Smart park energy consumption management method and platform based on multi-source data driving

Abstract

The invention discloses a smart park energy consumption management method and platform based on multi-source data driving, and relates to the technical field of energy consumption management, and the method comprises the steps: connecting a multi-mode monitoring device, and obtaining multi-source monitoring data; positioning a monitoring space of multi-source monitoring data, projecting the monitoring space to the space model of the smart park, and constructing a multi-dimensional space database; performing space energy consumption analysis according to the multi-dimensional space database, evaluating the energy consumption state of each space region, and identifying a space energy consumption excess region; and according to the multi-source monitoring data of the space energy consumption excess region, performing energy-saving analysis in combination with the space distribution characteristics to obtain an energy-saving management scheme. The technical problems of low park energy utilization efficiency and insufficient energy consumption management accuracy caused by single multi-modal data acquisition, disjunction of energy consumption and spatial information and lack of real-time dynamic mapping capability in static analysis in the prior art are solved, and the technical effect of improving the energy consumption management accuracy and the energy utilization efficiency of the smart park is achieved.

Description

Technical Field

[0001] The present application relates to the technical field related to energy consumption management, and specifically to a smart park energy consumption management method and platform driven by multi-source data. Background Art

[0002] As an important carrier of urban digital transformation, the energy consumption problem of smart parks has received increasing attention. The park covers multifunctional scenes such as industry, commerce, office, and life. The energy demand is complex and large in volume. The traditional extensive energy consumption management model can no longer meet the management needs of low-carbonization, refinement, and intelligence. The park wastes energy due to problems such as inefficient equipment operation, uneven energy distribution, and redundant environmental control. However, the current park energy consumption management mainly relies on single-dimensional data collection (such as meter readings) and manual experience-driven strategy formulation, which has significant limitations. On the one hand, the traditional monitoring equipment is single in type and it is difficult to cover multimodal parameters such as water, electricity, air (temperature and humidity, CO2 concentration), and equipment operation status, resulting in the correlation between energy use and environmental factors and equipment efficiency not being fully explored; on the other hand, the energy consumption data is disconnected from the spatial location information, and it is impossible to accurately locate the high-energy consumption area and its causes, which restricts the pertinence of energy-saving measures; and it is difficult to respond to energy consumption fluctuations in complex scenarios in the park in a timely manner, affecting the precise regulation of the park's energy system.

[0003] Therefore, in the current relevant technologies, there are technical problems such as the single multimodal data collection, the disconnection between energy consumption and spatial information, and the lack of real-time dynamic mapping capabilities in static analysis, which lead to low energy utilization efficiency in the park and insufficient accuracy of energy consumption management. Summary of the invention

[0004] This application provides a smart park energy consumption management method and platform driven by multi-source data, which solves the technical problems existing in the prior art, such as the single multimodal data collection, the disconnection between energy consumption and spatial information, and the lack of real-time dynamic mapping capability in static analysis, which leads to low energy utilization efficiency in the park and insufficient accuracy of energy consumption management. It achieves the technical effect of improving the accuracy of energy consumption management and energy utilization efficiency in the smart park.

[0005] The present application provides a smart park energy consumption management method driven by multi-source data, the method comprising: connecting a multi-modal monitoring device to obtain multi-source monitoring data, the multi-modal monitoring device comprising electricity, water, air, and equipment status monitoring equipment; locating the monitoring space of the multi-source monitoring data, projecting it into the spatial model of the smart park, and constructing a multi-dimensional spatial database; performing spatial energy consumption analysis based on the multi-dimensional spatial database, evaluating the energy consumption status of each spatial area, and identifying areas with excessive spatial energy consumption; performing energy-saving analysis based on the multi-source monitoring data of the areas with excessive spatial energy consumption in combination with spatial distribution characteristics to obtain an energy-saving management plan.

[0006] In a possible implementation, the energy consumption management method for a smart park driven by multi-source data also performs the following processing: identifying the structural distribution of the smart park and constructing a three-dimensional spatial model of the park; performing a primary classification according to the attributes of the park facilities and dividing the attribute space, where the facility attributes include buildings, roads, and greenery; performing a secondary classification according to the energy consumption level of the attribute space and dividing the energy consumption level space; marking the boundaries in the three-dimensional spatial model of the park according to the spatial boundaries of the attribute space and the energy consumption level space to obtain the spatial model of the smart park.

[0007] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: based on the multi-dimensional spatial database, the multi-source monitoring data are feature aligned and fused according to the spatial relationship to obtain multi-source fusion features; based on the multi-source monitoring data, a monitoring decision range is set, and the monitoring decision range is the decision threshold of the monitoring data, corresponding to the data source; energy consumption analysis and evaluation are performed according to the monitoring decision range and the multi-source fusion features to obtain energy consumption evaluation results.

[0008] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: using the monitoring decision range and the multi-source monitoring data to match and compare the data source, when the monitoring decision range is exceeded, the energy consumption evaluation result is the out-of-range abnormal energy consumption and the corresponding out-of-range value; when the monitoring decision range is not exceeded, the energy consumption evaluation result is the monitoring data of the corresponding data source; spatial energy consumption trend prediction is performed according to the multi-source fusion characteristics, and the energy consumption evaluation result is obtained based on the energy consumption prediction trend.

[0009] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: according to the spatial relationship of the multimodal monitoring equipment, the collection accuracy distribution is determined, and the monitoring data is compensated using the collection accuracy distribution; according to the spatial relationship, the compensated multi-source monitoring data is aligned, and the change characteristics of the monitoring data of each data source are calculated; according to the alignment relationship, the change characteristics of the monitoring data of each data source are fused to obtain the multi-source fusion characteristics.

[0010] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: based on the spatial relationship, the multi-source monitoring data with the same coordinates are positionally aligned according to the spatial coordinates; based on the acquisition frequency and acquisition timestamp of the multimodal monitoring equipment, the multi-source monitoring data are time-aligned; based on the position alignment and time alignment relationship, the change feature alignment calculation of the multi-source monitoring data is performed.

[0011] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: setting a space energy consumption comparison list based on the historical energy consumption records of each spatial area; using the energy consumption evaluation results to compare and match with the space energy consumption comparison list, identifying the excess energy consumption difference of each spatial area, and determining the space energy consumption excess area.

