Green building energy-saving optimization method and system based on big data

Abstract

The invention discloses an energy-saving optimization method and system for a green building based on big data, and relates to the technical field of big data analysis, and the system comprises a control center which is connected with a building collection module, an energy-saving processing module, an environmental protection analysis module and an intelligent upgrading module. Building resource data are collected for modulation conversion, analog resource signals are obtained, change judgment is conducted on the analog resource signals, and resource judgment coefficients are obtained; performing type integration on the resource judgment coefficients according to the building resource data to obtain a resource coefficient set, and updating and reflecting the building virtual perspective drawing based on the resource coefficient set to obtain a pre-judgment perspective drawing; a virtual simulation space is constructed to perform virtual mapping on the building virtual perspective drawing, a mapped virtual building is obtained, an analog modulation instruction is set to perform virtual regulation and control on the mapped virtual building, and an energy-saving replacement strategy is obtained; the energy consumption condition of the building is monitored in real time, the energy-saving strategy is quickly adjusted, and the energy-saving efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to an energy-saving optimization method and system for green buildings based on big data. Background Art

[0002] The construction industry is a major consumer of energy and resources. As the global energy crisis and environmental problems become increasingly serious, green building energy-saving technology has received widespread attention. Green buildings reduce energy consumption through energy-saving optimization, while effectively reducing greenhouse gas emissions and other pollutants, playing a positive role in environmental protection.

[0003] Traditional green building energy-saving methods mainly rely on the optimization of hardware aspects such as building design, building materials and building equipment, while the energy consumption monitoring and optimization during building operation still need to be strengthened. The development of big data technology has provided new possibilities and means for green building energy saving. To this end, a green building energy-saving optimization method and system based on big data is now provided. Summary of the invention

[0004] The purpose of the present invention is to provide a green building energy-saving optimization method and system based on big data.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A green building energy-saving optimization system based on big data, comprising a control center, wherein the control center is connected with a building acquisition module, an energy-saving processing module, an environmental protection analysis module and an intelligent upgrade module;

[0007] The process of constructing a virtual perspective view of a building by the building acquisition module and collecting building resource data includes:

[0008] Perform entity mapping on the building entity to obtain a virtual perspective view of the building, perform resource extraction on the virtual perspective view of the building to obtain a resource reference point;

[0009] A data capture terminal is set according to the obtained resource reference point, and resource collection is performed on the resource reference point through the data capture terminal to obtain building resource data, and the collected building resource data is time-stamped to obtain the collection time.

[0010] The process of performing digital map determination on analog resource signals and obtaining a signal determination array includes:

[0011] Modulating and converting the obtained building resource data to obtain a simulated resource signal, generating a simulated curve graph according to the simulated resource signal, and setting a peak and valley threshold axis for the obtained simulated curve graph;

[0012] The peak-valley threshold axis is uploaded to the simulation curve chart, and the simulation curve chart is slide-judged by the peak-valley threshold axis to obtain the peak-valley judgment signal segment;

[0013] The obtained peak and valley judgment signal segments are reorganized to obtain a signal judgment array.

[0014] The process of setting the decision extraction coefficient to extract and filter the signal decision array and characterize and capture the analog resource signal includes:

[0015] Set the determination extraction coefficient;

[0016] According to the signal determination array, an extraction base is set, and the determination extraction coefficient is cooperatively extracted through the extraction base to obtain a cooperative coefficient segment;

[0017] Performing primary screening on the signal determination array according to the synergy coefficient segment to obtain a synergy determination array;

[0018] The analog resource signal is characterized and captured according to the collaborative determination array to obtain the resource determination coefficient.

[0019] The process of updating and mapping the virtual perspective view of the building based on the resource coefficient set to obtain the predicted perspective view includes:

[0020] According to the building resource data, the resource determination coefficients are integrated into types to obtain a resource coefficient set, and the resource determination coefficients corresponding to the resource reference points are obtained from the resource coefficient set, which is recorded as a reference coefficient set;

[0021] Perform weight averaging on the obtained reference coefficient set to obtain the reference coefficient mean;

[0022] The reference original area is set according to the reference coefficient mean value, and the obtained reference original area is uploaded to the building virtual perspective map based on the resource reference point, and marked in the building virtual perspective map to obtain the original perspective drawing;

[0023] The reference coefficient set is uploaded to the original perspective drawing, and the original perspective drawing is mapped using the reference coefficient set to obtain the predicted perspective drawing.

