A building energy consumption adjusting control method and system based on energy-saving monitoring

Abstract

The application discloses a kind of building energy consumption regulation control method and system based on energy-saving monitoring, it is related to building technical field, the energy-saving monitoring method of the present application significantly improves the precision of building energy efficiency regulation by accurately capturing the temperature evolution sequence of different structural units and establishing building response vector group. By using the heat capacity, lag error and cold release curve of each structural unit, combined with the accurate identification of the cold release time window, the indoor temperature can be dynamically predicted and adjusted. Compared with the traditional heating control method based on fixed temperature control setting, this method can real-time perceive the thermal inertia and energy release of building structural units, thereby reducing excessive heating and energy waste. This precise control based on structural unit characteristics and time window feedback mechanism helps to reduce the energy consumption of buildings, while improving the comfort and energy-saving effect of indoor temperature control.

Description

Technical Field

[0001] This invention relates to the field of building technology, specifically to a building energy consumption regulation and control method and system based on energy-saving monitoring. Background Technology

[0002] Current building energy consumption regulation and control systems lack a dynamic modeling mechanism for the building's thermal inertia and cumulative heat dissipation characteristics, leading to decisions based solely on instantaneous monitoring data. This neglect of building physical properties, particularly in buildings with poor insulation, results in significant issues such as delayed heat release, ineffective regulation response, and energy compensation deviations, creating a critical regulatory flaw in energy-saving systems. Especially at night without heating, the cold released by structural units (such as walls and floors) may only cause a sudden drop in room temperature after a delay. For example, in northern residential buildings during winter, a common scenario is that after the heating system is turned off at 3:00 AM, the room temperature remains stable for about two hours, but then suddenly drops rapidly between 5:00 and 6:00 AM. Users subjectively experience unusually cold temperatures upon waking, even though the system has not yet restarted. Traditional systems are unable to respond to this, primarily because they fail to perceive and model the time-varying characteristics and rate of cold release, leading to delayed warnings of sudden room temperature drops and delayed heating responses, resulting in a break in thermal comfort and wasted energy. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a building energy consumption regulation and control method and system based on energy-saving monitoring, which solves the problems mentioned in the background technology.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a building energy consumption regulation and control method based on energy-saving monitoring, comprising the following steps:

[0005] Collect temperature evolution sequences within different structural units and fit the theoretical temperature rise curves of each structural unit to construct a building response vector set;

[0006] After the heating season ends, a complete cooling release curve is calculated for each structural unit. The cooling release time window for each structural unit is determined through multiple heating shutdown cycles. The cooling release time windows of each structural unit are then merged to form a cooling release window set.

[0007] Obtain the sequence of air temperature changes in the air unit from when the heating system is shut down to identify the actual temperature jump moment;

[0008] Trigger the advance heating reverse mechanism to determine the relative proportion of the current location within the cold energy release window;

[0009] Based on the relative position ratio and the actual temperature jump time, the start time of early heating and the corresponding preheating power path function are derived, which are used to dynamically update the adjustment strategy.

[0010] Preferably, the temperature evolution sequences in different structural units are collected according to temperature sensors deployed at each structural unit in the target building;

[0011] The structural units include at least an air unit, a wall unit, a window frame unit and a floor unit;

[0012] The heat capacity of each structural unit is calculated based on the physical definition of heat capacity;

[0013] The theoretical temperature rise curve of each structural unit is fitted according to the heat capacity and heating power of each structural unit, to extract the hysteresis error and integrate, which are summarized to build a building response vector group to represent the magnitude of structural heat storage and hysteresis.

[0014] Preferably, the theoretical temperature rise curve of each structural unit is fitted according to the heat capacity and heating power of each structural unit, to extract the hysteresis error and integrate, which are summarized to build a building response vector group, including:

[0015] The heating power in the target building during the response time period is recorded;

[0016] The theoretical temperature rise curve of each structural unit is derived based on the heat capacity and heating power of each structural unit;

[0017] The hysteresis error is obtained by subtracting the measured temperature from the theoretical temperature, and the residual thermal inertia index is obtained after integration.

[0018] Preferably, the complete cold release curve is calculated, including:

[0019] After the heating is completed, the heating termination time period is identified, and the temperature decay sequence of each structural unit is tracked from this time point;

[0020] The heat flow decay model is constructed by combining the building response vector group and the first-order Fourier heat transfer formula, which is used to calculate the cold release rate per unit time, i.e. negative heat flow;

[0021] The complete cold release curve is calculated for each structural unit.

[0022] Preferably, the cold release time window of each structural unit is determined through multiple heating off periods, including:

[0023] The internal temperature decay sequence of the structure in multiple heating off periods is extracted to build a cold release rate matrix, where the column represents the time point after the heating termination, and the row represents the multiple heating termination periods;

[0024] The average release rate curve is obtained by averaging the columns of the cold release rate matrix;

[0025] According to the average release rate curve, the start and end time of the cold release of the structural unit is identified by fitting an exponential decay model, and the corresponding cold release time window is defined;

[0026] The cold release time windows of the structural units are combined to form a cold release window set.

[0027] Preferably, the sequence of air temperature changes in the air unit since the heating system is turned off is obtained to identify the actual temperature jump time, including:

[0028] The sequence of air temperature changes in the air unit since the heating system is turned off is extracted from the temperature decay sequence;

[0029] According to the sequence of air temperature changes, the second-order difference at different times is calculated to depict the change in curvature.

[0030] If the second-order difference at consecutive N times exceeds the preset difference threshold, it indicates that the current air temperature is in the steep drop segment, at which time the position of the current air temperature in the cold release window set is determined, and is recorded as the actual temperature jump time.

[0031] Preferably, an early heating trigger mechanism is triggered to determine the relative position ratio of the current time in the cold release window set, including:

[0032] A sliding time window is set, and the total cold released by all structural units in the corresponding period is calculated in each sliding time window.

[0033] If the total cold released by all structural units in the current period exceeds the preset temperature jump warning threshold, it indicates that the current room temperature appears an irreversible temperature drop process, at which time the early heating trigger mechanism is triggered.

[0034] If the early heating trigger mechanism is triggered, the relative position ratio of the current time in the cold release window set is further identified, specifically: by calculating the time difference between the current time point and the start time of the cold release window set, and dividing it by the duration of the entire cold release window set, a standardized ratio value, i.e. the relative position ratio, is obtained.

[0035] Based on the relative position ratio, the start time of early heating and the corresponding preheating power path function are backstepped to dynamically update the adjustment strategy.

[0036] Preferably, the start time of early heating is backstepped, including:

[0037] When the relative position ratio exceeds , it indicates that the heat storage capacity of the overall structural unit has been greatly reduced.

