Big data information processing method and system

Abstract

The invention provides a big data information processing method and system. According to the method, an energy distribution model is generated by combining a building information model with infrared thermal imaging data, and a dynamic thermal balance equation set is constructed according to the model so as to generate three-dimensional temperature field reconstruction data. Then, thermal resistance parameters in the energy distribution model, heat flow abnormal area coordinates in the three-dimensional temperature field reconstruction data and air conditioning unit current data are analyzed through a deep learning algorithm, and space-time coupling characteristics of equipment power consumption and thermal environment parameters are extracted; then, a priority regulation and control chain of the temperature control equipment is formulated, and multi-stage linkage rules of the fresh air exchange rate, illumination dimming and cold release of the cold storage tank are covered; finally, the chilled water pump phase difference, the air conditioner group operation mode and the emergency lighting power limiting value are dynamically adjusted, and power load stabilization is achieved. According to the technical scheme provided by the invention, the data information processing efficiency and stability of the large building can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a big data information processing method and system. Background Art

[0002] As modern buildings develop 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 protection requirements, it is also particularly important to achieve effective management of power loads and reduce unnecessary energy losses.

[0003] At present, in response to the above technical needs, data collection and analysis systems based on the Internet of Things technology combined with cloud computing platforms have been widely used. The system collects various environmental data in the building in real time by distributing a variety of sensors (such as temperature sensors, humidity sensors, etc.) inside the building, and uploads these data to the cloud server for centralized processing. With the help of advanced algorithm models, the system can predict the trend of heat load changes in the future, thereby providing a basis for the automatic adjustment of temperature control equipment.

[0004] Although solutions based on the Internet of Things and cloud computing have improved the energy management problems of buildings to a certain extent, there are still some limitations. First, due to the reliance on cloud platforms for data analysis, this leads to the problem of data processing delays. Especially in the case of poor network conditions, real-time regulation may not be achieved, affecting the user experience. Secondly, existing systems often focus on monitoring a single type of environmental parameter and lack the ability to comprehensively analyze multi-source heterogeneous data (such as electrical parameters, spatial topology, etc.), making it difficult to fully capture the complex thermal environment characteristics inside the building. Finally, the system's scalability and flexibility are insufficient. When facing different building structures or special application scenarios, a lot of customized development work may be required, which increases the difficulty and cost of implementation. Summary of the invention

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

[0006] In a first aspect, an embodiment of the present application provides a method for processing big data information, including: 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; According to the sensitive node parameters of the priority control chain, a power load balancing strategy including phase difference control of chilled water pumps, staggered operation of air conditioning groups and dynamic power limit of emergency lighting is dynamically generated.

[0007] Optionally, the extracting the spatiotemporal coupling features of device power consumption and thermal environment parameters through the deep learning driven event feature extraction network includes: 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 device power consumption and thermal environment parameters are extracted.

[0008] Optionally, 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.

[0009] Optionally, 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 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.

[0010] Optionally, the generating three-dimensional temperature field reconstruction data by compensating the difference between the temperature field scanning data and the simulation result of the dynamic heat balance equation set 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.

[0011] Optionally, the coupling and iterating 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, 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.

[0012] Optionally, the step of constructing a priority control chain of temperature control equipment based on the spatiotemporal coupling characteristics and meteorological forecast data includes: 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.

[0013] In a second aspect, an embodiment of the present application provides a big data information processing system, including: An extraction module obtains big data information of buildings, including thermal resistance parameters and spatial topology data of enclosure structures extracted through building information models, and generates an energy distribution model with thermal attribute markers in combination with thermal distribution maps collected by infrared thermal imaging arrays; A scanning module, 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 by compensating the difference between the temperature field scanning data and the simulation results of the set of dynamic heat balance equations; An input module inputs 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 a deep learning-driven event feature extraction network to extract the spatiotemporal coupling characteristics of the equipment power consumption and the thermal environment parameters; A construction module is used to construct a priority control chain of temperature control equipment based on the spatiotemporal coupling characteristics and meteorological forecast data, and to generate multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization, and cold storage tank release triggering; A generation module dynamically generates a power load balancing strategy including phase difference control of chilled water pumps, staggered operation of air conditioning groups and dynamic power limit of emergency lighting according to the sensitive node parameters of the priority control chain.

[0014] In a third aspect, an embodiment of the present application provides a computing device, comprising 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.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a big data information processing method as described in the first aspect.

[0016] In an embodiment of the present application, big data information of a building is obtained, the big data information including thermal resistance parameters and spatial topological data of the enclosure structure extracted through a building information model, and combined with a thermal distribution map collected by an infrared thermal imaging array, an energy distribution model with thermal attribute labels is generated; 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 difference compensation between temperature field scanning data and simulation results of the dynamic heat balance equation set; 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 a deep learning-driven event feature extraction network 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; according to the sensitive node parameters of the priority control chain, a power load balancing strategy including phase difference control of chilled water pumps, peak-shifting operation of air-conditioning groups, and dynamic power limit of emergency lighting is dynamically generated.

[0017] The technical solution of this application has the following beneficial effects: This application obtains big data information of buildings, which includes thermal resistance parameters and spatial topological data of enclosure structures extracted through building information models, and combined with thermal distribution maps collected by infrared thermal imaging arrays, can accurately describe the thermal environment characteristics inside buildings. This process provides detailed basic data for subsequent analysis and improves the accuracy of understanding the heat distribution inside buildings. A set of dynamic heat balance equations is constructed based on the heat conduction path characteristics in the energy distribution model, and the temperature field scanning data is used for difference compensation to generate three-dimensional temperature field reconstruction data. 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 spatiotemporal coupling characteristics and meteorological forecast data, a priority control chain of temperature control equipment is constructed, and multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization, and cold storage tank release triggering are formulated. This method can realize intelligent adjustment of temperature control equipment, improve energy efficiency, and ensure indoor comfort. According to the sensitive node parameters of the priority control chain, a power load balancing strategy including phase difference control of chilled water pumps, peak-shifting operation of air conditioning groups, and dynamic power limit of emergency lighting is dynamically generated. This not only helps reduce power consumption peaks, but also ensures stable operation of the system.