[0024] The process of isomorphic mapping of the original perspective drawing by using the reference coefficient set includes:

[0025] Performing proportional replacement on the reference coefficient set according to the obtained reference coefficient mean value to obtain the reference coefficient ratio, and generating the temporal coefficient region according to the reference coefficient ratio based on the reference original region;

[0026] The obtained temporal coefficient area is uploaded to the original perspective drawing based on the resource reference point to obtain the predicted perspective drawing.

[0027] The process of obtaining the predicted area map includes:

[0028] Construct a virtual simulation space, virtually map the virtual perspective view of the building through the virtual simulation space to obtain a mapped virtual building, and mark the resource reference point in the mapped virtual building to obtain a mapped projection point;

[0029] The predicted perspective drawings are time-sorted based on the acquisition time to obtain a predicted perspective sequence, and the obtained predicted perspective sequence is uploaded to the mapped virtual building based on the mapped projection point;

[0030] The obtained prediction perspective image sequence is subjected to region conversion to obtain a prediction region area map.

[0031] The process of virtually regulating the mapped virtual building through simulated modulation instructions to obtain energy-saving replacement strategies includes:

[0032] Generate a simulation modulation instruction according to the obtained building resource data, upload the obtained simulation modulation instruction to the mapped virtual building, virtually control the mapped virtual building through the simulation modulation instruction, and perform homomorphic monitoring on the predicted area map to obtain the monitored area map;

[0033] Based on the analog modulation instruction, the area map of the monitoring region is continuously checked to obtain a comprehensive monitoring map, the comprehensive monitoring map is dynamically monitored to obtain predicted energy-saving points, and an energy-saving replacement strategy is generated according to the obtained predicted energy-saving points.

[0034] Based on the above-mentioned green building energy-saving optimization system based on big data, the present invention also provides a green building energy-saving optimization method based on big data, comprising the following steps:

[0035] Step 1: Construct a virtual perspective view of the building and collect building resource data;

[0036] Step 2: Modulate and convert the building resource data to obtain an analog resource signal, perform digital image determination on the analog resource signal to obtain a signal determination array, set the determination extraction coefficient to extract and screen the signal determination array and characterize and capture it with the analog resource signal to obtain a resource determination coefficient;

[0037] Step 3: According to the building resource data, the resource determination coefficients are integrated into types to obtain a resource coefficient set, and based on the resource coefficient set, the building virtual perspective drawing is updated and mapped to obtain a predicted perspective drawing;

[0038] Step 4: Construct a virtual simulation space to virtually map the virtual perspective view of the building, obtain the mapped virtual building, sort the predicted perspective drawings in time and convert the regions, obtain the predicted area map, generate simulation modulation instructions according to the building resource data, virtually regulate the mapped virtual building through the simulation modulation instructions, and obtain the energy-saving replacement strategy.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The collected building resource data is converted into a form, and data features are extracted to obtain resource determination coefficients. By integrating the resource determination coefficients into types, a resource coefficient set is obtained. Based on the resource coefficient set, the constructed building virtual perspective view is updated and mapped to obtain a predicted perspective drawing. This is conducive to real-time monitoring of the building's energy consumption, converting the collected energy comprehensive data of green buildings in different forms into the same standard data form, and converting them into the building's perspective view for comparison, thereby improving the data processing rate.

[0041] Construct a virtual simulation space to virtually map the virtual perspective of the building, obtain the mapped virtual building, set the simulation modulation instructions to virtually regulate the mapped virtual building, and obtain the energy-saving replacement strategy; by making adjustments in the virtual space to obtain the best adjustment nodes, the energy-saving measures are made more accurate and effective, the energy-saving efficiency is greatly improved, and it is ensured that the generated energy-saving replacement strategy can adapt to the ever-changing building energy-saving needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] like Figure 1 As shown, an energy-saving optimization system for green buildings based on big data includes a control center, and the control center is connected to a building collection module, an energy-saving processing module, an environmental analysis module, and an intelligent upgrade module;

[0046] The building acquisition module is used to construct a virtual perspective view of the building and collect building resource data. The specific process includes:

[0047] Physically map the building entity to obtain a virtual perspective view of the building;

[0048] The entity mapping means performing a three-dimensional virtual transformation on the building entity and mapping it to the plane view to obtain a virtual three-dimensional perspective view, which is recorded as a building virtual perspective view, wherein the structure, function and basic facilities of the building virtual perspective view are exactly the same as those of the building facilities in reality, and the building entity represents the building entity in reality;