[0038] At this time, the current time is compared with the actual temperature jump time, if the current time does not exceed the actual temperature jump time, the starting time of early heating is determined, otherwise, the heating operation is immediately executed;

[0039] The starting time of early heating is determined, specifically: , wherein, is the starting time of early heating, is the current time, is the preheating advance adjustment coefficient, is the relative position ratio, is the duration of the cold release window set; for reflecting that the closer the relative position ratio is to the end of the cold release, the earlier the preheating response should be triggered.

[0040] Preferably, the preheating power path function is constructed, including:

[0041] The total cold release rate of the whole at the current time is obtained, and the corresponding minimum heat supplement power reference value is converted according to the overall heat capacity of the building. In order to prevent the failure of lagging heat supplement, the minimum heat supplement power reference value is multiplied by a redundant power amplification coefficient, so that the adjustment power has a response margin on the basis of meeting the heating demand. The specific expression of the preheating power path function is: , wherein, is the adjustment heat supplement power at the current time, is the redundant power amplification coefficient, is the sum of the cold release rates of all structure units at the current time, is the equivalent heat capacity of the building structure, is the heating weight.

[0042] A building energy consumption regulation and control system based on energy-saving monitoring, comprising,

[0043] An information aggregation module collects temperature evolution sequences in different structure units and fits theoretical temperature rise curves of each structure unit to construct a building response vector group;

[0044] A release concentration module calculates complete cold release curves for each structure unit after heating is completed, and determines cold release time windows of each structure unit through multiple heating off periods to combine the cold release time windows of each structure unit to form a cold release window set;

[0045] An identification module obtains an air temperature change sequence in the air unit since the heating system is turned off to identify an actual temperature jump time;

[0046] A backstepping module triggers an early heating backstepping mechanism to determine a relative position ratio of the current time in the cold release window set;

[0047] The adjusting module reverses the starting time of early heating and a corresponding preheating power path function based on the relative position ratio and the actual temperature jump time, and is used for dynamically updating the adjusting strategy.

[0048] The application provides a building energy consumption adjusting control method and system based on energy-saving monitoring.

[0049] (1) The energy-saving monitoring method of the application can significantly improve the accuracy of building energy efficiency adjustment by accurately capturing the temperature evolution sequence of different structural units and establishing a building response vector group. By using the heat capacity, lag error and cold release curve of each structural unit, combined with the accurate identification of the cold release time window, the indoor temperature can be dynamically predicted and adjusted. Compared with the traditional heating control method based on fixed temperature control setting, the method can realize real-time sensing of the thermal inertia and energy release of the building structural unit, thereby reducing excessive heating and energy waste. The accurate control based on the structural unit characteristics and time window feedback mechanism helps to reduce the energy consumption of the building and improve the comfort and energy-saving effect of indoor temperature control.

[0050] (2) The application can effectively predict and adjust the starting time of heating in the building cold release window set by introducing the early heating reverse mechanism, especially when the indoor temperature is about to change (i.e. temperature jump phenomenon). By calculating the relative position ratio in the cold release window set at the current time, and according to the dynamic change of the actual temperature jump time and the cold release rate, the heating can be started in advance during the period of rapid temperature drop. This mechanism reduces the energy waste caused by lag response in the traditional heating method, especially in cold weather, which can quickly restore the indoor temperature and avoid the influence of rapid temperature drop on human comfort, improving the response speed and energy efficiency of the heating system.

[0051] (3) The scheme can accurately adjust the heating power when heating in advance by constructing a preheating power path function, considering the cold release rate and heat capacity characteristics of the whole building. By setting the redundant power amplification coefficient, the sufficiency of the preheating response is ensured, and the temperature fluctuation problem caused by lag heating failure is avoided. In addition, by nonlinearly adjusting the heating weight function in the time window, the distribution of heating power is further optimized, so that the adjustment process is not only accurate but also flexible, meeting the needs of different building structural units. The scheme ensures that the heating strategy meets the room temperature setting while improving the overall efficiency of the system, reducing unnecessary energy consumption, and ultimately helping to achieve the building energy-saving goal. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 It is a flowchart of the building energy consumption adjusting control method based on energy-saving monitoring.

[0053] Figure 2 It is a cold release time window identification schematic diagram of a building energy consumption adjustment control method based on energy-saving monitoring of the present application;

[0054] Figure 3 It is a block diagram of a building energy consumption adjustment control system based on energy-saving monitoring of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0056] Embodiment 1

[0057] Please refer to Figure 1 and Figure 2 The present application provides a building energy consumption adjustment control method based on energy-saving monitoring, comprising the following steps,

[0058] S1: collecting temperature evolution sequences in different structural units and fitting theoretical temperature rise curves of each structural unit to construct a building response vector group;

[0059] S2: after the heating is completed, calculating a complete cold release curve for each structural unit, and through multiple heating-off periods, determining cold release time windows of each structural unit, and merging the cold release time windows of each structural unit to form a cold release window set;

[0060] S3: obtaining a sequence of air temperature changes in the air unit since the heating system is turned off to identify the actual temperature jump time;

[0061] S4: triggering an early heating backstepping mechanism to determine the relative position proportion of the current time in the cold release window set;

[0062] S5: based on the relative position proportion and the actual temperature jump time, backstepping the starting time of early heating and the corresponding preheating power path function to dynamically update the adjustment strategy.

[0063] In the present embodiment, the S1 step can realize quantitative description of the thermal response behavior of various building structural units (such as walls, floors, window frames, and air), and through the construction of the building response vector group, provides structural differentiation and thermal inertia priori knowledge for subsequent heat flow prediction, risk assessment, and preheating adjustment strategy, effectively improving the accuracy and adaptability of the adjustment scheme.

[0064] Among them, the temperature evolution sequence refers to the hourly measured temperature curve of the structural unit during the heating process;

[0065] The theoretical temperature curve is a temperature value calculated based on a heat capacity model and a heating power;

[0066] The building response vector group is a multi-dimensional vector set with components such as heat capacity and residual thermal inertia, used to express structural differences and energy absorption response differences. For example, at the beginning of winter heating, the temperature of the wall unit rises slowly, and the measured temperature always lags behind the theoretical temperature rise model. At this time, the system identifies that the wall has high heat capacity and high lag characteristics, and inputs its response characteristics as parameters into the building response vector group.

[0067] The S2 step is performed during the natural cooling stage after heating is stopped, aiming to identify the passive heat dissipation capacity and heat storage decay rate of the structural unit, build a cold release behavior model, and extract a consistent time window set for use in the subsequent adjustment trigger logic.

[0068] The cold release curve is the negative heat flow curve of the structural unit to the indoor unit per unit time after heating is stopped;

[0069] The cold release time window is the continuous period of time when the structure releases cold energy most actively;

[0070] The cold release window set is the joint time range of all structure unit release time windows. For example, the heating system is turned off at 22:00, the window frame has a high release rate from 22:30 to 01:30, and the wall has a high release peak from 23:00 to 03:00. After extracting the time windows of all structure units, the system combines them to form a cold release window set from 22:30 to 03:00, which is used as a reference for subsequent heating regulation.

