Building energy consumption regulation control method and system based on energy-saving monitoring

Abstract

The invention discloses a building energy consumption regulation control method and system based on energy-saving monitoring, and relates to the technical field of buildings, the energy-saving monitoring method of the invention significantly improves the precision of building energy efficiency regulation by accurately capturing temperature evolution sequences of different structural units and establishing a building response vector group. The indoor temperature can be dynamically predicted and adjusted by utilizing the heat capacity, the lag error and the cooling capacity release curve of each structural unit and combining accurate recognition of the cooling capacity release time window. Compared with a traditional heat supply control method based on fixed temperature control setting, the method has the advantages that thermal inertia and energy release of the building structure unit can be sensed in real time, excessive heat supply and energy waste are reduced, accurate control based on the structure unit characteristics and the time window feedback mechanism is beneficial to reduction of energy consumption of the building, and the energy consumption of the building is reduced. And meanwhile, the indoor temperature control comfort and the energy-saving effect are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building, in particular to a building energy consumption regulation control method and system based on energy-saving monitoring. BACKGROUND

[0002] The current building energy consumption regulation control system does not establish a dynamic modeling mechanism for the thermal inertia and cumulative heat dissipation characteristics of the building body, resulting in the regulation system making decisions based only on instantaneous monitoring data. Especially in buildings with poor thermal insulation performance, this decision-making mode that ignores the physical properties of the building can cause obvious heat release lag, regulation response failure and energy consumption compensation deviation, etc., forming a key regulation defect in the energy-saving system. Especially at night without heating, the cold released by structural units (such as walls and floors) may cause a sudden drop in room temperature after a certain lag time. Taking a residential building in the northern region in winter as an example, the room temperature remains stable for about 2 hours after the heating system is turned off at 3:00 am, but suddenly drops rapidly between 5:00 and 6:00 am. The user's subjective experience is that it is extremely cold in the morning, but the system has not yet started. The traditional system has no response capability to this, and the core reason is that it fails to perceive and model the time characteristics and release rate of the cold release window, resulting in a lag in room temperature drop warning and a delay in heat compensation response, thus causing a breakpoint in thermal comfort experience and energy waste. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides a building energy consumption regulation control method and system based on energy-saving monitoring, which solves the problems in the background art.

[0004] To achieve the above purpose, the present application is implemented by the following technical scheme: a building energy consumption regulation control method based on energy-saving monitoring, 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 turned off, 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 an 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 early heating and the corresponding preheating power path function are backstepped to dynamically update the regulation strategy.

[0005] Preferably, the temperature evolution sequences in different structural units are collected according to temperature sensors deployed at each structural unit in the target building; The structural units include at least an air unit, a wall unit, a window frame unit and a floor unit; The heat capacity of each structural unit is calculated based on the physical definition of heat capacity; 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 is summarized to construct a building response vector group to represent the magnitude of structural heat storage and hysteresis.

[0006] 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 is summarized to construct a building response vector group, including: The heating power in the target building during the response time period is recorded; The theoretical temperature rise curve of each structural unit is derived based on the heat capacity and heating power of each structural unit; The hysteresis error is obtained by subtracting the measured temperature from the theoretical temperature, and the residual thermal inertia index is obtained after integration.

[0007] Preferably, the complete cold release curve is calculated, including: 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; Combined with the building response vector group, a heat flow decay model is constructed based on the first-order Fourier heat conduction formula, which is used to calculate the unit time cold release rate, i.e. negative heat flow; The complete cold release curve is calculated for each structural unit.

[0008] Preferably, the cold release time window of each structural unit is determined through multiple heating off periods, including: The internal temperature decay sequence of the structure in multiple heating off periods is extracted to construct 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; The average release rate curve is obtained by averaging the columns of the cold release rate matrix; According to the average release rate curve, the cold release start and end time of each structural unit is identified by fitting an 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.

