Public institution energy saving management system based on AI intelligent operation and maintenance

Abstract

The invention belongs to the technical field of energy management, and discloses a public institution energy-saving management system based on AI intelligent operation and maintenance, and the system comprises a multi-source data obtaining module which obtains building multi-source operation data; the coupling feature analysis module is used for constructing a building coupling feature data set based on the building multi-source operation data; the cold and hot hedging assessment module assesses the cold and hot hedging risk based on the building coupling feature data set and generates R cold and hot hedging risk areas; the hierarchical scheduling generation module is used for performing hierarchical sorting on the R cold and hot hedging risk areas and generating a cold and hot hedging priority relief list and a cold and hot hedging secondary adjustment list; the cold and hot hedging regulation and control module is used for carrying out cold and hot hedging grading self-adaptive regulation and control on the cold and hot hedging priority relieving list and the cold and hot hedging secondary regulation list; according to the invention, the problems of inaccurate cold and hot supply and demand matching and lack of pertinence of an adjustment strategy are solved through cold and hot hedging recognition and a self-adaptive regulation and control mechanism, and the energy-saving effect of building energy management is improved.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and more specifically, to an energy conservation management system for public institutions based on AI intelligent operation and maintenance. Background Art

[0002] In the process of energy system management of public institution buildings, how to achieve accurate allocation and dynamic balance of cold and hot resources is a key goal to ensure indoor comfort and improve energy efficiency. With the development of large-scale building group construction, the coupling relationship between building sub-areas in terms of air supply paths, return air loops, water system loops and heat transfer of enclosure structures has become increasingly complex, and the dynamic changes in cold and hot loads have shown stronger correlation and transmission. Especially in the context of coexistence of multi-functional areas and significant differences in energy demand, the phenomenon of cold and hot hedging occurs frequently, which not only reduces the energy efficiency of the system, but also easily causes operational and maintenance problems such as cold and hot fluctuations and frequent start and stop of equipment. To solve the above problems, the industry has gradually introduced an energy-saving management system with AI algorithms as the core, and tried to combine operating status parameters to achieve adaptive energy consumption control based on big data.

[0003] However, existing technologies often rely on static rule configuration or coarse-grained energy consumption aggregation data in the process of identifying and intervening in heat and cold hedging, which cannot accurately describe the spatial coupling structure and load interaction characteristics between building sub-areas. This results in coarse granularity and insufficient timeliness in identifying heat and cold hedging, making it difficult to achieve refined difference diagnosis and hierarchical regulation. In addition, existing solutions lack regional cooling efficiency, actual regulation margin and regulation intention. Figure 1 A systematic evaluation of the consistency of the regulation strategy may lead to problems such as inaccurate matching of heat and cold supply and demand, lack of targeted regulation strategy and poor energy-saving effect.

[0004] Therefore, there is an urgent need for a graph-structured heat and cold hedging diagnosis and control system that integrates building spatial structure, operational data, and semantic tags to achieve more targeted and intelligent heat and cold hedging adaptive control. In view of this, this paper proposes an energy conservation management system for public institutions based on AI intelligent operation and maintenance to address the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and achieve the above-mentioned objectives, the present invention provides the following technical solutions: an energy conservation management system for public institutions based on AI intelligent operation and maintenance, comprising:

[0006] Multi-source data acquisition module, used to obtain multi-source building operation data;

[0007] The coupling feature analysis module performs semantic fusion and coupling evaluation based on multi-source building operation data to construct a building coupling feature dataset. This dataset includes a heat and cold hedging difference map, an adjustment intention difference matrix, and the corresponding heat and cold demand directions, dynamic load semantic labels, and regional comprehensive cooling efficiency coefficients for N building sub-areas.

[0008] The heat-cold hedging assessment module performs heat-cold hedging risk assessment based on the building coupling feature dataset, obtains heat-cold hedging risk assessment scores corresponding to N building sub-areas, and identifies R heat-cold hedging risk areas;

[0009] A hierarchical scheduling generation module is used to hierarchically sort the R hot and cold hedging risk areas according to functional zoning attributes, building sub-area areas, and hot and cold hedging persistence types, and generate a hot and cold hedging priority mitigation list and a hot and cold hedging secondary adjustment list;

[0010] The hot and cold hedging control module is used to combine the air-conditioning system operating margin parameter set, the hot and cold hedging difference map and the adjustment intention difference matrix to perform hot and cold hedging hierarchical adaptive control on the hot and cold hedging priority relief list and the hot and cold hedging secondary adjustment list.

[0011] Furthermore, the method for performing adaptive control of cold and hot hedging levels includes:

[0012] For each building sub-area in the priority relief list for cold and hot hedging, which is recorded as a priority relief building sub-area, a sub-graph with a direct spatial coupling relationship with the priority relief building sub-area is obtained from the cold and hot hedging difference graph, which is recorded as a cold and hot hedging associated sub-graph; the matrix elements directly associated with the priority relief building sub-area on the graph structure edge are extracted from the adjustment intention difference matrix to construct an adjustment intention difference set; the air-conditioning system operation margin parameter set, the cold and hot hedging associated sub-graph, and the adjustment intention difference set are input into the priority relief parameter setting model to obtain an immediate adjustment instruction set; the immediate adjustment instruction set includes the target air supply volume set value, the terminal water valve target opening, the host load instantaneous correction gain, and the energy storage unit energy release instruction;

[0013] Send the real-time adjustment instruction set to the corresponding control unit for execution;

[0014] When the hot and cold hedge priority relief list is completed, the hot and cold hedge secondary adjustment list is updated and hot and cold hedge hierarchical adaptive regulation is performed.

[0015] Furthermore, the method for updating the cold and hot hedging secondary adjustment list and performing cold and hot hedging hierarchical adaptive control includes:

[0016] For each building sub-area in the secondary adjustment list for cold and heat hedging, record it as a secondary mitigation building sub-area, and re-evaluate and update the corresponding cold and heat hedging risk assessment score of the secondary mitigation building sub-area;

[0017] Remove the secondary mitigation building sub-areas whose cold and heat hedging risk assessment scores are not greater than the preset cold and heat hedging risk score threshold from the cold and heat hedging secondary adjustment list to obtain an updated cold and heat hedging secondary adjustment list;

[0018] Obtain the latest air conditioning system operating margin parameter set, and construct a heat and cold hedging risk assessment score set based on the heat and cold hedging risk assessment scores in the updated heat and cold hedging secondary adjustment list;

[0019] The air-conditioning system operating margin parameter set and the cold and hot hedging risk assessment score set are input into the secondary mitigation parameter setting model to obtain a planned adjustment instruction set, which is then sent to the corresponding control unit for execution; the planned adjustment instruction set is of the same type as the adjustment instruction in the immediate adjustment instruction set.

[0020] Furthermore, the method for obtaining the hot-cold hedging priority relief list and the hot-cold hedging secondary adjustment list includes:

[0021] For R cold-heat hedging risk areas, obtain the functional zoning attributes, cold-heat hedging duration ratio, building sub-area area, and cold-heat hedging risk assessment score of each cold-heat hedging risk area;

[0022] According to the preset functional zoning attribute matching table, the functional zoning attributes are converted into functional zoning attribute values; the hot and cold hedging duration ratio is compared with the preset duration ratio threshold to obtain the hot and cold hedging duration type, and converted into the hot and cold hedging duration type value; the hot and cold hedging duration type is continuous hot and cold hedging or intermittent hot and cold hedging;

[0023] Input the heat and cold hedging risk assessment score, functional zoning attribute value, heat and cold hedging persistence type value and building sub-area area into the heat and cold hedging priority assessment model to obtain the corresponding heat and cold hedging priority score;

[0024] Compare the hot and cold hedge priority scores with the preset hot and cold hedge priority score thresholds to obtain a hot and cold hedge priority mitigation list and a hot and cold hedge secondary adjustment list respectively;

[0025] The hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list are sorted in descending order according to the corresponding hot and cold hedge priority scores to obtain the final hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list.

[0026] Furthermore, the method for obtaining R cold-hot hedging risk areas includes:

[0027] Inputting the building coupling feature dataset into a pre-trained heat-cold hedging risk assessment model to obtain heat-cold hedging risk assessment scores corresponding to N building sub-areas;

[0028] For N cold-heat hedging risk assessment scores, compare each score with a preset cold-heat hedging risk score threshold. If the score is greater than the threshold, mark the building sub-area corresponding to the score as a cold-heat hedging risk area.

[0029] After the determination of N building sub-areas is completed, the total count of the cold-heat hedging risk areas is summarized and recorded as R, thereby obtaining R cold-heat hedging risk areas.