[0012] In a possible implementation, the energy consumption management method for a smart park driven by multi-source data also performs the following processing: based on the spatial model of the smart park, extracting the spatial distribution characteristics of the corresponding space energy consumption excess area, the spatial distribution characteristics including spatial attributes, energy consumption levels, and spatial placement characteristics; based on the multi-source monitoring data of the space energy consumption excess area, tracing abnormal factors and abnormal monitoring quantities; analyzing the influence relationship between the abnormal factors and the spatial attributes and spatial placement characteristics, and obtaining the energy consumption adjustment relationship; based on the energy consumption level, analyzing the energy-saving adjustment constraints of the region; based on the abnormal factors and abnormal monitoring quantities, performing energy-saving compensation control adjustment based on the energy consumption adjustment relationship and energy-saving adjustment constraints, and obtaining the energy-saving management plan.

[0013] In a possible implementation, the smart park energy consumption management method driven by multi-source data also performs the following processing: obtaining energy-saving management plans for multiple space areas with excessive energy consumption; identifying energy consumption correlations among the multiple space areas with excessive energy consumption; evaluating the energy-saving management plans for the multiple space areas with excessive energy consumption based on the energy consumption correlations, and jointly implementing the park operation loss amount; configuring balancing weights for each space area with excessive energy consumption based on the park operation loss amount; and balancing and adjusting the energy-saving management plans for the multiple space areas with excessive energy consumption based on the balancing weights and the park operation loss amount, to obtain a balanced energy-saving management plan for multiple areas of the park.

[0014] The present application also provides a smart park energy consumption management platform driven by multi-source data, including: a multi-source monitoring data acquisition module, used to connect multi-modal monitoring equipment to obtain multi-source monitoring data, the multi-modal monitoring equipment includes electricity, water, air, and equipment status monitoring equipment; a multi-dimensional spatial database construction module, used to locate the monitoring space of the multi-source monitoring data, project it into the spatial model of the smart park, and construct a multi-dimensional spatial database; a spatial energy consumption analysis module, used to perform spatial energy consumption analysis based on the multi-dimensional spatial database, evaluate the energy consumption status of each spatial area, and identify areas with excessive spatial energy consumption; an energy-saving management plan acquisition module, used to perform energy-saving analysis based on the multi-source monitoring data of the area with excessive spatial energy consumption in combination with spatial distribution characteristics, and obtain an energy-saving management plan.

[0015] It is proposed that the energy consumption management method and platform for smart parks driven by multi-source data proposed in this application be connected to multi-modal monitoring equipment to obtain multi-source monitoring data; locate the monitoring space of multi-source monitoring data, project it into the spatial model of the smart park, and build a multi-dimensional spatial database; perform spatial energy consumption analysis based on the multi-dimensional spatial database, evaluate the energy consumption status of each spatial area, and identify areas with excessive spatial energy consumption; perform energy-saving analysis based on the multi-source monitoring data of areas with excessive spatial energy consumption, combined with spatial distribution characteristics, and obtain energy-saving management solutions. This solves the technical problems existing in the prior art, such as the singleness of multi-modal data collection, the disconnection between energy consumption and spatial information, and the lack of real-time dynamic mapping capabilities for static analysis, which lead to low energy utilization efficiency and insufficient accuracy of energy consumption management in the park, and achieves the technical effect of improving the accuracy of energy consumption management and energy utilization efficiency in smart parks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0017] Figure 1 A flow chart of a smart park energy consumption management method driven by multi-source data provided in an embodiment of the present application.

[0018] Figure 2 A schematic diagram of the structure of a smart park energy consumption management platform driven by multi-source data provided in an embodiment of the present application.

[0019] Explanation of the reference numerals: multi-source monitoring data acquisition module 10, multi-dimensional spatial database construction module 20, spatial energy consumption analysis module 30, energy-saving management plan acquisition module 40. DETAILED DESCRIPTION

[0020] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0021] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0022] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0023] The present application embodiment provides a smart park energy consumption management method driven by multi-source data, such as Figure 1 As shown, the method includes: Step S100, connecting a multi-modal monitoring device to obtain multi-source monitoring data, wherein the multi-modal monitoring device includes electricity, water, air, and equipment status monitoring devices.

[0024] Preferably, multimodal monitoring equipment refers to a variety of sensors and intelligent terminal devices for real-time monitoring of different energy types, environmental parameters and equipment operating status, including electricity monitoring equipment, water monitoring equipment, air monitoring equipment and equipment status monitoring equipment. Specifically, multiple different types of sensors and monitoring equipment are connected through the Internet of Things (IoT) to collect energy consumption, environmental parameters and equipment operating status data of different dimensions in the park in real time, and obtain multi-source monitoring data. Among them, the electricity monitoring equipment is used to monitor the power consumption and related power parameters in the park, and may include smart meters (real-time collection of power consumption, power, voltage, current and other data), power quality analyzers (monitoring harmonics, power factor, voltage fluctuations, etc.), etc., which can analyze and identify power load distribution and identify high-energy-consuming equipment; water monitoring equipment is used to monitor water resource consumption, water quality and pipe network status in the park, and may include smart water meters (recording water consumption, flow rate, water pressure and other data), water quality sensors (detecting pH value, turbidity, residual chlorine and other water quality parameters), water leakage detection sensors, etc., which can track water use anomalies (such as leakage, excessive consumption).

[0025] Preferably, air monitoring equipment is used to collect indoor and outdoor environmental parameters of the park, which may include temperature and humidity sensors (monitoring indoor and outdoor temperature and humidity changes), CO2 concentration sensors (assessing air circulation and fresh air system operating efficiency), PM2.5 / PM10 sensors (detecting air quality and linking air purification equipment); equipment status monitoring equipment is used to monitor the operating status of key equipment in the park (such as air conditioning units, elevators, production lines), which may include vibration sensors (detecting abnormal vibrations of motors, pumps and other equipment), infrared thermal imagers (identifying equipment overheating or heat dissipation failures), and current / voltage monitoring modules (assessing equipment operating efficiency and aging). It can detect inefficient equipment operation and fault warnings, and avoid additional energy consumption caused by "running with illness". Through multi-modal monitoring equipment, multi-source monitoring data such as electricity, water, air, and equipment status are collected in real time, breaking through the limitations of traditional single-dimensional monitoring, thereby ensuring the accuracy of park energy consumption management and the dynamic nature of energy regulation.

[0026] Step S200, locating the monitoring space of the multi-source monitoring data, projecting it into the spatial model of the smart park, and constructing a multi-dimensional spatial database.