[0009] Preferably, the air temperature change sequence in the air unit since the heating system is turned off is obtained to identify the actual temperature jump time, including: extracting a sequence of air temperature variation in the air unit since the heating system is turned off from the temperature decay sequence; According to the sequence of air temperature variation, calculating its second-order difference at different time points for depicting the change of curvature; If the second-order difference at consecutive N time points all exceeds the preset difference threshold, it indicates that the current air temperature has been in the steep drop section, at this time, the position of the current time in the cold release window set will be determined, and it is recorded as the actual temperature jump time.

[0010] Preferably, the early heating backstepping mechanism is triggered to determine the relative position ratio of the current time in the cold release window set, including: Setting a sliding time window, and calculating the total cold release of all structure units in the corresponding period within each sliding time window; If the total cold release of all structure units in the current period exceeds the preset temperature jump warning threshold, it indicates that the current room temperature appears an irreversible jump-down process, at this time, the early heating backstepping mechanism is triggered; 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 starting time point 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.

[0011] 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.

[0012] Preferably, the starting time of early heating is backstepped, including: When the relative position ratio exceeds , it indicates that the heat storage capacity of the overall structure unit has been greatly reduced; At this time, the current time and the actual temperature jump time are compared, if the current time exceeds the actual temperature jump time, the starting time of early heating is determined, otherwise, the heating operation is immediately executed; 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.

[0013] Preferably, the preheating power path function is constructed, including: The sum of the overall cold release rate at the current moment is obtained, and the corresponding minimum heat supplement power reference value is converted according to the overall heat capacity of the building, and meanwhile, in order to prevent the lagging heat supplement from failing, 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, and the preheating power path function is specifically expressed as: Wherein, is the adjustment heat supplement power at the current moment, is the redundant power amplification coefficient, is the sum of the cold release rates of all structural units at the current moment, is the equivalent heat capacity of the building structure, is the heat supply weight.

[0014] A building energy consumption regulation control system based on energy-saving monitoring comprises, 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; A release concentration module calculates complete cold release curves of each structural unit after the heating is finished, and determines cold release time windows of each structural unit through multiple heating-off periods to combine the cold release time windows of each structural unit to form a cold release window set; 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 moment; A backstepping module triggers an early heating backstepping mechanism to determine a relative position ratio of the current moment in the cold release window set; An adjustment module backsteps a starting moment of early heating and a corresponding preheating power path function based on the relative position ratio and the actual temperature jump moment to dynamically update an adjustment strategy.

[0015] The application provides a building energy consumption regulation control method and system based on energy-saving monitoring, which has the following beneficial effects: (1) The energy-saving monitoring method of the application significantly improves the accuracy of building energy efficiency regulation by accurately capturing temperature evolution sequences 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 regulated. 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. This 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.

[0016] (2) The present application can effectively predict and adjust the heating start time in the building cold release window set by introducing the pre-heating countermechanism, especially when the indoor temperature is about to change abruptly (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 actual temperature jump time and the dynamic change of 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 the lag response in the traditional heating method, especially in cold weather, which can quickly restore the indoor temperature and avoid the impact of rapid temperature drop on human comfort, and improves the response speed and energy efficiency of the heating system.

[0017] (3) The present application can accurately adjust the heating power 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 preheating response is ensured, and the temperature fluctuation problem caused by lagging heating failure is avoided. In addition, by nonlinear adjustment of 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 structure 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 helps to achieve the building energy saving goal. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The present application is a building energy consumption adjustment control method based on energy saving monitoring process schematic diagram; Figure 2 The present application is a building energy consumption adjustment control method based on energy saving monitoring process schematic diagram; Figure 3 The present application is a building energy consumption adjustment control system block diagram. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] Embodiment 1 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, S1: Collect the temperature evolution sequence in different structural units and fit the theoretical temperature rise curve of each structural unit to construct a building response vector group; S2: After heating is stopped, 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; S3: Obtain the air temperature variation sequence in the air unit since the heating system is turned off to identify the actual temperature jump time; S4: Trigger the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set; S5: Based on the relative position ratio and the actual temperature jump time, the starting time of early heating and the corresponding preheating power path function are backstepped, which are used to dynamically update the adjustment strategy.