[0030] Furthermore, the method for constructing the building coupling feature dataset includes:

[0031] Based on temperature and humidity, determine the heating and cooling demand directions corresponding to N building sub-areas, where the heating and cooling demand directions include cooling demand, heating demand, and no comfort demand;

[0032] The air valve opening is matched with a pre-built air valve opening matching table to obtain the air valve opening gears corresponding to N building sub-areas; the air valve opening gears include low opening, medium opening and high opening.

[0033] Set dynamic load semantic labels for N building sub-areas based on cooling and heating demand direction, air valve opening gear, equipment power consumption, and occupant density;

[0034] According to the difference between the spatial coupling relationship of N building sub-areas and the dynamic load semantic labels, a heat and cold hedging difference map and a regulation intention difference matrix are constructed.

[0035] Based on the air volume, temperature difference of the air conditioning water system and valve opening, the regional comprehensive cooling efficiency coefficient corresponding to N building sub-areas is constructed;

[0036] The building coupling feature dataset is constructed by combining the heat and cold hedging difference map, the adjustment intention difference matrix, the heat and cold demand directions, dynamic load semantic labels and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas.

[0037] Furthermore, the method for determining the cooling and heating demand directions corresponding to N building sub-areas based on temperature and humidity includes:

[0038] For each building sub-area, if the temperature of the building sub-area is higher than the upper limit of the comfortable temperature and the humidity is higher than the upper limit of the comfortable humidity, the heating and cooling demand direction is marked as a cooling demand and the current process ends;

[0039] If the temperature of the building sub-area is lower than the lower limit of the comfort temperature, the cooling and heating demand direction is marked as the heat demand and the current process ends;

[0040] Mark the heating and cooling demand direction as comfortable and no demand, and end the current process.

[0041] Furthermore, the method for setting the dynamic load semantic labels corresponding to the N building sub-areas includes:

[0042] For each building sub-area, when the heating and cooling demand direction of the building sub-area is cooling demand, determine whether the equipment power consumption and personnel density exceed the corresponding thresholds. If the judgment result exceeds the threshold, determine whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as forced cooling required; if the air valve opening gear is not high, the dynamic load semantic label is recorded as warm cooling required; if the judgment result does not exceed the threshold, the dynamic load semantic label is recorded as warm cooling required;

[0043] When the cooling and heating demand direction of the building sub-area is heat demand, that is, the system has excessive cooling, it is determined whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as needing to reduce cooling. If the air valve opening gear is not high, the dynamic load semantic label is recorded as needing warm cooling.

[0044] When the cooling and heating demand direction of the building sub-area is comfortable with no demand, the dynamic load semantic label is recorded as no adjustment demand.

[0045] Furthermore, the method for constructing the hot-cold hedging difference map and the adjustment intention difference matrix includes:

[0046] Match the dynamic load semantic label with the pre-built dynamic load semantic label matching table to obtain the dynamic load semantic label values ​​corresponding to N building sub-areas;

[0047] Mapping N building sub-areas into a set of graph structure nodes;

[0048] The air supply path coupling edges, return air confluence coupling edges, water loop coupling edges and shared wall heat transfer edges corresponding to N building sub-areas are constructed into a graph structure edge set;

[0049] For each graph structure edge in the graph structure edge set, construct a graph structure edge combination with the graph structure edge adjacent to the graph structure edge, and determine whether the dynamic load semantic label values ​​corresponding to the graph structure edge combination are the same. If they are not the same, set the adjustment intention difference of the graph structure edge combination to the absolute value of the difference between the dynamic load semantic label values ​​corresponding to the graph structure edge combination; if they are the same, set the adjustment intention difference of the graph structure edge combination to zero;

[0050] The adjustment intention difference is written into the adjustment intention difference matrix, and the row and column coordinate positions in the adjustment intention difference matrix are the numbered corresponding positions formed by the edge combination of the graph structure;

[0051] When all graph structure edges in the graph structure edge set are processed, the assigned graph structure edge set is obtained;

[0052] The cold-hot hedge difference graph is constructed by combining the graph structure node set and the assigned graph structure edge set.

[0053] Furthermore, the method for constructing the regional comprehensive cooling efficiency coefficient corresponding to the N building sub-areas includes:

[0054] For each building sub-area, the air conditioning air volume, air conditioning water system temperature difference, and valve opening are normalized to a preset normalized range, and the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor are obtained respectively. The regional comprehensive cooling efficiency coefficient is calculated based on the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor.

[0055] When all building sub-areas are processed, the regional comprehensive cooling efficiency coefficients corresponding to the N building sub-areas are obtained.

[0056] Furthermore, the multi-source building operation data includes temperature, humidity, air-conditioning air volume, air-conditioning water system temperature difference, valve opening, air valve opening, equipment power consumption and personnel density corresponding to N building sub-areas.

[0057] Compared with the existing technology, the technical effects and advantages of the public institution energy conservation management system based on AI intelligent operation and maintenance of the present invention are as follows:

[0058] The building coupling feature dataset constructed in this application comprehensively considers the cooling and heating demand direction of each building sub-area, dynamic load semantic labels, regional comprehensive cooling efficiency coefficients, and the coupling map information formed by spatial paths and heat conduction relationships. This allows the assessment of cooling and heating hedging to not only rely on local indicators but also capture the propagation patterns of cooling and heating imbalances across regions, significantly improving the accuracy of cooling and heating hedging identification. By constructing a cooling and heating hedging difference map and a regulation intention difference matrix, the differences in cooling and heating supply and demand between building sub-areas can be intuitively quantified, providing data support for the rational formulation of subsequent cooling and heating conflict control strategies.

[0059] Furthermore, the system dynamically identifies conflict risk areas based on the conflict risk assessment scores output by the conflict risk assessment model. By combining key indicators such as functional zoning attributes, building sub-area area, and conflict duration type, it constructs a priority conflict mitigation list and a secondary conflict adjustment list, enabling the automatic deployment of differentiated conflict control strategies. The priority mitigation parameter setting model generates immediate adjustment instructions and quickly responds to sudden conflict issues. The secondary mitigation parameter setting model then formulates a plannable adjustment strategy based on the updated conflict risk level and air conditioning system operating margin parameters, ensuring overall system stability and energy efficiency.

[0060] In summary, this application effectively solves the problems of inaccurate matching of heat and cold supply and demand and lack of targeted adjustment strategies in traditional methods through data-driven and semantically integrated heat and cold hedging identification and adaptive control mechanism, improves the operating efficiency, responsiveness and energy-saving effect of public institution building energy management systems, and has good promotion and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of a public institution energy conservation management system based on AI intelligent operation and maintenance according to Example 1 of the present invention;

[0062] Figure 2 This is a flow chart of the energy conservation management method for public institutions based on AI intelligent operation and maintenance according to Example 2 of the present invention;

[0063] Figure 3 A flow chart of a method for performing adaptive control of hot and cold hedging grades;

[0064] Figure 4 A flow chart of a method for updating the secondary adjustment list of hot and cold hedging and performing adaptive control of hot and cold hedging levels;

[0065] Figure 5 A flow chart of a method for obtaining a hot and cold hedge priority mitigation list and a hot and cold hedge secondary adjustment list;

[0066] Figure 6 Flowchart of the method for constructing building coupling feature dataset. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present invention will be described in detail, clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. It should be noted that the specific embodiments described below are only used to better illustrate and describe the technical solutions of the present invention, and are intended to enable those skilled in the art to better understand and implement the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the spirit and essence of the present invention, those skilled in the art may modify, adjust or make equivalent replacements based on the contents disclosed in the present invention, and these should all be regarded as the scope of protection of the present invention.

[0068] Example 1:

[0069] See also Figure 1 As shown, this embodiment discloses an energy conservation management system for public institutions based on AI intelligent operation and maintenance, including a multi-source data acquisition module, a coupling feature analysis module, a cold and hot hedging evaluation module, a hierarchical scheduling generation module and a cold and hot hedging control module. Each module realizes data transmission through wired and / or wireless connections.

[0070] The multi-source data acquisition module is used to obtain multi-source building operation data; the multi-source building operation data includes temperature, humidity, air conditioning air volume, air conditioning water system temperature difference, valve opening, air valve opening, equipment power consumption and personnel density corresponding to N building sub-areas.

[0071] Specifically, the multi-source data acquisition module performs distributed data collection operations across multiple functional sub-areas within the building's operating environment, enabling comprehensive awareness of the building's energy consumption and environmental thermal status. The multi-source data acquisition module collects building operating data from multiple sources, including but not limited to temperature, humidity, air flow rate, air conditioning water system temperature difference, valve opening, damper opening, equipment power consumption, and occupancy density. Each data type has a clear data source path and system integration interface.