[0049] Constructing a transmission link between the building virtual perspective view and the building entity, wherein the transmission link is used to transmit data information of the building entity to the building virtual perspective view, or to transmit data information of the building virtual perspective view to the building entity;

[0050] Extract resources from the obtained virtual perspective view of the building to obtain resource reference points;

[0051] The resource extraction means analyzing the virtual perspective view of the building to obtain the locations where resources can be generated and consumed, which are recorded as resource reference points;

[0052] A data capture terminal is set according to the obtained resource reference point, resource collection is performed on the resource reference point through the data capture terminal to obtain building resource data, and time-stamping is performed on the collected building resource data to obtain collection time, wherein the collection time indicates the time corresponding to the acquisition of the building resource data through the data capture terminal, and the building resource data is associated with the corresponding collection time;

[0053] Further, the building resource data includes energy consumption data, environmental parameter data and building structure data, wherein the energy consumption data includes electricity consumption, heat consumption and water consumption, the environmental parameter data includes indoor temperature, indoor humidity, outdoor climate data, the outdoor climate data includes but is not limited to temperature, humidity, wind speed, light intensity, building structure data includes but is not limited to building area, building volume, building orientation, insulation layer thickness, window-to-wall ratio, glass type, shading coefficient;

[0054] The obtained building resource data is associated with corresponding resource reference points.

[0055] The energy-saving processing module is used to modulate and convert the building resource data to obtain a simulated resource signal, perform change determination on the simulated resource signal, and obtain a resource determination coefficient. The specific process includes:

[0056] Modulating and converting the acquired building resource data to obtain analog resource signals;

[0057] The modulation conversion means converting the obtained building resource data into a signal form, that is, an analog resource signal. According to the energy consumption data, environmental parameter data and building structure data included in the building resource data, the analog resource signal includes an energy consumption signal, an environmental parameter signal and a building structure signal;

[0058] Generate a simulation curve graph according to the obtained simulation resource signal, wherein the simulation curve graph is a signal curve graph generated according to the simulation resource signal, and the curve generated by the simulation resource signal in the graph is marked as a resource signal curve;

[0059] A peak-valley threshold axis is set for the obtained simulation curve graph, wherein the peak-valley threshold axis includes a peak axis and a valley axis, and the distance between the peak axis and the valley axis represents a threshold spacing, indicating that the distance between the peak axis and the valley axis is fixed, and even if the peak-valley threshold axis is moved, the interval between the peak axis and the valley axis can be ensured to be fixed, that is, the threshold spacing;

[0060] Uploading the obtained peak-valley threshold axis to the simulation curve graph, wherein the peak-valley threshold axis is parallel to the horizontal axis of the simulation curve graph, the peak axis and the valley axis are parallel to each other, and the peak axis is located above the valley axis;

[0061] Perform sliding judgment on the simulation curve graph through the peak-valley threshold axis to obtain the peak-valley judgment signal segment, wherein the peak-valley judgment signal segment includes a peak judgment signal segment, a middle judgment signal segment and a valley judgment signal segment;

[0062] Furthermore, the sliding determination process includes:

[0063] In the simulation curve graph, the peak-valley threshold axis is moved horizontally up and down. When the peak axis reaches the highest point of the resource signal curve, the intersection of the valley axis and the resource signal curve is obtained according to the valley axis at a threshold spacing distance from the peak axis, which are recorded as the pre-peak threshold point and the post-peak threshold point, and the resource signal curve between the pre-peak threshold point and the post-peak threshold point is marked as the peak determination signal segment;

[0064] When the peak axis reaches the midpoint of the resource signal curve, the intersection of the valley axis and the resource signal curve is obtained according to the valley axis at a threshold spacing distance from the peak axis, recorded as the middle front threshold point and the middle rear threshold point, and the resource signal curve between the middle front threshold point and the middle rear threshold point is marked as the middle determination signal segment, where the "midpoint" represents the middlemost position of the resource signal curve, that is, the position of half of the curve;

[0065] When the valley axis reaches the lowest point of the resource signal curve, the intersection of the peak axis and the resource signal curve is obtained according to the peak axis at a threshold spacing distance from the valley axis, recorded as the pre-valley threshold point and the post-valley threshold point, and the resource signal curve between the pre-valley threshold point and the post-valley threshold point is marked as a valley determination signal segment;