[0027] Preferably, the collected multi-source monitoring data (such as electricity, water, air, equipment status, etc.) are associated with their corresponding physical space location information, and visualized and stored through a digital smart park space model, so as to construct a multi-dimensional spatial database. Specifically, the physical space location corresponding to each piece of monitoring data is determined by positioning, that is, a unique identifier (ID) is assigned to each monitoring device (such as an electricity meter, a water meter, a temperature and humidity sensor, etc.), and its installation location (such as a floor, a room, etc.) is recorded. The device location is accurately located using indoor positioning technology (such as Bluetooth beacons, UWB ultra-wideband) or a spatial coordinate system (such as GPS, a local coordinate system), and the monitoring data is associated with a specific spatial area (such as a building, a floor, or a room); then the monitoring data with spatial location information is mapped to the digital smart park space model, for example, the monitoring data ( The spatial monitoring data is stored in a structured database (i.e., a multi-dimensional spatial database) to support multi-dimensional query and analysis. For example, the spatial dimension of the multi-dimensional spatial database records the physical location corresponding to the data (such as longitude and latitude, floor, room number), the time dimension records the timestamp of the data (such as real-time data, historical data), the data dimension stores the specific values ​​of the multi-source monitoring data (such as power consumption, water consumption, temperature, equipment status, etc.), and the metadata dimension records additional information such as equipment type, data unit, and collection frequency. By combining multi-source monitoring data with spatial models and constructing a multi-dimensional spatial database, the spatial distribution of energy consumption and environmental parameters can be intuitively displayed, and the energy use and environmental status of each area of ​​the smart park can be monitored in real time to ensure the refined management of the park's energy consumption.

[0028] Furthermore, step S200 also includes step S201, identifying the structural distribution of the smart park and constructing a three-dimensional spatial model of the park; step S202, performing a primary classification according to the attributes of the park facilities and dividing the attribute space, the facility attributes include buildings, roads, and greenery; step S203, performing a secondary classification according to the energy consumption level of the attribute space and dividing the energy consumption level space; step S204, marking the boundaries in the three-dimensional spatial model of the park according to the spatial boundaries of the attribute space and the energy consumption level space, and obtaining the spatial model of the smart park.

[0029] Preferably, the physical structure of the smart park is identified, classified and modeled through digital technology to form a visual three-dimensional spatial model containing multi-dimensional information such as facility attributes and energy consumption levels. Specifically, the physical structure of the smart park (such as buildings, roads, green areas, etc.) is identified and digitally modeled, that is, laser scanning (LiDAR), drone aerial photography, satellite images and other technologies are used to obtain spatial data such as the park's terrain, buildings, roads, etc., combined with building information modeling (BIM) or geographic information system (GIS) data, to supplement detailed information on park facilities, and then use three-dimensional modeling software (such as AutoCAD, Revit, SketchUp) or GIS platforms (such as Ar cGIS) is used to construct a 3D spatial model of the park, including basic spatial elements such as building outlines, road directions, and green areas. Then, according to the functional attributes of the smart park facilities (including buildings, roads, and green areas), the 3D spatial model of the park is divided into different attribute areas. For example, buildings include office buildings, factories, dormitories, warehouses, etc., roads include main roads, secondary roads, sidewalks, parking lots, etc., and green areas include lawns, woods, flower beds, landscape lakes, etc. In the 3D spatial model of the park, each facility attribute is assigned an independent layer or area. For example, all building areas are marked as "building attribute space" and all road areas are marked as "road attribute space" to realize the functional zoning management of the park space.

[0030] Preferably, on the basis of the attribute space, the energy consumption level space is further divided according to the energy consumption level (such as high, medium and low). Specifically, based on the historical energy consumption data, the energy consumption level thresholds of different attribute spaces are set. For example, in the building attribute space, the energy consumption level of the office building is "high" and the energy consumption level of the warehouse is "low". Then, in the attribute space, the area is further subdivided according to the energy consumption level. For example, the energy consumption level of an office building is marked as "high energy consumption space", and the energy consumption level of a green area is marked as "low energy consumption space", thereby realizing refined classification management of energy consumption; finally, the boundary information of the attribute space and the energy consumption level space is marked in the three-dimensional space model, that is, the boundaries of the attribute space and the energy consumption level space are marked with different colors, lines or layers, for example, high energy consumption space is marked with red, and low energy consumption space is marked with green. Then, the attribute space, energy consumption level space and their boundary information are integrated into the three-dimensional space model, thereby obtaining the spatial model of the smart park, which can realize refined spatial management and improve the efficiency of energy consumption analysis.

[0031] Step S300, performing spatial energy consumption analysis based on the multi-dimensional spatial database, evaluating the energy consumption status of each spatial area, and identifying areas with excessive spatial energy consumption.

[0032] Preferably, a constructed multi-dimensional spatial database (including information such as spatial location, time, and multi-source monitoring data) is used to perform spatial energy consumption analysis, evaluate the energy consumption status of each spatial area in the park, and identify areas with abnormal or excessive energy consumption. Specifically, based on the multi-dimensional spatial database, the energy consumption data of each area in the park are statistically analyzed and visualized, including extracting energy consumption data (such as electricity consumption and water consumption) from the database according to spatial areas (such as floors, rooms, equipment) and time ranges (such as days, months, and years), and then performing data analysis, such as calculating the average energy consumption, peak energy consumption, and energy consumption trends of each area, analyzing the correlation between energy consumption and time and environmental parameters (such as temperature and humidity), and comparing the energy consumption differences in different areas. Then, the spatial distribution and change trend of energy consumption in the smart park are visualized through heat maps, bar charts, line charts, etc.; then, the energy consumption data of each spatial area is quantitatively evaluated to determine whether its energy consumption level is reasonable. Specifically, based on historical data, According to the differences in regional functions (such as office area, production area, and living area), the energy consumption benchmark values ​​of each area in the smart park (such as electricity consumption per unit area and water consumption per capita) are set, and the energy consumption intensity (energy consumption per unit area or unit output value), energy consumption efficiency (the ratio of energy consumption to actual output or usage demand) and energy consumption volatility (the magnitude of change in energy consumption over time) of each area are evaluated. The actual energy consumption of each area is compared with the benchmark value to determine whether it exceeds the standard. According to the comparison results, the energy consumption status is quantitatively evaluated (such as divided into high, medium, and low); finally, by analyzing the energy consumption data, the areas with abnormal energy consumption or significantly higher than the benchmark are found, that is, the energy consumption data of each area is compared with the benchmark value, and the areas exceeding a certain threshold (such as 20%) are determined as areas with excessive energy consumption. Multi-source data (such as equipment status and environmental parameters) are associated to analyze the possible causes of excessive energy consumption (such as equipment failure, excessive environmental regulation, and human waste), so as to fully grasp the energy consumption of each area in the park and improve the energy utilization efficiency of the park.