[0018] Furthermore, by superimposing the thermal resistance parameters in the energy distribution model with the boundary data of the heat flow anomaly area in the three-dimensional temperature field reconstruction data, a spatial correlation map with the heat conduction hysteresis effect is generated, and the thermal disturbance propagation path is annotated based on the current data of the air-conditioning unit. The thermal resistance parameter attenuation weight is calculated and superimposed with the air flow characteristic data to obtain the thermal power coupling factor. Finally, the multi-dimensional association rules are generated by jointly analyzing the current data spectrum characteristics and the geometric topological co-occurrence probability of the heat flow anomaly area through the joint response sequence, so as to extract the spatiotemporal coupling characteristics of the equipment power consumption and thermal environment parameters. This method greatly enhances the understanding of the operating status of electrical equipment in complex thermal environments and its impact on the thermal environment, so that the system can more accurately identify the key areas and factors that cause energy loss. Through the precise positioning and analysis of these key points, not only can more refined regulation of temperature control equipment be achieved, but also energy consumption can be effectively reduced while ensuring comfort, while improving the intelligence level and response efficiency of the entire building energy management system.

[0019] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flowchart of a big data information processing method provided by the present application is shown; Figure 2 A schematic diagram of the structure of a big data information processing system provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0023] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between 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 of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to different types.

[0024] This application aims to use building information model and infrared thermal imaging technology to generate an energy distribution model with thermal attribute tags, then construct a set of dynamic heat balance equations based on the heat conduction path characteristics in the model, and perform difference compensation through temperature field scanning data to generate three-dimensional temperature field reconstruction data. Furthermore, the energy distribution model, three-dimensional temperature field reconstruction data and air-conditioning unit current data are combined and input into a deep learning network to extract the spatiotemporal coupling characteristics of equipment power consumption and thermal environment parameters. Finally, based on these characteristics and meteorological forecast data, a temperature control equipment control strategy is formulated to optimize the internal environment control of the building.

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0026] Figure 1 A flowchart of a method for processing big data information is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: 101. Obtain big data information of a building, wherein the big data information includes thermal resistance parameters and spatial topology data of the enclosure structure extracted through a building information model, combined with a thermal distribution map collected by an infrared thermal imaging array, to generate an energy distribution model with thermal attribute markers; In this step, the Building Information Model is a digital tool used to create and manage building project information, which contains all the physical and functional characteristics of the building.

[0027] The thermal resistance parameter of the building envelope structure refers to the resistance value of the building envelope structure such as the exterior wall and roof to heat conduction, and is an important indicator for measuring the thermal insulation performance of building materials.

[0028] Spatial topology data describes the spatial layout and relationships within a building, such as the location, size, and connection methods of rooms.

[0029] The infrared thermal imaging array is a device that uses infrared technology to capture the temperature distribution on the surface of an object. The thermal distribution map it generates can intuitively show the temperature differences between different areas inside and outside a building.

[0030] Thermal maps are images generated by infrared thermal imaging technology that show the distribution of surface temperature. In the construction field, they can be used to identify abnormal heat flow areas in buildings, such as cold bridges around windows or insulation failure points in walls.

[0031] The energy distribution model is a comprehensive digital model that integrates all the above information and adds thermal property tags to show the energy flow inside the building.

[0032] In the embodiment of the present application, first, the thermal resistance parameters and spatial topological data of the building envelope are extracted through the building information model. This process involves the use of specific software to parse the building design file 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, an image processing algorithm is used to identify and mark the abnormal heat flow areas, and this information is integrated into the energy distribution model. Finally, all the collected data are combined to generate an energy distribution model containing detailed thermal property information. This model accurately depicts the heat distribution inside the building and reflects its response characteristics to changes in the external environment.

[0033] In an office building project, the thermal resistance parameters and spatial topology data of the building envelope were first collected using building information modeling software, including the material properties of each floor and wall and their relative positions. Subsequently, in cold winter weather conditions, a high-resolution infrared thermal imaging device was used to conduct a comprehensive scan of the building, obtaining a detailed thermal distribution map and discovering obvious cold bridge effects around several windows. Based on these data, an energy distribution model that accurately reflects the actual heat flow was constructed, providing a basis for the subsequent optimization of the air conditioning system operation.

[0034] 102. 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; In this step, the energy distribution model is a comprehensive digital model that integrates all the above information and adds thermal property tags to show the energy flow inside the building.

[0035] The dynamic heat balance equations are a set of mathematical expressions used to simulate the heat transfer process in a building, taking into account factors such as the thermal conductivity of building materials and boundary conditions.

[0036] The temperature field scanning data is the temperature value of each location in the building collected in real time by the sensor network.

[0037] The three-dimensional temperature field reconstruction data is a compensated three-dimensional temperature distribution model, which more accurately reflects the actual temperature distribution inside the building.

[0038] In the embodiment of the present application, first, a set of dynamic heat balance equations is established based on the characteristics of the heat conduction path in the energy distribution model. Then, a distributed temperature sensor network deployed inside the building is used to collect temperature field scanning data. By comparing the temperature field scanning data with the simulation results of the set of dynamic heat balance equations, the differences are found and adjusted through the compensation algorithm, and finally three-dimensional temperature field reconstruction data that is 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.

[0039] 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 the 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.

[0040] 103. 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 the equipment power consumption and thermal environment parameters; In this step, thermal resistance parameters are data that describe the resistance of building envelope structures (such as walls, windows, etc.) to heat conduction. It reflects the ability of materials to prevent heat transfer and is crucial for evaluating the thermal insulation performance of buildings.

[0041] The coordinates of the heat flow anomaly area refer to the coordinates of the location where the temperature is significantly higher or lower than the surrounding area marked in the three-dimensional temperature field reconstruction data.

[0042] The current data of the air conditioning unit records the current consumption of the air conditioning system when it is running, and is one of the important indicators for evaluating the power consumption of the equipment.

[0043] The spatiotemporal coupling characteristics refer to the relationship patterns of these variables over time and space, revealing how device power consumption responds to different thermal environment conditions.

[0044] In the embodiment of the present application, first, the thermal resistance parameters, the coordinates of the abnormal heat flow 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 spatiotemporal coupling characteristics between the device power consumption and the thermal environment parameters through training. This process involves a lot of data preprocessing, feature engineering, and model training, and the final output results help to understand the operating status of the equipment and its impact on the environment.

[0045] Based on the data obtained in the first two steps, a deep learning model was built to analyze the energy consumption of different areas in 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 human activity.

[0046] 104. 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; In this step, the spatiotemporal coupling characteristics refer to the relationship patterns of these variables over time and space, which reveal how the device power consumption responds to different thermal environment conditions.

[0047] Meteorological forecast data contains weather forecast information for a period of time in the future, including changing trends of factors such as temperature and humidity.

[0048] Multi-level linkage rules refer to a series of operating instructions set according to different conditions, covering multiple aspects such as adjustment of the ventilation rate of the fresh air unit and optimization of the lighting dimming gradient, aiming to achieve energy-saving goals while ensuring comfort.