[0071] By constructing the cross-cycle cold release window set, this step can capture the overlapping period of the heat storage decay behavior of different structure units, avoid missing the potential heat release peak due to structural heterogeneity, and help accurately identify the "precursor stage" of the room temperature jump, providing a timing coordinate reference for pre-regulation action.

[0072] The S3 step focuses on the rapid drop of indoor air temperature, extracts the real occurrence time of the room temperature jump risk as a reference benchmark for heating regulation strategy, and verifies the causal relationship between the building structure cold release behavior and the room temperature change.

[0073] The temperature jump time is the time point at which the air temperature drop amplitude or rate suddenly increases;

[0074] Second-order difference is used to calculate the curvature change of air temperature, so as to detect the jump mutation point. For example, the air temperature stably decreases within 30 minutes after the heating is turned off, but an accelerated downward trend (the curvature is continuously positive and amplified) occurs for 3 consecutive minutes after the 35th minute, at which time the system records the 35th minute as the actual jump temperature time for subsequent heating backstepping mechanism.

[0075] This step can accurately identify the starting point of the irreversible temperature drop process caused by insufficient heat supply in the building space, form a quantitative judgment mechanism of temperature change events, and effectively improve the triggering accuracy and timeliness of risk response in the adjustment strategy.

[0076] S4, on the basis of identifying the cold release window and the actual jump temperature time, this step normalizes the position of the current time in the cold release behavior, and constructs a proportional index of adjustment urgency for backstepping the time and power strategy of heating. The relative position ratio is the normalized coordinate value of the current time point in the cold release window set. The larger the value, the closer to the end of the cold release, and the heat inertia has basically disappeared.

[0077] The advance heating backstepping mechanism is a method of predicting the starting time of adjustment response according to the time sequence position. For example, the cold release window is 22:00-02:00, and the current time is 01:30, then the relative position ratio is 0.875, and the system judges that the heating action should be triggered in advance to avoid the room temperature from falling out of the comfort zone. This step can make the heating adjustment have the ability of "time perception" and "behavior backstepping", avoid the loss of comfort caused by delayed heating, and realize the dynamic adjustment intervention strategy for different structure response speeds.

[0078] S5 is the response execution link of the method, which constructs a regulation path function with power regulation flexibility and nonlinear amplification capability according to the prediction index (time position and temperature change event) of the previous step, and realizes active heating before temperature drop. The preheating power path function is a curve of power changing dynamically with time, which is controlled by a step function. This step can realize real-time calculation and dynamic release of the heating amount before temperature drop, improve the forward-looking response ability of the system to temperature fluctuations, construct a adjustable, variable, continuous dynamic and response adaptive energy-saving control system, and effectively suppress the passive over-heating phenomenon and energy waste.

[0079] Embodiment 2

[0080] Please refer to Figure 1 , specifically: collecting temperature evolution sequences in different structural units according to temperature sensors deployed in each structural unit in the target building;

[0081] The structural unit includes at least one air unit, one wall unit, one window frame unit and one floor unit.

[0082] Among them, the multi-point temperature collection device is arranged: temperature sensor groups are arranged in each structural unit, wherein: the air layer adopts a middle hanging type thermal element; the wall body adopts an embedded or surface-mounted temperature measuring probe; the floor adopts a buried or surface contact type thermocouple.

[0083] Based on the definition of heat capacity physics, the heat capacity (i.e. the heat required for unit temperature rise) of each structural unit is calculated.

[0084] Heat capacity in thermodynamics refers to the heat absorbed or released by an object per unit temperature change, i.e. the heat absorbed (or released) to raise (or lower) the temperature of an object by 1K (or 1°C); its basic physical meaning is: the "response ability" or "thermal inertia" performance of the object to heat change, the greater the heat capacity, the more heat required for the structural unit to change in temperature, and the more "dull" the cooling or heating.

[0085] In engineering and physical systems, the heat capacity of a structural unit is usually calculated according to the following formula: , wherein, is the heat capacity of the i-th structural unit, is the structural material density of the i-th structural unit, is the specific heat capacity of the i-th structural unit, is the actual volume of the i-th structural unit, and i is the structural unit number;

[0086] The heat capacity is calculated to calculate the structural unit temperature response rate, together with the heating power to fit the theoretical temperature trajectory;

[0087] According to the heat capacity of each structural unit and the heating power, the theoretical temperature rise curve of each structural unit is fitted to extract the lag error and integrate, which is summarized to construct a building response vector group to represent the structure heat storage and lag amplitude.

[0088] The purpose of constructing the structural heat capacity sub-model and extracting the thermal inertia residual parameter is to construct a mathematical sub-model for describing the heat absorption and release capacity of each structural unit, and to identify the "residual thermal inertia effect" ignored in the regulation behavior.

[0089] According to the heat capacity of each structural unit and the heating power, the theoretical temperature rise curve of each structural unit is fitted to extract the lag error and integrate, which is summarized to construct a building response vector group, including:

[0090] The heating power in the target building during the response period is recorded, which is used for theoretical temperature curve derivation;

[0091] The heating power is the rate of heat input by the heating system to the building space (or a structural unit) per unit time, usually in units of watts (W) or kilowatts (kW). It not only describes the output intensity of the heating system, but also directly participates in the modeling of multiple thermal dynamic systems such as the structure temperature rise model, the heat flow compensation model, and the preheating power path function.

[0092] Based on the heat capacity and heating power of each structural unit, the theoretical temperature rise curve of each structural unit is derived, and the specific expression form is: Wherein, is the theoretical temperature fitted based on the heat capacity and heating power of the i th structural unit, is the initial temperature of the i th structural unit, is the heating start time, t is the current time, is the distribution factor of the i th structural unit receiving heat flow, the value range is 0 < a i < 1, is the heating power at the i th structural unit at the t th moment, is the time infinitesimal, is the integral variable; wherein, the formula is derived from the heat accumulation equation (heat capacity model), and the basic thermodynamic logic is as follows: The heat required for unit temperature rise, that is, heat = heat capacity * temperature rise amplitude of building structural unit, that is, temperature rise amplitude of building structural unit = heat divided by heat capacity;

[0093] And the heat actually comes from the energy integral generated by the heating power per unit time, so the theoretical temperature rise amount is:

[0094]

[0095] In the actual heating process, different structural units (such as walls, floors, window frames, etc.) receive different proportions of heat flow, so the distribution factor a i is introduced as the actual heat absorption correction, and the initial heating temperature is added to obtain the complete temperature rise curve.

