A method and system for processing big data information

Abstract

The present application provides a method and system for processing big data information. Among them, this method generates an energy distribution model by combining a building information model with infrared thermal imaging data, and constructs a set of dynamic thermal balance equations based on this model to generate three-dimensional temperature field reconstruction data. Then, a deep learning algorithm is used to analyze the thermal resistance parameters in the energy distribution model, the coordinates of the heat flux anomaly area in the three-dimensional temperature field reconstruction data, and the current data of the air conditioning unit, and the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters are extracted. Then, a priority control chain for temperature control equipment is formulated, covering multi-level linkage rules for fresh air ventilation rate, lighting dimming, and cold storage tank cold release. Finally, the phase difference of the chilled water pump, the operation mode of the air conditioning group, and the emergency lighting power limit are dynamically adjusted to achieve power load suppression. The technical solution provided by the present application can improve the efficiency and stability of data information processing in large buildings.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a big data information processing method and system. Background Art

[0002] With the development of modern buildings towards intelligence and energy conservation, precise control and optimized management of the internal environment of buildings have become the key to improving energy efficiency and reducing operating costs. Especially in high-energy-consuming buildings such as large commercial complexes and data centers, how to dynamically adjust the working state of temperature control equipment to adapt to changes in external meteorological conditions while ensuring indoor temperature comfort is one of the important challenges currently faced. In addition, in order to meet increasingly stringent environmental requirements, it is also particularly important to achieve effective management of power loads and reduce unnecessary energy losses.

[0003] Currently, in response to the above technical requirements, data collection and analysis systems based on Internet of Things technology combined with cloud computing platforms have been widely used. This system distributes a variety of sensors (such as temperature sensors, humidity sensors, etc.) in the building to collect various environmental data in the building in real time, and uploads these data to the cloud server for centralized processing. With the help of advanced algorithm models, the system can predict the change trend of heat load in the next period of time, thereby providing a basis for the automatic adjustment of temperature control equipment.

[0004] Although the solutions based on the Internet of Things and cloud computing have improved the building energy management problem to a certain extent, there are still some limitations. First, due to relying on the cloud platform for data analysis, this leads to the problem of data processing delay. Especially in the case of poor network conditions, real-time regulation may not be achieved, affecting the user experience. Second, existing systems often focus on monitoring single types of environmental parameters and lack the comprehensive analysis ability of multi-source heterogeneous data (such as electrical parameters, spatial topology, etc.), making it difficult to comprehensively capture the complex thermal environment characteristics inside the building. Finally, the scalability and flexibility of the system are insufficient. When facing different building structures or special application scenarios, a large amount of custom development work may be required, increasing the implementation difficulty and cost. Summary of the Invention

[0005] Embodiments of this application provide a big data information processing method and system to solve the problems of low efficiency and poor stability in the processing of building data information in the prior art.

[0006] In a first aspect, embodiments of this application provide a big data information processing method, including:

[0007] Obtain big data information of a building, where the big data information includes the thermal resistance parameters of the envelope structure and spatial topology data extracted through a building information model, and combine the thermal distribution map collected by an infrared thermal imaging array to generate an energy distribution model with thermal property marks;

[0008] Based on the heat conduction path characteristics in the energy distribution model, construct a set of dynamic heat balance equations, and generate three-dimensional temperature field reconstruction data through the difference compensation between the temperature field scan data and the simulation results of the set of dynamic heat balance equations;

[0009] Input the thermal resistance parameters in the energy distribution model, the coordinates of the heat flow anomaly area in the three-dimensional temperature field reconstruction data, and the current data of the air conditioning unit into an event feature extraction network driven by deep learning to extract the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters;

[0010] Based on the spatio-temporal coupling characteristics and meteorological forecast data, construct a priority control chain for temperature control equipment, and generate multi-level linkage rules including the adjustment of the ventilation rate of the fresh air unit, the optimization of the lighting dimming gradient, and the triggering of cold release from the cold storage tank;

[0011] According to the sensitive node parameters of the priority control chain, dynamically generate a power load suppression strategy including the phase difference control of the chilled water pump, the staggered operation of the air conditioning group, and the dynamic power limit of the emergency lighting.

[0012] Optionally, the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters extracted by the event feature extraction network driven by deep learning include:

[0013] Overlay the thermal resistance parameters in the energy distribution model and the boundary data of the heat flow anomaly area in the three-dimensional temperature field reconstruction data to generate a spatial correlation map with a heat conduction lag effect;

[0014] Based on the phase fluctuation characteristics of the current data of the air conditioning unit, mark the heat disturbance propagation path strongly related to the operation of electrical equipment in the spatial correlation map;

[0015] According to the spatial coverage range of the heat disturbance propagation path, attenuate and weight the thermal resistance parameters according to the conduction path length, and overlay the air flow characteristic data in the three-dimensional temperature field reconstruction data to generate a thermal power coupling factor;

[0016] Based on the three-dimensional distribution density of the thermal power coupling factor, construct a joint response sequence, and the joint response sequence generates multi-dimensional association rules by analyzing the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the heat flow anomaly area;

[0017] Perform cross-domain matching on the joint response sequence and the multi-dimensional association rules, and extract the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters.

[0018] Optionally, for the construction of the joint response sequence, by analyzing the co-occurrence probability of the spectral characteristics of the current data and the geometric topology of the heat flux anomaly area, multi-dimensional association rules are generated, including:

[0019] Based on the three-dimensional distribution density of the thermoelectric coupling factor, the dynamic association strength between the geometric topology of the heat flux anomaly area and the spectral characteristics of the current data in the spatial coordinates is superimposed in multiple channels to construct a joint response sequence;

[0020] Based on the coupling relationship between the axial gradient distribution of the joint response sequence and the frequency-domain energy attenuation rate, the boundary curvature change point set of the geometric topology of the heat flux anomaly area is extracted;

[0021] Match the boundary curvature change point set with the zero-crossing distribution of the spectral characteristics of the current data to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly area. The dynamic co-occurrence probability field contains the non-linear mapping relationship between the zero-crossing density and the boundary curvature change rate;

[0022] Based on the synchronous change interval of the thermal power fluctuation amplitude of the main axis and the frequency-domain energy attenuation rate, multi-dimensional association rules are generated by constraining the boundary curvature change threshold and the gradient range of the zero-crossing density.

[0023] Optionally, the matching of the boundary curvature change point set with the zero-crossing distribution of the spectral characteristics of the current data to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly area includes:

[0024] Perform gradient direction clustering on the boundary curvature change point set to generate a cluster group of curvature change point sets, and use the centroid connection line of the cluster group of curvature change point sets as the initial matching reference chain;

[0025] Perform circular neighborhood phase polarity analysis on the zero-crossing distribution of the spectral characteristics of the current data, screen out the zero-crossing sequence, and segment the zero-crossing sequence to generate a zero-crossing segmented trajectory chain;

[0026] According to the direction of the main axis of the geometric topology of the heat flux anomaly area, dynamically adjust the local curvature weight of the initial matching reference chain and the phase polarity weight of the zero-crossing segmented trajectory chain to generate a double-chain dynamic matching probability map;

[0027] According to the spatial aggregation degree of the double-chain dynamic matching probability map, perform coupling analysis on the cluster group of curvature change point sets, and generate a continuous probability field by superimposing the probability density gradients of adjacent cluster groups on the main axis;

[0028] Perform direction-aware probability diffusion on the continuous probability field, apply anisotropic diffusion constraints along the main axis, and generate a dynamic co-occurrence probability field according to the distribution density of the double-strand dynamic matching probability map.

[0029] Optionally, generating the three-dimensional temperature field reconstruction data by compensating for the difference between the temperature field scan data and the simulation results of the set of dynamic thermal equilibrium equations includes:

[0030] Perform spatial discretization on the simulation results of the set of dynamic thermal equilibrium equations based on the distribution of discrete sampling points of the temperature field scan data to generate simulated temperature field grid data;

[0031] Analyze the temperature difference between the temperature field scan data and the simulated temperature field grid data to obtain local compensation factors, and perform smooth diffusion according to spatial continuity constraints to generate continuous compensation field data;

[0032] Couple and iterate the continuous compensation field data with the simulation results of the set of dynamic thermal equilibrium equations, and perform reverse correction on the heat conduction term of the set of dynamic thermal equilibrium equations to generate a corrected set of dynamic thermal equilibrium equations;

[0033] Perform backpropagation analysis on the spatial gradient distribution of the temperature field scan data according to the corrected set of dynamic thermal equilibrium equations to generate compensated temperature field increment data, and generate three-dimensional temperature field reconstruction data by superimposing the simulated temperature field grid data.

[0034] Optionally, the coupling and iteration of the continuous compensation field data with the simulation results of the set of dynamic thermal equilibrium equations, and the reverse correction of the heat conduction term of the set of dynamic thermal equilibrium equations to generate a corrected set of dynamic thermal equilibrium equations includes:

[0035] Generate node correction coefficients based on the amplitude and direction of the compensation amount of the continuous compensation field data, and the absolute value of the node correction coefficient is positively correlated with the amplitude of the continuous compensation field data;

[0036] Define a heat conduction path weight factor according to the topological relationship of the heat conduction paths in the set of dynamic thermal equilibrium equations, and perform conductivity weighting and balancing of the node correction coefficients across nodes to generate a node correction coefficient distribution;

[0037] Reverse superimpose the node correction coefficient distribution with the heat conduction term of the set of dynamic thermal equilibrium equations to generate an intermediate correction equation set, and extract the heat flow residual parameters of the intermediate correction equation set and the set of dynamic thermal equilibrium equations;

[0038] Iteratively readjust the node correction coefficient distribution based on the heat flux residual parameter until the heat flux residual parameter satisfies the heat flux conservation constraint condition of the dynamic heat balance equation set, and generate a corrected dynamic heat balance equation set.