[0049] The priority control chain is a series of control strategies formulated based on the spatiotemporal coupling characteristics and meteorological forecast data to guide the effective operation of temperature control equipment.

[0050] In the embodiment of the present application, a priority control chain for temperature control equipment is formulated by combining the spatiotemporal coupling features extracted in the above steps with the weather forecast data. This chain contains multiple levels of operation instructions, which automatically adjust the equipment operation status according to different climatic conditions and indoor needs. For example, in hot weather, refrigeration equipment is started first, while in low temperature, ventilation is appropriately reduced. This saves energy and improves user experience.

[0051] During the summer high temperature warning period, a priority control chain for temperature control equipment was developed based on weather forecast data, the energy distribution model of the office building, and the three-dimensional temperature field reconstruction data. First, the ventilation rate of the fresh air unit was adjusted, and nighttime ventilation was increased to reduce the basic value of indoor temperature. At the same time, the dimming gradient of the lighting system was optimized to reduce the light intensity during non-working hours. This series of measures not only reduced the startup pressure of the air conditioning system, but also prepared for the upcoming high temperature weather.

[0052] 105. Based on the sensitive node parameters of the priority control chain, 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 is dynamically generated.

[0053] In this step, sensitive node parameters include but are not limited to the maximum power, startup time, operation mode and other information of the equipment. By monitoring and analyzing these parameters, an effective load management strategy can be formulated.

[0054] Chilled water pump phase difference control refers to adjusting the working phase of the chilled water pump motor to optimize its working state and avoid overloading the power grid due to starting multiple high-power devices at the same time.

[0055] Peak-shaving operation of air-conditioning groups is a load management technology that arranges different air-conditioning units to operate in different time periods to avoid peak loads caused by turning on all equipment at the same time.

[0056] The dynamic power limit of emergency lighting refers to the maximum power limit of a temporary lighting system set up to deal with emergencies.

[0057] Electricity load balancing strategy is a series of technical means and operating procedures aimed at balancing the relationship between electricity demand and supply.

[0058] 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. Through real-time monitoring of sensitive node parameters, the working phase difference of the chilled water pump is dynamically adjusted, the peak operation plan of the air conditioning system is implemented, and the power 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.

[0059] Based on the priority control chain, phase difference control is further implemented for the office building's chilled water pumps, and the air conditioning groups are arranged to run at staggered peaks to avoid overloading the power grid due to the simultaneous startup of all equipment. In addition, dynamic power limits are set for the emergency lighting system to ensure that basic lighting needs can be maintained during peak power 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 energy efficiency and ensuring power supply stability. These two steps work together to ensure that the office building can still operate efficiently and stably under extreme weather conditions.

[0060] In summary, steps 101 to 105 achieve refined management and efficient operation of the building's internal environment. From initial data collection to final strategy execution, each step makes full use of modern information technology to ensure that the building can achieve the best energy-saving effect while meeting user needs. This comprehensive solution not only improves energy efficiency, but also enhances the building's ability to adapt to changes in the external environment.

[0061] In order to further improve the understanding of the spatiotemporal coupling characteristics between device 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 flow anomaly area in the three-dimensional temperature field reconstruction data. According to the spatial coverage of the thermal disturbance propagation path, the co-occurrence probability of the current data spectrum characteristics and the geometric topology of the heat flow anomaly area is analyzed, and finally the spatiotemporal coupling characteristics of the device power consumption and thermal environment parameters are accurately extracted, providing a basis for subsequent intelligent regulation. In some embodiments, the event feature extraction network driven by deep learning described in step 103 extracts the spatiotemporal coupling characteristics of device power consumption and thermal environment parameters, including: 201. Superimpose the thermal resistance parameters in the energy distribution model 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 a heat conduction hysteresis effect; In step 201, the thermal resistance parameter describes the data of the resistance of the building envelope to heat transfer, reflecting the ability of different materials to prevent heat from being transferred from one side to the other. The boundary data of the heat flow anomaly area are the position coordinates marked in the three-dimensional temperature field reconstruction 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 to show the hysteresis effect and its impact range in the heat transfer process. The heat conduction hysteresis effect refers to the time delay phenomenon caused by material properties and distance in the heat transfer process.

[0062] In the embodiment of the present application, the thermal resistance parameters in the energy distribution model are firstly superimposed with the boundary data of the heat flow anomaly zone in the three-dimensional temperature field reconstruction data, and the image fusion technology is used to generate a spatial correlation map with a heat conduction hysteresis effect. This process not only reveals the basic pattern of heat flow inside the building, but also considers the influence of material properties on the heat transfer rate. The final result is a spatial correlation map that can accurately reflect the flow of heat inside the building.

[0063] 202. Based on the phase fluctuation characteristics of the current data of the air-conditioning unit, marking the thermal disturbance propagation path that is strongly related to the operation of the electrical equipment in the spatial correlation map; In step 202, the phase fluctuation characteristics of the air conditioning unit current data refer to data recording the current changes when the air conditioning system is working, which can reflect the working status and load level of the equipment. The thermal disturbance propagation path is the propagation path of the heat fluctuation caused by the operation of electrical equipment in the building, which identifies which areas are most affected by the operation of electrical equipment.

[0064] In the embodiment of the present application, based on the phase fluctuation characteristics of the current data of the air-conditioning unit, the current data is analyzed using a signal processing algorithm to identify the phase fluctuation characteristic points, and map them to the corresponding thermal disturbance propagation paths in the spatial correlation map, thereby identifying the key heat transfer paths. By marking the thermal disturbance propagation paths that are strongly related to the operation of the electrical equipment in the spatial correlation map, this step helps to accurately identify the hot spots of heat fluctuations caused by the operation of the electrical equipment.

[0065] 203. 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; In step 203, the thermal-electric coupling factor is an indicator that combines the thermal resistance parameter and the air flow characteristic data to quantify the interaction strength between heat and electrical energy. The conduction path length attenuation weighting is a method of adjusting the degree of influence of heat according to the distance it is transmitted along different paths. The air flow characteristic data describes the direction and speed of air flow inside the building and is crucial to understanding how heat spreads within the building.

[0066] In the embodiment of the present application, first, the spatial coverage of the thermal disturbance propagation path is analyzed, and the length of each conduction path is calculated. Then, the thermal resistance parameters are attenuated and weighted based on these lengths. Then, the air flow characteristic data in the three-dimensional temperature field reconstruction data is superimposed to generate a thermal-electric coupling factor. Specifically, the finite element analysis method is used to process complex multidimensional data sets to ensure that the generated factors can accurately reflect the actual situation. Finally, all information is integrated to form a comprehensive thermal-electric coupling factor.