[0096] Wherein, the distribution factor is derived from the average heating proportion of each structural unit in the debugging period or historical data, and the optimal distribution factor value (empirical value or training value) can also be obtained by actual response lag;

[0097] Based on the heat capacity principle and the law of conservation of heating energy, combined with the non-uniformity of heat flow received by each structural unit, a theoretical temperature rise curve model is constructed. The model takes the initial heating temperature as the initial value, uses the integral form to accumulate the heating power input, and corrects through the heat capacity coefficient and the distribution factor to form a multi-directional temperature rise prediction expression for each structural unit. The model can be used for temperature rise response prediction, and also provides theoretical support for subsequent residual heat inertia error extraction and dynamic power regulation strategy. ​​​​

[0098] The hysteresis error is obtained by subtracting the measured temperature from the theoretical temperature. After integration, the residual thermal inertia index is obtained, which is specifically: wherein, is the hysteresis error, is the measured temperature, is the theoretical temperature based on the heat capacity and heating power fitting of the ith structural unit;

[0099] The residual thermal inertia index is obtained as follows: wherein, is the residual thermal inertia index, indicating the integral value of the hysteresis error energy, reflecting the part of the thermal inertia accumulated that is not fully considered by the system;

[0100] The heat capacity and residual thermal inertia index of each structural unit are summarized to construct a building response vector group.

[0101] The temperature evolution sequence includes the measured temperature at each collection time;

[0102] In this embodiment, temperature sensors are deployed at multiple representative structural parts of the target building to form a local monitoring network for structural units with obvious differences in thermal behavior, such as air, walls, window frames, and floors. These structural units are the basic physical carriers of the building thermal inertia response. The trajectory of the temperature of each unit changing with time, i.e., the temperature evolution sequence, reflects the changes in its heat storage and release state.

[0103] Structural units are used to divide local entity modules with different heat capacity characteristics.

[0104] For example: air units (such as indoor free air volume) react quickly but store heat poorly;

[0105] Wall units (such as external wall structures) have slow heat transfer and strong heat storage;

[0106] Window frame units (such as metal frames) have fast heat conduction but weak heat storage capacity;

[0107] Floor units (such as reinforced concrete floors) have moderate responsiveness.

[0108] Temperature evolution sequence: for example, collect temperature at 8:00 in the evening to 6:00 the next day, record temperature every 10 hours to form 10 groups of temperature point sequences, reflecting the night heat dissipation curve. By classifying and deploying sensors, not only can the heat storage behavior of different materials be distinguished, but also basic data support can be provided for subsequent heat capacity modeling and heat flow analysis, improving the accuracy and resolution of the model. The basic physical properties (density, specific heat capacity, volume) of structural materials are used to calculate the unit heat capacity, which serves as the basis for subsequent energy transfer and temperature rise behavior quantification. For example, a 0.2-meter-thick, 20m 2concrete wall unit, whose volume is 4m 3 , the calculated heat capacity is: =2400*840*4=8064000J / K, even if the unit temperature rises 1K, it needs 806.4 million joules of heat, which shows that the wall has strong heat storage. Through this step, the thermal response baseline of each structural unit is determined, which provides key input parameters for subsequent construction of heating response curve and residual thermal inertia estimation, and realizes the quantifiable modeling of thermal characteristics difference.

[0109] According to the known heating power and heat capacity, the theoretical temperature rising trajectory is constructed, and compared with the actually collected temperature sequence, the "lag error" between the actual response and the ideal model is obtained, which is used to extract the structural delay heating characteristics.

[0110] Assuming that the floor heat capacity is high, the theoretical temperature rising prediction should rise 2℃ after 30 minutes of heating, but the actual collected temperature rising is only 0.5℃, so the lag error is 1.5℃, which shows that there is a slow heat storage process of "unabsorbed heat". The integral of the lag error quantifies the unresponsive part of the thermal inertia, i.e. the residual thermal inertia, which reflects the heat accumulation that has not been timely reflected in the heating regulation process, and helps to identify the regulation lag problem in advance, and then guides the heating regulation strategy design.

[0111] The heat capacity and residual thermal inertia index of each structural unit are combined to form a building response vector group, which is used to uniformly represent the overall thermal response characteristics of the building, support cold release trend identification and temperature jump prediction tasks. The building response vector group can be used as the key input of subsequent cold release path analysis, night temperature drop warning algorithm, etc., forming a parameterized and structured control basis, supporting active energy-saving regulation based on physical state. This section realizes the quantifiable modeling of building thermal inertia characteristics through the extraction of physical properties of structural units, the construction of theoretical temperature rising model, the identification of lag error and the generation of response vector group. This structure enables the subsequent modules to dynamically decide "when to start heating", "how much to preheat" and "whether the heat flow release process is abnormal", etc., thus significantly improving the intelligence, responsiveness and personalized regulation ability of building energy-saving system.

[0112] By combining the heating power to derive the theoretical temperature rising curve, and comparing the measured temperature to form a lag error sequence, and then integrating to extract the residual thermal inertia index, the system can effectively identify the thermal response lag and heat storage capacity difference of each structural unit in the heating process, which makes up for the technical gap of traditional energy regulation methods in lacking modeling of "thermal inertia accumulation effect". The response vector group not only can be used as the prior feature of subsequent cold release modeling, temperature jump risk prediction and heating regulation path generation, but also significantly improves the perception ability of energy regulation strategy to structural heterogeneity and dynamic heat storage state, thus realizing a more adaptive dynamic regulation mechanism and energy optimization decision.

[0113] Example 3

[0114] Please refer to Figure 1 and Figure 2 , among which, Figure 2 In the diagram, multiple gray broken lines represent the complete cold release curves showing the rate of cold release per unit time during different heating shutdown cycles. The black dashed line represents the average release rate curve after averaging different heating shutdown cycles. The horizontal axis represents the time elapsed since the heating season ended, and the vertical axis represents the amount of cold released by the structural unit per unit time, i.e., the intensity of heat flow to the outside. The closer the heat flow is to zero, the more stable the cold release process becomes. Figure 2 As is known, the cold energy release window set is defined as the distance from the start point to the end point of the cold energy release window set.

[0115] Specifically: During the nighttime period after the heating season ends, based on the heat capacity of each structural unit and the residual thermal inertia of the preceding phase, the natural decay path of its heat flow is derived, i.e., the trend of building cooling release.

[0116] Calculate the complete cooling release curve, including:

[0117] After the heating season ends, the heating termination period is identified, and the temperature decay sequence of each structural unit is tracked from that moment (obtained through actual measurement). The temperature decay sequence and temperature evolution sequence are actually only used to distinguish the changes monitored during and after the heating season.