[0021] In this embodiment, the S1 step can realize the quantitative characterization 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, it 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.

[0022] The temperature evolution sequence refers to the hourly measured temperature curve of the structural unit during the heating process; The theoretical temperature rise curve is the temperature theoretical value calculated based on the heat capacity model and the heating power; The building response vector group is a multi-dimensional vector set with heat capacity value, residual thermal inertia value, etc. as components, used to express structural differences and energy absorption response differences. For example, at the beginning of winter heating, the temperature rise of the wall unit is slow, 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 the response characteristics are input as parameters into the building response vector group.

[0023] 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, construct a cold release behavior model, and extract a consistent time window set for subsequent adjustment trigger logic.

[0024] The cold release curve is the negative heat flow curve of the structural unit released to the indoor unit per unit time after heating is stopped; The cold release time window is the most active continuous period of cold release of the structure; The cold release window set is the combined time range of all the structural 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 the system extracts the time window of all the structural units, the combined cold release window set formed is 22:30 to 03:00, which is used as a reference for subsequent heating regulation.

[0025] By constructing the cross-period cold release window set, this step can capture the overlapping period of the heat storage and dissipation behaviors of different structural units, avoid missing potential heat flow release peaks due to structural heterogeneity, and help accurately identify the "precursor stage" of the room temperature drop, providing a timing coordinate reference for pre-regulation actions.

[0026] S3 focuses on the rapid drop characteristics of indoor air temperature, extracts the true occurrence time point of the room temperature jump risk, and uses it as a response reference benchmark for heating regulation strategies to verify the causal relationship between building structure cold release behavior and room temperature change.

[0027] Among them, the temperature jump time is the time point at which the air temperature drop amplitude or rate suddenly increases; The second-order difference is used to calculate the curvature change of the air temperature, so as to detect the jump mutation point. For example, the air temperature stabilizes within 30 minutes after the heating is turned off, but after the 35th minute, there is an accelerated downward trend for 3 consecutive minutes (the curvature is continuously positive and amplified), at which time the system records the 35th minute as the actual temperature jump time for subsequent heating backstepping mechanism.

[0028] 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 for temperature change events, and effectively improve the triggering accuracy and timeliness of the risk response in the regulation strategy.

[0029] S4, based on the identification of the cold release window and the actual temperature jump time, normalizes the current time in the cold release behavior, constructs a proportion index of regulation urgency, and uses it to backstep the time and power strategy of the heating action. The relative position proportion is the normalized coordinate value of the current time point in the cold release window set, and the larger the value, the closer to the end of the cold release, and the heat inertia has basically dissipated. The advance heating backstepping mechanism is a method of predicting the starting time of the regulation response according to the timing position. For example, the cold release window is from 22:00 to 02:00, and the current time is 01:30, so the relative position proportion is 0.875. The system determines that the heating action should be triggered in advance to prevent the room temperature from falling out of the comfort zone. This step enables the heating regulation to have "time perception" and "behavior backstepping" capabilities, avoids the loss of comfort caused by delayed heating, and realizes dynamic adjustment intervention strategies for different structural response speeds.

[0030] S5 is the response execution step of the method, according to the previous prediction index (time position and temperature change event), a regulation path function with power regulation flexibility and nonlinear amplification capability is constructed, and active heat compensation before temperature drop is realized. The preheating power path function is a curve of power dynamic change with time, which is controlled by an ascending function. This step can realize real-time calculation and dynamic release of the heat compensation amount before room temperature jump, improve the forward response capability of the system to temperature fluctuation, build a adjustable, variable, continuous dynamic and response adaptive energy saving control system, and effectively inhibit the passive overheat and energy waste.