[0072] Temperature is the real-time collection of ambient air temperature within each building sub-area. This data is acquired through digital temperature sensors deployed in each building sub-area and can be connected to the building automation control network of public institutions via wired or wireless means. Temperature serves as a key indicator of regional cooling or heating load status and is used to identify coupling risks caused by uneven heating and cooling across the area.

[0073] Humidity is a real-time observation of relative humidity within each building subarea. This information is collected by humidity sensors, which can be integrated and installed at air conditioning supply and return vents, or representative indoor areas. Humidity can be used to comprehensively assess thermal comfort indicators and model and correct for condensation risks caused by high humidity. It also serves as a physical constraint in thermal field prediction.

[0074] Air conditioning air volume reflects the air supply intensity and ventilation capacity of each building sub-area's air supply system. It is collected through air volume sensors installed in air supply ducts or variable air volume (VAV) terminals. Air conditioning air volume is used to assess airflow efficiency and energy distribution, and participates in duct heat transfer calculations, serving as a core input for regional thermal energy transfer capacity modeling.

[0075] The air conditioning water system temperature difference represents the temperature difference between the inlet and outlet water circuits of the chilled water or hot water system. This difference is measured using temperature sensors placed at the chilled water inlet and outlet, and calculated by the control system. This temperature difference can be used to estimate the cooling and heating loads borne by the system in real time, making it a crucial parameter supporting overall building heat load forecasting and energy consumption modeling.

[0076] Valve opening describes the current opening state of control valves in terminal control units in water systems, such as fan coil units and cooling units. This data is collected using the position feedback unit on the electric control valve. Air valve opening reflects the current opening state of the air valves that regulate airflow in air conditioning systems, and is collected in a similar manner. Both valve and air valve openings are used to determine the execution strength and coverage area of ​​the current energy regulation strategy, providing direct support for physical state modeling of cooling and heating flow regulation.

[0077] Equipment power consumption reflects the current operating power of various energy-consuming devices in the system. Energy-consuming devices such as chillers, heat pumps, circulation pumps, and fans are collected through the electrical parameter interfaces in smart meters, energy metering units, or variable frequency control units. Equipment power consumption is used in system energy modeling to characterize the energy efficiency and operating load levels of each building sub-area.

[0078] Crowd density reflects the intensity of human activity and the distribution of heat sources within each building subarea. It is typically estimated using infrared human presence sensors, video image recognition systems, or location beacons such as ultra-wideband (UWB) or Bluetooth Low Energy (BLE). Crowd density can dynamically modify heat load models for building subareas and be used to construct unstructured heat source distribution maps, serving as a crucial input for constructing realistic thermal environments.

[0079] Further explanation: After the multi-source building operation data collection is completed, the multi-source data acquisition module enters the data preprocessing stage. Based on the timestamps of each sensor device, a unified calibration is performed to construct a global time axis based on high-precision server time. Raw data from different frequencies and time domains are aligned to a unified time window through interpolation or synchronous sampling. For outliers or missing values ​​caused by sensor errors, packet loss, or communication interruptions, data integrity detection rules are set, and a sliding window statistical method is used to identify abnormal fluctuation points. Missing data is then filled in by combining historical data distribution or spatial adjacent point interpolation methods to ensure data continuity and physical rationality.

[0080] After completing timing alignment and anomaly correction, the multi-source data acquisition module further performs spatial semantic attribution processing. For data collected from different subsystems, such as the air conditioning system, building automation system, and security system, spatial mapping is performed on each sensor data item based on the pre-integrated Building Information Model (BIM). The data is attributed to specific floors, rooms, or functional areas, achieving a unified spatial identification system. This spatial semantic mapping not only considers the physical location of the sensor, but also its service range, perception coverage boundary, and control link attribution to avoid data offset or misassociation caused by differences in naming conventions across systems.

[0081] Ultimately, the data processed by the multi-source data acquisition module is output as a multi-dimensional feature vector sequence structure, characterized by time synchronization, clear spatial attribution, semantic consistency, and controllable errors. By comprehensively acquiring and real-timely aggregating multi-source building operational data, this application meets the requirements of fusion modeling for spatial state continuity, physical drive consistency, and environmental semantic integrity, effectively supporting the operation of key functional modules such as thermal coupling identification, load drift diagnosis, and multi-objective collaborative optimization.

[0082] The coupling feature analysis module performs semantic fusion and coupling evaluation based on multi-source building operation data to construct a building coupling feature dataset; the building coupling feature dataset includes a cooling and heating hedging difference map, an adjustment intention difference matrix, and the cooling and heating demand directions, dynamic load semantic labels, and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas.

[0083] like Figure 6 As shown, the method for constructing the building coupling feature dataset includes:

[0084] Based on temperature and humidity, determine the heating and cooling demand directions corresponding to N building sub-areas, where the heating and cooling demand directions include cooling demand, heating demand, and no comfort demand;

[0085] Match the air valve opening with a pre-built air valve opening matching table to obtain the air valve opening gears corresponding to N building sub-areas; the air valve opening gears include low opening, medium opening and high opening;

[0086] Set dynamic load semantic labels for N building sub-areas based on cooling and heating demand direction, air valve opening gear, equipment power consumption, and occupant density;

[0087] According to the difference between the spatial coupling relationship of N building sub-areas and the dynamic load semantic labels, a heat and cold hedging difference map and a regulation intention difference matrix are constructed.

[0088] Based on the air volume, temperature difference of the air conditioning water system and valve opening, the regional comprehensive cooling efficiency coefficient corresponding to N building sub-areas is constructed;

[0089] The heat and cold hedging difference map, the adjustment intention difference matrix, the heat and cold demand directions, dynamic load semantic labels and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas are constructed into a building coupling feature dataset.

[0090] Methods for determining the cooling and heating demand directions corresponding to N building sub-areas based on temperature and humidity include:

[0091] For each building sub-area, if the temperature of the building sub-area is higher than the upper limit of the comfortable temperature and the humidity is higher than the upper limit of the comfortable humidity, the heating and cooling demand direction is marked as a cooling demand and the current process ends;

[0092] If the temperature of the building sub-area is lower than the lower limit of the comfort temperature, the cooling and heating demand direction is marked as the heat demand and the current process ends;

[0093] Mark the heating and cooling demand direction as comfortable and no demand, and end the current process.

[0094] The method for setting the dynamic load semantic labels corresponding to N building sub-areas includes:

[0095] For each building sub-area, when the cooling and heating demand direction of the building sub-area is cooling demand, determine whether the equipment power consumption and personnel density both exceed the corresponding threshold value. If the judgment result exceeds the threshold value, determine whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as forced cooling required; if the air valve opening gear is not high, the dynamic load semantic label is recorded as gentle cooling required; if the judgment result does not exceed the threshold value, the dynamic load semantic label is recorded as gentle cooling required; the forced cooling required label indicates that the current cooling supply is seriously insufficient and forced cooling is required; the gentle cooling required label indicates that the current cooling supply and heating are basically matched and gentle cooling is required.

[0096] When the cooling and heating demand direction of the building sub-area is heat demand, that is, the system has excessive cooling, it is determined whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as needing to reduce cooling. If the air valve opening gear is not high, the dynamic load semantic label is recorded as needing warm cooling.

[0097] When the cooling and heating demand direction of the building sub-area is comfortable with no demand, the dynamic load semantic label is recorded as no adjustment demand.

[0098] It should be noted that, for example, in this application, the threshold values ​​corresponding to the equipment power consumption can be set according to the functional zoning attributes, for example, the office area is set to 15W / m², the conference room is set to 18W / m², the reception area is set to 10W / m², and the data center room is set to 50W / m².

[0099] For example, the threshold corresponding to the personnel density can be set according to the functional zoning attributes, for example, the office area is set to 10 people / 100m², the conference room is set to 20 people / 100m², the reception area is set to 8 people / 100m², and the data center room is set to 3 people / 100m².

[0100] The construction method of the hot-cold hedging difference map and the adjustment intention difference matrix includes:

[0101] Match the dynamic load semantic label with the pre-built dynamic load semantic label matching table to obtain the dynamic load semantic label values ​​corresponding to N building sub-areas;

[0102] Mapping N building sub-areas into a set of graph structure nodes;

[0103] The air supply path coupling edges, return air confluence coupling edges, water loop coupling edges and shared wall heat transfer edges corresponding to N building sub-areas are constructed into a graph structure edge set;

[0104] It should be noted that the air supply path coupling edge refers to multiple building sub-areas sharing the same air supply branch or the same variable air volume terminal device VAV; the return air confluence coupling edge refers to the return air ducts flowing in parallel at the confluence node; the water loop coupling edge refers to the terminal coils sharing a supply and return water loop; the shared wall heat transfer edge refers to two building sub-areas being separated only by a lightweight wall or a glass curtain wall. In this application, the main function of the VAV variable air volume terminal device is to adjust the air flow rate supplied to the area according to the temperature demand in the area in the air-conditioning system to achieve fine control of the indoor temperature. The variable air volume terminal device is usually arranged at the end of the air supply branch, serving one or more adjacent areas, and has air valve adjustment, air supply control and reheating functions under some models. Therefore, when two sub-areas share the same variable air volume terminal device, it means that they have a coupling relationship in the air supply adjustment behavior, which is suitable as the basis for air supply path coupling in the identification of cold and hot hedging risks.