[0118] Combining the building response vector set, a heat flux decay model is constructed using the first-order Fourier heat transfer formula, which is used to represent the rate of cold release per unit time, i.e., negative heat flux; its specific expression is as follows: ,in, For the corresponding structural unit at time The heat released during the process For the corresponding structural unit at time The actual measured temperature at that time This represents the heat flow (power) released by the corresponding structural unit per unit time, i.e., the rate of cold energy release, and is also the output value of the heat flow decay model. This represents the temperature change rate of the corresponding structural unit; The thermal inertia sensitivity coefficient is an adjustment coefficient (empirical setting or fitting) used to balance the residual inertia compensation strength. It is initially set to an empirical value in the range of 0.5 to 1.0, and the optimal value can be obtained by training through residual minimization in historical heat flow fitting.

[0119] This is an inertial temperature compensation term, used to compensate for the neglect of heat storage release hysteresis behavior in conventional models. It characterizes the temperature contribution that should have been released but was not due to inertial hysteresis. Its basic logic is as follows:

[0120] After the heating system is turned off, the structural unit no longer receives external heat energy, but the heat stored in the structure will still be released slowly, and the actual measured temperature change rate is Only the surface or measuring point response is reflected, and the release behavior of internal heat storage has inertial lag, and this part of heat release is not fully expressed by the first-order derivative of temperature, and needs to be compensated by RHI modeling, therefore, The term is introduced as an inertial compensation term of heat flow;

[0121] The residual heat inertia index represents the historical integral residual value of the thermal lag effect of each structural unit;

[0122] By introducing the residual heat inertia index into the heat flow decay model, the evaluation error caused by ignoring the structure heat storage lag in the traditional heat capacity model is effectively corrected, the cold release curve is more in line with the actual physical behavior, and the accuracy of the cold release time window identification and the response accuracy of the dynamic heating strategy are improved.

[0123] The complete cold release curve of each structural unit is calculated to obtain the total cold released by each structural unit from the time when the heating system is turned off to the time when the heat flow decreases to stable after the heating is turned off, and the specific expression is: , wherein, is the total cold released by each structural unit from the time when the heating system is turned off to the time when the heat flow decreases to stable after the heating is turned off, is the heating system turn-off time, is the time when the heat flow decreases to stable, is the heat released by the corresponding structural unit at time ;

[0124] Wherein, the decrease to stable period is from the heating system turn-off time point, the change of the internal heat flow rate of the structural unit tends to be stable, that is, the absolute value of the cold release rate gradually decreases, the temperature change rate tends to be flat or stable, the system no longer significantly releases explicit heat energy, and the building structure enters the passive heat balance section, which can be determined according to the change rate of the heat flow per unit time approaching zero, and a minimum change threshold is set, which is usually 5%-10% of the initial cold release rate, when the cold release rate of the structural unit is less than the minimum change threshold, it is determined that it enters the heat flow stable interval;

[0125] The complete cold release curve is calculated to evaluate the passive cooling amplitude that the structure can bring in the absence of active heating.

[0126] The first-order Fourier heat transfer formula is a fundamental expression in heat conduction theory, used to describe the rate at which heat is transferred along a temperature gradient. Fourier's law states that heat always flows from a high-temperature region to a low-temperature region, and its flow rate is proportional to the temperature gradient, with the direction of flow being the same as the direction of temperature decrease. In engineering applications, Fourier's law is often rewritten to apply to heat transfer relationships within finite volume units. For example, it is used to describe the temperature rise or cooling process of building structural units under the influence of a heating system.

[0127] By determining the time window for cooling release of each structural unit through multiple heating shutdown cycles, including:

[0128] Extract the internal temperature decay sequence of the structure in multiple heating shutdown cycles (e.g., 7 consecutive days) to construct a cold energy release rate matrix. The columns in the cold energy release rate matrix represent the time points after the heating is terminated, and the rows represent multiple heating termination cycles.

[0129] The heating shutdown cycle refers to the continuous non-heating period from the moment the heating system is shut down until the moment before the next heating system is restarted.

[0130] The average release rate curve is obtained by averaging the columns of the cold energy release rate matrix.

[0131] The expression for the average release rate curve is: ,in, Let K be the average release rate at time t during the j-th heating shutdown cycle, where j is the heating shutdown cycle number and K is the total number of heating shutdown cycles included in the statistics.

[0132] The average release rate curve is used to address single-cycle disturbance issues in order to establish a unified benchmark for evaluating cold release.

[0133] Based on the average release rate curve, the start and end times of cold energy release of the structural unit are identified by fitting the exponential decay model, and the corresponding cold energy release time window is defined.

[0134] The expression for the exponential decay model is: ,in, The cold energy release intensity is the rate at which cold energy is released immediately after the heating system is turned off. For the corresponding structural unit at time Predicted value of the rate of cold energy release at that time. With a natural base, the value is approximately 2.71828. This refers to the heating system shutdown time. This is the cold energy release attenuation factor, whose value is obtained by taking the actual measured cold energy release rate. For each data point, perform least squares fitting or exponential regression fitting to obtain the optimal parameters. i.e. the exponential rate of cold release.

[0135] After the heating stops, the residual heat energy in the structural unit is slowly released outward in the form of conduction and radiation. According to the Fourier heat conduction equation, the natural decay process of heat approximately conforms to the exponential law, i.e. the proportion of residual heat released per unit time is basically constant, forming a geometrically decreasing structure, which is suitable for expression by an exponential model.

[0136] The logic of the exponential decay model is to describe the natural decay trend of the gradual release of heat by the structural unit after the heating is turned off by using an exponential decay function based on the physical heat diffusion process. This model can efficiently depict the non-linear characteristics of the time-decreasing cold release and provide calculation support for subsequent control strategies such as cold release time window, heat inertia compensation, and temperature jump warning.

[0137] The cold release time windows of each structural unit are combined to form a cold release window set; this window set can be used as a key control variable input in the subsequent prediction stage to dynamically determine the room temperature jump risk and wake up the heating system in advance.

[0138] The fitted exponential decay model is used to simplify the complex heat flow release curve into an exponential decay model for better parameter extraction and prediction modeling.

[0139] The cold release window set is used to aggregate the cold release time windows of each structural unit to determine a maximum time window, which is the union of the cold release time windows of all structural units.

[0140] In this embodiment, the purpose of this step is to identify the passive cooling process of each unit of the building structure under the condition of no heating after the heating system is stopped. Specifically, by combining the temperature decay sequence with the heat capacity parameter, the cold release rate per unit time, i.e. the heat flow power, is calculated to form a complete cold release curve.

[0141] Heat capacity is the heat required for unit temperature rise, reflecting the heat storage capacity of the structure.

[0142] The temperature decay sequence is the sequence data of the measured temperature of the structural unit decreasing over time after the heating is stopped.