[0031] Embodiment 2 Please refer to Figure 1 , specifically: according to the temperature sensors deployed at each structural unit in the target building, the temperature evolution sequence in different structural units is collected; The structural unit includes at least an air unit, a wall unit, a window frame unit and a floor unit; Among them, the multi-point temperature acquisition device is arranged: temperature sensor groups are arranged at each structural unit, wherein: the air layer adopts a space middle hanging type thermosensitive element; the wall adopts an embedded or surface-mounted temperature measuring probe; the floor adopts a buried or surface contact type thermocouple.

[0032] 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; 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 "sluggish" the temperature drop or rise.

[0033] 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; The calculation of heat capacity is to calculate the temperature response rate of the structural unit, together with the heating power to fit the theoretical temperature trajectory; 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 build a building response vector group to represent the structure heat storage and lag amplitude.

[0034] The purpose of constructing the structural thermal capacity sub-model and extracting the thermal inertia residual error 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.

[0035] According to the heat capacity and heating power of each structural unit, the theoretical temperature rise curve of each structural unit is fitted to extract the lag error and integrate, which is summarized to construct the building response vector group, including: The heating power in the target building during the response period is recorded, which is used for theoretical temperature curve derivation; The heating power is the heat rate input by the heating system to the building space (or a structural unit) per unit time, usually in 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 structural temperature rise model, heat flow compensation model, preheating power path function, etc.

[0036] 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 < 1, is the heating power at the i-th time, is the heating power at the i-th time, 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; And the heat actually comes from the energy integral of the heating power per unit time, then the theoretical temperature rise amount is: 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 is introduced as the actual heat absorption correction, plus the initial heating temperature, to obtain the complete temperature rise curve; Wherein, the distribution factor is derived from the average heating proportion of each structural unit in the debugging period or historical data, or the optimal distribution factor value (empirical value or training value) can be derived from the actual response lag; ​​The present application is based on the principle of heat capacity and the law of conservation of heat energy, combined with the non-uniformity of each structural unit receiving heat flow, a theoretical temperature rise curve model is constructed. The model takes the starting temperature of heat supply as the initial value, uses the integral form to accumulate the heat supply 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. This model can be used for temperature rise response prediction, and also provides theoretical support for subsequent residual thermal inertia error extraction and dynamic power regulation strategy.

[0037] The lag error is obtained by subtracting the theoretical temperature from the measured temperature. After integration, the residual thermal inertia index is obtained, which is: wherein, is the lag error, is the measured temperature, is the theoretical temperature based on the heat capacity and heating power fitting of the i-th structural unit; The residual thermal inertia index is obtained as follows: wherein, is the residual thermal inertia index, indicating the integral value of the lag error energy, reflecting the part of the thermal inertia that is not fully considered by the system; The heat capacity and residual thermal inertia index of each structural unit are summarized to construct a building response vector group.

[0038] The temperature evolution sequence includes the measured temperature at each collection time; In this embodiment, temperature sensors are deployed at multiple representative structural parts of the target building to form a local monitoring network facing structural units with obvious differences in thermal behavior such as air, wall, window frame and floor. These structural units are the basic physical carriers of building thermal inertia response. The temperature evolution sequence of each unit reflects its heat storage and release state change.

[0039] Structural units are used to divide local entity modules with different heat capacity characteristics.

[0040] For example: air units (such as indoor free air volume) react quickly but store heat poorly; Wall units (such as external wall structures) have slow heat transfer and strong heat storage; Window frame units (such as metal frames) have fast heat conduction but weak heat storage capacity; Floor units (such as reinforced concrete floors) have moderate responsiveness.