[0067] 204. 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 heat flow abnormal area; 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 heat flow anomaly area geometric topology. Multidimensional association rules are extracted from these sequences to describe the interaction pattern between different variables. The current data spectrum characteristics are the results obtained after frequency domain analysis of the current data, and the heat flow anomaly area geometric topology refers to the spatial form and positional relationship of the heat flow anomaly area.

[0068] In the embodiment of the present application, the fast Fourier transform is used to analyze the spectral characteristics of the current data, and the geometric topology of the heat flow anomaly area is analyzed in combination with the geometric modeling tool to find the co-occurrence pattern between the two. Then, multidimensional 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 information is integrated to form a comprehensive multidimensional association rule to guide subsequent optimization strategies.

[0069] 205. Cross-domain matching is performed between the joint response sequence and the multi-dimensional association rule, and spatiotemporal coupling characteristics of device power consumption and thermal environment parameters are extracted.

[0070] 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 spatiotemporal coupling feature is extracted from this matching to describe the relationship pattern between the device power consumption and the thermal environment parameters over 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.

[0071] In the embodiment of the present application, a time series analysis method, such as an autoregressive integrated moving average model, is used in combination with geographic information system data to analyze the variation patterns of device power consumption and thermal environment parameters over time and space. Then, based on the above analysis results, a spatiotemporal coupling feature is generated. This step uses data mining techniques, such as cluster analysis and principal component analysis, to extract key features. Finally, all information is integrated to form a comprehensive spatiotemporal coupling feature to guide subsequent optimization strategies.

[0072] Here is a specific example: In an office building project, the building information modeling system was first used to extract the thermal resistance parameters and spatial topology data of the building's envelope structure, and infrared thermal imaging equipment was used to obtain a detailed thermal distribution map. Then, the temperature field scanning data was collected through a distributed temperature sensor network to generate three-dimensional temperature field reconstruction data. On this basis, the technicians superimposed the thermal resistance parameters with the boundary data of the heat flow anomaly area to generate a spatial correlation map. Then, based on the phase fluctuation characteristics of the air-conditioning unit current data, the main thermal disturbance propagation paths were marked in the spatial correlation map. Next, based on the spatial coverage of these paths, the thermal-electric coupling factor was calculated, and a joint response sequence was further constructed. Finally, the spatiotemporal coupling characteristics of equipment power consumption and thermal environment parameters were extracted through cross-domain matching technology, providing a scientific basis for optimizing the overall temperature control strategy of the office building.

[0073] In summary, through a series of in-depth data analysis and technical means from steps 201 to 205, an accurate understanding and description of the complex relationship between the power consumption of equipment inside the building and the thermal environment parameters is 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. Through a detailed analysis of the interaction between heat and electric energy inside the building, unnecessary energy loss 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 and ensures the comfort and stability of the indoor environment.

[0074] In order 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 scheme extracts the boundary curvature change point set of the geometric topology of the heat flow anomaly area by multi-channel superposition of the three-dimensional distribution density of the thermal and electrical coupling factors, and generates a dynamic co-occurrence probability field. Based on the synchronous change interval of the thermal fluctuation amplitude of the main axis and the frequency domain energy attenuation rate, multi-dimensional association rules are generated to improve the system's understanding accuracy and response speed to changes in complex thermal environments. In some embodiments, the construction of a joint response sequence described in step 204 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: 301. 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 current data spectrum characteristics in the spatial coordinates is superimposed in multiple channels to construct a joint response sequence; In step 301, the thermal-electric coupling factor is an indicator that combines thermal resistance parameters and air flow characteristic data to quantify the interaction intensity between heat and electric energy. The three-dimensional distribution density refers to the spatial distribution of the thermal-electric coupling factor throughout the building. The geometric topology of the heat flow anomaly zone describes the spatial morphology and positional relationship of the heat flow anomaly zone. The current data spectrum characteristics are the results obtained after frequency domain analysis of the current data. The joint response sequence is a set of data sequences obtained by analyzing these data to reveal the dynamic correlation intensity between different variables.

[0075] In the embodiment of the present application, firstly, based on the three-dimensional distribution density of the thermal-electric coupling factor, the geometric topology of the heat flow anomaly area and the spectrum characteristics of the current data are superimposed in multiple channels in the spatial coordinates using image processing technology to generate a joint response sequence. This process involves complex mathematical modeling and signal processing algorithms, such as fast Fourier transform, and the final result is a joint response sequence that can accurately reflect the dynamic correlation strength between different variables.

[0076] 302. Based on the coupling relationship between the axial gradient distribution of the joint response sequence and the frequency domain energy attenuation rate, extract the boundary curvature change point set of the geometric topology of the heat flow anomaly area; 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 decay rate refers to the speed at which energy decreases with frequency changes in the frequency domain. The boundary curvature change point set is extracted from the joint response sequence and represents the set of locations where the boundary curvature changes in the geometric topology of the heat flow anomaly area.

[0077] 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 boundary curvature change point set of the geometric topology of the heat flow anomaly area. In specific implementation, the boundary curvature change points are calculated using differential geometry technology and marked to form a set containing all key points. This step helps to accurately identify important change areas in the heat flow anomaly area.

[0078] 303. Match 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; In step 303, the boundary curvature change point set is a set of locations where the boundary curvature changes in the geometric topology of the heat flow anomaly area. The zero-crossing point distribution of the current data spectrum feature refers to the distribution of locations where the signal crosses the zero point in the current data spectrum diagram. The dynamic co-occurrence probability field is a probability distribution diagram generated by combining the above two types of data, which 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 flow anomaly area.

[0079] In the embodiment of the present application, the boundary curvature change point set is matched with the zero-crossing point distribution of the current data spectrum characteristics, and the 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 flow anomaly area. In this process, by comparing the boundary curvature change point set with the zero-crossing point distribution, the co-occurrence probability between the two is calculated, and a dynamic co-occurrence probability field containing a nonlinear mapping relationship between the zero-crossing point density and the boundary curvature change rate is generated. This step helps to understand the complex relationship between the current data and the heat flow anomaly area.

[0080] 304. 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.

[0081] In step 304, the thermal fluctuation amplitude of the main axis refers to the temperature fluctuation amplitude of the heat flow anomaly zone along its main extension direction. The frequency domain energy decay rate is the speed at which the energy in the frequency domain decreases with the change of frequency. The multidimensional association rules are 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 boundary curvature. The zero crossing density refers to the number of zero crossings of the signal in the current data spectrum diagram.