[0105] For each graph structure edge in the graph structure edge set, the graph structure edge and the graph structure edges adjacent to the graph structure edge are constructed into a graph structure edge combination, and it is determined whether the dynamic load semantic label values ​​corresponding to the graph structure edge combination are the same. If they are not the same, the adjustment intention difference of the graph structure edge combination is set to the absolute value of the numerical difference between the dynamic load semantic label values ​​corresponding to the graph structure edge combination; if they are the same, the adjustment intention difference of the graph structure edge combination is set to zero; the adjustment intention difference is written into the adjustment intention difference matrix, and the row and column coordinate positions in the adjustment intention difference matrix are the numbered positions corresponding to the graph structure edge combination; for example, the two edges in the graph structure edge combination are numbered 1 and 3 respectively, and the coordinate position of the graph structure edge combination is (1, 3), that is, the position in the adjustment intention difference matrix is ​​the 1st row and the 3rd column.

[0106] When all graph structure edges in the graph structure edge set are processed, the assigned graph structure edge set is obtained;

[0107] The cold-hot hedge difference graph is constructed by combining the graph structure node set and the assigned graph structure edge set.

[0108] It should be noted that, in a preferred embodiment of the present invention, the constructed heat and cold hedging difference graph and the adjustment intention difference matrix serve as the core data structures in this application, and are mainly used to reflect the real-time conflicting relationships between building sub-areas in heat and cold regulation behaviors, thereby supporting the execution of key functions such as multi-area collaborative control, heat and cold load regulation optimization, and heat and cold hedging risk identification. Specifically, the adjustment intention difference matrix uses graph structure edge combinations as index units, and expresses the inconsistency of the adjustment direction and adjustment intensity of adjacent sub-areas at the current moment through numerical differences, which significantly improves the quantifiability and identifiability of heat and cold hedging behaviors.

[0109] Furthermore, the heat and cold hedging difference graph, while retaining the physical topological structure of the building, is weighted by combining the difference of each graph structure edge, thereby realizing the graphical modeling of the conflicting paths of heat and cold regulation inside the building. The heat and cold hedging difference graph not only reflects the existence of the building's energy transfer channels, namely the supply air path coupling edge, return air confluence coupling edge, water loop coupling edge and shared wall heat transfer edge in this application, but also integrates the adjustment state differences on each channel, and can accurately locate the spatial area and causal path of local heat and cold hedging, that is, quantify the potential heat and cold hedging risk paths between adjacent areas, and provide visual analysis support for the control strategy. Based on the heat and cold hedging difference graph, the heat and cold hedging risk area can be identified, and a heat and cold hedging priority relief list and a heat and cold hedging secondary adjustment list can be constructed to achieve targeted intervention and strategy adjustment in the heat and cold hedging risk area, thereby effectively improving the pertinence of the system response and the accuracy of energy-saving control.

[0110] The method for constructing the regional comprehensive cooling efficiency coefficient corresponding to N building sub-areas includes:

[0111] For each building sub-area, the air conditioning air volume, air conditioning water system temperature difference, and valve opening are normalized to a preset normalized range, and the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor are obtained respectively. The regional comprehensive cooling efficiency coefficient is calculated based on the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor.

[0112] When all building sub-areas are processed, the regional comprehensive cooling efficiency coefficients corresponding to the N building sub-areas are obtained.

[0113] The calculation method of the regional comprehensive cooling efficiency coefficient includes:

[0114] ;

[0115] in, is the comprehensive cooling efficiency coefficient of the nth building sub-area, is the wind side transmission factor corresponding to the nth building sub-area, is the water-side heat transfer factor corresponding to the nth building sub-area, is the valve adjustment factor corresponding to the nth building sub-area. 、 and is the corresponding weight coefficient, 、 and The sum is 1. For example, in a preferred embodiment of the present application, The value of is set to 0.4, The value of is set to 0.3, The value of is set to 0.3.

[0116] It should be noted that in the diagnosis of cold and heat hedging, it is difficult to accurately determine whether the "insufficient cooling" is due to limited system transmission and distribution capacity or due to hedging in neighboring areas based solely on the dynamic load semantic label and the adjustment intention difference matrix. To this end, this application further introduces the regional comprehensive cooling efficiency coefficient. By simultaneously utilizing three operating data items: air-conditioning air volume, air-conditioning water system temperature difference, and valve opening, it quantifies the instantaneous cooling efficiency that can actually be obtained by the building sub-area under the current working conditions, providing a more physically constrained criterion for subsequent adjustment decisions, realizing real-time quantification of regional cooling potential, greatly improving the accuracy of determining the cause of cold and heat hedging, and providing an explainable and quantifiable cooling capacity constraint for subsequent control strategies, thereby further improving the accuracy of energy-saving adjustment.

[0117] It is further explained that the air volume of the air conditioner is normalized to the normalized interval range to obtain the air side distribution factor, the temperature difference of the air conditioner water system is normalized to the normalized interval range to obtain the water side heat exchange factor, and the valve opening is normalized to the normalized interval range to obtain the valve adjustment factor. For example, the normalized interval range can be set to or In the preferred embodiment of the present application, the normalized interval range is set to .

[0118] An example of the air valve opening matching table is shown in Table 1:

[0119] Table 1 Air valve opening matching table

[0120]

[0121] It should be noted that in the embodiments of the present invention, the damper opening matching table is used to classify the current air supply adjustment behavior into different levels, thereby assisting in determining whether the air supply system is effectively responding to the current cooling and heating loads. Specifically, the system compares the damper opening value of each building sub-area with the preset damper opening matching table and classifies it into low opening, medium opening, and high opening. Among them, low opening indicates that the air supply strategy of the current building sub-area is in a passive suppression state, which is usually used in the load reduction stage; medium opening is the baseline air supply intensity when the system is not adjusted; and high opening indicates that the system has enhanced its responsiveness to the regional cooling / heating demand.

[0122] An example of the dynamic load semantic label matching table is shown in Table 2:

[0123] Table 2 Dynamic load semantic label matching table

[0124]

[0125] It should be noted that, in a preferred embodiment of the present invention, in order to achieve quantitative analysis of the cooling and heating regulation behavior and identification of hedging risks between building sub-areas, the present application constructs a numerical mapping mechanism based on dynamic load semantic labels. Specifically, the present application generates a dynamic load semantic label for each building sub-area, and assigns label values ​​of 4, 3, 2, and 1 to the requirements for forced cooling, the requirements for mild cooling, the requirements for no adjustment, and the requirements for reduced cooling, respectively. The above label values ​​not only reflect the directionality of the cooling and heating demand, but also reflect the relative level of the regulation intensity. Based on this label numerical system, for any pair of building sub-areas adjacent to each other through the supply air path, return air path, water circuit or physical wall, the absolute value of the difference between the label values ​​is calculated as the adjustment intention difference of the pair of building sub-areas. The greater the adjustment intention difference, the more it indicates that the adjacent building sub-areas present a state of opposite direction or extremely inconsistent adjustment amplitude in the air-conditioning system, and there is a higher risk of cooling and heating hedging, thereby achieving efficient identification of the contradiction between cooling and heating flow within the building and decision support for control optimization.

[0126] The heat-cold hedging assessment module performs heat-cold hedging risk assessment based on the building coupling feature dataset, obtains the heat-cold hedging risk assessment scores corresponding to N building sub-areas, and identifies R heat-cold hedging risk areas.

[0127] The method for obtaining R cold and hot hedging risk areas includes:

[0128] Inputting the building coupling feature dataset into a pre-trained heat-cold hedging risk assessment model to obtain heat-cold hedging risk assessment scores corresponding to N building sub-areas;

[0129] For N cold-heat hedging risk assessment scores, compare each score with a preset cold-heat hedging risk score threshold. If the score is greater than the threshold, mark the building sub-area corresponding to the score as a cold-heat hedging risk area.