[0039] Optionally, constructing a priority control chain for the temperature control device based on the spatio-temporal coupling feature and meteorological forecast data includes:

[0040] According to the correlation between the distributed thermal inertia parameter of the temperature control device in the spatio-temporal coupling feature and the future period temperature fluctuation parameter in the meteorological forecast data, extract the spatial thermal inertia index and the time response sensitivity index of the temperature control device;

[0041] Based on the correlation relationship between the spatial thermal inertia index and the time response sensitivity index, establish the regulation influence weight of the temperature control device, and combine the topological relationship of the heat conduction path between the temperature control devices to generate a preliminary priority sequence;

[0042] According to the spatio-temporal distribution characteristics of extreme temperature events in the meteorological forecast data, dynamically adjust the preliminary priority sequence, and correct the regulation influence weight through the matching degree between the area covered by the extreme temperature event and the spatial position of the temperature control device to generate a dynamic priority sequence;

[0043] Construct a priority control chain for the temperature control device by iteratively screening the temperature control device nodes that violate the energy consumption threshold or the thermal stability threshold in the dynamic priority sequence.

[0044] In a second aspect, an embodiment of the present application provides a big data information processing system, including:

[0045] An extraction module that obtains big data information of a building, where the big data information includes the thermal resistance parameter of the envelope structure and the spatial topology data extracted through a building information model, and combines the thermal distribution map collected by an infrared thermal imaging array to generate an energy distribution model with thermal property marks;

[0046] A scanning module that constructs a set of dynamic heat balance equations based on the heat conduction path characteristics in the energy distribution model, and generates three-dimensional temperature field reconstruction data through the difference compensation between the temperature field scanning data and the simulation results of the set of dynamic heat balance equations;

[0047] An input module that inputs the thermal resistance parameter in the energy distribution model, the coordinates of the heat flux anomaly area in the three-dimensional temperature field reconstruction data, and the air conditioner unit current data into an event feature extraction network driven by deep learning to extract the spatio-temporal coupling characteristics of device power consumption and thermal environment parameters;

[0048] A construction module, based on the spatio-temporal coupling characteristics and meteorological forecast data, constructs a priority control chain for temperature control equipment, and generates multi-level linkage rules including the adjustment of the ventilation rate of fresh air units, the optimization of lighting dimming gradients, and the triggering of chilled water tank cold release.

[0049] A generation module dynamically generates a power load suppression strategy including the phase difference control of chilled water pumps, the off-peak operation of air-conditioning groups, and the dynamic power limit of emergency lighting according to the sensitive node parameters of the priority control chain.

[0050] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data information processing method as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a big data information processing method as described in the first aspect.

[0052] In the embodiment of the present application, big data information of a building is obtained. The big data information includes the thermal resistance parameters of the building envelope and spatial topology data extracted through a building information model, and combines the thermal distribution map collected by an infrared thermal imaging array to generate an energy distribution model with thermal property marks; based on the heat conduction path characteristics in the energy distribution model, a set of dynamic heat balance equations is constructed, and through the differential compensation between the temperature field scan data and the simulation results of the set of dynamic heat balance equations, three-dimensional temperature field reconstruction data is generated; the thermal resistance parameters in the energy distribution model, the coordinates of the heat flow anomaly area in the three-dimensional temperature field reconstruction data, and the current data of the air-conditioning unit are input into an event feature extraction network driven by deep learning to extract the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters; based on the spatio-temporal coupling characteristics and meteorological forecast data, a priority control chain for temperature control equipment is constructed, and multi-level linkage rules including the adjustment of the ventilation rate of fresh air units, the optimization of lighting dimming gradients, and the triggering of chilled water tank cold release are generated; according to the sensitive node parameters of the priority control chain, a power load suppression strategy including the phase difference control of chilled water pumps, the off-peak operation of air-conditioning groups, and the dynamic power limit of emergency lighting is dynamically generated.

[0053] The technical solution of the present application has the following beneficial effects:

[0054] This application obtains the big data information of a building, and the big data information includes the thermal resistance parameters of the envelope structure and the spatial topology data extracted through the building information model, and combines with the thermal distribution map collected by the infrared thermal imaging array, which can accurately describe the thermal environment characteristics inside the building. This process provides detailed basic data for subsequent analysis and improves the understanding accuracy of the heat distribution inside the building. Based on the heat conduction path characteristics in the energy distribution model, a set of dynamic heat balance equations is constructed, and the temperature field scanning data is used for differential compensation to generate the reconstructed data of the three-dimensional temperature field. This method can more accurately simulate the actual temperature field inside the building and improve the accuracy and response speed of temperature control. Based on the spatio-temporal coupling characteristics and meteorological forecast data, a priority control chain for temperature control equipment is constructed, and multi-level linkage rules including the adjustment of the ventilation rate of the fresh air unit, the optimization of the lighting dimming gradient, and the triggering of cold release from the cold storage tank are formulated. This method can realize the intelligent adjustment of temperature control equipment, improve energy efficiency while ensuring indoor comfort. According to the sensitive node parameters of the priority control chain, a power load suppression strategy including the phase difference control of the chilled water pump, the staggered operation of the air conditioning group, and the dynamic power limit of the emergency lighting is dynamically generated. This not only helps to reduce the peak power consumption but also ensures the stable operation of the system.

[0055] Furthermore, by superimposing the thermal resistance parameters in the energy distribution model and the boundary data of the heat flow anomaly area in the reconstructed data of the three-dimensional temperature field, a spatial correlation map with a heat conduction lag effect is generated, and the heat disturbance propagation path is marked based on the current data of the air conditioning unit. The thermal power coupling factor is obtained by superimposing the weighted decay of the thermal resistance parameters and the air flow characteristic data. Finally, the multi-dimensional association rules are generated by jointly analyzing the spectral characteristics of the current data and the geometric topology co-occurrence probability of the heat flow anomaly area, so as to extract the spatio-temporal coupling characteristics of the equipment power consumption and the thermal environment parameters. This method greatly enhances the understanding of the operating state of electrical equipment in a complex thermal environment and its impact on the thermal environment, enabling the system to more accurately identify the key areas and factors causing energy loss. Through the precise positioning and analysis of these key points, not only can the temperature control equipment be more refinedly regulated, but also the energy consumption can be effectively reduced while ensuring comfort, and at the same time, the intelligent level and response efficiency of the entire building energy management system can be improved.

[0056] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0057] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0058] Figure 1 It shows a flowchart of a big data information processing method provided by the present application;

[0059] Figure 2 It shows a schematic structural diagram of a big data information processing system provided by the present application;

[0060] Figure 3 It shows a schematic structural diagram of a computing device provided by the present application. Detailed implementation manners

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application.

[0062] In some processes described in the specification, claims and the above accompanying drawings of the present application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0063] The present application aims to generate an energy distribution model with thermal property marks by using building information models and infrared thermal imaging technology, then construct a set of dynamic heat balance equations based on the heat conduction path characteristics in the model, and perform differential compensation through temperature field scan data to generate three-dimensional temperature field reconstruction data. Further, by combining the energy distribution model, three-dimensional temperature field reconstruction data and air conditioner unit current data, input them into a deep learning network to extract the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters, and finally formulate a temperature control device regulation strategy according to these characteristics and meteorological forecast data to optimize the control of the internal environment of the building.

[0064] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0065] Figure 1 The following is a flowchart of a big data information processing method provided by an embodiment of the present application. As Figure 1 shown, the method includes:

[0066] 101. Obtain the big data information of the building, where the big data information includes the thermal resistance parameters of the envelope structure and the spatial topology data extracted through the building information model, and combine the thermal distribution map collected by the infrared thermal imaging array to generate an energy distribution model with thermal property marks;

[0067] In this step, the building information model is a digital tool for creating and managing building project information, which contains all the physical and functional characteristic data of the building.

[0068] The thermal resistance parameter of the envelope structure refers to the resistance value of the building envelope such as the exterior wall and roof to heat conduction, and is an important index to measure the heat insulation performance of building materials.

[0069] The spatial topology data describes the internal spatial layout of the building and its mutual relationship, such as the location, size and connection method of the rooms.

[0070] The infrared thermal imaging array is a device that uses infrared technology to capture the temperature distribution on the surface of an object, and the generated thermal distribution map can intuitively display the temperature differences in different areas inside and outside the building.

[0071] The thermal distribution map is an image generated by infrared thermal imaging technology, which shows the temperature distribution on the surface of the object. In the field of architecture, it can be used to identify the abnormal heat flow areas in the building, such as the cold bridge phenomenon around the window or the insulation layer failure points in the wall.

[0072] The energy distribution model is a comprehensive digital model that integrates all the above information and attaches thermal property marks to show the energy flow status inside the building.

[0073] In the embodiments of the present application, first, the thermal resistance parameters and spatial topology data of the building envelope are extracted through a building information model. This process involves using specific software to parse the building design documents to obtain relevant parameters. Then, the building is scanned using an infrared thermal imaging array to collect its thermal distribution map. When processing these data, image processing algorithms are used to identify and label the heat flow abnormal areas, and this information is integrated into the energy distribution model. Finally, combining all the collected data, an energy distribution model containing detailed thermal property information is generated. This model accurately depicts the internal heat distribution of the building and reflects its response characteristics to changes in the external environment.

[0074] In an office building project, first, the building information model software was used to collect the thermal resistance parameters and spatial topology data of the entire building envelope, including the material properties and relative positions of each floor slab and wall. Subsequently, under the cold weather conditions in winter, a high-resolution infrared thermal imaging device was used to conduct a comprehensive scan of the building, obtaining a detailed thermal distribution map, and several obvious cold bridge effects were found around the windows. Based on these data, an energy distribution model that accurately reflects the actual heat flow was constructed, providing a basis for subsequent optimization of the air conditioning system operation.

[0075] 102. Based on the heat conduction path characteristics in the energy distribution model, a set of dynamic heat balance equations is constructed. Through the differential compensation between the temperature field scan data and the simulation results of the set of dynamic heat balance equations, three-dimensional temperature field reconstruction data is generated.

[0076] In this step, the energy distribution model is a comprehensive digital model that integrates all the above information and is attached with thermal property marks to show the energy flow status inside the building.

[0077] The set of dynamic heat balance equations is a set of mathematical expressions used to simulate the heat transfer process in the building, taking into account factors such as the thermal conductivity of building materials and boundary conditions.

[0078] The temperature field scan data is the temperature values at various positions inside the building collected in real time by a sensor network.

[0079] The three-dimensional temperature field reconstruction data is a three-dimensional temperature distribution model after compensation, which more accurately reflects the actual temperature distribution inside the building.