[0033] Furthermore, step S300 also includes step S310, based on the multi-dimensional spatial database, aligning and fusing the features of the multi-source monitoring data according to the spatial relationship to obtain the multi-source fusion features; step S320, based on the multi-source monitoring data, setting the monitoring decision range, the monitoring decision range is the decision threshold of the monitoring data, corresponding to the data source; step S330, performing energy consumption analysis and evaluation according to the monitoring decision range and the multi-source fusion features, to obtain energy consumption evaluation results.

[0034] Preferably, the multi-source monitoring data (such as electricity, water, air, equipment status, etc.) in the multi-dimensional spatial database are used to extract comprehensive features through spatial alignment and feature fusion technology, and the energy consumption status is analyzed and evaluated in combination with the preset decision threshold. Specifically, the monitoring data from different data sources (such as electricity meters, water meters, temperature and humidity sensors, etc.) are aligned and fused according to their spatial position relationship to extract comprehensive features, that is, the spatial position information of the multi-source data (such as floor, room, equipment number) is matched with the spatial model in the multi-dimensional spatial database to ensure that the multi-source data in the same spatial area are aligned in time and space, and then the multi-source data are integrated into comprehensive features using data fusion algorithms (such as weighted average, principal component analysis PCA, deep learning model), which may include energy consumption intensity (energy consumption per unit area or unit time), energy consumption efficiency (the ratio of energy consumption to actual output or usage demand) and environmental energy consumption index (the correlation between energy consumption and environmental parameters).

[0035] Preferably, a monitoring decision range is set according to multi-source monitoring data, that is, a decision threshold of the monitoring data is set, and it corresponds to the data source, so as to determine whether the energy consumption status is normal or excessive. Specifically, decision thresholds of different data sources are set based on historical data, including electricity decision threshold, water decision threshold, air decision threshold, etc. Exceeding the decision threshold means high electricity consumption, excessive water use or air pollution; then, the energy consumption status of each area of ​​the park is analyzed and evaluated by using multi-source fusion characteristics and decision thresholds to obtain energy consumption evaluation results, that is, the multi-source fusion characteristics are compared with the decision threshold to determine whether the energy consumption status is normal. For example, if the comprehensive energy consumption characteristics of a certain area exceed the threshold, it is determined that the energy consumption is excessive; the energy consumption status is quantitatively evaluated according to the level classification (such as high, medium, and low). For example, the energy consumption score of a certain area is "high", which means that the energy consumption efficiency of the area is low; finally, the energy consumption abnormal area is identified, the abnormal cause (such as equipment failure, excessive environmental regulation) is analyzed, reasonable energy-saving measures are formulated, and the energy consumption management of the park is continuously optimized.

[0036] Furthermore, step S310 also includes step S311, determining the acquisition accuracy distribution according to the spatial relationship of the multimodal monitoring equipment, and using the acquisition accuracy distribution to compensate the monitoring data; step S312, aligning the compensated multi-source monitoring data according to the spatial relationship, and calculating the change characteristics of the monitoring data of each data source; step S313, fusing the change characteristics of the monitoring data of each data source according to the alignment relationship to obtain the multi-source fusion characteristics.

[0037] Preferably, by analyzing the spatial distribution of multimodal monitoring equipment and its data acquisition accuracy, the monitoring data is compensated for accuracy and spatially aligned, and then the change characteristics of each data source are extracted and merged into comprehensive characteristics. Specifically, the spatial distribution of multimodal monitoring equipment (such as electricity meters, water meters, temperature and humidity sensors, etc.) is analyzed, and the data acquisition accuracy is evaluated, including determining the spatial position of each monitoring device (such as floor, room, equipment number), and analyzing the spatial distance and coverage between devices; the data acquisition accuracy is evaluated according to factors such as equipment type, installation location, and environmental conditions, and then the monitoring data with lower accuracy is compensated according to the acquisition accuracy distribution to improve the data quality. For example, interpolation algorithms (such as linear interpolation and Kriging interpolation) are used to fill in missing data, and filtering algorithms (such as Kalman filtering) are used to remove noise data. The data in the area with lower accuracy is corrected to make it closer to the true value, so as to improve the accuracy and consistency of the monitoring data.

[0038] Preferably, the multi-source monitoring data (such as electricity, water, air, equipment status, etc.) after compensation processing are aligned according to spatial position, that is, the monitoring data of each data source are mapped to a unified spatial coordinate system and synchronized by timestamp to ensure the consistency of data in time and space, and the change characteristics are extracted from the aligned multi-source monitoring data to describe the dynamic changes of energy consumption, environmental parameters, etc. Specifically, the time series characteristics of each data source, such as mean, variance, trend, periodicity, etc., are calculated, and then the spatial distribution characteristics of each data source, such as regional differences, spatial correlation, etc., are calculated to capture the dynamic change law of multi-source monitoring data; finally, weighted average, principal component analysis (PCA), deep learning model, etc. are used to merge the change characteristics of the monitoring data of each data source into comprehensive characteristics, extract a more comprehensive and accurate description of the energy consumption status, thereby improving the data quality and ensuring the refinement of the energy consumption management of the park.

[0039] Furthermore, step S312 also includes step A, performing position alignment of multi-source monitoring data with the same coordinates according to the spatial coordinates based on the spatial relationship; step B, performing time alignment of the multi-source monitoring data according to the acquisition frequency and acquisition timestamp of the multimodal monitoring device; and step C, performing change feature alignment calculation of the multi-source monitoring data based on the position alignment and time alignment relationship.

[0040] Preferably, the multi-source monitoring data (such as electricity, water, air, equipment status, etc.) are aligned in the spatial and temporal dimensions through spatial coordinates and timestamps, and the change characteristics of each data source are calculated based on the alignment relationship. Specifically, the monitoring data from different data sources (such as electricity meters, water meters, temperature and humidity sensors, etc.) are aligned according to their spatial coordinates (such as longitude and latitude, floor, room number) to ensure that the data matches at the same spatial position. Then, according to the collection frequency and collection timestamp of the multimodal monitoring equipment, the multi-source monitoring data are time-aligned to ensure that the data is synchronized in the time dimension. For example, the electricity consumption at a certain moment is aligned with the temperature and humidity at the same moment. Data alignment, and if the data sources have different collection frequencies, use interpolation or downsampling methods to adjust the data to the same time dimension; finally, based on the spatial and temporal alignment, calculate the change characteristics of the monitoring data of each data source, and ensure that these characteristics are aligned in space and time, including calculating the change characteristics of the monitoring data of each data source, such as mean, variance, trend, periodicity, etc., and then aligning the change characteristics of each data source according to spatial coordinates and timestamps, integrating the aligned change characteristics into comprehensive characteristics, describing the multi-dimensional information of energy consumption status, and then achieving accurate matching of multi-source data in space and time, ensuring refined energy consumption management in the park.