[0066] Reorganize the obtained peak and valley judgment signal segments to obtain a signal judgment array;

[0067] The interval reorganization means that the corresponding signal segments are arranged in a matrix form according to the arrangement order of the peak determination signal segment, the middle determination signal segment and the valley determination signal segment, that is, the signal determination array;

[0068] Setting a determination extraction coefficient, wherein the determination extraction coefficient is expressed in a function form;

[0069] Setting an extraction base number according to the obtained signal determination array, wherein the extraction base number is equal to the number of elements in the signal determination array;

[0070] According to the obtained extraction base number, the extraction coefficient is determined to be extracted in a coordinated manner to obtain a coordinated coefficient segment;

[0071] The collaborative extraction means that the determined extraction coefficients are evenly trimmed according to the number of extraction bases to obtain collaborative coefficient segments of equal length;

[0072] Performing primary screening on the signal determination array according to the obtained synergy coefficient segments to obtain a synergy determination array;

[0073] The primary screening means uploading the obtained synergy coefficient segments to the signal determination array, making one-to-one correspondence between the synergy coefficient segments and the elements in the signal determination array according to the order of synergy extraction, and convolving the synergy coefficient segments with the elements in the signal determination array to obtain synergy determination segments, and replacing the elements at the original positions with the obtained synergy determination segments to obtain a synergy determination array;

[0074] Characterize and capture the analog resource signal according to the obtained collaborative determination array to obtain a resource determination coefficient;

[0075] Furthermore, the characterization capture process includes:

[0076] Uploading the obtained coordination determination array to the simulated resource signal, convolving the coordination determination array with the simulated resource signal to obtain a convolution coordination signal, and performing discrete cosine transformation on the obtained convolution coordination signal to obtain a resource determination coefficient;

[0077] The obtained resource determination coefficient is associated with the corresponding simulated resource signal, that is, the resource determination coefficient is used to represent the characteristic identity of the simulated resource signal, and each simulated resource signal has a corresponding resource determination coefficient.

[0078] The environmental analysis module is used to integrate resource determination coefficients according to building resource data to obtain a resource coefficient set, and to update and map the building virtual perspective view based on the resource coefficient set to obtain a predicted perspective drawing. The specific process includes:

[0079] According to the obtained building resource data, the obtained resource determination coefficients are type-integrated to obtain a resource coefficient set;

[0080] The type integration means classifying the obtained resource determination coefficients according to the energy consumption data, environmental parameter data and building structure data included in the building resource data, dividing the corresponding resource determination coefficients according to the energy consumption data, environmental parameter data and building structure data respectively, and constructing a set of the divided resource determination coefficients to obtain a resource coefficient set, wherein the resource coefficient set includes an energy consumption set, an environmental parameter set and a building structure set; in particular, the order of the resource determination coefficients in the resource coefficient set is the same as the order of the collection time associated with the building resource data, that is, they are sorted in the order of the collection time;

[0081] Obtaining a resource determination coefficient corresponding to a resource reference point in a resource coefficient set, recorded as a reference coefficient set, and associating the obtained reference coefficient set with the corresponding resource reference point, wherein, according to the resource coefficient set including an energy consumption set, an environmental parameter set, and a building structure set, the reference coefficient set includes a reference consumption set, a reference environmental parameter set, and a reference structure set;

[0082] Perform weight averaging on the obtained reference coefficient set to obtain the reference coefficient mean;

[0083] The weight averaging means averaging the resource determination coefficients included in the obtained reference coefficient set, and the obtained mean is the reference coefficient mean. According to the reference consumption set, the reference environmental parameter set and the reference structure set included in the reference coefficient set, the reference coefficient mean includes the consumption mean and the environmental parameter mean. The reference structure set is not weighted and kept unchanged.