[0143] The cold release rate (negative heat flow) is the heat released by the structural unit to the air per unit time, reflecting the passive heat dissipation capacity. For example, after the heating system is turned off at 22:00 at night, the sequence of the surface temperature of the wall decreasing over time is recorded as 21.5°C, 20.8°C, and 19.6°C. Combined with the heat capacity of the wall, the cold release rate per unit time can be calculated, and a complete cold release curve can be further obtained. By establishing a natural decay path of heat flow, the heat dissipation trend of the building structure can be objectively quantified to provide a heat flow basis for subsequent heat imbalance prediction.

[0144] For each structural unit, the total cooling release value is obtained by integrating the release rate from the heating termination time to the period when the heat flow tends to be stable. This process measures the passive cooling capacity of the structure in a natural state.

[0145] The heat flow drops to stable refers to the period when the heat flow rate changes slowly and tends to be zero, which usually reflects the room temperature tends to be stable.

[0146] The total cooling release value is the area integral of the cooling release curve in the termination interval, which measures the total heat dissipation capacity of the structure.

[0147] If the floor unit releases at a fast-slow rate after heating ends, the cumulative release time is 8 hours, and the total cooling release is 12.8 MJ, which can be used as a benchmark for its passive heat dissipation capacity. This value provides a data basis for quantitative evaluation of building regulation capacity and is a key basis for subsequent cooling release time window division.

[0148] By sampling multiple heating-off periods, a cooling release rate matrix is formed, the average release rate curve is calculated, and an exponential decay model is fitted to identify the cooling release start and end time of each structural unit and form a time window.

[0149] The average release rate curve is the average of multiple periods, which is used to eliminate accidents. For example, after 7 consecutive nights of heating-off at night, the cooling release rate of the window frame unit reaches a peak in the first hour, and then decreases exponentially. The cooling release window is determined to be [22:00, 06:00]. Establishing an averaged time window model can provide a fixed reference boundary for dynamic regulation strategies and enhance the system's feedforward response capability.

[0150] Taking the union of the cooling release time windows of all structural units, a unified cooling release window set is generated, which serves as a unified reference time domain for subsequent jump temperature prediction and early heating strategies.

[0151] The application establishes a heat flow decay function through a Fourier first-order heat transfer formula, and combines the temperature decay sequence and the temperature change rate of each structural unit to accurately depict the "passive heat dissipation capacity" of the structural unit in a non-heating state, and further evaluate the contribution of the structural unit to the temperature maintenance of the overall space. Through the hourly calculation and integration of the cold release rate, the technology can quantify the "delayed cooling effect" caused by the heat storage release of each structural unit after heating is stopped, make up for the shortcomings of traditional energy-saving monitoring systems in identifying structural thermal inertia, and provide structural level data support for subsequent jump risk prediction. Using continuous multi-day heating off period data, extracting the cold release rate matrix, and fitting the exponential decay model can effectively eliminate incidental temperature variation noise and obtain an average release rate curve with stable time sequence characteristics, improving the robustness and generalization ability of the model in multi-period temperature variation prediction. By fitting the release rate with an exponential decay model, the system can quickly identify the start and end time of the cold release of each structural unit, and then combine to form a complete cold release window set. This window set can be regarded as a "silent threshold reference" of the heating response system, which can predict the trend of structural heat flow exhaustion in advance when the room temperature does not mutate, thereby significantly improving the forward-looking and energy efficiency of the adjustment response. Compared with traditional polynomials or high-order physical models, the exponential decay function has the advantages of fast calculation, few parameters, and high curve convergence. By simplifying the cold release behavior with an exponential function model, the algorithm overhead of the control system can be greatly reduced while ensuring accuracy, which is beneficial to the deployment of low-power devices or edge control terminals, and realizes the rapid deployment and online update of lightweight thermal control logic.

[0152] Embodiment 4

[0153] Please refer to Figure 1 , specifically: obtaining an air temperature change sequence in the air unit since the heating system is turned off to identify the actual temperature jump time, including:

[0154] extracting the air temperature change sequence in the air unit since the heating system is turned off from the temperature decay sequence;

[0155] According to the air temperature change sequence, calculate its second-order difference at different time, for depicting the curvature change;

[0156] If the second-order difference at consecutive N time points exceeds the preset difference threshold, it indicates that the current air temperature is in the steep drop section, the curvature is large, and the temperature jump risk is high, at this time the position of the air unit in the cold release window set is determined, and is recorded as the actual temperature jump time.

[0157] Trigger the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set, including:

[0158] Set a sliding time window, and calculate the total cold release of all structural units in each sliding time window.

[0159] If the total cold quantity released by all structural units in the current period exceeds the preset temperature jump warning threshold, it indicates that the current room temperature has entered an irreversible temperature jump process, at which time the early heating backstepping mechanism is triggered, otherwise it is not triggered.

[0160] If the early heating backstepping mechanism is triggered, the relative position ratio of the current time in the cold quantity release window set is further identified, specifically: by calculating the time difference between the current time point and the starting time of the cold quantity release window set, and dividing it by the duration of the entire cold quantity release window set, a normalized ratio value, i.e. the relative position ratio, is obtained, which ranges from zero to one.

[0161] The specific expression is: wherein, is the relative position ratio, is the current time, is the starting time of the cold quantity release window set, is the end time of the cold quantity release window set;

[0162] The formula is positioned through the standardization process, and its value can directly reflect the stage position of the current in the entire cold quantity release period.

[0163] Based on the relative position ratio, the starting time of early heating and the corresponding preheating power path function are backstepped to dynamically update the adjustment strategy.

[0164] The size of the sliding time window is much smaller than the cold quantity release window set, and is usually set to 2 to 4 hours;

[0165] In this embodiment, the temperature change sequence in the air unit after the heating is turned off is extracted, and the curvature trend of the temperature change is described by calculating the second-order difference, successfully capturing the steep drop point. When the continuous time difference exceeds the set threshold, it can be judged that the current air temperature has entered the temperature jump state section, thereby replacing the traditional static threshold triggering mode, realizing dynamic local identification and accurate positioning of the sudden jump risk.

[0166] For example, if the air temperature curve is obviously concave at 23:00 at night, and the second-order difference is greater than the threshold value, the system immediately marks this time as the actual jump temperature time. When the air temperature jump is identified, the system does not immediately execute heating, but compares the position structure of the cold release window set, calculates the relative position ratio of the current time in the window set, and judges the degree of heat flow decay. Thus, the most reasonable pre-heating start time is deduced, which can ensure pre-response while avoiding energy waste caused by early heating. For example, if the cold release window set is 8 hours long, and the current time has passed 6 hours from the start, the relative position ratio is 0.75, indicating that the heat storage is about to be exhausted, and heating should be quickly intervened. The system sets a sliding time window (such as 2 hours), and calculates the total cold release value in each window. If the value exceeds the jump temperature warning threshold, it indicates that the structure unit group has released most of the heat storage, and the air temperature starts to jump down, and heating strategy needs to be triggered immediately. This method is based on the double-factor coupling identification mechanism of "structure cold-air temperature variation", which is significantly better than the traditional criterion of only looking at the slope of the temperature curve.