[0041] Temperature evolution sequence: for example, collect temperature at 8:00 in the evening to 6:00 the next day, record temperature every 10 hours, form 10 groups of temperature point sequence, reflect its night heat dissipation curve. Through the classification deployment of sensors, not only can distinguish the heat storage behavior of different materials, but also provide basic data support for subsequent heat capacity modeling and heat flow analysis, improve the accuracy and resolution of the model. Using the basic physical properties of structural materials (density, specific heat capacity, volume) to calculate the unit heat capacity, as the quantitative basis for subsequent energy transfer and temperature rise behavior. For example, a concrete wall unit with a thickness of 0.2 meters and an area of 20 m 2 The volume is 4 m 3 The calculated heat capacity is: = 2400 * 840 * 4 = 8064000 J / K, even if the temperature rises by 1 K, 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 property differences.

[0042] According to the known heating power and heat capacity, the theoretical temperature rise 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 delayed temperature rise characteristics of the structure.

[0043] Assuming that the floor heat capacity is high, the theoretical temperature rise prediction should rise by 2℃ after 30 minutes of heating, but the actual collected temperature rise is only 0.5℃, 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 guide the design of heating regulation strategy.

[0044] 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 rise model, the identification of lag error and the generation of response vector group. The 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" and other key issues, thereby significantly improving the intelligence, responsiveness and personalized regulation ability of building energy-saving system.

[0045] By combining the theoretical temperature rise curve derived from heating power and comparing it with the measured temperature to form a hysteresis error sequence, and then extracting the residual thermal inertia index through integration, the system can effectively identify the differences in thermal response hysteresis and heat storage capacity of each structural unit during the heating process. This fills the technical gap in traditional energy consumption regulation methods that lack modeling of the "cumulative effect of thermal inertia." This response vector set can not only serve as a priori features for subsequent cold release modeling, temperature jump risk prediction, and heating regulation path generation, but also significantly improves the energy consumption regulation strategy's ability to perceive structural heterogeneity and dynamic heat storage status, thereby achieving a more adaptive dynamic regulation mechanism and energy consumption optimization decision-making.

[0046] Example 3 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. 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.

[0047] Calculate the complete cooling release curve, including: 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. 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; is the thermal inertia sensitivity coefficient, which is used to weigh the adjustment coefficient (empirical setting or fitting) of the residual inertia compensation strength, and is initially set to an empirical value in the range of 0.5 to 1.0, and can be obtained by residual minimization training in historical heat flow fitting to obtain the optimal value; is the inertia temperature compensation term, which is used to compensate for the neglect of the release lag behavior of the thermal storage by the conventional model, that is, to represent the temperature contribution that should be released due to inertia lag but is not released in time, and the basic logic is as follows: After the heating system is turned off, the structure unit no longer receives external heat energy, but the heat stored in the structure will be slowly released, and the actual measured temperature change rate only reflects the response of the surface or the measuring point, and the release behavior of the internal thermal storage has inertia lag, and this part of the heat release is not completely expressed by the temperature first-order derivative, and needs to be compensated by RHI for modeling, therefore, is introduced as the inertia compensation term of the heat flow; the residual thermal inertia index represents the historical integral residual value of the thermal lag effect of each structure unit; By incorporating the residual thermal inertia index into the heat flow decay model, the evaluation error caused by neglecting the structure thermal 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.

[0048] The complete cold release curve is calculated for each structure unit to obtain the total cold released by each structure 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 structure 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 structure unit at time ; and wherein, the period when the heat flow decreases to stable is from the heating system turn-off time point, the change rate of the internal heat flow of the structure 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% to 10% of the initial cold release rate, and when the cold release rate of the structure unit is less than the minimum change threshold, it is determined that the heat flow has entered the stable interval; The complete cold release curve is calculated to evaluate the passive cooling amplitude that the structure can bring without active heating.

[0049] 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. By determining the time window for cooling release of each structural unit through multiple heating shutdown cycles, including: 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. 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.

[0050] The average release rate curve is obtained by averaging the columns of the cold energy release rate matrix. 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. The average release rate curve is used to address single-cycle disturbance issues in order to establish a unified benchmark for evaluating cold release. 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. 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. , which is the exponential rate of cold energy release.