[0084] The reference original area is set according to the obtained reference coefficient mean value, and the original area represents a preset fixed-size area, and the area is used to upload to the building virtual perspective view. In this embodiment, the area shape of the reference original area is circular, and the area is filled with color, that is, the reference original area corresponding to each type of reference coefficient mean value corresponds to a color;

[0085] Based on the resource reference point, the obtained reference original area is uploaded to the building virtual perspective view, and marked in the building virtual perspective view to obtain the original perspective drawing, which includes the consumption mean drawing and the ring parameter mean drawing, wherein the reference original area is uploaded to the resource reference point corresponding to the building virtual perspective view, and the center point of the reference original area coincides with the resource reference point, until all resource reference points of the building virtual perspective view are uploaded with the corresponding reference original area;

[0086] In particular, according to the consumption mean and the environmental parameter mean included in the reference coefficient mean, the reference original area generated for each type of reference coefficient mean is divided into a building virtual perspective view, and the original perspective drawings corresponding to the consumption mean and the environmental parameter mean can be obtained, and the original perspective drawings include the consumption mean drawings and the environmental parameter mean drawings, wherein the original perspective drawings represent the summary of the same type of reference coefficient mean marks in the building virtual perspective view, that is, each building virtual perspective view of the reference original area marked on the drawing corresponds to a type of reference coefficient mean, that is, the consumption mean drawings are the marks of the reference original area corresponding to the consumption mean in the building virtual perspective view, and the environmental parameter mean drawings are the marks of the reference original area corresponding to the environmental parameter mean in the building virtual perspective view;

[0087] The obtained reference coefficient set is uploaded to the original perspective drawing, and the original perspective drawing is mapped by the reference coefficient set to obtain the predicted perspective drawing, wherein the predicted perspective drawing includes a predicted consumption drawing and a predicted ring reference drawing;

[0088] It should be further explained that, in the specific implementation process, the process of equal example mapping includes:

[0089] Performing proportional replacement on the reference coefficient set according to the obtained reference coefficient mean value to obtain the reference coefficient ratio, and generating the temporal coefficient region according to the obtained reference coefficient ratio based on the reference original region;

[0090] The ratio replacement means calculating the ratio of the resource determination coefficient in the reference coefficient set to the original reference coefficient mean, which is recorded as the reference coefficient ratio, wherein the reference coefficient ratio = resource determination coefficient ÷ reference coefficient mean, and the reference coefficient mean is the mean corresponding to the reference coefficient set, that is, the reference consumption set and the corresponding consumption mean, the reference environment parameter set and the corresponding environment parameter mean, then the reference coefficient ratio includes the consumption ratio and the environment parameter ratio;

[0091] According to the proportional relationship of the reference coefficient ratio, the reference original area corresponding to the original reference coefficient mean is obtained, and the resource determination coefficient in the reference coefficient set generates an area of ​​corresponding proportion, which is recorded as the temporal coefficient area, that is, the area size of the temporal coefficient area generated by each resource determination coefficient is generated according to the proportional relationship with the original reference coefficient mean. For example, if the reference coefficient ratio is 2.1, the generated temporal coefficient area is 2.1 times the corresponding reference original area, and the temporal coefficient area includes the consumption temporal area and the ring parameter temporal area; in particular, the area shape of the temporal coefficient area is the same as that of the reference original area, both of which are circular, and are filled with the same color as the corresponding reference original area;

[0092] Based on the resource reference point, the obtained temporal coefficient area is uploaded to the original perspective drawing to obtain the predicted perspective drawing. Similarly, the center point of the temporal coefficient area is uploaded to the resource reference point and overlaps with the resource reference point, and is filled with the corresponding color, and the reference original area is highlighted, for example, the color brightness of the reference original area is increased or the edge display is enhanced, so that the reference original area is not covered by other temporal coefficient areas, wherein the consumption temporal area is uploaded to the consumption mean drawing to obtain the predicted consumption drawing, and the ring parameter temporal area is uploaded to the ring parameter mean drawing to obtain the predicted ring parameter drawing; in particular, according to the collection time corresponding to the building resource data, each predicted perspective drawing corresponds to a collection time, that is, in one collection time, there is a predicted consumption drawing and a predicted ring parameter drawing, and these two drawings can find the corresponding same collection time.