[0080] In the embodiments of the present application, first, based on the heat conduction path characteristics in the energy distribution model, a set of dynamic heat balance equations is established. Then, a distributed temperature sensor network deployed inside the building is used to collect temperature field scan data. By comparing the temperature field scan data with the simulation results of the set of dynamic heat balance equations, the difference points are found and adjusted through a compensation algorithm, and finally, three-dimensional temperature field reconstruction data closer to the actual situation is generated. This step ensures that the temperature field model can truly reflect the actual temperature distribution inside the building.

[0081] Continuing with the data from the previous step, a set of dynamic heat balance equations was established based on the energy distribution model, and a distributed temperature sensor network was deployed to monitor the temperature changes inside the office building in real time. By analyzing the collected data, some local hot spots were found. These areas were finely adjusted using a compensation algorithm, and finally, a three-dimensional temperature field reconstruction model that accurately reflects the actual situation was generated, providing a scientific basis for optimizing the operation of the air conditioning system.

[0082] 103. Input the thermal resistance parameters in the energy distribution model, the coordinates of the heat flux anomaly area in the three-dimensional temperature field reconstruction data, and the current data of the air conditioning unit into the event feature extraction network driven by deep learning to extract the spatio-temporal coupling features of equipment power consumption and thermal environment parameters;

[0083] In this step, the thermal resistance parameters are data describing the resistance of the building envelope (such as walls, windows, etc.) to heat conduction. It reflects the ability of the material to prevent heat transfer and is crucial for evaluating the insulation performance of the building.

[0084] The coordinates of the heat flux anomaly area refer to the position coordinates marked in the three-dimensional temperature field reconstruction data where the temperature is significantly higher or lower than the surrounding area.

[0085] The current data of the air conditioning unit records the current consumption during the operation of the air conditioning system and is one of the important indicators for evaluating equipment power consumption.

[0086] The spatio-temporal coupling features refer to the relationship patterns of these variables changing with time and space, revealing how equipment power consumption responds to different thermal environment conditions.

[0087] In the embodiments of the present application, first, the thermal resistance parameters, the coordinates of the heat flux anomaly area, and the current data of the air conditioning unit in the energy distribution model are input into the event feature extraction network driven by deep learning. The network learns the spatio-temporal coupling features between equipment power consumption and thermal environment parameters through training. This process involves a large amount of data preprocessing, feature engineering, and model training work, and the finally output results help to understand the operating state of the equipment and its impact on the environment.

[0088] Based on the data obtained from the first two steps, a deep learning model was constructed to analyze the energy consumption in different areas of the office building. By continuously monitoring the current consumption of the air conditioning system and the corresponding temperature changes, it was found that the energy consumption increased significantly during certain specific time periods, and the specific reasons for this situation were determined, such as sudden changes in external temperature or increased personnel activities, etc.

[0089] 104. Based on the spatio-temporal coupling characteristics and meteorological forecast data, construct a priority control chain for temperature control equipment, and generate multi-level linkage rules including adjustment of the ventilation rate of fresh air units, optimization of lighting dimming gradients, and triggering of chilled water tank cold release.

[0090] In this step, the spatio-temporal coupling characteristics refer to the relationship patterns of these variables changing over time and space, which reveal how the power consumption of equipment responds to different thermal environment conditions.

[0091] Meteorological forecast data contains weather prediction information for a future period of time, including the change trends of elements such as temperature and humidity.

[0092] The multi-level linkage rules refer to a series of operation instructions set according to different conditions, covering multiple aspects such as adjustment of the ventilation rate of fresh air units and optimization of lighting dimming gradients, aiming to achieve energy-saving goals while ensuring comfort.

[0093] The priority control chain is a series of control strategies formulated based on spatio-temporal coupling characteristics and meteorological forecast data to guide the effective operation of temperature control equipment.

[0094] In the embodiment of the present application, combined with the spatio-temporal coupling characteristics and meteorological forecast data extracted in the above steps, a priority control chain for temperature control equipment is formulated. This chain contains multiple levels of operation instructions, and automatically adjusts the operation status of the equipment according to different climate conditions and indoor requirements. For example, in high-temperature weather, the refrigeration equipment is preferentially started, while in low-temperature conditions, the ventilation volume is appropriately reduced. This not only saves energy but also improves the user experience.

[0095] During the high-temperature warning period in summer, based on the meteorological forecast data, the energy distribution model of the office building, and the reconstructed data of the three-dimensional temperature field, a priority control chain for temperature control equipment was formulated. First, the ventilation rate of the fresh air unit was adjusted to increase night ventilation to reduce the indoor temperature base value, and at the same time, the lighting system dimming gradient was optimized to reduce the light intensity during non-working hours. This series of measures not only reduced the starting pressure of the air conditioning system but also prepared for the upcoming high-temperature weather.

[0096] 105. According to the sensitive node parameters of the priority control chain, dynamically generate a power load smoothing strategy including phase difference control of chilled water pumps, staggered operation of air conditioning groups, and dynamic power limit of emergency lighting.

[0097] In this step, the sensitive node parameters include, but are not limited to, information such as the maximum power of the device, start-up time, and operating mode of the device. By monitoring and analyzing these parameters, effective load management strategies can be formulated.

[0098] The phase difference control of the chilled water pump refers to adjusting the working phase of the chilled water pump motor to optimize its working state and avoid overloading the power grid caused by starting multiple high-power devices simultaneously.

[0099] Staggered operation of the air-conditioning group is a load management technique that arranges different air-conditioning units to operate in different time periods to avoid peak loads caused by all devices starting simultaneously.

[0100] The dynamic power limit of emergency lighting refers to the maximum power limit of the temporary lighting system set to cope with emergencies.

[0101] The power load smoothing strategy is a series of technical means and operating procedures aimed at balancing the relationship between power demand and supply.

[0102] In the embodiment of the present application, based on the priority control chain of the above steps, the power load management strategy is further refined. By real-time monitoring of the sensitive node parameters, the working phase difference of the chilled water pump is dynamically adjusted, the staggered operation plan of the air-conditioning system is implemented, and the power upper limit of the emergency lighting is set. These measures work together to effectively reduce the peak power load and improve the stability of the power grid.

[0103] Based on the priority control chain, the phase difference control is further implemented for the chilled water pumps in the office building, and the air-conditioning groups are arranged to operate in a staggered manner to avoid overloading the power grid caused by all devices starting simultaneously. In addition, the dynamic power limit of the emergency lighting system is set to ensure that the basic lighting requirements can be maintained during peak electricity consumption periods without affecting normal office activities. Through these refined management measures, the peak power load of the office building has been successfully reduced, significantly improving the energy use efficiency and ensuring the power supply stability. These two steps work together to ensure that the office building can still operate efficiently and stably under extreme weather conditions.

[0104] In summary, steps 101 to 105 achieve the refined management and high-efficiency operation of the internal environment of the building. From the initial data collection to the final strategy execution, each step makes full use of modern information technology means to ensure that the building can achieve the best energy-saving effect while meeting the user's needs. This comprehensive solution not only improves the energy utilization efficiency but also enhances the building's ability to adapt to changes in the external environment.

[0105] To further improve the understanding of the spatio-temporal coupling characteristics between equipment power consumption and thermal environment parameters, a spatial correlation map is generated by superimposing the thermal resistance parameters in the energy distribution model and the boundary data of the heat flux anomaly region in the reconstructed three-dimensional temperature field data. According to the spatial coverage range of the thermal disturbance propagation path, the co-occurrence probability of the spectral characteristics of the current data and the geometric topology of the heat flux anomaly region is analyzed, and finally the accurate extraction of the spatio-temporal coupling characteristics between equipment power consumption and thermal environment parameters is realized, providing a basis for subsequent intelligent regulation. In some embodiments, the spatio-temporal coupling characteristics of equipment power consumption and thermal environment parameters are extracted by the event feature extraction network driven by deep learning in step 103, including:

[0106] 201. Superimpose the thermal resistance parameters in the energy distribution model and the boundary data of the heat flux anomaly region in the reconstructed three-dimensional temperature field data to generate a spatial correlation map with a heat conduction lag effect;

[0107] In step 201, the thermal resistance parameters describe the data of the building envelope's resistance to heat transfer, reflecting the ability of different materials to prevent heat from transferring from one side to the other. The boundary data of the heat flux anomaly region are the position coordinates marked in the reconstructed three-dimensional temperature field data where the temperature is significantly different from the surrounding area. The spatial correlation map is a chart generated by combining these two types of data, used to display the lag effect and its influence range during the heat transfer process. The heat conduction lag effect refers to the time delay phenomenon caused by material properties and distance during the heat transfer process.

[0108] In the embodiments of the present application, first, the thermal resistance parameters in the energy distribution model and the boundary data of the heat flux anomaly region in the reconstructed three-dimensional temperature field data are superimposed and processed, and an image fusion technology is used to generate a spatial correlation map with a heat conduction lag effect. This process not only reveals the basic mode of heat flow inside the building but also considers the influence of material properties on the heat transfer speed. The final result is a spatial correlation map that can accurately reflect the heat flow situation inside the building.

[0109] 202. Based on the phase fluctuation characteristics of the air conditioner unit current data, mark the heat disturbance propagation paths strongly related to the operation of electrical equipment in the spatial correlation map;

[0110] In step 202, the phase fluctuation characteristics of the air conditioner unit current data refer to the data recording the current change situation when the air conditioning system is working, and these data can reflect the working state and load level of the equipment. The heat disturbance propagation path is the propagation path of the heat fluctuation caused by the operation of electrical equipment in the building, identifying which areas are most affected by the operation of electrical equipment.

[0111] In the embodiments of the present application, based on the phase fluctuation characteristics of the current data of the air conditioner unit, the signal processing algorithm is used to analyze the current data, identify the phase fluctuation characteristic points, and map them to the corresponding heat disturbance propagation paths in the spatial correlation map, thereby identifying the key heat transfer paths. By marking the heat disturbance propagation paths strongly related to the operation of electrical equipment in the spatial correlation map, this step helps to accurately identify the heat fluctuation hot spot areas caused by the operation of electrical equipment.