[0051] After the heating stops, the remaining 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, that is, the proportion of the remaining heat released per unit time is basically constant, forming a geometrically decreasing structure, which is suitable for expression by an exponential model.

[0052] The logic of the exponential decay model is based on the physical heat diffusion process, which uses an exponential decay function to describe the natural decay trend of the gradual release of heat by the structural unit after the heating is turned off. This model can efficiently depict the nonlinear characteristics of the decreasing cold energy release over time and provide computational support for subsequent control strategies such as cold energy release time window, thermal inertia compensation, and temperature jump warning.

[0053] The cold energy release time windows of each structural unit are combined to form a cold energy release window set. This window set can be used as a key control variable input in the subsequent prediction phase to dynamically determine the room temperature jump risk and wake up the heating system in advance.

[0054] The fitted exponential decay model is used to simplify the complex heat flow release curve into an exponential decay model to better extract parameters and build prediction models.

[0055] The cold energy release window set is used to aggregate the cold energy release time windows of each structural unit to determine a maximum time window, which is the union of the cold energy release time windows of all structural units. 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 energy release rate per unit time, i.e., the heat flow power, is calculated to form a complete cold energy release curve.

[0056] Heat capacity is the amount of heat required for a unit temperature rise, reflecting the heat storage capacity of the structure.

[0057] The temperature decay sequence is the sequence data of the measured temperature of the structural unit decreasing over time after the heating is stopped.

[0058] The cold energy release rate (negative heat flow) is the amount of 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 energy release rate per unit time can be calculated, and a complete cold energy release curve can be obtained. By establishing a natural heat flow decay path, the heat dissipation trend of the building structure can be objectively quantified, providing a heat flow basis for subsequent thermal imbalance prediction.

[0059] The total cooling release value is obtained by integrating the release rate of each structural unit 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] The average release rate curve is the average of multiple periods, which is used to eliminate accidental factors. 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 adjustment strategies and enhance the system's feedforward response capability.

[0065] 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 strategy decision-making.

[0066] The present application establishes a heat flow decay function through the 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 the non-heating state, and further evaluate the contribution capacity of the structural unit to the overall space temperature maintenance. 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 the heating is stopped, make up for the defects of the traditional energy-saving monitoring system 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, obtain an average release rate curve with stable time sequence characteristics, and improve the robustness and generalization ability of the model in multi-period temperature variation prediction. By fitting the release rate through the 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. The window set can be regarded as the "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 through the exponential function model, the algorithm overhead of the control system can be greatly reduced on the premise of 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.

[0067] Embodiment 4 Please refer to Figure 1 , specifically: obtaining the air temperature change sequence in the air unit since the heating system is turned off to identify the actual temperature jump time, including: 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, calculate its second-order difference at different time, for depicting the curvature change; 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.

[0068] Triggering the early heating backstepping mechanism to determine the relative position ratio of the current time in the cold release window set, including: Set a sliding time window, calculate the total cold release of all structural units in the corresponding period in each sliding time window; If the total cooling capacity released by all structural units in the current period exceeds the preset temperature jump warning threshold, it indicates that the current room temperature has an irreversible temperature jump process, at which time the early heating backstepping mechanism is triggered, otherwise it is not triggered. If the early heating backstepping mechanism is triggered, the relative position ratio of the current time in the cooling capacity release window set is further identified, specifically: by calculating the time difference between the current time point and the starting time of the cooling capacity release window set, and dividing it by the duration of the entire cooling capacity release window set, a standardized ratio value, i.e. the relative position ratio, is obtained, which ranges from zero to one.

[0069] The specific expression is: wherein, is the relative position ratio, is the current time, is the starting time of the cooling capacity release window set, is the end time of the cooling capacity release window set; The formula is positioned by the standardization process, and its value can directly reflect the stage position of the current in the entire cooling capacity release period.

