, by having them perform personal activities of AI NPCs such as moving, taking lunch breaks, and eating, interacting with other virtual characters, and interacting with equipment placed in the virtual building environment, thereby generating simulation activity features (e.g., actions, location, heat generation, etc.) in real time, and simultaneously having them interact with the virtual building environment during the interaction between virtual characters and equipment, for example, by performing operations such as changing the status of equipment in the environment, turning lamps on / off, and adjusting and controlling air conditioners; inputting building information, environmental information, equipment information, and acquired simulation activity features in the virtual building environment into a calculation module in a building-related load calculation device and calculating; and the calculation module outputting the calculated building load in Step S6.

[0250] According to the embodiment of the present invention, by calculating building-related loads based on the characteristics of simulation activities performed in a virtual building environment by multiple virtual individuals simulating a crowd inside a building, the cost and risk of data collection can be reduced, and the calculation steps can be optimized and operations simplified by automatically setting parameters and acquiring data. As a result, crowd activity characteristics that are more true and accurate than pre-set fixed values ​​can be simulated in a simple and efficient manner. Furthermore, when simulation activity characteristics are used to calculate building-related loads, the accuracy of the calculation of building-related loads can be improved.

[0251] Although only the components or modules related to this application have been described above, this application is not limited thereto. The above-mentioned device may further include other components or modules, and the specific details of these components or modules can be found by referring to related technologies.

[0252] For simplicity, Figure 15 only shows illustrative examples of the connection relationships or signal flows between each component or module; however, it will be clear to those skilled in the art that various related technologies, such as bus connections, can be employed. Each of the above components or modules can be implemented by hardware such as a processor or memory, for example. This application is not limited thereto.

[0253] The above embodiments are merely illustrative examples of the embodiments of the present application, and the present application is not limited thereto. Furthermore, appropriate modifications may be made based on the above embodiments. For example, each of the above embodiments may be used individually, or one or more of the above embodiments may be combined.

[0254] Although the present application has been described above with reference to specific embodiments, it will be clear to those skilled in the art that these descriptions are illustrative and do not limit the scope of the claims of this application. Those skilled in the art can make various modifications and alterations to the present application based on the principles of this application, and these modifications and alterations also fall within the scope of this application.

[0255] CN106524295ACN116341246A

Claims

1. A method for calculating building-related loads, comprising: a step of obtaining simulation activity characteristics of a plurality of virtual people by performing simulation activities in a virtual building environment that simulates the environment inside the building, wherein the simulation activity characteristics include the amount of heat generated by the plurality of virtual people; and a step of calculating the building-related loads based on at least the amount of heat generated by the plurality of virtual people.

2. The method for calculating building-related loads according to claim 1, wherein the step of obtaining simulation activity characteristics of a plurality of virtual people by having them perform simulation activities in a virtual building environment that simulates the environment inside the building, the step of calculating the amount of heat generated by the basal metabolism of the plurality of virtual people based on the static influence elements of the plurality of virtual people; the step of calculating the dynamic amount of heat generated by the plurality of virtual people based on the dynamic influence elements related to the simulation activities of the plurality of virtual people; and the step of calculating the amount of heat generated by the plurality of virtual people based on the amount of heat generated by the basal metabolism and the dynamic amount of heat generated.

3. The method for calculating building-related loads according to claim 2, wherein the static influencing element includes at least one of the gender, age, height, and weight of the plurality of virtual persons, and / or the dynamic influencing element includes at least one of the physical activity level, environmental data, state data, and lifestyle rhythm data of the plurality of virtual persons.

4. The method for calculating building-related loads according to claim 2, wherein the dynamic influencing elements are automatically acquired in real time during the simulation activities of the plurality of virtual characters.

5. A method for calculating building-related loads according to claim 3, further comprising the step of obtaining state data of a plurality of virtual persons, wherein the state data is identified based on at least one of fatigue level, health level, comfort level, and emotion, and the health level is related to a disease that may affect a person's heat output.

6. The method for calculating building-related loads according to claim 3, wherein the physical activity level affects the amount of heat generated by the energy consumed by physical activity, the environmental data affects the amount of heat generated by changing body temperature, the state data affects the amount of heat generated by affecting metabolic efficiency, and the lifestyle rhythm data affects the amount of heat generated by adjusting and controlling metabolic rhythms by the biological clock.

7. The method for calculating building-related loads according to claim 1, wherein the simulation activity features further include the actions and / or locations of the plurality of virtual persons.

8. The method for calculating building-related loads according to claim 1, wherein the plurality of virtual characters are generated by a virtual character generation means based on a general-purpose virtual character model and placement data corresponding to the plurality of virtual characters, and the plurality of virtual characters are virtual characters driven by artificial intelligence.

9. The method for calculating building-related loads according to claim 8, wherein the placement data includes at least one of the number of people, gender ratio, age distribution, position ratio, and relationship network, and the relationship network includes at least one of the closeness relationship, project cooperation relationship, and organizational architecture relationship.

10. The method for calculating building-related loads according to claim 1, wherein the virtual building environment is generated based on building information and environmental information.

11. The method for calculating building-related loads according to claim 10, wherein the building information includes at least one of building drawings, building models, and building heat transfer coefficients, and the building model is a three-dimensional or two-dimensional building model.

12. The method for calculating building-related loads according to claim 10, wherein the environmental information includes information on equipment within the building and / or weather information.

13. The method for calculating building-related loads according to claim 12, wherein the equipment information is updated based on the actions and / or locations of the plurality of virtual persons.

14. The method for calculating building-related loads according to claim 10, wherein the environmental information is updated based on information in the actual environment.

15. The method for calculating building-related loads according to claim 1, wherein the simulation activity features further include the amount of moisture released by the plurality of virtual persons.

16. The method for calculating building-related loads according to claim 1, wherein the step of calculating the building-related load based on the heat generation of at least the plurality of virtual persons includes the step of calculating the building-related load based on equipment information, weather information and the heat generation in the virtual building environment.

17. A method for calculating building-related loads according to claim 1, further comprising the step of calculating the load and / or energy consumption of equipment within the building based on the building-related loads, wherein the equipment includes an air conditioning system.

18. A method for calculating building-related loads according to claim 1, further comprising the step of calculating at least one of the loads in at least one area within the building, the loads in the building during a predetermined time period, and the real-time loads of the building, based on the building-related loads.

19. The method for calculating building-related loads according to any one of claims 1 to 18, wherein the building-related loads include at least one of the total load, heat load, cold load, and wet load of the building.

20. A building-related load calculation device, comprising: an acquisition module that acquires simulation activity characteristics of a plurality of virtual people who simulate a crowd inside a building, through simulation activities performed in a virtual building environment that simulates the environment inside the building, wherein the simulation activity characteristics include the amount of heat generated by the plurality of virtual people; and a calculation module that calculates the building-related load based on at least the amount of heat generated by the plurality of virtual people.

Images