[0093] The intelligent upgrade module is used to construct a virtual simulation space to virtually map the virtual perspective view of the building, obtain the mapped virtual building, set the simulation modulation instruction to virtually regulate the mapped virtual building, and obtain the energy-saving replacement strategy. The specific process includes:

[0094] Construct a virtual simulation space, virtually map the virtual perspective view of the building through the virtual simulation space to obtain a mapped virtual building, and mark the resource reference point in the mapped virtual building to obtain a mapping projection point, wherein the mapping projection point is exactly the same as the resource reference point, except that the corresponding resource reference point is marked as a mapping projection point in the mapped virtual building;

[0095] The virtual mapping means that the building is virtually perspective Figure 1 1. Projected into the virtual simulation space, the obtained virtual building has the same equipment, structure, function, location and scale as the building entity, and can realize the corresponding function of the virtual building. The virtual simulation space is a virtual space constructed to provide a simulation space for the virtual perspective view of the building;

[0096] Time-sorting the obtained predicted perspective drawings based on the acquisition time to obtain a predicted perspective drawing sequence;

[0097] The time sorting means sorting the predicted perspective drawings according to the order of acquisition time to obtain a sequence of predicted perspective drawings;

[0098] Uploading the obtained predicted perspective image sequence to the mapped virtual building based on the mapped projection point, and associating the obtained predicted perspective image sequence with the corresponding mapped projection point;

[0099] Performing a region conversion on the obtained prediction perspective image sequence to obtain a prediction region area graph, wherein the region conversion means transferring the reference original region and the temporal coefficient region of the prediction perspective image in the prediction perspective image sequence into a two-dimensional rectangular coordinate system to obtain a prediction area curve, wherein the prediction area curve includes an original area curve and a temporal area curve, and the region is represented by a corresponding shape with the center point as the origin in the coordinate system, that is, the prediction area curve is a circular region with the center point as the origin; in particular, each prediction perspective image sequence corresponds to a prediction region area graph, that is, the temporal coefficient regions included in the prediction perspective image sequence are all represented in an area graph;

[0100] Constructing a transmission link between the predicted area map and the virtual simulation space, and associating the obtained predicted area map with the corresponding mapping transmission point;

[0101] Generate a simulation modulation instruction according to the obtained building resource data, upload the obtained simulation modulation instruction to the mapped virtual building, virtually control the mapped virtual building through the simulation modulation instruction, and perform homomorphic monitoring on the predicted area map to obtain the monitored area map;

[0102] Further, the analog modulation instruction is generated according to the building resource data, indicating that the environmental parameter data and the building structure data are adjusted, and the adjusted data is recorded, that is, the analog modulation instruction; the virtual control indicates that the analog modulation instruction is executed by mapping the virtual building, and when executing each analog modulation instruction, the corresponding prediction area map is monitored to obtain the monitoring area map, and each time an analog modulation instruction is executed, the prediction area map is recorded once, wherein each analog modulation instruction has a corresponding mapping transmission point and adjustment content; in particular, the generated analog modulation instructions are carried out in order, and are set in order from small to large or from large to small, each instruction only changes one variable, and the phase difference interval between two adjacent analog modulation instructions is small enough to generate enough analog modulation instructions, so as to obtain the best energy effect value of the mapping transmission point and improve the accuracy of energy-saving optimization;

[0103] The obtained monitoring area area map is continuously checked based on the analog modulation command to obtain a comprehensive monitoring map;

[0104] The continuous inspection means that the obtained monitoring area area maps are sorted according to the order of the analog modulation instructions, and the obtained monitoring area area maps are mapped to the monitoring area area map corresponding to the first analog modulation instruction to obtain a comprehensive monitoring map, that is, the graphic areas corresponding to the monitoring area area maps generated by all analog modulation instructions are mapped to the same monitoring map, that is, the comprehensive monitoring map;

[0105] According to the predicted consumption diagram and the predicted environmental reference diagram included in the predicted perspective diagram, the comprehensive monitoring diagram only represents the consumption monitoring diagram, and the predicted environmental reference diagram is not changed;

[0106] Dynamically monitor the obtained comprehensive monitoring map to obtain predicted energy-saving points, and generate energy-saving replacement strategies based on the obtained predicted energy-saving points;

[0107] It should be further explained that, in the specific implementation process, the dynamic monitoring process includes:

[0108] According to the analog modulation instructions corresponding to the environmental parameter data and the building structure data in the building resource data, based on the setting order of the analog modulation instructions, the prediction area curve in the comprehensive monitoring map is dynamically generated, that is, according to the order of the analog modulation instructions, the prediction area curve in the comprehensive monitoring map is dynamically displayed to obtain a dynamic comprehensive monitoring map, that is, according to the order of the analog modulation instructions, it is sequentially displayed in the same monitoring map, and what is obtained is the dynamic change of the prediction area curve;