[0041] Furthermore, step S330 also includes step S331, using the monitoring decision range and the multi-source monitoring data to match and compare the data sources. When the monitoring decision range is exceeded, the energy consumption evaluation result is the out-of-range abnormal energy consumption and the corresponding out-of-range value; step S332, when it does not exceed the monitoring decision range, the energy consumption evaluation result is the monitoring data of the corresponding data source; step S333, performing spatial energy consumption trend prediction according to the multi-source fusion characteristics, and obtaining the energy consumption evaluation result based on the energy consumption prediction trend.

[0042] Preferably, by comparing the multi-source monitoring data with the preset decision threshold, it is determined whether the energy consumption status is abnormal, and the future energy consumption trend is predicted in combination with the multi-source fusion characteristics, so as to obtain a more comprehensive energy consumption evaluation result. Specifically, the multi-source monitoring data (such as electricity, water, air, equipment status, etc.) are compared with the preset decision threshold to determine whether the energy consumption status is normal, including matching the monitoring data of each data source (such as electricity consumption, water consumption) with its corresponding decision threshold. If the monitoring data exceeds the threshold, it is determined to be "abnormal energy consumption out of range", and the exceeded value is recorded as the energy consumption analysis result. If the monitoring data does not exceed the threshold, it is determined to be "normal energy consumption", and the actual monitoring data is recorded as the energy consumption analysis result.

[0043] Preferably, multi-source fusion features (such as energy consumption intensity, energy consumption efficiency, and environmental energy consumption index) are used to predict future energy consumption trends. Specifically, the impact of multi-source fusion features (such as environmental parameters and equipment status) on energy consumption is considered, and time series analysis (such as ARIMA), machine learning (such as LSTM neural network) or deep learning models are used to predict future energy consumption trends based on historical data, and predict future energy consumption changes in various regions (such as the electricity consumption of an office building will increase by 20% in summer); then, based on the predicted energy consumption trend, the future energy consumption status is evaluated, including determining whether the predicted energy consumption trend is likely to exceed the decision threshold. If the predicted trend exceeds the threshold, it is marked as "potential out-of-range abnormal energy consumption"; if the predicted trend does not exceed the threshold, it is marked as "expected normal energy consumption". These are used as energy consumption evaluation results, thereby realizing active and intelligent energy consumption management and avoiding the occurrence of abnormal energy consumption.

[0044] Furthermore, step S300 also includes step S340, setting a space energy consumption comparison list according to the historical energy consumption records of each space area; step S350, using the energy consumption evaluation results to compare and match with the space energy consumption comparison list, identifying the excess energy consumption difference of each space area, and determining the space energy consumption excess area.

[0045] Preferably, an energy consumption benchmark (comparison list) is established for each space area through historical energy consumption data, and the current energy consumption evaluation result is compared with the benchmark to calculate the excess energy consumption difference, so as to identify the area with excess energy consumption. Specifically, the energy consumption data (such as electricity consumption and water consumption) of each space area over a period of time (such as one year) is collected, and based on the historical energy consumption data of each space area (such as floor, room, equipment), the statistical characteristics of the historical data (such as mean, median, standard deviation) are calculated as the energy consumption benchmark, so as to generate an energy consumption comparison list for each space area, including the benchmark value and its allowable fluctuation range; The current energy consumption evaluation results (such as electricity consumption and water consumption in a certain area) are compared with the benchmark values ​​in the space energy consumption comparison list to determine whether the energy consumption status is normal. If the current energy consumption data exceeds the allowable range of the benchmark value, it is determined to be excessive energy consumption, so as to quickly identify abnormal energy consumption areas, and then calculate the excess difference in the energy consumption excess area (that is, the difference between the current energy consumption and the benchmark value), and determine the spatial position of these areas, that is, calculate the difference between the current energy consumption data and the benchmark value, and then associate the excess difference with the spatial position, mark the excess area, thereby accurately locating the spatial energy consumption excess area and further improving the energy efficiency management level of the park.

[0046] Step S400, performing energy-saving analysis based on the multi-source monitoring data of the spatial energy consumption excess area in combination with the spatial distribution characteristics to obtain an energy-saving management plan.

[0047] Preferably, after identifying the area with excessive energy consumption, use the multi-source monitoring data (such as electricity, water, air, equipment status, etc.) and spatial distribution characteristics (such as regional functions, equipment layout, environmental conditions, etc.) of the area to conduct energy-saving analysis to obtain the cause of excessive energy consumption and formulate targeted energy-saving management plans. Specifically, integrate the multi-source data such as electricity, water, air, equipment status, etc. in the area with excessive energy consumption, and conduct correlation analysis, including time synchronization and spatial matching of data such as electricity consumption, water consumption, temperature and humidity, and equipment operating status, analyze whether the environmental parameters (such as temperature and humidity) during the period of excessive energy consumption are abnormal, and check The operating status of the equipment (such as whether it is overloaded or inefficiently operating) is compared with historical data to identify the reasons for the sudden increase in energy consumption and clarify the reasons for excessive energy consumption. The spatial characteristics of the area with excessive energy consumption (such as regional functions, equipment layout, building structure, etc.) are then considered to analyze the spatial influencing factors of excessive energy consumption, including regional function analysis, such as the different energy consumption characteristics of office areas, production areas, and living areas; equipment layout analysis, such as the high density of equipment in a certain area leading to excessive local power load; building structure analysis, such as the poor thermal insulation performance in a certain area leading to increased air-conditioning energy consumption; and environmental conditions analysis, such as insufficient light in a certain area leading to increased lighting energy consumption.

[0048] Preferably, based on the causes of excessive energy consumption and spatial characteristics, specific energy-saving measures and optimization plans are formulated, including equipment optimization, environmental control optimization, behavior management and technology application. Specifically, equipment optimization includes upgrading or replacing high-energy-consuming equipment (such as replacing high-efficiency motors, installing inverters), and adjusting equipment operation strategies (such as time-sharing operation and load balancing); environmental control optimization includes optimizing the control logic of air-conditioning and lighting systems (such as adjusting air-conditioning temperature according to pedestrian flow, and adjusting lighting brightness according to natural light), and improving building thermal insulation performance (such as adding insulation materials and optimizing window design); behavior management includes formulating energy-saving behavior norms (such as reminding employees to turn off unused equipment) and improving the energy-saving awareness of park users through visualization of energy consumption data; technology application includes introducing intelligent control systems (such as AI-based energy consumption prediction and regulation) and deploying renewable energy (such as solar photovoltaic panels and wind power generation); these measures are used to form an energy-saving management plan, and through targeted energy-saving measures, energy consumption in areas with excessive energy consumption can be reduced to improve the overall energy efficiency of the park.