[0082] In an embodiment of the present application, first, the thermal fluctuation amplitude and frequency domain energy attenuation rate of the main axis 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. Next, a 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, multidimensional 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 information is integrated to form a comprehensive multidimensional association rule to guide subsequent optimization strategies.

[0083] Here is a specific example: In an office building project, technicians first used image processing technology to perform multi-channel superposition of the geometric topology of the heat flow anomaly area and the spectral characteristics of the current data in spatial coordinates to generate a joint response sequence. Next, differential geometry technology was used to extract the boundary curvature change point set of the geometric topology of the heat flow anomaly area. Then, pattern recognition technology was used to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flow anomaly area. Finally, multi-dimensional association rules were generated by constraining the boundary curvature change threshold and the gradient range of the zero-crossing point density. These rules helped optimize the air-conditioning system operation strategy of the office building, improved energy efficiency, and ensured the comfort of the indoor environment.

[0084] In summary, through a series of in-depth data analysis and technical means from steps 301 to 304, an accurate understanding and description of the complex relationship between the spectral characteristics of the current data and the geometric topology of the heat flow anomaly area is 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. Through a detailed analysis of the interaction between heat and electric energy 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 and ensures 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.

[0085] In order to further improve the understanding of the match between the boundary curvature change point set and the zero-crossing point distribution of the current data spectrum characteristics, the scheme generates clusters by clustering the boundary curvature change point set to form a zero-crossing segmented trajectory chain, and further analyzes the spatial aggregation degree and superimposes the probability density gradient of adjacent clusters to generate a continuous probability field to obtain a dynamic co-occurrence probability field. In some embodiments, the step 303 matches the boundary curvature change point set with the zero-crossing point distribution of the current data spectrum characteristics to generate a dynamic co-occurrence probability field constrained by the main axis of the geometric topology of the heat flow anomaly area, including: 401. 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; In step 401, the boundary curvature change point set is a set of locations where the boundary curvature changes in the geometric topology of the heat flow anomaly area. Gradient direction clustering is a clustering method based on the similarity of the gradient directions of each point in the point set, which is used to divide the boundary curvature change point set into a number of clusters. The centroid line refers to the line segment formed by connecting the centroids of all points in each cluster, which is used as the initial matching reference chain. The initial matching reference chain is the basic reference line for the subsequent matching process.

[0086] In the embodiment of the present application, first, the boundary information in the design drawings of the exterior wall of the office building is collected, and the curvature change at each boundary point is calculated. Then, the mean clustering algorithm is used to classify these curvature change points according to their gradient direction, thereby forming multiple clusters with similar characteristics. Then, the centroid position is determined for each cluster, and these centroids are connected in sequence to form an initial matching reference chain. This chain not only summarizes the design characteristics of the building exterior wall, but also provides a basis for further analysis.

[0087] 402. Performing annular neighborhood phase polarity analysis on the zero-crossing point distribution of the current data spectrum feature, screening out a zero-crossing point sequence, and segmenting the zero-crossing point sequence to generate a zero-crossing point segmented trajectory chain; In step 402, the zero-crossing distribution of the current data spectrum characteristics refers to the position distribution of the signal crossing the zero point in the current data spectrum diagram. The annular neighborhood phase polarity analysis is a method for local area analysis of the current data spectrum characteristics, which is used to screen out a zero-crossing sequence with a specific phase and polarity. The zero-crossing sequence is an ordered set of signals crossing zero points in the current data spectrum characteristics obtained after screening. The zero-crossing segmented trajectory chain is a trajectory chain obtained by segmenting the zero-crossing sequence.

[0088] In the embodiment of the present application, first, the current data spectrum characteristics of the internal circuit of the office building are obtained to identify all zero crossings. Then, a circular neighborhood phase polarity analysis is performed on each zero crossing to screen out a zero crossing sequence that meets specific conditions. Then, these zero crossings are segmented according to the current fluctuation pattern, and each segment represents a continuous current feature segment. Finally, these segments are connected in series to form a zero crossing segmented trajectory chain, which helps in subsequent troubleshooting and positioning work.

[0089] 403. 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; In step 403, the main axis refers to the main extension direction of the geometric topology of the heat flow anomaly area. The centroid line generated by the initial matching benchmark chain. The local curvature weight is the different weight values ​​assigned to the degree of curvature change of each point on the initial matching benchmark chain. The segmented trajectory chain of the current data spectrum characteristics generated by the zero-crossing segmented trajectory chain. The phase polarity weight is the different weight values ​​assigned to the phase and polarity of each point on the zero-crossing segmented trajectory chain. The dual-chain dynamic matching probability diagram is a probability distribution diagram generated by combining the above two types of data, which is used to show the matching relationship between the two.

[0090] In the embodiment of the present application, first, based on the geometric topology of the heat flow anomaly area, the direction of the main axis is determined. This step is crucial for dynamically adjusting the local curvature weight of the initial matching reference chain. Then, combined with the adjusted weight parameters, the degree of matching between the initial matching reference chain and the zero-crossing segmented trajectory chain is calculated. Then, based on the above matching results, a double-chain dynamic matching probability map is generated, in which the high-probability area indicates a high degree of consistency between the two chains. This figure shows the matching possibility of the two chains at different positions, laying the foundation for the next step of analysis.

[0091] 404. According to the spatial aggregation degree of the double-chain dynamic matching probability graph, coupling analysis is performed on the curvature change point clusters, and a continuous probability field is generated by superimposing probability density gradients of adjacent clusters on the main axis; In step 404, the spatial concentration refers to the density of data points in the probability map. The probability density gradient refers to the rate of change of the probability density between adjacent clusters on the main axis. The continuous probability field is a probability distribution field generated by superimposing the probability density gradients of adjacent clusters.

[0092] In the embodiment of the present application, first, the spatial aggregation of the curvature change point clusters is calculated to quantify the density of the curvature change point clusters in space. Next, a coupling analysis is performed to evaluate the impact between adjacent clusters. Then, 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 in the entire study area, providing a scientific basis for optimizing architectural design.

[0093] 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 graph.

[0094] In step 405, direction-aware probability diffusion refers to a method of probability diffusion along the main axis, taking into account the influence of directional factors. Anisotropic diffusion constraints refer to diffusion restrictions in different directions imposed during the diffusion process. The dynamic co-occurrence probability field is the final generated probability distribution map, which shows the co-occurrence probability relationship between the geometric topology of the heat flow anomaly area and the spectral characteristics of the current data.