[0130] It should be noted that the building sub-areas that are not marked as cold-heat hedging risk areas can be marked as non-cold-heat hedging risk areas. In a preferred embodiment of the present invention, for example, the range of the cold-heat hedging risk assessment score is limited to . The closer the cold-heat hedging risk assessment score is to 1, the higher the possibility of cold-heat hedging risk in the building sub-area under the current operating state, and the more urgent the adjustment demand; conversely, the closer the cold-heat hedging risk assessment score is to 0, the better the matching degree of cold and heat supply in the building sub-area and the stable operating state. For example, the cold-heat hedging risk score threshold is set to 0.75. In specific implementation, this application supports configuring the cold-heat hedging risk score threshold as either dynamically adjustable or statically set to meet the needs of refined energy-saving strategies in different scenarios.

[0131] After the determination of N building sub-areas is completed, the total number of cold-heat hedging risk areas is summarized and recorded as R, thereby obtaining R cold-heat hedging risk areas.

[0132] The training method of the cold-hot hedging risk assessment model includes:

[0133] A hot-cold hedging risk assessment dataset is pre-constructed, wherein the hot-cold hedging risk assessment dataset includes Y groups of hot-cold hedging risk assessment data and hot-cold hedging risk assessment scores corresponding to the Y groups of hot-cold hedging risk assessment data, where Y is a positive integer; the hot-cold hedging risk assessment data includes a building coupling feature dataset; the hot-cold hedging risk assessment dataset is divided into a training set and a validation set, wherein the training set is used for parameter learning of the hot-cold hedging risk assessment model, and the validation set is used for real-time monitoring of the generalization performance and overfitting degree of the hot-cold hedging risk assessment model;

[0134] A node-output graph neural network architecture is used as the cold-hot hedging risk assessment model. The cold-hot hedging risk assessment data is standardized and vectorized and then input into the node-output graph neural network architecture. The node-output graph neural network architecture consists of an input layer, a hidden layer, and an output layer. Each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a Sigmoid activation function to obtain the probability distribution corresponding to each cold-hot hedging risk assessment score. Finally, the cold-hot hedging risk assessment score corresponding to the maximum probability is taken as the prediction result of the cold-hot hedging risk assessment model. During the training process, the cross-entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, the cold-hot hedging risk assessment model is determined to have converged and the training is terminated.

[0135] It should be noted that within the technical framework of this invention, the building coupling feature dataset provides a structured representation of the operating status and mutual coupling relationships of N building sub-areas. This includes both node-level features (i.e., the heating and cooling demand directions, dynamic load semantic labels, and regional comprehensive cooling efficiency coefficients in this application), as well as edge-level difference information aggregated through graph convolution (i.e., the heating and cooling hedging difference graph and the adjustment intention difference matrix in this application). Therefore, the building coupling feature dataset can simultaneously reveal the instantaneous heating and cooling load conditions of building sub-areas and the intensity of coupling contradictions between these building sub-areas in the overall energy network, meeting the input dimensionality and semantic integrity required for heating and cooling hedging risk assessment.

[0136] Node-level features provide the heat load intensity signal within the building sub-areas for the heat-cold hedging risk assessment model. Edge-level dissimilarity information propagates coupling conflict information to associated nodes via the graph convolutional layer. The heat-cold hedging risk assessment model, combining both node-level features and edge-level dissimilarity information, can comprehensively determine the probability of each building sub-area triggering heat-cold hedging at the current moment. Therefore, using the building coupling feature dataset as the overall input to the heat-cold hedging risk assessment model, a heat-cold hedging risk assessment score corresponding to each of the N building sub-areas can be obtained.

[0137] This application identifies R hot and cold hedging risk areas, and has significant rationality and technical advantages. Specifically, this application avoids relying solely on static rules based on thresholds and can capture complex, multi-source nonlinear interaction effects. It utilizes the local aggregation mechanism of the node-output graph neural network architecture to make the edge-level difference directly contribute to the hot and cold conflict risk assessment of adjacent nodes, significantly improving the local hedging positioning accuracy. The node-level independent output format allows the assessment results to be directly mapped to regional risk alerts, facilitating the generation and execution of subsequent partition adjustment strategies. Therefore, it meets the technical requirements of this application to achieve refined hot and cold hedging diagnosis and adaptive energy-saving control.

[0138] The hierarchical scheduling generation module is used to hierarchically sort the R hot and cold hedging risk areas according to functional zoning attributes, building sub-area areas and hot and cold hedging duration types, and generate a hot and cold hedging priority mitigation list and a hot and cold hedging secondary adjustment list, thereby supporting the hierarchical response and differentiated resource allocation of the energy-saving control system.

[0139] like Figure 5 As shown, the method for obtaining the hot and cold hedging priority mitigation list and the hot and cold hedging secondary adjustment list includes:

[0140] S100: let the initial value of r be 1, and the value range of r be 1 to R;

[0141] S101: Obtain the functional zoning attributes of the rth hot and cold hedging risk area, match the functional zoning attributes with the pre-built functional zoning attribute matching table, and obtain the corresponding functional zoning attribute values; record the proportion of the monitored hot and cold hedging duration in unit time as the hot and cold hedging duration ratio, and determine whether the hot and cold hedging duration ratio exceeds the preset hot and cold hedging duration ratio threshold; if the judgment result is yes, mark the hot and cold hedging duration type of the rth hot and cold hedging risk area as continuous hot and cold hedging; if the judgment result is no, mark the hot and cold hedging duration type of the rth hot and cold hedging risk area as intermittent hot and cold hedging; digitize the hot and cold hedging duration type into a hot and cold hedging duration type value; for example, the hot and cold hedging duration type value corresponding to the continuous hot and cold hedging can be marked as 10, and the hot and cold hedging duration type value corresponding to the intermittent hot and cold hedging can be marked as 11. Exemplarily, in a preferred embodiment of the present application, the hot and cold hedging duration ratio threshold can be set to 70%.

[0142] S102: Inputting the heat and cold hedging risk assessment score, functional zoning attribute value, heat and cold hedging persistence type value, and building sub-area area of ​​the rth heat and cold hedging risk area into the heat and cold hedging priority assessment model to obtain a corresponding heat and cold hedging priority score;

[0143] S103: Determine whether the hot and cold hedge priority score is greater than a preset hot and cold hedge priority score threshold. If so, add the rth hot and cold hedge risk area to the hot and cold hedge priority mitigation list. If not, add the rth hot and cold hedge risk area to the hot and cold hedge secondary adjustment list.

[0144] S104: Let r=r+1. If r is less than or equal to R, return to S101 to continue execution. If r is greater than R, sort the hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list in descending order according to the corresponding hot and cold hedge priority scores to obtain the final hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list.

[0145] The training method of the hot-cold hedging priority evaluation model includes:

[0146] A hot and cold hedging priority evaluation dataset is pre-constructed, wherein the hot and cold hedging priority evaluation dataset includes the hot and cold hedging priority evaluation data of the PG group and the hot and cold hedging priority scores corresponding to the hot and cold hedging priority evaluation data of the PG group, where PG is a positive integer; the hot and cold hedging priority evaluation data includes the hot and cold hedging risk assessment score, the functional zoning attribute value, the hot and cold hedging persistence type value, and the building sub-area area; the hot and cold hedging priority evaluation dataset is divided into a training set and a validation set, wherein the training set is used for learning the parameters of the hot and cold hedging priority evaluation model, and the validation set is used for real-time monitoring of the generalization performance and overfitting degree of the hot and cold hedging priority evaluation model;

[0147] A deep neural network with a multi-layer perceptron as the core is used as the hot and cold hedging priority evaluation model. The hot and cold hedging priority evaluation data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer; each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a softmax activation function to obtain the probability distribution corresponding to each hot and cold hedging priority score. Finally, the hot and cold hedging priority score corresponding to the maximum probability is taken as the prediction result of the hot and cold hedging priority evaluation model; during the training process, the cross entropy loss function is used as the optimization target, and the gradient descent optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, it is determined that the hot and cold hedging priority evaluation model has converged and the training is terminated.

[0148] It should be noted that in this application, the hot / cold hedging priority assessment model outputs a corresponding hot / cold hedging priority score based on input features such as the hot / cold hedging risk assessment score of the building sub-area, the functional zoning attribute value, the hot / cold hedging persistence type value, and the sub-area area. The hot / cold hedging priority score is used to quantify the urgency and response priority of each hot / cold hedging risk area during the operation and maintenance adjustment process.

[0149] For example, the value range of the thermal hedge priority score is set to In the continuous interval of , the higher the cold-heat hedging priority score value, the more serious the cold-heat hedging phenomenon corresponding to the building sub-area is, the greater the impact on the energy system loss, the more significant the threat to the indoor environmental comfort, and the higher the priority of the adjustment response.