[0109] The dynamic comprehensive monitoring map is recorded, and the capture condition is set in the dynamic comprehensive monitoring map. The best prediction area map is obtained according to the obtained capture condition. The capture condition is that the prediction area curve reaches the maximum area and is less than the threshold area, wherein the threshold area satisfies less than CN, , n represents the weight multiple, 1<n, SY represents the area of ​​the reference original area;

[0110] The obtained optimal prediction area map is recorded as a predicted energy-saving point, indicating that energy-saving optimization can be performed at the optimal prediction area map, and the analog modulation instruction corresponding to the optimal prediction area map is obtained. The content corresponding to the obtained analog modulation instruction is recorded as an energy-saving replacement strategy. Energy-saving measures are executed on the resource reference point corresponding to the building entity through the energy-saving replacement strategy to adjust and optimize;

[0111] Furthermore, the setting of the analog modulation instructions is carried out in an orderly manner, in a sequence from small to large or from large to small, and each instruction only changes one variable. Then, according to the adjustment instructions in the instructions, the resource reference points that can change the environmental parameter data and the building structure data can be adjusted at the predicted energy-saving points to obtain the optimized resource reference points, that is, to optimize the energy consumption of green buildings, so as to reduce energy waste and apply effective resources to the required building nodes.

[0112] Based on the above-mentioned green building energy-saving optimization system based on big data, the present invention also provides a green building energy-saving optimization method based on big data, comprising the following steps:

[0113] Step 1: Construct a virtual perspective view of the building and collect building resource data;

[0114] Step 2: Modulate and convert the building resource data to obtain an analog resource signal, perform digital image determination on the analog resource signal to obtain a signal determination array, set the determination extraction coefficient to extract and screen the signal determination array and characterize and capture it with the analog resource signal to obtain a resource determination coefficient;

[0115] Step 3: According to the building resource data, the resource determination coefficients are integrated into types to obtain a resource coefficient set, and based on the resource coefficient set, the building virtual perspective drawing is updated and mapped to obtain a predicted perspective drawing;

[0116] Step 4: Construct a virtual simulation space to virtually map the virtual perspective view of the building, obtain the mapped virtual building, sort the predicted perspective drawings in time and convert the regions, obtain the predicted area map, generate simulation modulation instructions according to the building resource data, virtually regulate the mapped virtual building through the simulation modulation instructions, and obtain the energy-saving replacement strategy.

[0117] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A green building energy-saving optimization system based on big data, including a control center, characterized in that: The control center is connected to a building collection module, an energy-saving processing module, an environmental analysis module and an intelligent upgrade module; The building collection module is used to construct a virtual perspective view of the building and collect building resource data; The energy-saving processing module is used to modulate and convert the building resource data to obtain the analog resource signal, perform digital image determination on the analog resource signal to obtain the signal determination array, set the determination extraction coefficient to extract and screen the signal determination array and characterize and capture the analog resource signal to obtain the resource determination coefficient; The environmental analysis module is used to integrate resource determination coefficients according to building resource data to obtain a resource coefficient set, and to update and map the building virtual perspective view based on the resource coefficient set to obtain a predicted perspective drawing; The intelligent upgrade module is used to construct a virtual simulation space to virtually map the virtual perspective view of the building, obtain the mapped virtual building, time-sort and area-convert the predicted perspective drawings, obtain the predicted area map, generate simulation modulation instructions according to the building resource data, virtually regulate the mapped virtual building through the simulation modulation instructions, and obtain an energy-saving replacement strategy.

2. According to the energy-saving optimization system of green buildings based on big data according to claim 1, it is characterized in that: The process of constructing a virtual perspective view of a building by the building acquisition module and collecting building resource data includes: Perform entity mapping on the building entity to obtain a virtual perspective view of the building, perform resource extraction on the virtual perspective view of the building to obtain a resource reference point; A data capture terminal is set according to the obtained resource reference point, and resource collection is performed on the resource reference point through the data capture terminal to obtain building resource data, and the collected building resource data is time-stamped to obtain the collection time.

3. According to the energy-saving optimization system of green buildings based on big data as described in claim 2, it is characterized in that: The process of performing digital map determination on analog resource signals and obtaining a signal determination array includes: Modulating and converting the obtained building resource data to obtain a simulated resource signal, generating a simulated curve graph according to the simulated resource signal, and setting a peak and valley threshold axis for the obtained simulated curve graph; The peak-valley threshold axis is uploaded to the simulation curve chart, and the simulation curve chart is slide-judged by the peak-valley threshold axis to obtain the peak-valley judgment signal segment; The obtained peak and valley judgment signal segments are reorganized to obtain a signal judgment array.