[0049] Furthermore, step S400 also includes step S410, extracting the spatial distribution characteristics of the corresponding space energy consumption excess area according to the spatial model of the smart park, and the spatial distribution characteristics include spatial attributes, energy consumption levels, and spatial placement characteristics; step S420, tracing abnormal factors and abnormal monitoring quantities according to multi-source monitoring data of the space energy consumption excess area; step S430, parsing the influence relationship between the abnormal factors and the spatial attributes and spatial placement characteristics, and obtaining the energy consumption adjustment relationship; step S440, based on the energy consumption level, analyzing the energy-saving adjustment constraints of the area; step S450, according to the abnormal factors and abnormal monitoring quantities, performing energy-saving compensation control adjustment based on the energy consumption adjustment relationship and energy-saving adjustment constraints, and obtaining the energy-saving management plan.

[0050] Preferably, by analyzing the spatial characteristics and multi-source monitoring data of the area with excessive energy consumption, the causes of abnormal energy consumption are traced, and a targeted energy-saving management plan is formulated in combination with factors such as spatial attributes and energy consumption levels. Specifically, the spatial characteristics of the area with excessive energy consumption are extracted from the spatial model of the smart park, including spatial attributes, energy consumption levels and spatial placement characteristics, wherein the spatial attributes describe the functional type of the area (such as office area, production area, living area), the energy consumption level describes the energy consumption level of the area (such as high, medium, low), and the spatial placement characteristics describe the equipment layout, building structure, environmental conditions, etc. of the area; through multi-source monitoring data (such as electricity, water, air, equipment status, etc.), the specific causes of excessive energy consumption (abnormal factors) and their corresponding abnormal data (abnormal monitoring quantities) are analyzed, including analyzing multi-source data to identify abnormal factors, finding out the key factors leading to excessive energy consumption, and then extracting monitoring data related to the abnormal factors, for example, extracting data such as power consumption, operating time, and ambient temperature of the air-conditioning system.

[0051] Preferably, the relationship between abnormal factors and spatial attributes and spatial placement characteristics is analyzed to clarify the direction and method of energy consumption adjustment. For example, the excessive air-conditioning energy consumption in a certain office area is related to the spatial attributes (office area) and spatial placement characteristics (dense equipment and poor ventilation). The causal relationship between abnormal factors (high-power operation of air conditioners) and spatial characteristics (poor ventilation) is analyzed to determine the energy consumption adjustment relationship, that is, the specific direction and measures of energy consumption adjustment; then, according to the energy consumption level of the area with excessive energy consumption, the energy-saving adjustment constraints of the area are analyzed, for example, the technical and economic constraints of energy-saving adjustment are analyzed to ensure the feasibility of the energy-saving plan; finally, according to the abnormal factors, energy consumption adjustment relationship and energy-saving adjustment constraints, a specific energy-saving management plan is formulated, including determining energy-saving measures, compensating for excessive energy consumption by adjusting equipment operating parameters, improving spatial layout, etc., and finally generating an energy-saving management plan including specific measures, implementation steps and expected effects to reduce energy consumption in the area with excessive energy consumption and improve the energy efficiency of the park.

[0052] Furthermore, step S450 also includes step S451, obtaining an energy-saving management plan for multiple space areas with excessive energy consumption; step S452, identifying energy consumption correlations among the multiple space areas with excessive energy consumption; step S453, evaluating the energy-saving management plans for the multiple space areas with excessive energy consumption based on the energy consumption correlations, and jointly implementing the park operation loss amount; step S454, configuring balancing weights for the areas with excessive energy consumption in each space based on the park operation loss amount; and step S456, performing balanced adjustments to the energy-saving management plans for the multiple space areas with excessive energy consumption based on the balancing weights and the park operation loss amount, to obtain a balanced energy-saving management plan for multiple areas in the park.

[0053] Preferably, after formulating energy-saving management plans for multiple spatial energy-consumption-excess areas, the energy consumption correlation between these areas is analyzed, the impact of the implementation of the plans on the overall operation of the park is evaluated, and the plans are optimized and adjusted based on the equilibrium weights and the amount of operating losses, so as to ultimately achieve balanced management of energy consumption in multiple areas of the park. Specifically, energy-saving management plans for multiple spatial energy-consumption-excess areas are obtained, and the energy consumption correlation between multiple energy-consumption-excess areas is analyzed, that is, through multi-source monitoring data, whether the energy consumption changes between areas are synchronized or affect each other is analyzed, and the spatial position relationship between areas (such as adjacent and shared equipment) is analyzed; then, the energy-saving management plans for multiple spatial energy-consumption-excess areas are evaluated based on the energy consumption correlation, that is, the impact of multiple energy-saving management plans on the overall operation of the park during implementation is evaluated, and the amount of operating losses (such as reduced energy efficiency and increased costs) is quantified. For example, the impact of each plan on the associated areas after implementation is analyzed, the energy efficiency loss and economic cost of the park after the implementation of the plan are quantified, and the percentage of energy efficiency reduction caused by power load fluctuations is calculated to evaluate the comprehensive impact of the plan.

[0054] Preferably, a balanced weight is assigned to each area with excess energy consumption according to the amount of operating losses in the park, which is used to adjust the priority of the energy-saving management plan. Specifically, a higher weight is assigned to an area with a large operating loss, and its energy-saving plan is adjusted preferentially, and the weight of each area is standardized to a weight distribution with a total of 1; finally, the energy-saving management plan of each area is optimized and adjusted according to the balanced weight and the operating loss, so as to achieve balanced management of energy consumption in multiple areas of the park, including giving priority to optimizing plans for high-weight areas to reduce their impact on the overall operation of the park, for example, adjusting the air-conditioning operation strategy of area A to reduce the impact on the power load of area B; comprehensively considering the energy-saving goals and operating losses of each area, and formulating a balanced energy-saving management plan, so as to achieve coordinated optimization of energy consumption in multiple areas of the park and improve the overall energy efficiency level and operational management efficiency.

[0055] In the above, refer to Figure 1 The energy consumption management method of a smart park based on multi-source data driving according to an embodiment of the present invention is described in detail. Figure 2A smart park energy consumption management platform driven by multi-source data according to an embodiment of the present invention is described.