[0167] Embodiment 5

[0168] Please refer to Figure 1 , specifically: deduce the pre-heating start time, including:

[0169] When the relative position ratio exceeds , it indicates that the heat storage capacity of the overall structure unit (the entire target building) has been significantly reduced, and the risk of room temperature drop is significantly increased;

[0170] At this time, compare the current time with the actual jump temperature time. If the current time does not exceed the actual jump temperature time, determine the pre-heating start time, otherwise, immediately execute the heating operation;

[0171] Determine the pre-heating start time, specifically: , wherein, is the pre-heating start time, is the current time, is the pre-heating adjustment coefficient (empirical value is 0.5-1.0), is the relative position ratio, is the duration of the cold release window set; represents the proportion of the remaining cold release process;

[0172] When the system has entered the end of the cold release (i.e. D tends to 1), it indicates that the room temperature will drop soon, the value tends to 0, tends to t, i.e. immediate pre-heating is required. If D is small, it indicates that it is in the early stage of release, and the pre-heating time is increased, which has sufficient time to prepare.

[0173] The preheating advance adjustment coefficient is used to control the extent to which the preheating time is pushed back, avoiding energy waste caused by starting the preheating process too early, and preventing sudden drops in room temperature due to preheating response lag. Methods for obtaining this coefficient include backtracking based on historical temperature jump data, using simulated thermal inertia models, or empirical rules. The simulated thermal inertia model method, in particular, involves simulating different... right The impact of temperature fluctuations was evaluated under various outdoor environments and indoor temperature settings, and the system that minimized the maximum temperature fluctuation and the average deviation was selected. ;

[0174] Construct the preheating power path function, including:

[0175] Obtain the total cold release rate at the current moment, and then calculate the corresponding minimum supplementary heating power benchmark value based on the overall heat capacity of the building. Simultaneously, to prevent delayed supplementary heating failure, multiply it by the redundancy power amplification factor, ensuring that the adjustment power has a response margin while meeting heating demand. The specific expression of the preheating power path function is as follows: ,in, Adjust the supplementary heating power at the current moment. This is the redundant power amplification factor (adjusted for sensitivity, typically taken as 1.2 to 1.5). This is the sum of the cooling release rates of all current structural units. The equivalent heat capacity of the building structure is the sum of the aforementioned heat capacity calculations;

[0176] Before restarting the building's heating system, based on the proportional relationship between the current rate of cold release within the structural units (i.e., passive heat dissipation intensity) and the equivalent heat capacity of the entire building, a minimum supplementary heating power baseline value is first derived. Then, combined with a certain redundancy power amplification factor and a nonlinear ascending-order adjustment function, a heating power path function with incremental characteristics is formed. This function not only considers the matching relationship between the current degree of heat loss and the building's heat storage capacity but also incorporates the time urgency of achieving the temperature rise target. That is, as the target time approaches, the preheating power will increase exponentially, thereby achieving a stable and efficient temperature regulation strategy while avoiding energy waste. Therefore, this formula not only has the rationality of a thermal physics foundation but also takes into account the responsiveness and robustness of the dynamic control strategy.

[0177] The minimum reheating power reference value represents the minimum reheating power reference value calculated based on the current heat release status and heat capacity, i.e., the reheating rate required per unit heat capacity, when the cold release rate... When the heat capacity is high and the heat capacity is low, it indicates that the building is experiencing severe heat loss and poor insulation, requiring rapid heat replenishment and thus high power output.

[0178] The redundant power amplification coefficient is introduced to reserve redundancy space for the control system to avoid the delay of heat supplement caused by lagging judgment. The value can be based on the existing similar building adjustment data experience, and the selection range is usually 1.2 to 1.5, that is, on the basis of the minimum heat supplement power reference value, 20% to 50% response margin is reserved. This method is suitable for buildings not connected to adaptive control systems, and the feedback is obtained through long-term operation and engineering debugging.

[0179] In order to adjust the heating weight function in the time window, the following ascending function is usually used for modeling: , wherein, is the target time, which represents the time when the set temperature needs to be reached, is the starting time of early heating, is the current time, and m is the ascending index, which controls the nonlinearity of the increase of the control power (the larger m is, the faster the rise is). Usually, m is an integer (m≥2), wherein when the time tends to , , the heating power approaches the maximum;

[0180] The ascending index (nonlinear power) is used to control the growth slope of the weight function in the time window and the power concentration characteristics, so that the adjustment strategy has power response flexibility and temperature protection advance. The heating power in the last segment of the control time window is increased through nonlinear amplification control, so as to make up for the temperature response delay caused by building thermal inertia, reduce the risk of temperature jump, and optimize the energy consumption structure. The recommended value range is usually 2 to 4; the trend is as follows:

[0181] When m=1, the power increases linearly, which is not conducive to heat supplement in the critical stage;

[0182] When m=2: smooth but slightly accelerated, suitable for buildings with moderate thermal inertia;

[0183] m=3 or more: obvious acceleration in the last segment, suitable for thick walls or slow reaction systems;

[0184] In the intelligent control system, m can be used as a training adjustable parameter or a dynamic adaptive index for automatic optimization.

[0185] In this embodiment, compared with the traditional passive mechanism of triggering heating depending on the room temperature threshold, the present application constructs a relative position ratio index with the cold release window set as the reference system. When the ratio exceeds the window duration, the system determines that the building as a whole has approached the heat storage exhaustion point.

[0186] By comparing the current time with the actual temperature jump time, it can be intelligently decided whether to immediately heat or delay intervention, so as to realize the process driving and proportional regulation of heating response. For example: when the release window is 8 hours, the current has entered 7.2 hours, the system automatically determines that it is near the end of cold release, and heating should be provided in advance. The introduced preheating advance regulation coefficient makes the advance heating starting time adjustable according to the building response characteristics, so that the regulation model has the self-adaptive advance control ability under different building structures and different climate regions.

[0187] In the construction of the preheating power path function, instead of statically setting the power value, the current total cold release rate is divided by the overall equivalent heat capacity of the building to obtain the theoretical minimum heating power reference value, which is used to dynamically measure the lower limit of passive heat loss of the current heating hedge. This value multiplied by the redundancy coefficient can provide a margin at the beginning of heating start to resist structural thermal inertia delay, effectively improving response efficiency. The preheating power path presents a nonlinear temperature rise trend control effect of slow start-increasing-peak, which can avoid instantaneous peak at start and complete temperature rise before the target time, improving user comfort and control flexibility.