4. According to the energy-saving optimization system for green buildings based on big data according to claim 3, it is characterized in that: The process of setting the decision extraction coefficient to extract and filter the signal decision array and characterize and capture the analog resource signal includes: Set the determination extraction coefficient; According to the signal determination array, an extraction base is set, and the determination extraction coefficient is cooperatively extracted through the extraction base to obtain a cooperative coefficient segment; Performing primary screening on the signal determination array according to the synergy coefficient segment to obtain a synergy determination array; The analog resource signal is characterized and captured according to the collaborative determination array to obtain the resource determination coefficient.

5. According to the big data-based green building energy-saving optimization system of claim 4, it is characterized in that: The process of updating and mapping the virtual perspective view of the building based on the resource coefficient set to obtain the predicted perspective view includes: According to the building resource data, the resource determination coefficients are integrated into types to obtain a resource coefficient set, and the resource determination coefficients corresponding to the resource reference points are obtained from the resource coefficient set, which is recorded as a reference coefficient set; Perform weight averaging on the obtained reference coefficient set to obtain the reference coefficient mean; The reference original area is set according to the reference coefficient mean value, and the obtained reference original area is uploaded to the building virtual perspective map based on the resource reference point, and marked in the building virtual perspective map to obtain the original perspective drawing; The reference coefficient set is uploaded to the original perspective drawing, and the original perspective drawing is mapped using the reference coefficient set to obtain the predicted perspective drawing.

6. The energy-saving optimization system for green buildings based on big data according to claim 5 is characterized in that: The process of isomorphic mapping of the original perspective drawing by using the reference coefficient set includes: Performing proportional replacement on the reference coefficient set according to the obtained reference coefficient mean value to obtain the reference coefficient ratio, and generating the temporal coefficient region according to the reference coefficient ratio based on the reference original region; The obtained temporal coefficient area is uploaded to the original perspective drawing based on the resource reference point to obtain the predicted perspective drawing.

7. The energy-saving optimization system for green buildings based on big data according to claim 6 is characterized in that: The process of obtaining the predicted area map includes: Construct a virtual simulation space, virtually map the virtual perspective view of the building through the virtual simulation space to obtain a mapped virtual building, and mark the resource reference point in the mapped virtual building to obtain a mapped projection point; The predicted perspective drawings are time-sorted based on the acquisition time to obtain a predicted perspective sequence, and the obtained predicted perspective sequence is uploaded to the mapped virtual building based on the mapped projection point; The obtained prediction perspective image sequence is subjected to region conversion to obtain a prediction region area map.

8. The energy-saving optimization system for green buildings based on big data according to claim 7 is characterized in that: The process of virtually regulating the mapped virtual building through simulated modulation instructions to obtain energy-saving replacement strategies includes: Generate a simulation modulation instruction according to the obtained building resource data, upload the obtained simulation modulation instruction to the mapped virtual building, virtually control the mapped virtual building through the simulation modulation instruction, and perform homomorphic monitoring on the predicted area map to obtain the monitored area map; Based on the analog modulation instruction, the area map of the monitoring region is continuously checked to obtain a comprehensive monitoring map, the comprehensive monitoring map is dynamically monitored to obtain predicted energy-saving points, and an energy-saving replacement strategy is generated according to the obtained predicted energy-saving points.

9. The energy-saving optimization method of the energy-saving optimization system of a green building based on big data according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step 1: Construct a virtual perspective view of the building and collect building resource data; Step 2: Modulate and convert the building resource data to obtain an analog resource signal, perform digital image determination on the analog resource signal to obtain a signal determination array, set the determination extraction coefficient to extract and screen the signal determination array and characterize and capture it with the analog resource signal to obtain a resource determination coefficient; Step 3: According to the building resource data, the resource determination coefficients are integrated into types to obtain a resource coefficient set, and based on the resource coefficient set, the building virtual perspective drawing is updated and mapped to obtain a predicted perspective drawing; Step 4: Construct a virtual simulation space to virtually map the virtual perspective view of the building, obtain the mapped virtual building, sort the predicted perspective drawings in time and convert the regions, obtain the predicted area map, generate simulation modulation instructions according to the building resource data, virtually regulate the mapped virtual building through the simulation modulation instructions, and obtain the energy-saving replacement strategy.

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