[0150] In order to achieve hierarchical regulation of hot and cold hedging risk areas, a hot and cold hedging priority score threshold is set in this application to divide the priority mitigation area and the secondary regulation area. The hot and cold hedging priority score threshold can be dynamically set based on historical operation data and actual operation and maintenance experience, and preferably a numerical range between 0.70 and 0.80 is used for parameter optimization. In the implementation example, the hot and cold hedging priority score threshold is preferably set to 0.75, that is, when the priority score of a hot and cold hedging risk area is greater than 0.75, it is determined that the area should be included in the hot and cold hedging priority mitigation list; if it is lower than or equal to 0.75, it is included in the hot and cold hedging secondary regulation list.

[0151] By setting the hot and cold hedging priority scoring threshold, not only the response classification of the hot and cold hedging areas is achieved, but also the unnecessary tilt of scheduling resources is effectively avoided, and the overall system's coordination scheduling capabilities and energy-saving response accuracy when facing large-scale hot and cold hedging risks are improved, reflecting the innovative value of this application in intelligent operation control and energy efficiency collaborative management.

[0152] An example of the functional partition attribute matching table is shown in Table 3:

[0153] Table 3 Functional zoning attribute matching table

[0154]

[0155] It should be noted that in this application, for example, combined with the functional zoning attribute matching table set in the embodiment, the four typical areas of "office", "conference room", "reception area" and "data center room" are numerically modeled according to the sensitivity of the area to the indoor hot and cold environment, and on the basis of the hot and cold hedging risk assessment, a hot and cold hedging persistence identification mechanism is introduced to construct a hot and cold hedging priority relief list and a hot and cold hedging secondary adjustment list to achieve hierarchical scheduling control. The hierarchical scheduling strategy of this application not only reflects the priority level of different functional areas in operation and maintenance adjustment, but also introduces the dynamic characteristics of hot and cold hedging persistence, which improves the pertinence of the policy response and the practicality of energy-saving measures.

[0156] Specifically, for example, data center rooms are assigned the highest level in the numerical setting of functional zoning attributes due to their high density of equipment, heavy operating load, and extremely high requirements for temperature control accuracy. Their adjustment priority is significantly higher than areas with a higher tolerance for temperature and humidity fluctuations, such as conference rooms and reception areas. At the same time, by monitoring the duration of the hot and cold hedging phenomenon per unit time, we can further distinguish between continuous hot and cold hedging and intermittent hot and cold hedging. Among them, continuous hot and cold hedging means that the imbalance between hot and cold in the area exists for a long time. If it is not handled in time, it will cause continuous energy waste or local thermal comfort collapse, and it needs to be included in the hot and cold hedging priority mitigation list; intermittent hot and cold hedging means that the hot and cold hedging fluctuates periodically or sporadically, and can be postponed for processing and included in the hot and cold hedging secondary adjustment list, thereby achieving a phased response.

[0157] By integrating the hot and cold hedging risk assessment scores, functional zoning attribute values, hot and cold hedging duration type values ​​and building sub-area areas into modeling, and using the priority scoring results to drive the scheduling order allocation of hot and cold hedging areas, it is possible to achieve dynamic optimization matching of resource regulation intensity, response rhythm and target areas, thereby enhancing the efficiency and accuracy of the system's energy-saving response.

[0158] The hot and cold hedging control module combines the air conditioning system's operating margin parameter set, the hot and cold hedging difference map, and the adjustment intention difference matrix to perform hierarchical and adaptive hot and cold hedging control on the hot and cold hedging priority relief list and the hot and cold hedging secondary adjustment list. The air conditioning system operating margin parameter set describes the air conditioning system's remaining adjustment headroom without overloading, that is, its current effective output capacity.

[0159] The air conditioning system operation margin parameter set includes real-time air supply volume margin, cold water flow regulation margin, hot water flow regulation margin, terminal valve stroke remaining percentage, host frequency conversion adjustment range, cold storage unit remaining energy release capacity and heat storage unit remaining energy release capacity.

[0160] It should be noted that the real-time air supply margin is calculated by obtaining the current air supply data of the terminal fan coil unit or variable air volume box terminal device and calculating the difference between it and the designed maximum air supply volume. The real-time air supply volume can be collected by the air volume sensor or the built-in feedback unit of the variable air volume terminal device; the designed maximum air supply volume is obtained based on the initial system settings. The difference between the two is the current air supply adjustment margin.

[0161] The cold water flow regulation margin and hot water flow regulation margin represent the remaining adjustable flow capacity in the system's cold water and hot water branches, respectively. These margins are determined by comparing the current flow rate with the pump's variable frequency control upper flow limit, using electromagnetic flowmeters installed at the outlets of the cold and hot water pumps. For pump stations with bidirectional regulation capabilities, a comprehensive upper and lower bound assessment can also be performed in conjunction with a set minimum flow threshold.

[0162] The remaining percentage of terminal valve travel reflects the adjustable range of the terminal control valve in the current building sub-area. The current valve opening is obtained in real time through the position feedback unit built into the valve electric actuator, and the remaining percentage is calculated based on the maximum travel of the valve structure. This remaining percentage of terminal valve travel can be used to determine whether the terminal control capacity is saturated, thereby influencing the mitigation strategy for localized hot and cold hedging issues.

[0163] The host frequency conversion adjustment range indicates the remaining margin between the chiller or heat source host's current operating frequency and its upper and lower allowable operating frequency limits. The host frequency conversion adjustment range is calculated by comparing the operating frequency feedback provided by the host control system with the host's set maximum and minimum operating frequencies. The remaining percentage of terminal valve travel is used to assess system-level regulation responsiveness.

[0164] The remaining energy release capacity of the cold and heat storage units indicates the amount of cold and heat energy currently available for release from the centralized energy storage system. The energy management controllers of the cold and heat storage units obtain real-time information about the current energy storage medium temperature, volume, and energy storage status, and calculate the remaining energy release capacity based on the heat capacity calculation formula.

[0165] By collecting and summarizing the above-mentioned various operating margin parameters, this application can dynamically construct an operating margin parameter set that reflects the overall adjustment capability of the air-conditioning system, providing a multi-dimensional and real-time resource support basis for cold and hot hedging adjustment.

[0166] like Figure 3 As shown, the method for performing adaptive control of cold and hot hedging levels includes:

[0167] For each building sub-area in the priority relief list of heat and cold hedging, it is recorded as the priority relief building sub-area. A sub-graph that has a direct spatial coupling relationship with the priority relief building sub-area is obtained from the heat and cold hedging difference graph, which is recorded as the heat and cold hedging associated sub-graph. Matrix elements that are directly associated with the priority relief building sub-area on the graph structure edge are extracted from the adjustment intention difference matrix to construct an adjustment intention difference set. The air-conditioning system operation margin parameter set, the heat and cold hedging associated sub-graph, and the adjustment intention difference set are input into the priority relief parameter setting model to obtain an immediate adjustment instruction set. The immediate adjustment instruction set includes the target air supply volume setting value, the terminal water valve target opening, the host load instantaneous correction gain, and the energy storage unit energy release instruction.

[0168] The real-time adjustment command set is written to the building automation system control bus according to the node address mapping. The command is then encapsulated into a protocol based on the device type and sent to the corresponding control unit for execution. For example, the target air volume setpoint is written to the air volume setting register of the variable air volume terminal device or fan coil module, the target opening of the terminal water valve is written to the target travel register of the electric control valve driver, the instantaneous correction gain of the host load is written to the variable frequency control interface of the chiller / boiler, and the energy release command of the energy storage unit is sent to the energy management controller of the cold / heat storage device.

[0169] When the hot and cold hedge priority relief list is completed, the hot and cold hedge secondary adjustment list is updated and hot and cold hedge hierarchical adaptive regulation is performed.

[0170] like Figure 4 As shown, the method for updating the cold and hot hedging secondary adjustment list and performing cold and hot hedging hierarchical adaptive control includes:

[0171] For each building sub-area in the secondary adjustment list for cold and heat hedging, record it as a secondary mitigation building sub-area, and re-evaluate and update the corresponding cold and heat hedging risk assessment score of the secondary mitigation building sub-area;

[0172] The secondary mitigation building sub-areas whose cold and heat hedging risk assessment scores are not greater than the preset cold and heat hedging risk score threshold are removed from the cold and heat hedging secondary adjustment list to obtain an updated cold and heat hedging secondary adjustment list; ensure that the cold and heat hedging secondary adjustment list only retains the cold and heat hedging risk areas that still need to be adjusted.