[0070] 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.

[0071] The size of the sliding time window is much smaller than the cooling capacity release window set, and is usually set to 2 to 4 hours; 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 difference of continuous time 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.

[0072] For example, if the air temperature curve shows a significant 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 temperature jump time. At the same time of identifying the air temperature jump, 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 temperature jump 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 change", which is significantly better than the traditional criterion of only looking at the slope of the temperature curve.

[0073] Embodiment 5 Please refer to Figure 1 Specifically, the pre-heating start time is deduced, including: 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; At this time, the current time is compared with the actual temperature jump time. If the current time exceeds the actual temperature jump time, the pre-heating start time is determined, otherwise, heating operation is immediately executed; The pre-heating start time is determined, specifically: Wherein, is the pre-heating start time, is the current time, is the pre-heating advance adjustment coefficient (the 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; 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, that is, pre-heating should be immediately performed. 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.

[0074] The preheating advance adjustment coefficient is used to control the magnitude of the forward push of the preheating time, to avoid the waste of energy caused by the early start of the preheating process, and to prevent the sudden drop of room temperature caused by the lag of the preheating response. The obtaining method includes a backtracking method based on historical temperature jump data, a simulation thermal inertia model method, or an empirical rule method. The simulation thermal inertia model method is used to simulate the influence of different on in various outdoor environments and indoor temperature settings, to select the that minimizes the maximum temperature fluctuation and the average deviation. The preheating power path function is constructed, including: 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. 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 adjusted power has a response margin based on meeting the heating demand. The specific expression of the preheating power path function is: wherein, is the adjusted heat supplement power at the current time, is the redundant power amplification coefficient (adjustment sensitivity, usually 1.2-1.5), 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, which is obtained by summing and aggregating the heat capacity calculated in the foregoing, Before the building heating system is restarted, the minimum heat supplement power reference value is derived based on the proportional relationship between the internal cold release rate (i.e., the passive heat dissipation intensity) of the structural unit and the equivalent heat capacity of the whole building. Then, combined with a certain redundant power amplification coefficient and a nonlinear step-up adjustment function, a heating power path function with increasing characteristics is formed. This function not only considers the matching relationship between the current heat loss degree and the building heat storage capacity, but also integrates the time urgency of reaching the temperature rise target, i.e., the preheating power will increase exponentially as the target time approaches, thereby avoiding energy waste while achieving a smooth and efficient temperature regulation strategy. Therefore, this formula not only has the rationality of thermal physical basis, but also takes into account the responsiveness and robustness of dynamic control strategy.

[0075] is the minimum heat supplement power reference value, which represents the minimum heat supplement power reference value calculated based on the current heat release condition and heat capacity. When the cold release rate is high and the heat capacity is low, it indicates that the building loses heat seriously and the insulation is poor, so the power should be high. ​The redundant power amplification coefficient is introduced to reserve redundancy space for the control system to avoid the delay of heat compensation 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 compensation power reference value, 20% to 50% response margin is reserved. This method is suitable for buildings that are not connected to adaptive control systems, and the feedback is obtained through long-term operation and engineering debugging.

[0076] In order to adjust the heating weight function in the time window, the following ascending function model is usually used: wherein, is the target time, which represents the time when the set temperature needs to be reached, is the starting time of advance heating, is the current time, and m is the ascending index, which controls the non-linear degree of power increase (the larger m is, the faster the rise is). Usually, m is an integer (m≥2), wherein when time tends to , tends to 1, and the heating power approaches the maximum; The ascending index (non-linear 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 non-linear amplification control, so as to compensate 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: When m=1, the power growth is linear, which is not conducive to heat compensation in the critical stage; When m=2: smooth but slightly accelerated, suitable for buildings with moderate thermal inertia; m=3 or above: obvious acceleration in the last segment, suitable for thick walls or slow reaction systems; In the intelligent control system, m can be used as a training adjustable parameter or a dynamic adaptive index for automatic optimization.