[0095] In the embodiment of the present application, first, a direction-aware probability diffusion algorithm is applied along the main axis to propagate the probability information in a specific direction while considering the influence of directional factors. Next, anisotropic diffusion constraints are introduced to ensure that this diffusion process is more in line with the actual situation. Then, a dynamic co-occurrence probability field is generated based on the distribution density of the double-chain dynamic matching probability graph. Ultimately, this probability field comprehensively reflects the probability distribution under the combined effect of various factors, helping to predict possible structural problems or energy consumption patterns in the future.

[0096] Here is a specific example: In an office building project, technicians began to process the design drawings of the building's exterior walls, extracting boundary information and calculating the set of curvature change points. The points were successfully classified into several clusters, and the centroids of each cluster were connected to form an initial matching benchmark chain. Subsequently, the technicians analyzed the current data of the internal circuits of the office building to form a zero-crossing segmented trajectory chain. Based on these two chains, the technicians further analyzed the temperature distribution in the building, determined the main axis of the heat flow anomaly area, and generated a dual-chain dynamic matching probability map. Next, by calculating the spatial aggregation and performing coupling analysis, the technicians created a continuous probability field that fully reflects the structural characteristics of the office building. Finally, the technicians applied the direction-aware probability diffusion algorithm along the main axis to generate a dynamic co-occurrence probability field. These steps helped optimize the office building's air conditioning system operation strategy, improve energy efficiency, and ensure the comfort of the indoor environment.

[0097] In summary, through steps 401 to 405, a series of in-depth data analysis and technical means are used to achieve an accurate understanding and description of the complex relationship between the boundary curvature change point set and the current data spectrum characteristics. 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. Through a detailed analysis of the interaction between heat and electric energy inside the building, unnecessary energy loss 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 and ensures 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.

[0098] In order to further improve the matching accuracy between the temperature field scanning data and the simulation results of the dynamic heat balance equation set, firstly, the simulated temperature field grid data is generated based on the spatial discretization processing of the temperature field scanning data, and then the temperature difference between the actual and simulated data is analyzed to generate a local compensation factor and generate continuous compensation field data through smooth diffusion. The compensation field data is coupled with the simulation results to iteratively correct the heat conduction term to generate a corrected dynamic heat balance equation set. Finally, the compensated temperature field incremental data is generated by performing a back propagation analysis on the spatial gradient distribution of the temperature field scanning data, and the simulation data is superimposed to generate the final three-dimensional temperature field reconstruction data. In some embodiments, the generation of three-dimensional temperature field reconstruction data by compensating the difference between the temperature field scanning data and the simulation results of the dynamic heat balance equation set in step 102 includes: 501. 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; In step 501, the discrete sampling point distribution of the temperature field scanning data refers to the actual temperature values ​​of various locations inside the building collected from the actual environment. These data provide the true 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 of various points inside the building under simulated conditions. The simulated temperature field grid data provides a basic reference for subsequent steps.

[0099] In the embodiment of the present application, the temperature field scanning data from the temperature sensor network is first collected and combined with the known set of dynamic heat balance equations. Then, the dynamic heat balance equation is solved using numerical simulation techniques such as the finite element method to obtain a preliminary temperature distribution prediction. Then, this prediction result is discretized according to the spatial coordinates to form a temperature field grid data composed of multiple small grid cells. Finally, by integrating the information of these grid cells, a simulated temperature field grid data that fully reflects the ambient temperature distribution is generated.

[0100] 502. Analyze the temperature difference between the temperature field scanning data and the simulated temperature field grid data to obtain a local compensation factor, and perform smooth diffusion according to the spatial continuity constraint to generate continuous compensation field data; In step 502, the local compensation factor is calculated by comparing the temperature difference between the temperature field scanning 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 continuous compensation field data, the compensation factor is required to maintain a smooth transition in the entire space to avoid sudden changes. The continuous compensation field data is a compensation factor distribution map after smooth diffusion processing, which is used to adjust the simulated temperature field grid data.

[0101] In the embodiment of the present application, the temperature difference in each grid unit is first calculated to identify the area that needs to be corrected. Then, the local compensation factor is smoothly diffused based on the spatial continuity constraint using an interpolation algorithm and a smoothing filter. Then, these compensation factors are applied to the original simulated temperature field grid data to generate a more accurate continuous compensation field data. Ultimately, this data can effectively improve the error in the initial simulation results.

[0102] 503. Iteratively couple the continuous compensation field data with the simulation results of the dynamic heat balance equation set, and reversely correct the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set; In step 503, coupling iteration refers to the process of combining the continuous compensation field data with the simulation results of the dynamic heat balance equation set to perform multiple iteration optimization. The reverse correction of the heat conduction term refers to 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 set of equations generated after multiple iteration optimizations, which is used to more accurately simulate the temperature changes inside the building.

[0103] In the embodiment of the present application, the continuous compensation field data is first coupled with the simulation results of the original dynamic heat balance equation set for iterative calculation, and the matching degree between the two is gradually adjusted. Then, based on the error pattern found in the iterative process, the heat conduction term in the dynamic heat balance equation set is reversely corrected. Then, this process is repeated 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.

[0104] 504. Perform back propagation analysis on the spatial gradient distribution of the temperature field scanning data according to the modified 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.

[0105] In step 504, the compensated temperature field incremental data is generated by back propagation analysis of the spatial gradient distribution of the temperature field scanning data, and is used to correct the error 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 incremental data and the simulated temperature field grid data, which can more accurately reflect the actual temperature distribution inside the building.

[0106] In the embodiment of the present application, firstly, the spatial gradient distribution of the temperature field scanning data is back-propagated and analyzed according to the revised set of dynamic heat balance equations to determine the temperature field increments that need to be supplemented. Then, these incremental 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 actual 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.

[0107] Here is a specific example: In a large commercial building project, technicians first deployed a network of temperature sensors throughout the building to collect temperature field scanning 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 difference between the actual data and the simulated data, the local compensation factor was calculated and applied to create continuous compensation field data. On this basis, the technicians further optimized the set of dynamic heat balance equations and adjusted the model parameters through repeated iterations 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 building's internal environmental control system, but also provides a scientific basis for energy-saving renovation.

[0108] In summary, through steps 501 to 504, a series of in-depth data analysis and technical means are used to achieve an accurate understanding and description of the temperature field inside the building. 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. Through a detailed analysis of 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 and ensures 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 occupants.