[0112] 203. According to the spatial coverage range of the heat disturbance propagation path, attenuate and weight the thermal resistance parameters according to the conduction path length, and superimpose the air flow characteristic data in the reconstructed three-dimensional temperature field data to generate a thermal-electricity coupling factor;

[0113] In step 203, the thermal-electricity coupling factor is an index that combines the thermal resistance parameters and the air flow characteristic data, and is used to quantify the interaction strength between heat and electric energy. The attenuation weighting of the conduction path length is a method of adjusting the influence degree of heat transmission along different paths according to the distance. The air flow characteristic data describes the direction and speed of air flow inside the building, which is crucial for understanding how heat diffuses in the building.

[0114] In the embodiments of the present application, first, analyze the spatial coverage range of the heat disturbance propagation path and calculate the length of each conduction path. Then, perform attenuation weighting processing on the thermal resistance parameters based on these lengths. Next, superimpose the air flow characteristic data in the reconstructed three-dimensional temperature field data to generate a thermal-electricity coupling factor. Specifically, the finite element analysis method is used to process the complex multi-dimensional data set to ensure that the generated factor can accurately reflect the actual situation. Finally, integrate all the information to form a comprehensive thermal-electricity coupling factor.

[0115] 204. Based on the three-dimensional distribution density of the thermal-electricity coupling factor, construct a joint response sequence, and the joint response sequence generates multi-dimensional association rules by analyzing the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the heat flow abnormal area;

[0116] In step 204, the joint response sequence is a set of data sequences obtained by analyzing the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the heat flow abnormal area. The multi-dimensional association rules are extracted from these sequences to describe the interaction patterns between different variables. The current data spectrum characteristics are the results obtained by performing frequency domain analysis on the current data, and the geometric topology of the heat flow abnormal area refers to the spatial form and positional relationship of the heat flow abnormal area.

[0117] In the embodiments of the present application, the spectral characteristics of current data are analyzed using the fast Fourier transform, and the geometric topology of the heat flux anomaly area is analyzed in combination with geometric modeling tools to find the co-occurrence patterns between the two. Then, multi-dimensional association rules are generated. This step uses statistical methods such as Bayesian networks or decision trees to establish association rules between different variables. Finally, all the information is integrated to form a comprehensive multi-dimensional association rule for guiding subsequent optimization strategies.

[0118] 205. Perform cross-domain matching between the joint response sequence and the multi-dimensional association rules, and extract the spatio-temporal coupling characteristics of device power consumption and thermal environment parameters.

[0119] In step 205, cross-domain matching refers to comparing the joint response sequence with the multi-dimensional association rules to find the corresponding relationship between the two. The spatio-temporal coupling characteristics are extracted from this matching to describe the relationship pattern of device power consumption and thermal environment parameters changing with time and space. The cross-domain matching technology aims to discover the potential connections between different data sources to provide a more comprehensive perspective to understand the dynamic behavior of the system.

[0120] In the embodiments of the present application, time series analysis methods such as the autoregressive integrated moving average model are used in combination with geographic information system data to analyze the changing laws of device power consumption and thermal environment parameters with time and space. Then, based on the above analysis results, spatio-temporal coupling characteristics are generated. This step uses data mining techniques such as clustering analysis and principal component analysis to extract key characteristics. Finally, all the information is integrated to form a comprehensive spatio-temporal coupling characteristic for guiding subsequent optimization strategies.

[0121] The following is a specific example:

[0122] In an office building project, first, the building information model system is used to extract the thermal resistance parameters of the building envelope and spatial topology data of the whole building, and a detailed thermal distribution map is obtained using an infrared thermal imaging device. Then, temperature field scan data is collected through a distributed temperature sensor network to generate reconstructed three-dimensional temperature field data. On this basis, the technical personnel overlay the thermal resistance parameters with the boundary data of the heat flux anomaly area to generate a spatial association map. Then, based on the phase fluctuation characteristics of the current data of the air conditioning unit, the main heat disturbance propagation paths are marked on the spatial association map. Next, according to the spatial coverage of these paths, the thermal power coupling factor is calculated, and a joint response sequence is further constructed. Finally, the spatio-temporal coupling characteristics of device power consumption and thermal environment parameters are extracted through cross-domain matching technology, providing a scientific basis for optimizing the overall temperature control strategy of the office building.

[0123] In summary, through steps 201 to 205, through a series of in-depth data analysis and technical means, an accurate understanding and description of the complex relationship between the power consumption of building internal equipment and the thermal environment parameters have been achieved. This method not only improves the efficiency of energy management but also provides a solid foundation for formulating more intelligent and personalized energy-saving strategies. By carefully analyzing the interaction between heat and electricity inside the building, unnecessary energy losses can be effectively reduced, and the overall energy efficiency performance of the building can be improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment.

[0124] To further improve the understanding of the co-occurrence probability between the spectral characteristics of current data and the geometric topology of the heat flow anomaly area, the solution performs multi-channel superposition on the three-dimensional distribution density of the thermoelectric coupling factor, extracts the set of boundary curvature change points of the geometric topology of the heat flow anomaly area, and generates a dynamic co-occurrence probability field. Based on the synchronous change interval of the thermal fluctuation amplitude and the frequency-domain energy attenuation rate of the main axis, multi-dimensional association rules are generated to improve the understanding accuracy and response speed of the system to complex thermal environment changes. In some embodiments, in step 204, the construction of the joint response sequence, the joint response sequence generates multi-dimensional association rules by analyzing the co-occurrence probability of the spectral characteristics of current data and the geometric topology of the heat flow anomaly area, including:

[0125] 301. Based on the three-dimensional distribution density of the thermoelectric coupling factor, perform multi-channel superposition on the dynamic association strength between the geometric topology of the heat flow anomaly area and the spectral characteristics of current data in the spatial coordinates to construct a joint response sequence;

[0126] In step 301, the thermoelectric coupling factor is an index that combines the thermal resistance parameter and the air flow characteristic data, and is used to quantify the interaction strength between heat and electricity. The three-dimensional distribution density refers to the spatial distribution of the thermoelectric coupling factor inside the entire building. The geometric topology of the heat flow anomaly area describes the spatial form and positional relationship of the heat flow anomaly area. The spectral characteristics of current data are the results obtained after performing frequency-domain analysis on the current data. The joint response sequence is a set of data sequences obtained by analyzing these data, used to reveal the dynamic association strength between different variables.

[0127] In the embodiment of the present application, first, based on the three-dimensional distribution density of the thermoelectric coupling factor, use image processing technology to perform multi-channel superposition on the geometric topology of the heat flow anomaly area and the spectral characteristics of current data in the spatial coordinates to generate a joint response sequence. This process involves complex mathematical modeling and signal processing algorithms, such as the fast Fourier transform. The final result is a joint response sequence that can accurately reflect the dynamic association strength between different variables.

[0128] 302. Extract the set of boundary curvature change points of the geometric topology of the heat flux anomaly region based on the coupling relationship between the axial gradient distribution of the joint response sequence and the frequency-domain energy attenuation rate;

[0129] In step 302, the axial gradient distribution of the joint response sequence refers to the change trend of the sequence in a certain direction. The frequency-domain energy attenuation rate refers to the speed at which energy decreases with frequency in the frequency domain. The set of boundary curvature change points is extracted from the joint response sequence and represents the set of positions where the boundary curvature changes in the geometric topology of the heat flux anomaly region.

[0130] In the embodiment of the present application, based on the coupling relationship between the axial gradient distribution of the joint response sequence and the frequency-domain energy attenuation rate, a mathematical analysis method is used to extract the set of boundary curvature change points of the geometric topology of the heat flux anomaly region. Specifically, differential geometry techniques are used to calculate the change points of the boundary curvature and mark them to form a set containing all key points. This step helps to accurately identify the important change regions in the heat flux anomaly region.

[0131] 303. Match the set of boundary curvature change points with the zero-crossing distribution of the spectral characteristics of the current data to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly region, and the dynamic co-occurrence probability field contains the non-linear mapping relationship between the zero-crossing density and the boundary curvature change rate;

[0132] In step 303, the set of boundary curvature change points is a set composed of the positions where the boundary curvature changes in the geometric topology of the heat flux anomaly region. The zero-crossing distribution of the spectral characteristics of the current data refers to the position distribution where the signal crosses zero in the current data spectrogram. The dynamic co-occurrence probability field is a probability distribution map generated by combining the above two types of data and is used to show the co-occurrence probability relationship between the two. The main axis refers to the main extension direction of the geometric topology of the heat flux anomaly region.

[0133] In the embodiment of the present application, the set of boundary curvature change points is matched with the zero-crossing distribution of the spectral characteristics of the current data, and pattern recognition techniques are used to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly region. In this process, by comparing the set of boundary curvature change points with the zero-crossing distribution, the co-occurrence probability between the two is calculated, and a dynamic co-occurrence probability field containing the non-linear mapping relationship between the zero-crossing density and the boundary curvature change rate is generated. This step helps to understand the complex relationship between the current data and the heat flux anomaly region.

[0134] 304. Based on the synchronous change interval between the thermal fluctuation amplitude of the main axis and the frequency-domain energy attenuation rate, generate multi-dimensional association rules by constraining the boundary curvature change threshold and the gradient range of the zero-crossing density.

[0135] In step 304, the thermal fluctuation amplitude of the main axis refers to the temperature fluctuation amplitude along its main extension direction in the heat flux anomaly area. The frequency-domain energy attenuation rate is the speed at which energy decreases with frequency in the frequency domain. The multi-dimensional association rule is extracted from the dynamic co-occurrence probability field to describe the interaction pattern between different variables. The boundary curvature change threshold is a numerical range used to limit the rate of change of the boundary curvature. The zero-crossing density refers to the number of times the signal crosses zero in the current data spectrum diagram.

[0136] In the embodiment of the present application, first, the thermal fluctuation amplitude of the main axis and the frequency-domain energy attenuation rate are obtained, and the synchronous change interval is determined. This step uses sensor data and spectrum analysis techniques, such as fast Fourier transform, to extract key features. Then, the constraint boundary curvature change threshold is set, and the gradient range of the zero-crossing density is calculated. Specifically, mathematical modeling tools, such as finite element analysis, are used to set reasonable boundary conditions, and statistical methods are used to calculate the zero-crossing density and its gradient range. Then, based on the above processing results, multi-dimensional association rules are generated. This step uses machine learning algorithms, such as decision trees or neural networks, to establish association rules between different variables. Finally, all the information is integrated to form a comprehensive multi-dimensional association rule for guiding subsequent optimization strategies.