[0077] 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.

[0078] By comparing the current time with the actual jump temperature time, it can be intelligently decided whether to immediately heat or delay intervention, so as to realize the process driving and proportional adjustment of the 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 the cold release, and the heating should be advanced. The introduced preheating advance adjustment coefficient can adjust the advance heating starting time according to the building response characteristics, so that the adjustment model has the self-adaptive advance control ability under different building structures and different climate regions.

[0079] In the construction of the preheating power path function, instead of setting the power value statically, 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 the passive heat loss of the heating hedge. This value multiplied by the redundancy coefficient can provide a margin at the initial stage of heating start to resist the delay of structural thermal inertia, effectively improving the response efficiency. The preheating power path presents a nonlinear temperature rise trend control effect of slow start-increase-peak, which can avoid the instantaneous peak at the start and complete temperature rise before the target time, improving user comfort and control flexibility.

[0080] Embodiment 6 Please refer to Figure 3 Specifically, a building energy consumption regulation and control system based on energy-saving monitoring, comprising, 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; 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 combining the cold release time windows of each structural unit; An identification module obtains an air temperature change sequence in the air unit since the heating system is turned off to identify the actual jump temperature time; A backstepping module triggers an advance heating backstepping mechanism to determine the relative position proportion of the current time in the cold release window set; An adjustment module backsteps the starting time of the advance heating and the corresponding preheating power path function based on the relative position proportion and the actual jump temperature time, and dynamically updates the regulation strategy.

[0081] Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended 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; Obtain the air temperature variation sequence in the air unit since the heating system is turned off to identify the actual temperature jump time; Trigger the early heating mechanism, 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 early heating and the corresponding preheating power path function are back calculated, which are used to dynamically update the adjustment strategy.

2. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 1, characterized in that: According to the temperature sensors deployed in each structural unit in the target building, the temperature evolution sequences in different structural units are collected; 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; 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 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: 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 hysteresis error and integrate, and after summarizing, a building response vector group is constructed, including: Record the heating power in the target building during the response period; Based on the heat capacity of each structural unit and the heating power, the theoretical temperature rise curve of each structural unit is derived; 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: Calculate the complete cold release curve, including: 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, which is used 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: Through multiple heating-off periods, the cold release time window of each structural unit is determined, including: Extract the internal temperature decay sequence of the structure in multiple heating-off periods to construct a cold release rate matrix, where the column represents the time point after the heating is over, and the row represents multiple heating termination periods; By averaging the columns of the cold release rate matrix, the average release rate curve is obtained; According to the average release rate curve, the cold release start and end time of each structural unit is identified by fitting an exponential decay model, and the corresponding cold release time window is defined; Combine the cold release time windows of each structural unit 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 an actual temperature jump time, comprising: an air temperature change sequence in the air unit since the heating system is turned off is extracted from the temperature decay sequence; a second-order difference at different time points is calculated according to the air temperature change sequence, for depicting the change in curvature; if the second-order difference at continuous N time points exceeds a preset difference threshold, it is indicated that the current air temperature is in a steep drop section, at this 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.

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

8. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 7, characterized in that: the starting time of early heating is backstepped, 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, the current time is compared with the actual temperature jump time, if the current time exceeds the actual temperature jump time, the starting time of early heating is determined, otherwise, the heating operation is immediately performed. 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; 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.

9. The building energy consumption adjustment control method based on energy-saving monitoring according to claim 8, characterized in that: the preheating power path function is constructed, 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 structure units at the current time, is the equivalent heat capacity of the building structure, is the heat supply weight.

10. 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 1-9. including, an information collection 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; a release concentration module calculates complete cold release curves of each structure unit after the 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; 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; 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; an adjustment module backsteps the starting time of early heating and the corresponding preheating power path function based on the relative position ratio and the actual temperature jump time, for dynamically updating the adjustment strategy.

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