[0056] The smart park energy consumption management platform based on multi-source data drive according to the embodiment of the present invention is used to solve the technical problems existing in the prior art, such as the single multi-modal data collection, the disconnection between energy consumption and spatial information, and the lack of real-time dynamic mapping capability in static analysis, which leads to low energy utilization efficiency and insufficient energy consumption management accuracy in the park, and achieves the technical effect of improving the accuracy of energy consumption management and energy utilization efficiency in the smart park. Figure 2 As shown, the smart park energy consumption management platform driven by multi-source data includes: a multi-source monitoring data acquisition module 10, a multi-dimensional spatial database construction module 20, a spatial energy consumption analysis module 30, and an energy-saving management solution acquisition module 40.

[0057] A multi-source monitoring data acquisition module 10 is used to connect to multi-modal monitoring equipment to obtain multi-source monitoring data, and the multi-modal monitoring equipment includes electricity, water, air, and equipment status monitoring equipment; a multi-dimensional spatial database construction module 20 is used to locate the monitoring space of the multi-source monitoring data, project it into the spatial model of the smart park, and construct a multi-dimensional spatial database; a spatial energy consumption analysis module 30 is used to perform spatial energy consumption analysis based on the multi-dimensional spatial database, evaluate the energy consumption status of each spatial area, and identify areas with excessive spatial energy consumption; an energy-saving management plan acquisition module 40 is used to perform energy-saving analysis based on the multi-source monitoring data of the area with excessive spatial energy consumption in combination with spatial distribution characteristics to obtain an energy-saving management plan.

[0058] The specific configuration of the multi-dimensional spatial database construction module 20 will be described in detail below. The multi-dimensional spatial database construction module 20 further includes: identifying the structural distribution of the smart park and constructing a three-dimensional spatial model of the park; performing a primary classification according to the attributes of the park facilities and dividing the attribute space, and the facility attributes include buildings, roads, and greening; performing a secondary classification according to the energy consumption level of the attribute space and dividing the energy consumption level space; marking the boundaries in the three-dimensional spatial model of the park according to the spatial boundaries of the attribute space and the energy consumption level space, and obtaining the spatial model of the smart park.

[0059] The specific configuration of the spatial energy consumption analysis module 30 will be described in detail below. The spatial energy consumption analysis module 30 further includes: based on the multi-dimensional spatial database, aligning and fusing the multi-source monitoring data according to the spatial relationship to obtain the multi-source fusion feature; based on the multi-source monitoring data, setting the monitoring decision range, the monitoring decision range is the decision threshold of the monitoring data, corresponding to the data source; performing energy consumption analysis and evaluation according to the monitoring decision range and the multi-source fusion feature, and obtaining the energy consumption evaluation result.

[0060] The specific configuration of the space energy consumption analysis module 30 will be described in detail below. The space energy consumption analysis module 30 further includes: using the monitoring decision range and the multi-source monitoring data to match and compare the data source, when the monitoring decision range is exceeded, the energy consumption evaluation result is the abnormal energy consumption beyond the range and the corresponding out-of-range value; when the monitoring decision range is not exceeded, the energy consumption evaluation result is the monitoring data of the corresponding data source; according to the multi-source fusion characteristics, the space energy consumption trend is predicted, and the energy consumption evaluation result is obtained based on the energy consumption prediction trend.

[0061] The specific configuration of the spatial energy consumption analysis module 30 will be described in detail below. The spatial energy consumption analysis module 30 further includes: determining the acquisition accuracy distribution according to the spatial relationship of the multimodal monitoring equipment, and using the acquisition accuracy distribution to compensate the monitoring data; aligning the compensated multi-source monitoring data according to the spatial relationship, and calculating the change characteristics of the monitoring data of each data source; according to the alignment relationship, fusing the change characteristics of the monitoring data of each data source to obtain the multi-source fusion characteristics.

[0062] The specific configuration of the spatial energy consumption analysis module 30 will be described in detail below. The spatial energy consumption analysis module 30 further includes: according to the spatial relationship, position alignment of multi-source monitoring data with the same coordinates according to the spatial coordinates; according to the acquisition frequency and acquisition timestamp of the multi-modal monitoring device, time alignment of the multi-source monitoring data; and according to the position alignment and time alignment relationship, alignment calculation of the change characteristics of the multi-source monitoring data.

[0063] The specific configuration of the space energy consumption analysis module 30 will be described in detail below. The space energy consumption analysis module 30 further includes: setting a space energy consumption comparison list according to the historical energy consumption records of each space area; using the energy consumption evaluation results to compare and match with the space energy consumption comparison list, identifying the energy consumption excess difference of each space area, and determining the space energy consumption excess area.

[0064] The specific configuration of the energy-saving management scheme acquisition module 40 will be described in detail below. The energy-saving management scheme acquisition module 40 further includes: extracting the spatial distribution characteristics of the corresponding space energy consumption excess area according to the spatial model of the smart park, the spatial distribution characteristics including spatial attributes, energy consumption levels, and spatial placement characteristics; tracing abnormal factors and abnormal monitoring quantities according to the multi-source monitoring data of the space energy consumption excess area; analyzing the influence relationship between the abnormal factors and the spatial attributes and spatial placement characteristics, and obtaining the energy consumption adjustment relationship; based on the energy consumption level, analyzing the energy-saving adjustment constraints of the region; according to the abnormal factors and abnormal monitoring quantities, performing energy-saving compensation control adjustment based on the energy consumption adjustment relationship and energy-saving adjustment constraints, and obtaining the energy-saving management scheme.

[0065] The specific configuration of the energy-saving management scheme acquisition module 40 will be described in detail below. The energy-saving management scheme acquisition module 40 further includes: obtaining energy-saving management schemes for multiple spatial energy-consumption excess areas; identifying the energy consumption correlation of the multiple spatial energy-consumption excess areas; evaluating the energy-saving management schemes for the multiple spatial energy-consumption excess areas according to the energy consumption correlation, and jointly executing the park operation loss amount; configuring the balancing weights of each spatial energy-consumption excess area according to the park operation loss amount; based on the balancing weights and the park operation loss amount, balancing and adjusting the energy-saving management schemes for the multiple spatial energy-consumption excess areas to obtain a balanced energy-saving management scheme for multiple areas in the park.