[0173] Obtain the latest air conditioning system operating margin parameter set, and construct the hot and cold hedging risk assessment score set from the updated hot and cold hedging secondary adjustment list;

[0174] The air conditioning system's operating margin parameter set and the heat and cold hedging risk assessment score set are input into the secondary mitigation parameter setting model to generate a planned adjustment instruction set. The adjustment instructions in the planned adjustment instruction set are the same type as those in the immediate adjustment instruction set, and are then distributed to the corresponding control units for execution. This ensures that the heat and cold hedging phenomenon in the secondary heat and cold hedging risk area is smoothly mitigated within a controllable range, while maximizing energy savings and system operational safety, thereby achieving adaptive control of the heat and cold hedging phenomenon and optimizing energy efficiency.

[0175] The training method of the priority mitigation parameter setting model includes:

[0176] A priority mitigation parameter setting data set is pre-constructed, the priority mitigation parameter setting data set including YX groups of priority mitigation parameter setting data and a set of immediate adjustment instructions corresponding to the YX groups of priority mitigation parameter setting data, where YX is a positive integer; the priority mitigation parameter setting data includes an air-conditioning system operating margin parameter set, a cold and hot hedging association subgraph, and an adjustment intention difference set; the priority mitigation parameter setting data set is divided into a training set and a validation set, the training set is used for learning the parameters of the priority mitigation parameter setting model, and the validation set is used for real-time monitoring of the generalization performance and overfitting degree of the priority mitigation parameter setting model;

[0177] A node-output graph neural network architecture is adopted as the priority relief parameter setting model. The priority relief parameter setting data is standardized and vectorized and then input into the node-output graph neural network architecture. The node-output graph neural network architecture consists of an input layer, a hidden layer and an output layer. Each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a Sigmoid activation function to obtain the probability distribution corresponding to each set of immediate adjustment instructions. Finally, the set of immediate adjustment instructions corresponding to the maximum probability is taken as the prediction result of the priority relief parameter setting model. During the training process, the cross-entropy loss function is used as the optimization target, and the gradient descent optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, it is determined that the priority relief parameter setting model has converged and the training is terminated.

[0178] The training method of the secondary mitigation parameter setting model includes:

[0179] Pre-constructing a secondary mitigation parameter setting data set, the secondary mitigation parameter setting data set including CJ group secondary mitigation parameter setting data and a set of planned adjustment instructions corresponding to the CJ group secondary mitigation parameter setting data, where CJ is a positive integer; the secondary mitigation parameter setting data including an air-conditioning system operating margin parameter set and a cold and hot hedging risk assessment score set; dividing the secondary mitigation parameter setting data set into a training set and a validation set, the training set being used for secondary mitigation parameter setting model parameter learning, and the validation set being used for real-time monitoring of the generalization performance and overfitting degree of the secondary mitigation parameter setting model;

[0180] A deep neural network with a multilayer perceptron as the core is used as the secondary relief parameter setting model. The secondary relief parameter setting data is input into the deep neural network after standardization and vectorization processing. The deep neural network consists of an input layer, a hidden layer and an output layer. Each hidden layer uses a nonlinear activation function to extract features, and the output layer uses a softmax activation function to obtain the probability distribution corresponding to each plan adjustment instruction set. Finally, the plan adjustment instruction set corresponding to the maximum probability is taken as the prediction result of the secondary relief parameter setting model. During the training process, the cross entropy loss function is used as the optimization target, and a gradient descent optimization algorithm is used to update the network weights, and an early stopping strategy is set: when the prediction accuracy on the validation set reaches or exceeds the preset threshold, it is determined that the secondary relief parameter setting model has converged and the training is terminated.

[0181] It should be noted that in the overall control framework of this application, the cold and heat hedging risk areas are divided into a cold and heat hedging priority mitigation list and a cold and heat hedging secondary adjustment list. Different control schemes are adopted for the two lists. Specifically, the technical considerations of this hierarchical design are that in terms of the differences in risk urgency and business continuity, the building sub-areas in the cold and heat hedging priority mitigation list mostly belong to functional areas such as data rooms or densely populated areas, and are much more sensitive to temperature and humidity stability and energy consumption impacts than general offices or public reception areas; any continued existence of cold and heat hedging may directly affect core business or generate comfort complaints. Therefore, the priority mitigation scheme is designed with instantaneous conflict suppression as the design goal, and adopts an instant adjustment strategy of seconds to minutes. The instruction parameters emphasize fast and effective cold / heat compensation, such as rapid increase in target air supply volume, large adjustment of valve stroke, sudden increase in host cooling gain, and instant energy release of energy storage units. High-frequency closed-loop feedback is configured after adjustment to ensure that cold and heat risks are suppressed in the shortest time.

[0182] In terms of differences in system resource scheduling and energy-saving coordination, the cold and heat hedging risk of the building sub-areas covered by the cold and heat hedging secondary adjustment list is relatively low, and adjustment lag or time-sharing processing can be tolerated. In order to avoid the impact of system load peaks or frequent starts and stops on equipment caused by large-scale simultaneous adjustments, the secondary adjustment scheme adopts hourly planning and batch scheduling strategies with the goal of smooth conflict reduction and coordinated energy saving. The secondary mitigation parameter setting model places more emphasis on resource sharing and load balancing, and sets a slow gradient for the instruction parameters, such as slow rise of the air supply curve, fine-tuning of valves, smooth correction of the main engine base load, and batch charging and discharging of energy storage. At the same time, as a supplementary note, this application can also be combined with off-peak electricity prices and night maintenance windows to achieve peak shaving and valley filling and energy efficiency optimization.

[0183] Based on the above differences, this application constructs a priority mitigation parameter setting model and a secondary mitigation parameter setting model respectively. The priority mitigation parameter setting model focuses on real-time margins and calculates the adjustment amplitude with high weights; the secondary mitigation parameter setting model focuses on full-time margins and optimizes the adjustment parameters in a time-sharing manner. Both the priority mitigation parameter setting model and the secondary mitigation parameter setting model are based on the air-conditioning system operation margin parameter set, and are respectively adapted to the technical scenarios of immediate conflict suppression and planned energy-saving scheduling, thereby achieving differentiated and precise control of areas with different risk levels.

[0184] Example 2:

[0185] See also Figure 2 As shown, this embodiment provides a method for energy conservation management of public institutions based on AI intelligent operation and maintenance, including:

[0186] Obtain multi-source building operation data;

[0187] Based on semantic fusion and coupling evaluation of multi-source building operation data, a building coupling feature dataset is constructed. The building coupling feature dataset includes a cooling and heating hedging difference map, an adjustment intention difference matrix, and the cooling and heating demand directions, dynamic load semantic labels, and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas.

[0188] Based on the building coupling feature dataset, a heat-cold hedging risk assessment is performed to obtain the heat-cold hedging risk assessment scores corresponding to N building sub-areas, and R heat-cold hedging risk areas are identified.

[0189] It is used to hierarchically sort the R hot and cold hedging risk areas according to functional zoning attributes, building sub-area areas, and hot and cold hedging persistence types, and generate a hot and cold hedging priority mitigation list and a hot and cold hedging secondary adjustment list;

[0190] Combined with the air-conditioning system operating margin parameter set, the hot and cold hedging difference map and the adjustment intention difference matrix, the hot and cold hedging priority relief list and the hot and cold hedging secondary adjustment list are adaptively controlled by hot and cold hedging hierarchical control.

[0191] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0192] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The energy conservation management system for public institutions based on AI intelligent operation and maintenance is characterized by: include: Multi-source data acquisition module, used to obtain multi-source operation data of buildings; The coupling feature analysis module performs semantic fusion and coupling evaluation based on multi-source building operation data to construct a building coupling feature dataset; The building coupling feature dataset includes a cooling and heating hedging difference map, an adjustment intention difference matrix, and cooling and heating demand directions, dynamic load semantic labels, and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas; The heat-cold hedging assessment module performs heat-cold hedging risk assessment based on the building coupling feature dataset, obtains heat-cold hedging risk assessment scores corresponding to N building sub-areas, and identifies R heat-cold hedging risk areas; A hierarchical scheduling generation module is used to hierarchically sort the R hot and cold hedging risk areas according to functional zoning attributes, building sub-area areas, and hot and cold hedging persistence types, and generate a hot and cold hedging priority mitigation list and a hot and cold hedging secondary adjustment list; The hot and cold hedging control module is used to combine the air-conditioning system operating margin parameter set, the hot and cold hedging difference map and the adjustment intention difference matrix to perform hot and cold hedging hierarchical adaptive control on the hot and cold hedging priority relief list and the hot and cold hedging secondary adjustment list.

2. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 1 is characterized in that: The methods for performing adaptive control of hot and cold hedging levels include: For each building sub-area in the priority relief list for cold and hot hedging, which is recorded as a priority relief building sub-area, a sub-graph with a direct spatial coupling relationship with the priority relief building sub-area is obtained from the cold and hot hedging difference graph, which is recorded as a cold and hot hedging associated sub-graph; the matrix elements directly associated with the priority relief building sub-area on the graph structure edge are extracted from the adjustment intention difference matrix to construct an adjustment intention difference set; the air-conditioning system operation margin parameter set, the cold and hot hedging associated sub-graph, and the adjustment intention difference set are input into the priority relief parameter setting model to obtain an immediate adjustment instruction set; the immediate adjustment instruction set includes the target air supply volume set value, the terminal water valve target opening, the host load instantaneous correction gain, and the energy storage unit energy release instruction; Send the real-time adjustment instruction set to the corresponding control unit for execution; When the hot and cold hedge priority relief list is completed, the hot and cold hedge secondary adjustment list is updated and hot and cold hedge hierarchical adaptive regulation is performed.

3. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 2 is characterized in that: The method for updating the cold and hot hedging secondary adjustment list and performing cold and hot hedging hierarchical adaptive control includes: For each building sub-area in the secondary adjustment list for cold and heat hedging, record it as a secondary mitigation building sub-area, and re-evaluate and update the corresponding cold and heat hedging risk assessment score of the secondary mitigation building sub-area; Remove the secondary mitigation building sub-areas whose cold and heat hedging risk assessment scores are not greater than the preset cold and heat hedging risk score threshold from the cold and heat hedging secondary adjustment list to obtain an updated cold and heat hedging secondary adjustment list; Obtain the latest air conditioning system operating margin parameter set, and construct a heat and cold hedging risk assessment score set based on the heat and cold hedging risk assessment scores in the updated heat and cold hedging secondary adjustment list; The air-conditioning system operating margin parameter set and the cold and hot hedging risk assessment score set are input into the secondary mitigation parameter setting model to obtain a planned adjustment instruction set, which is then sent to the corresponding control unit for execution; the planned adjustment instruction set is of the same type as the adjustment instruction in the immediate adjustment instruction set.

4. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 1 is characterized in that: Methods for obtaining the hot and cold hedge priority mitigation list and the hot and cold hedge secondary adjustment list include: For R cold-heat hedging risk areas, obtain the functional zoning attributes, cold-heat hedging duration ratio, building sub-area area, and cold-heat hedging risk assessment score of each cold-heat hedging risk area; According to the preset functional zoning attribute matching table, the functional zoning attributes are converted into functional zoning attribute values; the hot and cold hedging duration ratio is compared with the preset duration ratio threshold to obtain the hot and cold hedging duration type, and converted into the hot and cold hedging duration type value; the hot and cold hedging duration type is continuous hot and cold hedging or intermittent hot and cold hedging; Input the heat and cold hedging risk assessment score, functional zoning attribute value, heat and cold hedging persistence type value and building sub-area area into the heat and cold hedging priority assessment model to obtain the corresponding heat and cold hedging priority score; Compare the hot and cold hedge priority scores with the preset hot and cold hedge priority score thresholds to obtain a hot and cold hedge priority mitigation list and a hot and cold hedge secondary adjustment list respectively; The hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list are sorted in descending order according to the corresponding hot and cold hedge priority scores to obtain the final hot and cold hedge priority relief list and the hot and cold hedge secondary adjustment list.

5. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 1 is characterized in that: The method for obtaining R cold and hot hedging risk areas includes: Inputting the building coupling feature dataset into a pre-trained heat-cold hedging risk assessment model to obtain heat-cold hedging risk assessment scores corresponding to N building sub-areas; For N cold-heat hedging risk assessment scores, compare each score with a preset cold-heat hedging risk score threshold. If the score is greater than the threshold, mark the building sub-area corresponding to the score as a cold-heat hedging risk area. After the determination of N building sub-areas is completed, the total count of the cold-heat hedging risk areas is summarized and recorded as R, thereby obtaining R cold-heat hedging risk areas.

6. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 1 is characterized in that: The method for constructing the building coupling feature dataset includes: Based on temperature and humidity, determine the heating and cooling demand directions corresponding to N building sub-areas, where the heating and cooling demand directions include cooling demand, heating demand, and no comfort demand; Match the air valve opening with a pre-built air valve opening matching table to obtain the air valve opening gears corresponding to N building sub-areas; the air valve opening gears include low opening, medium opening and high opening; Set dynamic load semantic labels for N building sub-areas based on cooling and heating demand direction, air valve opening gear, equipment power consumption, and occupant density; According to the difference between the spatial coupling relationship of N building sub-areas and the dynamic load semantic labels, a heat and cold hedging difference map and a regulation intention difference matrix are constructed. Based on the air volume, temperature difference of the air conditioning water system and valve opening, the regional comprehensive cooling efficiency coefficient corresponding to N building sub-areas is constructed; The building coupling feature dataset is constructed by combining the heat and cold hedging difference map, the adjustment intention difference matrix, the heat and cold demand directions, dynamic load semantic labels and regional comprehensive cooling efficiency coefficients corresponding to N building sub-areas.

7. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 6 is characterized in that: Methods for determining the cooling and heating demand directions corresponding to N building sub-areas based on temperature and humidity include: For each building sub-area, if the temperature of the building sub-area is higher than the upper limit of the comfortable temperature and the humidity is higher than the upper limit of the comfortable humidity, the heating and cooling demand direction is marked as a cooling demand and the current process ends; If the temperature of the building sub-area is lower than the lower limit of the comfort temperature, the cooling and heating demand direction is marked as the heat demand and the current process ends; Mark the heating and cooling demand direction as comfortable and no demand, and end the current process.

8. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 6 is characterized in that: The method for setting the dynamic load semantic labels corresponding to N building sub-areas includes: For each building sub-area, when the heating and cooling demand direction of the building sub-area is cooling demand, determine whether the equipment power consumption and personnel density exceed the corresponding thresholds. If the judgment result exceeds the threshold, determine whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as forced cooling required; if the air valve opening gear is not high, the dynamic load semantic label is recorded as warm cooling required; if the judgment result does not exceed the threshold, the dynamic load semantic label is recorded as warm cooling required; When the cooling and heating demand direction of the building sub-area is heat demand, that is, the system has excessive cooling, it is determined whether the air valve opening gear is high. If the air valve opening gear is high, the dynamic load semantic label is recorded as needing to reduce cooling. If the air valve opening gear is not high, the dynamic load semantic label is recorded as needing warm cooling. When the cooling and heating demand direction of the building sub-area is comfortable with no demand, the dynamic load semantic label is recorded as no adjustment demand.

9. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 6 is characterized in that: The construction method of the hot-cold hedging difference map and the adjustment intention difference matrix includes: Match the dynamic load semantic label with the pre-built dynamic load semantic label matching table to obtain the dynamic load semantic label values ​​corresponding to N building sub-areas; Mapping N building sub-areas into a set of graph structure nodes; The air supply path coupling edges, return air confluence coupling edges, water loop coupling edges and shared wall heat transfer edges corresponding to N building sub-areas are constructed into a graph structure edge set; For each graph structure edge in the graph structure edge set, construct a graph structure edge combination with the graph structure edge adjacent to the graph structure edge, and determine whether the dynamic load semantic label values ​​corresponding to the graph structure edge combination are the same. If they are not the same, set the adjustment intention difference of the graph structure edge combination to the absolute value of the difference between the dynamic load semantic label values ​​corresponding to the graph structure edge combination; if they are the same, set the adjustment intention difference of the graph structure edge combination to zero; The adjustment intention difference is written into the adjustment intention difference matrix, and the row and column coordinate positions in the adjustment intention difference matrix are the numbered corresponding positions formed by the edge combination of the graph structure; When all graph structure edges in the graph structure edge set are processed, the assigned graph structure edge set is obtained; The cold-hot hedge difference graph is constructed by combining the graph structure node set and the assigned graph structure edge set.

10. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 6 is characterized in that: The method for constructing the regional comprehensive cooling efficiency coefficient corresponding to N building sub-areas includes: For each building sub-area, the air conditioning air volume, air conditioning water system temperature difference, and valve opening are normalized to a preset normalized range, and the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor are obtained respectively. The regional comprehensive cooling efficiency coefficient is calculated based on the air-side distribution factor, water-side heat exchange factor, and valve adjustment factor. When all building sub-areas are processed, the regional comprehensive cooling efficiency coefficients corresponding to the N building sub-areas are obtained.

11. The public institution energy conservation management system based on AI intelligent operation and maintenance according to claim 1 is characterized in that: The multi-source building operation data includes temperature, humidity, air-conditioning air volume, air-conditioning water system temperature difference, valve opening, air valve opening, equipment power consumption and personnel density corresponding to N building sub-areas.

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