[0137] The following is a specific example:

[0138] In an office building project, technicians first use image processing technology to perform multi-channel superposition of the geometric topology of the heat flux anomaly area and the spectral characteristics of the current data in the spatial coordinates to generate a joint response sequence. Then, differential geometry technology is used to extract the boundary curvature change point set of the geometric topology of the heat flux anomaly area. Then, pattern recognition technology is used to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly area. Finally, by constraining the boundary curvature change threshold and the gradient range of the zero-crossing density, multi-dimensional association rules are generated. These rules help optimize the operation strategy of the air conditioning system in the office building, improve energy use efficiency, and ensure the comfort of the indoor environment at the same time.

[0139] In summary, through steps 301 to 304, through a series of in-depth data analysis and technical means, an accurate understanding and description of the complex relationship between the spectral characteristics of current data and the geometric topology of the heat flux anomaly area are achieved. This method not only improves the efficiency of energy management, but also provides a solid foundation for formulating more intelligent and personalized energy-saving strategies. By carefully analyzing the interaction between heat and electricity inside the building, the overall energy efficiency performance of the building is improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment. This method also improves the response speed and accuracy of the system, making the energy management system more efficient and intelligent.

[0140] To further improve the understanding of the matching between the set of boundary curvature change points and the zero-crossing distribution of the spectral characteristics of current data, the solution generates clusters by clustering the set of boundary curvature change points to form a zero-crossing segmented trajectory chain, and further analyzes the spatial aggregation degree to superimpose the probability density gradient of adjacent clusters to generate a continuous probability field, obtaining a dynamic co-occurrence probability field. In some embodiments, the matching of the set of boundary curvature change points with the zero-crossing distribution of the spectral characteristics of current data in step 303 to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flux anomaly region includes:

[0141] 401. Perform gradient direction clustering on the set of boundary curvature change points to generate a cluster of curvature change point sets, and use the line connecting the centroids of the cluster of curvature change point sets as the initial matching reference chain;

[0142] In step 401, the set of boundary curvature change points is a set composed of the positions where the boundary curvature changes in the geometric topology of the heat flux anomaly region. Gradient direction clustering is a clustering method based on the similarity of the gradient directions of points in the point set, used to divide the set of boundary curvature change points into several clusters. The line connecting the centroids is the line segment formed by connecting the centroids of all points in each cluster, used as the initial matching reference chain. The initial matching reference chain is the basic reference line for the subsequent matching process.

[0143] In the embodiments of the present application, first, collect the boundary information in the design drawings of the outer wall of the office building and calculate the curvature change at each boundary point. Then, use the k-means clustering algorithm to classify these curvature change points according to their gradient directions, thus forming multiple clusters with similar characteristics. Then, determine the centroid positions for each cluster and connect these centroids in sequence to form an initial matching reference chain. This chain not only summarizes the design characteristics of the outer wall of the building but also provides a basis for further analysis.

[0144] 402. Perform circular neighborhood phase polarity analysis on the zero-crossing distribution of the spectral characteristics of the current data, filter out the zero-crossing sequence, and segment the zero-crossing sequence to generate a zero-crossing segmented trajectory chain;

[0145] In step 402, the zero-crossing distribution of the spectral characteristics of the current data refers to the position distribution where the signal in the current data spectrogram crosses zero. Circular neighborhood phase polarity analysis is a method for local region analysis of the spectral characteristics of current data, used to filter out the zero-crossing sequence with specific phase and polarity. The zero-crossing sequence is an ordered set of signals crossing zero in the spectral characteristics of the current data obtained after filtering. The zero-crossing segmented trajectory chain is a trajectory chain obtained by segmenting the zero-crossing sequence.

[0146] In the embodiments of the present application, first, the current data spectral characteristics of the internal circuit of the office building are obtained, and all zero-crossing points are identified. Then, circular neighborhood phase polarity analysis is performed on each zero-crossing point, and a sequence of zero-crossing points that meet specific conditions is screened out. Next, these zero-crossing points are segmented according to the current fluctuation pattern, and each segment represents a continuous current feature segment. Finally, a zero-crossing point segmented trajectory chain is formed by concatenating these segments, which helps subsequent fault detection and location work.

[0147] 403. Dynamically adjust the local curvature weight of the initial matching reference chain and the phase polarity weight of the zero-crossing point segmented trajectory chain according to the direction of the main axis of the geometric topology of the heat flow anomaly area, and generate a double-chain dynamic matching probability map.

[0148] In step 403, the main axis refers to the main extension direction of the geometric topology of the heat flow anomaly area, which is the centroid connection line generated by the initial matching reference chain. The local curvature weight is different weight values assigned to the degree of curvature change of each point on the initial matching reference chain. The segmented trajectory chain of the current data spectral characteristics is generated by the zero-crossing point segmented trajectory chain. The phase polarity weight is different weight values assigned to the phase and polarity of each point on the zero-crossing point segmented trajectory chain. The double-chain dynamic matching probability map is a probability distribution map generated by combining the above two types of data, which is used to show the matching relationship between the two.

[0149] In the embodiments of the present application, first, based on the geometric topology of the heat flow anomaly area, the direction of the main axis is determined, and this step is crucial for dynamically adjusting the local curvature weight of the initial matching reference chain. Then, in combination with the adjusted weight parameters, the matching degree between the initial matching reference chain and the zero-crossing point segmented trajectory chain is calculated. Next, a double-chain dynamic matching probability map is generated based on the above matching results, where the high-probability area indicates a high degree of consistency between the two chains. This map shows the matching possibilities of the two chains at different positions, laying a foundation for the next analysis.

[0150] 404. Perform coupling analysis on the cluster group of curvature change points according to the spatial aggregation degree of the double-chain dynamic matching probability map, and generate a continuous probability field by superimposing the probability density gradients of adjacent cluster groups on the main axis.

[0151] In step 404, the spatial aggregation degree refers to the density of data points in this probability map. The probability density gradient refers to the change rate of the probability density between adjacent cluster groups on the main axis. The continuous probability field is a probability distribution field generated by superimposing the probability density gradients of adjacent cluster groups.

[0152] In the embodiments of the present application, first, the spatial aggregation degree of the curvature change point set clusters is calculated to quantify the density of the curvature change point set clusters in space. Then, coupling analysis is performed to evaluate the influence between adjacent clusters. Next, the probability density gradients of these clusters are superimposed on the main axis to form a smooth and continuous probability field. This probability field reveals the potential patterns and trends within the entire research area, providing a scientific basis for optimizing building design.

[0153] 405. Perform direction-aware probability diffusion on the continuous probability field, apply anisotropic diffusion constraints along the main axis, and generate a dynamic co-occurrence probability field according to the distribution density of the double-chain dynamic matching probability map.

[0154] In step 405, direction-aware probability diffusion refers to a method of probability diffusion along the main axis, taking into account the influence of direction factors. Anisotropic diffusion constraints refer to the diffusion limitations in different directions imposed during the diffusion process. The dynamic co-occurrence probability field is the finally generated probability distribution map, showing the co-occurrence probability relationship between the geometric topology of the heat flow anomaly area and the spectral characteristics of the current data.

[0155] In the embodiments of the present application, first, apply the direction-aware probability diffusion algorithm along the main axis to make the probability information propagate in a specific direction while considering the influence of direction factors. Then, introduce anisotropic diffusion constraints to ensure that this diffusion process is more in line with the actual situation. Next, generate a dynamic co-occurrence probability field according to the distribution density of the double-chain dynamic matching probability map. Finally, this probability field comprehensively reflects the probability distribution under the combined action of various factors, helping to predict possible future structural problems or energy consumption patterns.

[0156] The following is a specific example:

[0157] In an office building project, technicians start processing the design drawings of the building exterior wall, extract the boundary information from them and calculate the curvature change point set. These points are successfully classified into several clusters, and the centroids of each cluster are connected to form an initial matching reference chain. Subsequently, the technicians analyze the current data of the internal circuit of the office building to form a zero-crossing segmentation trajectory chain. Based on these two chains, the technicians further analyze the temperature distribution inside the building, determine the main axis of the heat flow anomaly area, and generate a double-chain dynamic matching probability map. Next, by calculating the spatial aggregation degree and performing coupling analysis, the technicians create a continuous probability field that comprehensively reflects the structural characteristics of the office building. Finally, the technicians apply the direction-aware probability diffusion algorithm along the main axis to generate a dynamic co-occurrence probability field. These steps help optimize the operation strategy of the air conditioning system in the office building, improve the energy use efficiency, and ensure the comfort of the indoor environment at the same time.

[0158] In summary, through steps 401 to 405, through a series of in-depth data analysis and technical means, an accurate understanding and description of the complex relationship between the boundary curvature change point set and the spectral characteristics of current data have been achieved. This method not only improves the efficiency of energy management but also provides a solid foundation for formulating more intelligent and personalized energy-saving strategies. By carefully analyzing the interaction between heat and electricity inside the building, unnecessary energy losses can be effectively reduced, and the overall energy efficiency performance of the building can be improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment. In addition, this method also improves the response speed and accuracy of the system, making the energy management system more efficient and intelligent.

[0159] To further improve the matching accuracy between the temperature field scan data and the simulation results of the dynamic heat balance equation set, first, based on the spatial discretization of the temperature field scan data, simulated temperature field grid data is generated. Then, the temperature difference between the actual and simulated data is analyzed to generate local compensation factors, and continuous compensation field data is generated through smooth diffusion. The compensation field data is coupled with the simulation results to iteratively correct the heat conduction term, generating a corrected dynamic heat balance equation set. Finally, through backpropagation analysis of the spatial gradient distribution of the temperature field scan data, compensated temperature field increment data is generated, and the simulated data is superimposed to generate the final three-dimensional temperature field reconstruction data. In some embodiments, generating three-dimensional temperature field reconstruction data by compensating for the difference between the temperature field scan data and the simulation results of the dynamic heat balance equation set in step 102 includes:

[0160] 501. Based on the discrete sampling point distribution of the temperature field scan data, the simulation results of the dynamic heat balance equation set are spatially discretized to generate simulated temperature field grid data;

[0161] In step 501, the discrete sampling point distribution of the temperature field scan data refers to the actual temperature values at various positions inside the building collected from the actual environment. These data provide the real situation of the temperature field. The simulated temperature field grid data is generated by spatially discretizing the results of the dynamic heat balance equation set and is used to represent the temperature distribution at each point inside the building under simulated conditions. The simulated temperature field grid data provides a basic reference for subsequent steps.