[0066] The smart park energy consumption management platform based on multi-source data drive provided in the embodiment of the present invention can execute the smart park energy consumption management method based on multi-source data drive provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0067] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0068] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A smart park energy consumption management method driven by multi-source data, characterized in that: The method comprises: Connecting multi-modal monitoring equipment to obtain multi-source monitoring data, wherein the multi-modal monitoring equipment includes electricity, water, air, and equipment status monitoring equipment; Locate the monitoring space of the multi-source monitoring data, project it into the spatial model of the smart park, and build a multi-dimensional spatial database; Performing spatial energy consumption analysis based on the multi-dimensional spatial database, evaluating the energy consumption status of each spatial area, and identifying areas with excessive spatial energy consumption; Based on the multi-source monitoring data of the area with excessive energy consumption in the space, energy-saving analysis is performed in combination with spatial distribution characteristics to obtain an energy-saving management plan.

2. The smart park energy consumption management method based on multi-source data drive according to claim 1 is characterized in that: Projected into the spatial model of the smart park, it previously included: Identify the structural distribution of the smart park and build a three-dimensional spatial model of the park; Classify the park facilities into primary categories and divide the attribute space according to their attributes. The attributes of the facilities include buildings, roads, and green areas. Perform secondary classification according to the energy consumption level of the attribute space and divide the energy consumption level space; According to the spatial boundaries of the attribute space and the energy consumption level space, boundary marking is performed in the three-dimensional spatial model of the park to obtain the spatial model of the smart park.

3. The method for managing energy consumption in a smart park based on multi-source data drive according to claim 2 is characterized in that: Performing spatial energy consumption analysis based on the multi-dimensional spatial database includes: Based on the multi-dimensional spatial database, feature alignment and fusion of multi-source monitoring data are performed according to spatial relationships to obtain multi-source fusion features; Based on the multi-source monitoring data, a monitoring decision range is set, where the monitoring decision range is a decision threshold of the monitoring data, corresponding to the data source; Energy consumption analysis and evaluation are performed respectively according to the monitoring decision range and the multi-source fusion characteristics to obtain energy consumption evaluation results.

4. The method for managing energy consumption in a smart park based on multi-source data drive according to claim 3 is characterized in that: Energy consumption analysis and evaluation are performed respectively according to the monitoring decision range and the multi-source fusion characteristics to obtain energy consumption evaluation results, including: The monitoring decision range is used to match and compare data sources with the multi-source monitoring data. When the monitoring decision range is exceeded, the energy consumption evaluation result is the out-of-range abnormal energy consumption and the corresponding out-of-range value; When the monitoring decision range is not exceeded, the energy consumption evaluation result is the monitoring data of the corresponding data source; The spatial energy consumption trend is predicted according to the multi-source fusion characteristics, and the energy consumption evaluation result is obtained based on the energy consumption prediction trend.

5. The method for managing energy consumption in a smart park based on multi-source data drive according to claim 3 is characterized in that: According to the spatial relationship, the multi-source monitoring data is aligned and fused to obtain multi-source fusion features, including: According to the spatial relationship of multi-modal monitoring equipment, the acquisition accuracy distribution is determined, and the monitoring data is compensated using the acquisition accuracy distribution; According to the spatial relationship, the multi-source monitoring data after compensation are aligned, and the change characteristics of the monitoring data from each data source are calculated; According to the alignment relationship, the change characteristics of the monitoring data of each data source are fused to obtain the multi-source fusion feature.

6. The method for managing energy consumption in a smart park based on multi-source data drive according to claim 5 is characterized in that: According to the spatial relationship, the multi-source monitoring data after compensation are aligned, and the change characteristics of the monitoring data from each data source are calculated, including: According to the spatial relationship, position alignment of multi-source monitoring data with the same coordinates is performed according to the spatial coordinates; Time alignment of multi-source monitoring data is performed based on the acquisition frequency and acquisition timestamp of multi-modal monitoring equipment; The change feature alignment calculation of multi-source monitoring data is performed according to the position alignment and time alignment relationship.

7. The smart park energy consumption management method based on multi-source data drive according to claim 4 is characterized in that: Evaluate the energy consumption status of each space area and identify areas with excessive energy consumption, including: According to the historical energy consumption records of each space area, set up a space energy consumption comparison list; The energy consumption evaluation result is compared and matched with the space energy consumption comparison list to identify the energy consumption excess difference of each space area and determine the space energy consumption excess area.

8. The method for managing energy consumption in a smart park based on multi-source data drive according to claim 2 is characterized in that: Based on the multi-source monitoring data of the area with excessive energy consumption, energy-saving analysis is performed in combination with the spatial distribution characteristics to obtain an energy-saving management plan, including: According to the spatial model of the smart park, extract the spatial distribution characteristics of the corresponding space energy consumption excess area, wherein the spatial distribution characteristics include spatial attributes, energy consumption level, and spatial placement characteristics; According to the multi-source monitoring data of the area with excessive energy consumption in the space, abnormal factors and abnormal monitoring quantities are traced; Analyze the influence relationship between the abnormal factor and the space attribute and space placement characteristics to obtain the energy consumption adjustment relationship; Analyzing energy-saving adjustment constraints of the region based on the energy consumption level; According to the abnormal factors and abnormal monitoring quantities, energy-saving compensation control adjustment is performed based on the energy consumption adjustment relationship and energy-saving adjustment constraints to obtain the energy-saving management plan.

9. The smart park energy consumption management method based on multi-source data drive according to claim 8 is characterized in that: Get an energy saving management plan, also including: Obtain energy-saving management solutions for areas with excessive energy consumption in multiple spaces; Identifying energy consumption correlations among the multiple spatial energy consumption excess areas; Evaluate the energy-saving management plan of the multi-space energy-consumption excess area according to the energy consumption correlation, and jointly implement the park operation loss amount; According to the park operation loss, the balancing weight of each space energy consumption excess area is configured; Based on the balancing weight and the park operation loss, the energy-saving management plans for the multiple space energy-excess consumption areas are balanced and adjusted to obtain a balanced energy-saving management plan for multiple areas of the park.

10. The smart park energy consumption management platform driven by multi-source data is characterized by: The platform is used to implement the smart park energy consumption management method based on multi-source data drive according to any one of claims 1 to 9, and the platform includes: A multi-source monitoring data acquisition module, used to connect to a multi-modal monitoring device to obtain multi-source monitoring data, wherein the multi-modal monitoring device includes electricity, water, air, and equipment status monitoring devices; A multi-dimensional spatial database construction module is used to locate the monitoring space of the multi-source monitoring data, project it into the spatial model of the smart park, and construct a multi-dimensional spatial database; A space energy consumption analysis module, used to perform space energy consumption analysis based on the multi-dimensional space database, evaluate the energy consumption status of each space area, and identify the space energy consumption excess area; The energy-saving management plan acquisition module is used to perform energy-saving analysis based on the multi-source monitoring data of the area with excessive energy consumption in the space in combination with the spatial distribution characteristics to obtain an energy-saving management plan.

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