[0109] In order to further improve the matching accuracy between the continuous compensation field data and the simulation results of the dynamic heat balance equation set, a method based on the node correction coefficient is designed. First, the heat conduction path weight factor is used to perform weighted balancing of the conductivity across nodes to generate a node correction coefficient distribution. Then, the distribution is reversely superimposed with the heat conduction term to generate an intermediate correction equation set to generate a corrected dynamic heat balance equation set. This method improves the accuracy and adaptability of the model. In some embodiments, the coupling and iteration of the continuous compensation field data and the simulation results of the dynamic heat balance equation set described in step 503, and the reverse correction of the heat conduction term of the dynamic heat balance equation set to generate a corrected dynamic heat balance equation set, includes: 601. 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; In step 601, the continuous compensation field data is a distribution diagram of the compensation factor after smooth diffusion processing. The compensation amplitude 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 amplitude and direction for adjusting the parameters of the heat conduction equation, and its absolute value is proportional to the compensation amplitude. The node correction coefficient is used to correct the error in the simulation result to make the simulation closer to the actual situation.

[0110] In the embodiment of the present application, the compensation amount and direction required for each node are first determined based on the continuous compensation field data. Then, the corresponding correction coefficient is calculated for each node according to the size and direction of these compensation amounts. Then, these correction coefficients are applied to the corresponding nodes to ensure that they can accurately reflect the actual temperature changes. Finally, by integrating the correction coefficients of all nodes, a node correction coefficient distribution map that fully reflects the temperature field correction requirements is generated.

[0111] 602. Define a heat conduction path weight factor according to the heat conduction path topological relationship of the dynamic heat balance equation set, perform cross-node conductivity weighted balance on the node correction coefficient, and generate a node correction coefficient distribution; In step 602, the heat conduction path topology describes the spatial structure of the heat transfer path inside the building. The heat conduction path weight factor is a different weight value assigned to different paths to reflect the importance of the path. The node correction coefficient distribution is a distribution map generated by weighted balancing the node correction coefficients across the node conductivity. This distribution map takes into account the mutual influence between different paths and provides a basis for subsequent corrections.

[0112] In the embodiment of the present application, the topological relationship of the heat conduction paths in the dynamic heat balance equation set is first analyzed to identify the main heat conduction paths. Then, the corresponding weight factors are assigned according to the importance of these paths. Then, the correction coefficients of each node are recalculated using these weight factors to ensure that the heat can be evenly distributed throughout the system. Finally, through this process, a node correction coefficient distribution map after conductivity weighted balance is generated, which improves the overall stability of the system.

[0113] 603. 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; In step 603, the reverse superposition of the heat conduction term refers to combining the node correction coefficient distribution with the heat conduction term of the dynamic heat balance equation set to generate a new equation set. The intermediate correction equation set is a new equation set generated by reverse superposition. The heat flow residual parameter is extracted from the intermediate correction equation set and is used as an indicator to measure the difference between the equation set and the actual temperature field scanning data.

[0114] In the embodiment of the present application, the node correction coefficient distribution is first combined with the heat conduction term 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. Then, these residual parameters are used to guide subsequent adjustments to ensure that the final result is as close to the actual situation as possible. Finally, the above process generates an intermediate correction equation set that can more accurately describe the system behavior.

[0115] 604. Iteratively readjust the node correction coefficient distribution by using the heat flow residual parameter until the heat flow residual parameter satisfies the heat flow conservation constraint of the dynamic heat balance equation set, thereby generating a corrected dynamic heat balance equation set.

[0116] In step 604, the heat flow residual parameter is an extracted indicator that measures the difference between the equation set and the actual data. Iterative readjustment refers to multiple adjustments to the node correction coefficient distribution according to the heat flow residual parameter until the heat flow conservation constraint is satisfied. The modified dynamic heat balance equation set is a new equation set generated after multiple iterative optimizations, which is used to more accurately simulate the temperature changes inside the building.

[0117] In the embodiment of the present application, the node correction coefficient distribution is first adjusted according to the heat flow 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. Then, in each iteration, it is checked whether the heat flow conservation constraint is met. Finally, when the predetermined accuracy requirement is reached, a modified set of dynamic heat balance equations is generated, which can more accurately simulate the heat conduction behavior of the actual system.

[0118] Here is a specific example: In a large data center project, technicians first generated continuous compensation field data based on the data collected by the temperature sensor network, and calculated the correction coefficients of each cooling device node based on this. Next, the topological relationship of the heat conduction path inside the data center was analyzed, and appropriate heat conduction path weight factors were defined to perform conductivity weighted balancing on the node correction coefficients. Subsequently, the adjusted node correction coefficient distribution was combined with the heat conduction term of the dynamic heat balance equation set to generate an intermediate correction equation set, from which the heat flow residual parameters were extracted. Finally, the node correction coefficient distribution was adjusted through multiple iterations until the heat flow residual parameters met the heat flow conservation constraints, effectively improving the efficiency and stability of the data center cooling system.

[0119] In summary, through steps 601 to 604, an accurate understanding and description of the temperature field inside the building is 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. Through a detailed analysis of 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 and ensures 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 flow distribution inside the building can be better understood, thereby optimizing energy use and improving the comfort of occupants.

[0120] In order to further improve the intelligence and personalization of the temperature control device control strategy, the solution constructs a priority control chain for temperature control devices, using a method that combines spatiotemporal coupling characteristics with meteorological forecast data. First, the spatial thermal inertia index and time response sensitivity index of the temperature control device are extracted, and a preliminary priority sequence is generated by combining the topological relationship of the heat conduction path. Then, the control weight is corrected by the matching degree between the coverage area and the device location to generate a dynamic priority sequence. Finally, the device nodes that violate the energy consumption or stability threshold are iteratively screened out to construct a priority control chain. This method improves the response speed and accuracy of the system. In some embodiments, the priority control chain of the temperature control device is constructed based on the spatiotemporal coupling characteristics and meteorological forecast data in step 104, including: 701. Extracting a spatial thermal inertia index and a 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; In step 701, the spatiotemporal coupling characteristics describe the relationship between the power consumption of the device and the thermal environment parameters over time and space. The distributed thermal inertia parameter is an indicator to measure the reaction speed of different locations in the building to temperature changes. The temperature fluctuation parameter in the future period in the weather forecast data provides the temperature forecast information for a period of time in the future. The spatial thermal inertia index reflects the resistance of each point inside the building to temperature changes, and the time response sensitivity index indicates the response speed of the temperature control device to temperature changes.