[0162] In the embodiments of the present application, first, temperature field scan data from a temperature sensor network is collected and combined with a known dynamic heat balance equation set. Then, numerical simulation techniques such as the finite element method are used to solve the dynamic heat balance equation to obtain a preliminary temperature distribution prediction. Then, this prediction result is discretized according to spatial coordinates to form a temperature field grid data composed of multiple small grid units. Finally, by integrating the information of these grid units, simulated temperature field grid data that comprehensively reflects the environmental temperature distribution is generated.

[0163] 502. Obtain a local compensation factor by analyzing the temperature difference between the temperature field scan data and the simulated temperature field grid data, and perform smooth diffusion according to the spatial continuity constraint to generate continuous compensation field data;

[0164] In step 502, the local compensation factor is calculated by comparing the temperature difference between the temperature field scan data and the simulated temperature field grid data, and is used to correct the error in the simulation result. The spatial continuity constraint means that when generating the continuous compensation field data, it is required that the compensation factor maintains a smooth transition throughout the space to avoid sudden changes. The continuous compensation field data is a distribution map of the compensation factor after smooth diffusion processing and is used to adjust the simulated temperature field grid data.

[0165] In the embodiment of the present application, first calculate the temperature difference in each grid cell to identify the area that needs to be corrected. Then, use the interpolation algorithm and the smoothing filter to perform smooth diffusion processing on the local compensation factor based on the spatial continuity constraint. Then, apply these compensation factors to the original simulated temperature field grid data to generate a more accurate continuous compensation field data. Finally, this data can effectively improve the error in the initial simulation result.

[0166] 503. Couple and iterate the continuous compensation field data with the simulation result of the dynamic heat balance equation set, and perform reverse correction on the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set;

[0167] In step 503, the coupled iteration refers to the process of combining the continuous compensation field data with the simulation result of the dynamic heat balance equation set and performing multiple iterative optimizations. The reverse correction of the heat conduction term means adjusting the parameters in the heat conduction equation according to the compensation field data to improve the accuracy of the model. The corrected dynamic heat balance equation set is a new equation set generated after multiple iterative optimizations and is used to more accurately simulate the temperature change inside the building.

[0168] In the embodiment of the present application, first perform coupled iterative calculation on the continuous compensation field data and the simulation result of the original dynamic heat balance equation set to gradually adjust the matching degree between the two. Then, based on the error pattern found during the iteration process, perform reverse correction on the heat conduction term in the dynamic heat balance equation set. Then, repeat this process until the predetermined accuracy requirement is met, thereby generating a corrected dynamic heat balance equation set. This process improves the adaptability and accuracy of the model to the actual situation.

[0169] 504. Perform backpropagation analysis on the spatial gradient distribution of the temperature field scan data according to the corrected dynamic heat balance equation set to generate compensated temperature field increment data, and generate three-dimensional temperature field reconstruction data by superimposing the simulated temperature field grid data.

[0170] In step 504, the compensated temperature field increment data is data generated by performing backpropagation analysis on the spatial gradient distribution of the temperature field scan data, and is used to correct the errors in the simulated temperature field grid data. The three-dimensional temperature field reconstruction data is the final temperature distribution map generated by superimposing the compensated temperature field increment data and the simulated temperature field grid data, and can more accurately reflect the actual temperature distribution inside the building.

[0171] In the embodiment of the present application, first, according to the corrected set of dynamic heat balance equations, backpropagation analysis is performed on the spatial gradient distribution of the temperature field scan data to determine the temperature field increments to be supplemented. Then, these increment data are superimposed on the original simulated temperature field grid data to construct a more accurate three-dimensional temperature field reconstruction data. Finally, using this improved data, the true temperature distribution of the environment can be more accurately reflected. This process not only improves the accuracy of the simulation results, but also ensures that the temperature field reconstruction data can truly reflect the actual temperature distribution inside the building.

[0172] The following is a specific example:

[0173] In a large commercial building project, technicians first deployed a network of temperature sensors throughout the building to collect temperature field scan data. Subsequently, the finite element method was used to solve the dynamic heat balance equation to generate simulated temperature field grid data. Then, by analyzing the differences between the actual data and the simulated data, local compensation factors were calculated and applied to create continuous compensation field data. On this basis, the technicians further optimized the set of dynamic heat balance equations, and repeatedly adjusted the model parameters through iteration until a satisfactory revised version was obtained. Finally, the original data was enhanced using the revised model to generate three-dimensional temperature field reconstruction data with more details. This not only helps to improve the efficiency of the indoor environment control system in the building, but also provides a scientific basis for energy-saving renovation.

[0174] In summary, through steps 501 to 504, through a series of in-depth data analysis and technical means, an accurate understanding and description of the temperature field inside the building are achieved. This method not only improves the accuracy of temperature field simulation, but also provides a solid foundation for formulating more intelligent and personalized temperature control strategies. By carefully analyzing the heat transfer process inside the building, the overall energy efficiency performance of the building is improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment. In addition, this method also improves the response speed and accuracy of the system, making the temperature control management system more efficient and intelligent. Through accurate temperature field reconstruction, the heat flow distribution inside the building can be better understood, thereby optimizing energy use and improving the comfort of the occupants.

[0175] To further improve the matching accuracy between the continuously compensated field data and the simulation results of the dynamic heat balance equation set, a method based on node correction coefficients is designed. First, the cross-node conductivity is weighted and balanced using the heat conduction path weight factor to generate the node correction coefficient distribution. Then, this distribution is reversely superimposed with the heat conduction term to generate an intermediate correction equation set, and a corrected dynamic heat balance equation set is generated. This method improves the accuracy and adaptability of the model. In some embodiments, the step of coupling and iterating the continuously compensated field data with the simulation results of the dynamic heat balance equation set and reversely correcting the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set includes:

[0176] 601. Generate node correction coefficients based on the magnitude and direction of the compensation amount in the continuously compensated field data, where the absolute value of the node correction coefficient is positively correlated with the magnitude of the continuously compensated field data;

[0177] In step 601, the continuously compensated field data is a compensated factor distribution map after smoothing and diffusion processing. The compensation amount magnitude refers to the size of the compensation factor, and the direction refers to the direction of the temperature difference. The node correction coefficient is a coefficient generated based on the compensation amount magnitude and direction for adjusting the parameters of the heat conduction equation, and its absolute value is proportional to the compensation amount magnitude. The node correction coefficient is used to correct the errors in the simulation results to make the simulation closer to the actual situation.

[0178] In the embodiments of the present application, first, the compensation amount and its direction required for each node are determined based on the continuously compensated field data. Then, according to the magnitudes and directions of these compensation amounts, the corresponding correction coefficients are calculated for each node. Next, these correction coefficients are applied to the corresponding nodes to ensure that they can accurately reflect the actual temperature change. Finally, by integrating the correction coefficients of all nodes, a node correction coefficient distribution map that comprehensively reflects the temperature field correction requirements is generated.

[0179] 602. Define a heat conduction path weight factor according to the heat conduction path topology relationship of the dynamic heat balance equation set, and perform cross-node conductivity weighted balancing on the node correction coefficients to generate a node correction coefficient distribution;

[0180] In step 602, the heat conduction path topology relationship describes the spatial structure of the heat transfer path inside the building. The heat conduction path weight factor is different weight values assigned to different paths to reflect the importance of the paths. The node correction coefficient distribution is a distribution map generated by performing cross-node conductivity weighted balancing on the node correction coefficients. This distribution map takes into account the mutual influence between different paths and provides a basis for subsequent corrections.

[0181] In the embodiments of the present application, first, the topological relationship of the heat conduction paths in the dynamic heat balance equation set is analyzed to identify the main heat conduction paths. Then, corresponding weight factors are assigned according to the importance of these paths. Next, these weight factors are used to recalculate the correction coefficients of each node to ensure that heat can be evenly distributed throughout the system. Finally, through this process, a distribution map of the node correction coefficients weighted by the conductivity is generated, improving the overall stability of the system.

[0182] 603. Reverse superpose the node correction coefficient distribution with the heat conduction terms of the dynamic heat balance equation set to generate an intermediate correction equation set, and extract the heat flow residual parameters of the intermediate correction equation set and the dynamic heat balance equation set.

[0183] In step 603, the reverse superposition of the heat conduction terms means combining the node correction coefficient distribution with the heat conduction terms of the dynamic heat balance equation set to generate a new equation set. The intermediate correction equation set is the new equation set generated through reverse superposition. The heat flow residual parameter is extracted from the intermediate correction equation set and is an index used to measure the difference between the equation set and the actual temperature field scan data.

[0184] In the embodiments of the present application, first, the node correction coefficient distribution is combined with the heat conduction terms of the dynamic heat balance equation set to form an intermediate correction equation set. Then, the heat flow residual parameters are extracted from this new equation set to evaluate the correction effect. Next, these residual parameters are used to guide the subsequent adjustment work to ensure that the final result is as close to the actual situation as possible. Finally, through the above process, an intermediate correction equation set that can more accurately describe the system behavior is generated.

[0185] 604. Iteratively readjust the node correction coefficient distribution through the heat flow residual parameters until the heat flow residual parameters meet the heat flow conservation constraint conditions of the dynamic heat balance equation set, and generate a corrected dynamic heat balance equation set.

[0186] In step 604, the heat flow residual parameter is an index extracted to measure the difference between the equation set and the actual data. The iterative readjustment means making multiple adjustments to the node correction coefficient distribution according to the heat flow residual parameters until the heat flow conservation constraint conditions are met. The corrected dynamic heat balance equation set is a new equation set generated after multiple iterations of optimization and is used to more accurately simulate the temperature changes inside the building.

[0187] In the embodiments of the present application, first, the node correction coefficient distribution is initially adjusted according to the heat flux residual parameter. Then, this process is repeated multiple times, and each adjustment is based on the latest residual parameter to gradually narrow the error range. Next, it is checked whether the heat flux conservation constraint condition is satisfied during each iteration process. Finally, when the predetermined accuracy requirement is met, a set of corrected dynamic heat balance equations is generated, and this equation set can more accurately simulate the heat conduction behavior of the actual system.