[0188] Embodiment 6

[0189] Please refer to Figure 3 , specifically: a building energy consumption regulation control system based on energy-saving monitoring, comprising,

[0190] An information aggregation module collects temperature evolution sequences in different structural units and fits theoretical temperature rise curves of each structural unit to construct a building response vector group;

[0191] A release concentration module calculates a complete cold release curve for each structural unit after heating is completed, and determines the cold release time window of each structural unit through multiple heating off periods to form a cold release window set by merging the cold release time windows of each structural unit;

[0192] An identification module obtains a sequence of air temperature changes in the air unit since the heating system is turned off to identify the actual temperature jump time;

[0193] A backstepping module triggers an advance heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set;

[0194] An adjustment module backsteps the starting time of advance heating and the corresponding preheating power path function based on the relative position ratio and the actual temperature jump time, and dynamically updates the regulation strategy.

[0195] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A building energy consumption regulation and control method based on energy-saving monitoring, characterized in that: comprising the following steps, collecting temperature evolution sequences in different structural units and fitting theoretical temperature rise curves of each structural unit to construct a building response vector group; After the heating is over, the complete cold release curve of each structural unit is calculated, and through multiple heating-off periods, the cold release time window of each structural unit is determined, and the cold release time windows of each structural unit are combined to form a cold release window set; obtaining the air temperature change sequence in the air unit since the heating system is turned off to identify the actual temperature jump time; triggering the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set; based on the relative position ratio and the actual temperature jump time, the starting time of the early heating and the corresponding preheating power path function are backstepped to dynamically update the adjustment strategy; backstepping the starting time of the early heating, comprising: When the relative position ratio exceeds , it indicates that the heat storage capacity of the overall structural unit has been greatly reduced; At this time, compare the current time with the actual temperature jump time. If the current time does not exceed the actual temperature jump time, the starting time of the early heating is determined. Otherwise, the heating operation is immediately executed. determining a starting time of early heating, specifically, wherein, is the starting time of early heating, is a current time, is a preheating advance adjustment coefficient, is a relative position ratio, is a duration of a cold release window set; used to reflect that the closer the relative position ratio is to the end of the cold release, the earlier the preheating response should be triggered; constructing a preheating power path function, comprising: The total cold release rate of the whole building at the current time is obtained, and the corresponding minimum heat supplement power reference value is converted according to the overall heat capacity of the building. In order to prevent the failure of lagging heat supplement, the minimum heat supplement power reference value is multiplied by a redundant power amplification coefficient, so that the adjustment power has a response margin on the basis of meeting the heat supply demand. The specific expression of the preheating power path function is: wherein, is the current adjustment heat supplement power, is the redundant power amplification coefficient, is the sum of the cold release rates of all structural units at the current time, is the equivalent heat capacity of the building structure, is the heat supply weight.

2. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 1, characterized in that: collecting temperature evolution sequences in different structural units according to temperature sensors deployed at each structural unit in the target building; the structural unit includes at least an air unit, a wall unit, a window frame unit and a floor unit; based on the definition of heat capacity physics, the heat capacity of each structural unit is calculated; fitting the theoretical temperature rise curve of each structural unit based on the heat capacity of each structural unit and the heating power to extract the hysteresis error and integrate, and after summarizing, a building response vector group is constructed to represent the magnitude of structural heat storage and hysteresis.

3. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 2, characterized in that: fitting the theoretical temperature rise curve of each structural unit based on the heat capacity of each structural unit and the heating power to extract the hysteresis error and integrate, and after summarizing, a building response vector group is constructed, comprising: record the heating power in the target building during the response period; deduce the theoretical temperature rise curve of each structural unit based on the heat capacity of each structural unit and the heating power; by subtracting the measured temperature from the theoretical temperature, the hysteresis error is obtained, and after integration, the residual thermal inertia index is obtained.

4. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 3, characterized in that: calculating the complete cold release curve, comprising: After the heating is over, identify the heating termination time period, and track the temperature decay sequence of each structural unit from this time; combined with the building response vector group, a heat flow decay model is constructed based on the first-order Fourier heat conduction formula to calculate the unit time cold release rate, i.e. negative heat flow; calculate the complete cold release curve of each structural unit.

5. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 4, characterized in that: determining the cold release time window of each structural unit through multiple heating-off periods, comprising: extract the internal temperature decay sequence in multiple heating-off periods to construct a cold release rate matrix, wherein the list in the cold release rate matrix represents the time point after the heating is terminated, and the row represents multiple heating termination periods; The average release rate curve is obtained by averaging the column of the cold release rate matrix; According to the average release rate curve, the cold release start and end time of the structural unit is identified by fitting the exponential decay model, and the corresponding cold release time window is defined; The cold release time windows of each structural unit are combined to form a cold release window set.

6. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 5, characterized in that: an air temperature change sequence in the air unit since the heating system is turned off is obtained to identify the actual temperature jump time, comprising: extracting the air temperature change sequence in the air unit since the heating system is turned off from the temperature decay sequence; According to the air temperature change sequence, the second-order difference at different times is calculated to describe the curvature change; If the second-order difference at N consecutive times exceeds the preset difference threshold, it indicates that the current air temperature is in the steep drop section, and the position of the current time in the cold release window set is determined, and is recorded as the actual temperature jump time.

7. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 6, characterized in that: triggering the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set, comprising: setting a sliding time window, and calculating the total cold released by all structural units in the corresponding period in each sliding time window; If the total cold released by all structural units in the current period exceeds the preset temperature jump warning threshold, it indicates that the current room temperature appears an irreversible temperature drop process, and the early heating backstepping mechanism is triggered at this time; If the early heating backstepping mechanism is triggered, the relative position ratio of the current time in the cold release window set is further identified, specifically: by calculating the time difference between the current time point and the start time of the cold release window set, and dividing it by the duration of the entire cold release window set, a standardized ratio value, i.e. the relative position ratio, is obtained; Based on the relative position ratio, the start time of early heating and the corresponding preheating power path function are backstepped to dynamically update the adjustment strategy.

8. A building energy consumption adjustment control system based on energy-saving monitoring, used to implement the building energy consumption adjustment control method based on energy-saving monitoring in any one of claims 1-7. including, The information aggregation module collects the temperature evolution sequence in different structural units and fits the theoretical temperature rise curve of each structural unit to construct the building response vector group; The release concentration module calculates the complete cold release curve of each structural unit after the heating is completed, and determines the cold release time window of each structural unit through multiple heating-off periods, and combines the cold release time windows of each structural unit to form a cold release window set; The identification module obtains the air temperature change sequence in the air unit since the heating system is turned off to identify the actual temperature jump time; The backstepping module triggers the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set; The adjustment module backsteps the start time of early heating and the corresponding preheating power path function based on the relative position ratio and the actual temperature jump time to dynamically update the adjustment strategy.

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