[0121] In the embodiment of the present application, the spatial distribution information and thermal inertia parameters of the temperature control equipment are first collected, and the weather forecast data for the future period is obtained. Then, the spatial thermal inertia index and time response sensitivity index of each temperature control equipment are extracted through data analysis algorithms (such as regression analysis). Then, based on these indicators, the adaptability of each device to future temperature fluctuations is evaluated. Finally, all information is integrated to form a data set that comprehensively reflects the adaptability and response speed of the temperature control equipment.

[0122] 702. Based on the correlation between the spatial thermal inertia index and the time response sensitivity index, establish the control influence weight of the temperature control device, and generate a preliminary priority sequence in combination with the topological relationship of the heat conduction path between the temperature control devices; In step 702, the control influence weight is established based on these two indicators to evaluate the influence of each temperature control device in the control process. The thermal conduction path topology relationship describes the heat transfer path between temperature control devices. The preliminary priority sequence is generated based on the above parameters and relationships to guide the priority sorting of temperature control devices.

[0123] In the embodiment of the present application, the control influence weight is first calculated based on the spatial thermal inertia index and the time response sensitivity index. Next, the topological relationship of the heat conduction path between the temperature control devices is analyzed to identify the key nodes. Then, a graph theory algorithm is used to assign a priority to each device based on the control influence weight. Finally, a preliminary priority sequence is generated that takes into account the interaction between the devices and their respective importance to the system temperature control.

[0124] 703. 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 device to generate a dynamic priority sequence; In step 703, extreme temperature events refer to significantly high or low temperature weather that may occur in a certain period of time in the future. The spatiotemporal distribution characteristics describe the time and location of these extreme events. The control impact weight is further adjusted on this basis, and a dynamic priority sequence is generated by considering the matching degree between the area covered by the extreme temperature event and the spatial location of the temperature control equipment. This sequence is more in line with actual needs and improves the ability to cope with extreme weather.

[0125] In an embodiment of the present application, weather forecast data is first analyzed to identify extreme temperature events that may occur in the future and their specific locations and times. Next, the coverage of these events is compared with the location of the temperature control equipment to assess the degree of impact. Then, the control influence weights of each device are adjusted according to the evaluation results, and the dynamic priority sequence is reordered to generate. Finally, ensure that the sequence can effectively cope with the upcoming extreme temperature challenges. This process helps to identify the temperature control devices that need the most regulation, improve the system's ability to cope with extreme weather, and ensure the efficient operation of the system.

[0126] 704. Construct a priority regulation chain of temperature control devices by iteratively screening the temperature control device nodes that violate the energy consumption threshold or the thermal stability threshold in the dynamic priority sequence.

[0127] In step 704, the dynamic priority sequence is the adjusted device priority order generated in step 703. The energy consumption threshold and the thermal stability threshold are the set upper limit of energy consumption and the minimum temperature requirement for maintaining indoor comfort, respectively. By iteratively screening the temperature control device nodes that violate the above thresholds in the dynamic priority sequence, a priority regulation chain of the temperature control device is constructed. This regulation chain ensures the efficient operation of the system while meeting the user's comfort requirements.

[0128] In the embodiment of the present application, energy consumption and thermal stability thresholds are first set as screening criteria. Next, each device node in the dynamic priority sequence is checked one by one to see if these conditions are met. Then, the nodes that do not meet the conditions are marked and removed from the sequence or their priorities are rearranged. Finally, a priority control chain that meets all the standards is constructed to ensure efficient and stable operation of the entire system. The priority control chain provides a guarantee for the optimized operation of the temperature control system.

[0129] Here is a specific example: In an office building project, technicians first extracted 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 equipment, combined with the meteorological forecast data for the future period. Then, based on these indicators, the control impact weights were established, and the topological relationship of the heat conduction path between the temperature control devices was considered to generate a preliminary priority sequence. Subsequently, the extreme temperature events in the meteorological forecast data were analyzed, and the preliminary priority sequence was dynamically adjusted to better cope with extreme weather conditions. Finally, through an iterative screening process, the device nodes that violated the energy consumption or thermal stability thresholds were removed, and an efficient priority control chain was constructed, which significantly improved the response speed and energy efficiency of the temperature control system in the building.

[0130] In summary, through steps 701 to 704, a series of in-depth data analysis and technical means are used to achieve an accurate understanding and optimization of the temperature control equipment control strategy. 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. Through a detailed analysis of the internal heat transfer process of the building and the external meteorological conditions, unnecessary energy loss 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 and ensures 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 a precise priority control chain, energy consumption can be better managed and occupant satisfaction can be improved.

[0131] Figure 2 A schematic diagram of a big data information processing system is provided for an embodiment of the present application. Figure 2 As shown, the system includes: An acquisition module 21 acquires big data information of a building, wherein the big data information includes thermal resistance parameters and spatial topology data of the enclosure structure extracted by the building information model, and combines the thermal distribution map collected by the infrared thermal imaging array to generate an energy distribution model with thermal attribute tags; A generation module 22 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 by compensating the difference between the temperature field scanning data and the simulation results of the set of dynamic heat balance equations; The extraction module 23 inputs 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 the equipment power consumption and the thermal environment parameters; The generation module is also used to construct a priority control chain for temperature control equipment based on the spatiotemporal coupling characteristics and meteorological forecast data, and to generate multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization, and cold storage tank release triggering; and to dynamically generate a power load balancing strategy for buildings based on sensitive node parameters of the priority control chain.

[0132] Figure 2 The big data information processing system can execute Figure 1 The implementation principle and technical effect of the big data information processing method described in the embodiment are not repeated here. The specific way in which each module and unit performs operations in the big data information processing system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0133] In one possible design, Figure 2 A big data information processing system of the embodiment shown 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; 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 .

[0134] The processing component 32 is used for the above Figure 1 A big data information processing method according to the embodiment.

[0135] 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 to perform the above method.

[0136] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can 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.

[0137] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0138] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0139] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0140] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0141] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A big data information processing method of the illustrated embodiment.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0144] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for 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.

[0145] 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 it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the 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; According to the sensitive node parameters of the priority regulation chain, a power load balancing strategy for the building is dynamically generated.

2. The method according to claim 1, characterized in that The event feature extraction network driven by deep learning extracts 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 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. The method according to claim 1, characterized in that 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.

8. 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 build a priority control chain of temperature control equipment based on the spatiotemporal coupling characteristics and meteorological forecast data, and generate multi-level linkage rules including ventilation rate adjustment of fresh air units, lighting dimming gradient optimization and cold storage tank release triggering; according to the sensitive node parameters of the priority control chain, the power load balancing strategy of the building is dynamically generated.

9. 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 7.

10. 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 7 is implemented.

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