[0188] The following is a specific example:

[0189] In a large data center project, technicians first generate continuous compensation field data based on the data collected by the temperature sensor network, and calculate the correction coefficients of each cooling equipment node accordingly. Then, analyze the topological relationship of the heat conduction paths inside the data center, define appropriate heat conduction path weight factors, and perform conductivity weighted balancing processing on the node correction coefficients. Subsequently, combine the adjusted node correction coefficient distribution with the heat conduction terms of the dynamic heat balance equation set to generate an intermediate correction equation set, and extract the heat flux residual parameter from it. Finally, adjust the node correction coefficient distribution through multiple iterations until the heat flux residual parameter satisfies the heat flux conservation constraint condition, effectively improving the efficiency and stability of the data center cooling system.

[0190] In summary, the accurate understanding and description of the internal temperature field of the building are achieved through steps 601 to 604. This method not only improves the accuracy of temperature field simulation, but also provides a solid foundation for formulating more intelligent and personalized temperature control strategies. By carefully analyzing the heat transfer process inside the building, the overall energy efficiency performance of the building is improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment. In addition, this method also improves the response speed and accuracy of the system, making the temperature control management system more efficient and intelligent. Through accurate temperature field reconstruction and correction of the dynamic heat balance equation set, the heat flux distribution inside the building can be better understood, thereby optimizing energy use and improving the comfort of the occupants.

[0191] In order to further improve the intelligence and personalization of the temperature control equipment regulation strategy, the solution constructs a priority regulation chain for the temperature control equipment, using a method that combines spatio-temporal coupling characteristics and meteorological forecast data. First, extract the spatial thermal inertia index and time response sensitivity index of the temperature control equipment, and generate a preliminary priority sequence in combination with the heat conduction path topological relationship. Then, correct the regulation weight through the matching degree between the coverage area and the equipment location to generate a dynamic priority sequence. Finally, iteratively screen out the equipment nodes that violate the energy consumption or stability threshold to construct a priority regulation chain. This method improves the response speed and accuracy of the system. In some embodiments, constructing a priority regulation chain for the temperature control equipment based on the spatio-temporal coupling characteristics and meteorological forecast data in step 104 includes:

[0192] 701. Extract the spatial thermal inertia index and time response sensitivity index of the temperature control device according to the correlation between the distributed thermal inertia parameter of the temperature control device and the future period temperature fluctuation parameter in the meteorological forecast data in the spatio-temporal coupling characteristics.

[0193] In step 701, the spatio-temporal coupling characteristics describe the relationship pattern of device power consumption and thermal environment parameters changing with time and space. The distributed thermal inertia parameter is an index to measure the reaction speed of different positions in the building to temperature changes. The future period temperature fluctuation parameter in the meteorological forecast data provides temperature prediction information for a period of time in the future. The spatial thermal inertia index reflects the resistance ability of each point inside the building to temperature changes, and the time response sensitivity index represents the response speed of the temperature control device to temperature changes.

[0194] In the embodiment of the present application, first, collect the spatial distribution information of the temperature control device and its thermal inertia parameter, and obtain the meteorological forecast data for the future period. Then, extract the spatial thermal inertia index and time response sensitivity index of each temperature control device through a data analysis algorithm (such as regression analysis). Then, evaluate the adaptability of each device to future temperature fluctuations based on these indexes. Finally, integrate all the information to form a data set that comprehensively reflects the adaptability and response speed of the temperature control device.

[0195] 702. Based on the correlation relationship between the spatial thermal inertia index and the time response sensitivity index, establish the regulation influence weight of the temperature control device, and combine the topological relationship of the heat conduction path between the temperature control devices to generate a preliminary priority sequence.

[0196] In step 702, the regulation influence weight is established based on these two indexes and is used to evaluate the influence of each temperature control device in the regulation process. The topological relationship of the heat conduction path describes the heat transfer path between the temperature control devices. The preliminary priority sequence is generated according to the above parameters and relationships to guide the priority ranking of the temperature control devices.

[0197] In the embodiment of the present application, first, calculate the regulation influence weight according to the spatial thermal inertia index and the time response sensitivity index. Then, analyze the topological relationship of the heat conduction path between the temperature control devices to identify the key nodes. Then, use a graph theory algorithm to assign priorities to each device based on the regulation influence weight. Finally, generate a preliminary priority sequence, which considers the interaction between the devices and their importance to the system temperature control.

[0198] 703. According to the spatio-temporal distribution characteristics of extreme temperature events in the meteorological forecast data, dynamically adjust the preliminary priority sequence, and correct the regulation influence weight through the matching degree between the area covered by the extreme temperature event and the spatial position of the temperature control device to generate a dynamic priority sequence.

[0199] In step 703, an extreme temperature event refers to significant high or low temperature weather that may occur within a certain future time period. The spatio-temporal distribution characteristics describe the time and location of the occurrence of these extreme events. The regulation influence weight is further adjusted on this basis. By considering the matching degree between the area covered by the extreme temperature event and the spatial location of the temperature control equipment, a dynamic priority sequence is generated. This sequence is more in line with the actual needs and improves the ability to respond to extreme weather.

[0200] In the embodiment of the present application, first, meteorological forecast data is analyzed to identify possible extreme temperature events in the future and their specific locations and times. Then, the coverage ranges of these events are compared with the locations of the temperature control equipment to evaluate the affected degree. Next, according to the evaluation results, the regulation influence weights of each device are adjusted, and a dynamic priority sequence is re-generated. Finally, it is ensured that this sequence can effectively cope with the upcoming extreme temperature challenges. This process helps to identify the temperature control equipment that most needs to be regulated, improves the system's ability to respond to extreme weather, and ensures the efficient operation of the system.

[0201] 704. By iteratively screening the temperature control equipment nodes in the dynamic priority sequence that violate the energy consumption threshold or the thermal stability threshold, a priority regulation chain for the temperature control equipment is constructed.

[0202] In step 704, the dynamic priority sequence is the adjusted device priority ranking generated in step 703. The energy consumption threshold and the thermal stability threshold are respectively the set upper limit of energy consumption and the minimum temperature requirement for maintaining indoor comfort. By iteratively screening the temperature control equipment nodes in the dynamic priority sequence that violate the above thresholds, a priority regulation chain for the temperature control equipment is constructed. This regulation chain ensures the efficient operation of the system and at the same time meets the comfort requirements of users.

[0203] In the embodiment of the present application, first, the energy consumption and thermal stability thresholds are set as the screening criteria. Then, each device node in the dynamic priority sequence is checked one by one to see if it meets these conditions. Next, the nodes that do not meet the conditions are marked and removed from the sequence or their priorities are rearranged. Finally, a priority regulation chain that meets all the criteria is constructed to ensure the efficient and stable operation of the entire system. The priority regulation chain provides a guarantee for the optimized operation of the temperature control system.

[0204] The following is a specific example:

[0205] In an office building project, technicians first extract the spatial thermal inertia index and time response sensitivity index of each device based on the spatial distribution and thermal inertia parameters of the temperature control devices, combined with the meteorological forecast data for future periods. Then, based on these indices, a regulation influence weight is established, and considering the topological relationship of the heat conduction paths between the temperature control devices, a preliminary priority sequence is generated. Subsequently, extreme temperature events in the meteorological forecast data are analyzed to dynamically adjust the preliminary priority sequence to better cope with extreme weather conditions. Finally, through an iterative screening process, device nodes that violate the energy consumption or thermal stability thresholds are removed, and an efficient priority regulation chain is constructed, significantly improving the response speed and energy utilization efficiency of the temperature control system in the building.

[0206] In summary, through steps 701 to 704, through a series of in-depth data analyses and technical means, an accurate understanding and optimization of the regulation strategy for temperature control devices are achieved. This method not only improves the response speed and accuracy of the temperature control system but also provides a solid foundation for formulating more intelligent and personalized energy-saving strategies. By carefully analyzing the internal heat transfer process of the building and the external meteorological conditions, unnecessary energy losses can be effectively reduced, and the overall energy efficiency performance of the building can be improved. At the same time, this method enhances the ability to cope with extreme weather conditions, ensuring the comfort and stability of the indoor environment. In addition, this method also improves the response speed and accuracy of the system, making the temperature control management system more efficient and intelligent. Through an accurate priority regulation chain, energy consumption can be better managed, and the satisfaction of the occupants can be improved.

[0207] Figure 2 The present application provides a structural schematic diagram of a big data information processing system, as Figure 2 shown. The system includes:

[0208] An acquisition module 21, which acquires big data information of a building, where the big data information includes the thermal resistance parameters of the envelope structure and spatial topology data extracted through a building information model, and combines the thermal distribution map collected by an infrared thermal imaging array to generate an energy distribution model with thermal property marks;

[0209] A generation module 22, which constructs a set of dynamic heat balance equations based on the heat conduction path characteristics in the energy distribution model, and generates three-dimensional temperature field reconstruction data through the difference compensation between the temperature field scan data and the simulation results of the set of dynamic heat balance equations;

[0210] An extraction module 23, which inputs the thermal resistance parameters in the energy distribution model, the coordinates of the heat flow anomaly area in the three-dimensional temperature field reconstruction data, and the current data of the air conditioning unit into an event feature extraction network driven by deep learning to extract the spatio-temporal coupling features of device power consumption and thermal environment parameters;

[0211] The generating module is further configured to construct a priority control chain for the temperature control device by using the spatio-temporal coupling features and meteorological forecast data, and generate multi-level linkage rules including adjustment of the ventilation rate of the fresh air unit, optimization of the lighting dimming gradient, and triggering of chilled water release from the chilled water storage tank; and dynamically generate a power load smoothing strategy for the building according to the sensitive node parameters of the priority control chain.

[0212] Figure 2 The described big data information processing system can execute Figure 1 For the big data information processing method described in the foregoing embodiments, its implementation principle and technical effects will not be elaborated further. For the big data information processing system in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0213] In a possible design, Figure 2 The big data information processing system of the foregoing embodiments can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0214] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0215] The processing component 32 is used for the above Figure 1 The big data information processing method of the foregoing embodiments.

[0216] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0217] The storage component 31 is configured to store various types of data to support operations on the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0218] Of course, the computing device may necessarily further include other components, such as input / output interfaces, display components, communication components, etc.

[0219] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above-mentioned peripheral interface module can be an output device, an input device, etc.

[0220] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0221] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above-mentioned processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0222] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 big data information processing method shown in the above embodiments.

[0223] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0224] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0225] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for processing big data information, characterized in that: include: Obtaining big data information of buildings, including thermal resistance parameters and spatial topology data of enclosure structures extracted through building information models, combined with thermal distribution maps collected by infrared thermal imaging arrays, to generate an energy distribution model with thermal attribute markers; Based on the heat conduction path characteristics in the energy distribution model, a dynamic heat balance equation set is constructed, and three-dimensional temperature field reconstruction data is generated by compensating the difference between the temperature field scanning data and the simulation results of the dynamic heat balance equation set; Input the thermal resistance parameters in the energy distribution model, the coordinates of the heat flow abnormal area in the three-dimensional temperature field reconstruction data, and the current data of the air-conditioning unit into the event feature extraction network driven by deep learning to extract the spatiotemporal coupling characteristics of equipment power consumption and thermal environment parameters; Based on the spatiotemporal coupling characteristics and meteorological forecast data, a priority control chain of temperature control equipment is constructed to generate multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization, and cold storage tank release triggering; Dynamically generate a power load balancing strategy for a building based on the sensitive node parameters of the priority control chain; The priority control chain of the temperature control equipment is constructed based on the spatiotemporal coupling characteristics and the meteorological forecast data, including: Extracting the spatial thermal inertia index and the time response sensitivity index of the temperature control device according to the correlation between the distributed thermal inertia parameters of the temperature control device in the spatiotemporal coupling characteristics and the temperature fluctuation parameters of the future period in the meteorological forecast data; Based on the correlation between the spatial thermal inertia index and the time response sensitivity index, the control influence weight of the temperature control device is established, and the preliminary priority sequence is generated in combination with the topological relationship of the heat conduction path between the temperature control devices; According to the spatiotemporal distribution characteristics of extreme temperature events in the meteorological forecast data, the preliminary priority sequence is dynamically adjusted, and the control influence weight is corrected by the matching degree between the extreme temperature event coverage area and the spatial position of the temperature control equipment to generate a dynamic priority sequence; By iteratively screening the temperature control device nodes that violate the energy consumption threshold or the thermal stability threshold in the dynamic priority sequence, a priority regulation chain of the temperature control device is constructed.

2. The method according to claim 1, characterized in that: The deep learning-driven event feature extraction network is used to extract the spatiotemporal coupling features of device power consumption and thermal environment parameters, including: The thermal resistance parameters in the energy distribution model are superimposed with the boundary data of the heat flow anomaly area in the three-dimensional temperature field reconstruction data to generate a spatial correlation map with the heat conduction hysteresis effect; Based on the phase fluctuation characteristics of the current data of the air-conditioning unit, the thermal disturbance propagation path that is strongly related to the operation of the electrical equipment is marked in the spatial correlation map; According to the spatial coverage of the thermal disturbance propagation path, the thermal resistance parameter is attenuated and weighted according to the conduction path length, and the air flow characteristic data in the three-dimensional temperature field reconstruction data is superimposed to generate a thermal-electric coupling factor; Based on the three-dimensional distribution density of the thermal-electric coupling factor, a joint response sequence is constructed, wherein the joint response sequence generates multi-dimensional association rules by analyzing the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the thermal flow anomaly area; The joint response sequence is cross-domain matched with the multi-dimensional association rules, and the spatiotemporal coupling characteristics of the device power consumption and thermal environment parameters are extracted.

3. The method according to claim 2, characterized in that The joint response sequence is constructed, and the joint response sequence generates multi-dimensional association rules by analyzing the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the heat flow abnormal area, including: Based on the three-dimensional distribution density of the thermal-electric coupling factor, the dynamic correlation intensity of the geometric topology of the heat flow anomaly area and the spectrum characteristics of the current data in the spatial coordinates is superimposed in multiple channels to construct a joint response sequence; Based on the coupling relationship between the axial gradient distribution of the joint response sequence and the frequency domain energy attenuation rate, a set of boundary curvature change points of the geometric topology of the heat flow anomaly area is extracted; Matching the boundary curvature change point set with the zero-crossing point distribution of the current data spectrum feature to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flow anomaly area, wherein the dynamic co-occurrence probability field includes a nonlinear mapping relationship between the zero-crossing point density and the boundary curvature change rate; Based on the synchronous change interval of the thermal fluctuation amplitude of the main axis and the frequency domain energy attenuation rate, a multi-dimensional association rule is generated by constraining the boundary curvature change threshold and the gradient range of the zero-crossing point density.

4. The method according to claim 3, characterized in that The step of matching the boundary curvature change point set with the zero-crossing point distribution of the current data spectrum feature to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flow anomaly area includes: Performing gradient direction clustering on the boundary curvature change point set to generate a curvature change point cluster group, and using the centroid connection line of the curvature change point cluster group as an initial matching reference chain; Performing annular neighborhood phase polarity analysis on the zero-crossing point distribution of the current data spectrum characteristics, screening out a zero-crossing point sequence, and segmenting the zero-crossing point sequence to generate a zero-crossing point segmented trajectory chain; According to the direction of the main axis of the geometric topology of the heat flow anomaly area, dynamically adjust the local curvature weight of the initial matching reference chain and the phase polarity weight of the zero-crossing segmented trajectory chain to generate a double-chain dynamic matching probability map; According to the spatial aggregation degree of the double-chain dynamic matching probability map, coupling analysis is performed on the clusters of the curvature change points, and a continuous probability field is generated by superimposing the probability density gradients of adjacent clusters on the main axis; Direction-aware probability diffusion is performed on the continuous probability field, anisotropic diffusion constraints are applied along the main axis, and a dynamic co-occurrence probability field is generated according to the distribution density of the double-chain dynamic matching probability map.

5. The method according to claim 1, characterized in that The method of compensating the difference between the temperature field scanning data and the simulation results of the dynamic heat balance equation set to generate three-dimensional temperature field reconstruction data includes: Based on the discrete sampling point distribution of the temperature field scanning data, the simulation results of the dynamic heat balance equation set are spatially discretized to generate simulated temperature field grid data; The local compensation factor is obtained by analyzing the temperature difference between the temperature field scanning data and the simulated temperature field grid data, and the continuous compensation field data is generated by smooth diffusion according to the spatial continuity constraint; Iteratively coupling the continuous compensation field data with the simulation results of the dynamic heat balance equation set, and reversely correcting the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set; The spatial gradient distribution of the temperature field scanning data is subjected to back propagation analysis according to the modified dynamic heat balance equation set to generate compensated temperature field increment data, and three-dimensional temperature field reconstruction data is generated by superimposing the simulated temperature field grid data.

6. The method according to claim 5, characterized in that The coupling and iteration of the continuous compensation field data and the simulation results of the dynamic heat balance equation set, and reverse correction of the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set, includes: Generate a node correction coefficient based on the compensation amount amplitude and direction of the continuous compensation field data, wherein the absolute value of the node correction coefficient is positively correlated with the amplitude of the continuous compensation field data; According to the heat conduction path topological relationship of the dynamic heat balance equation set, a heat conduction path weight factor is defined, and the node correction coefficient is weightedly balanced for the conductivity across nodes to generate a node correction coefficient distribution; Reversely superimpose the node correction coefficient distribution and the heat conduction term of the dynamic heat balance equation set to generate an intermediate correction equation set, and extract heat flow residual parameters of the intermediate correction equation set and the dynamic heat balance equation set; The node correction coefficient distribution is iteratively readjusted by using the heat flow residual parameter until the heat flow residual parameter satisfies the heat flow conservation constraint condition of the dynamic heat balance equation set, thereby generating a corrected dynamic heat balance equation set.

7. A big data information processing system, characterized in that: include: An acquisition module is used to acquire big data information of buildings, wherein the big data information includes thermal resistance parameters and spatial topology data of enclosure structures extracted through building information models, combined with thermal distribution maps collected by infrared thermal imaging arrays, to generate an energy distribution model with thermal attribute markers; A generation module, for constructing a set of dynamic heat balance equations based on the heat conduction path characteristics in the energy distribution model, and generating three-dimensional temperature field reconstruction data by compensating for the difference between the temperature field scanning data and the simulation results of the set of dynamic heat balance equations; An extraction module, used to input the thermal resistance parameters in the energy distribution model, the coordinates of the heat flow abnormal area in the three-dimensional temperature field reconstruction data, and the current data of the air-conditioning unit into the event feature extraction network driven by deep learning, and extract the spatiotemporal coupling characteristics of the equipment power consumption and thermal environment parameters; The generation module is also used to construct a priority control chain of temperature control equipment based on the spatiotemporal coupling characteristics and meteorological forecast data, generate multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization and cold storage tank release triggering; dynamically generate power load balancing strategies for buildings based on sensitive node parameters of the priority control chain; The priority regulation chain of the temperature control equipment is constructed based on the spatiotemporal coupling characteristics and the meteorological forecast data, including: extracting the spatial thermal inertia index and the time response sensitivity index of the temperature control equipment according to the correlation between the distributed thermal inertia parameters of the temperature control equipment in the spatiotemporal coupling characteristics and the temperature fluctuation parameters of the future time period in the meteorological forecast data; establishing the regulation influence weight of the temperature control equipment based on the correlation between the spatial thermal inertia index and the time response sensitivity index, and generating a preliminary priority sequence in combination with the topological relationship of the heat conduction path between the temperature control equipment; dynamically adjusting the preliminary priority sequence according to the spatiotemporal distribution characteristics of extreme temperature events in the meteorological forecast data, correcting the regulation influence weight by the matching degree between the extreme temperature event coverage area and the spatial position of the temperature control equipment, and generating a dynamic priority sequence; and constructing the priority regulation chain of the temperature control equipment by iteratively screening the temperature control equipment nodes that violate the energy consumption threshold or the thermal stability threshold in the dynamic priority sequence.

8. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data information processing method as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a big data information processing method as described in any one of claims 1 to 6 is implemented.

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