Building operation and maintenance management system based on digital twinning

Abstract

The invention discloses a building operation and maintenance management system based on digital twinning, and relates to the technical field of digital twinning, and the system comprises a data collection module, a conference hall reservation response module, a conference hall pre-cooling time prediction module, a digital twinning simulation and optimization module, and an execution terminal. Operation data and response data of a conference reservation system, physical data of an environment sensor and time sequence data of equipment operation are integrated into a unified analysis framework for the first time, the precooling time point is dynamically predicted in combination with the number of participants and indoor and outdoor temperatures, a traditional static rule is replaced, the precooling efficiency is high, the real-time performance is high, and the precooling efficiency is high. Precise matching of cold supply and real requirements is achieved; according to the method, the model parameters are adaptively adjusted through the equipment operation quality score, the reservation time point is predicted again, the operation quality of the refrigeration equipment is enabled to be optimal under the condition that the refrigeration requirement is met, and the balance of the efficiency, the energy consumption and the power performance of the refrigeration equipment is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to a building operation and maintenance management system based on digital twinning. BACKGROUND

[0002] Modern buildings have many internal facilities, including power systems, water supply and drainage systems, ventilation and air conditioning systems, elevators and other equipment. The normal operation of these devices is directly related to the use of the building. In particular, good air conditioning management can ensure indoor air quality, reduce harmful substances such as pollutants, bacteria and viruses in the air, and reduce the risk of respiratory diseases and other health problems, providing a comfortable environment for people and improving the comfort of living and working.

[0003] The prior art, such as the intelligent adjustment system for commercial air conditioning refrigeration equipment disclosed in the patent application with publication number CN116857763B, accurately analyzes the performance of the air conditioning refrigeration equipment, and adjusts the equipment in a timely manner when the performance does not meet the requirements, thereby improving the working efficiency of the air conditioning refrigeration equipment and preventing real-time load abnormalities from reducing the operating efficiency of the analysis object.

[0004] In combination with the above-mentioned scheme, it can be found that the prior art has the following deficiencies: On the one hand, the prior art mostly adopts fixed-time pre-cooling or real-time adjustment of air conditioner power through indoor temperature and humidity sensors to maintain the set temperature. Fixed-time pre-cooling cannot take into account the dynamic adjustment of the participants, making it difficult to balance the relationship between the fluctuation of the participants and the indoor refrigeration demand. The feedback adjustment of the indoor temperature control needs to wait for the temperature to deviate before responding, which is low in pre-cooling efficiency and has a lag. Moreover, the outdoor temperature is not combined with the refrigeration load for prediction, resulting in unreasonable distribution of cold energy. On the other hand, there are few verifications of the reservation time point, making it difficult to ensure that the refrigeration equipment operates at the optimal quality while meeting the refrigeration demand, and it is difficult to balance the relationship between the efficiency, energy consumption and power performance of the refrigeration equipment. SUMMARY

[0005] The present application aims to provide a building operation and maintenance management system based on digital twinning, which solves the problems in the background art.

[0006] To solve the above technical problems, the present application adopts the following technical scheme: The present application provides a building operation and maintenance management system based on digital twinning, comprising: a data collection module for collecting historical operation data, historical response data and historical environment data of a conference hall in a building, dividing the historical response data and the historical environment data based on the time stamp of the historical operation data, and establishing a mapping relationship table among the three.

[0007] A conference hall reservation response module for responding to reservations of conference halls in a building and collecting reservation time periods, target temperatures and participant ID sets of the conference halls.

[0008] The conference hall precooling time prediction module is configured to set a conference hall attendance confirmation interval length based on the mapping relationship table and feed back to the attendees, dynamically predict a precooling time point of the conference hall, and until the precooling time point and the current time point are separated by a pre-set compensation analysis interval length or less.

[0009] The digital twin simulation and optimization module is configured to construct a conference hall 3D model, define a digital twin model of a refrigeration system, input the predicted precooling time point of the conference hall, the operating parameters of the refrigeration equipment, and the real-time indoor and outdoor temperatures into the digital twin model, obtain real-time response event records, determine whether the predicted precooling time point of the conference hall meets the reservation requirements, and make corresponding adaptive adjustments based on the determination result.

[0010] The execution terminal is configured to control the refrigeration system of the conference hall according to the adaptive adjustment result of the digital twin simulation and optimization module.

[0011] The present application has the following advantages: (1) The present application first integrates the operation data, response data, physical data of environmental sensors, and time sequence data of equipment operation into a unified analysis framework, constructs a "human-machine-environment" collaborative optimization model, and dynamically predicts the precooling time point in combination with the number of attendees and indoor and outdoor temperatures, thereby making up for the shortcomings of the prior art, replacing traditional static rules, achieving high precooling efficiency, and achieving real-time performance, and realizing accurate matching of cold supply and real demand.

[0012] (2) The present application verifies the accuracy of the reservation time point through the digital twin model, and adaptively adjusts the model parameters through the equipment operation quality score to re-predict the reservation time point, thereby ensuring that the operation quality of the refrigeration equipment is optimal under the condition of meeting the refrigeration demand, and ensuring the balance of the efficiency, energy consumption, and power performance of the refrigeration equipment. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0014] Figure 1 The present application is a system structure connection diagram. DETAILED DESCRIPTION

[0015] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described, 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 labor are within the scope of protection of the present application.

[0016] With reference to Figure 1 As shown in the figure, the present application provides a building operation and maintenance management system based on digital twinning, comprising: a data collection module, a conference hall reservation response module, a conference hall pre-cooling time prediction module, a digital twinning simulation and optimization module and an execution terminal.

[0017] It should be noted that the present application is explained by conference room reservation cooling, and in fact, conference room reservation heating can also be deduced.

[0018] The data collection module is used for collecting historical operation data, historical response data and historical environment data of the conference hall in the building, dividing the historical response data and the historical environment data based on the time stamp of the historical operation data, and establishing a mapping relationship table among the three.

[0019] In the specific embodiments of the present application, the historical response data and the historical environment data are divided based on the time stamp of the historical operation data, and a mapping relationship table among the three is established, and the specific content includes: obtaining the time interval corresponding to all operation events in the historical operation data, for each operation event, extracting the initial time stamp and the end time stamp of the operation event from the time interval, thereby obtaining the initial time stamp and the end time stamp of all operation events in the operation event record.

[0020] Based on the initial time stamp and the end time stamp of each operation event, the time period of the historical response data is compared in turn, the historical response data corresponding to the same initial time stamp and end time stamp is intercepted, and the historical environment data corresponding to the same initial time stamp and end time stamp is intercepted, thereby establishing a mapping relationship table of the historical operation data, the historical response data and the historical environment data, and the order of the mapping relationship in the mapping relationship table is sequentially represented as: operation event-historical response data-historical environment data.

[0021] Specifically, the historical operation data, the historical response data and the historical environment data of the conference hall in the building can be obtained through a conference hall reservation monitoring system and a building Internet of Things monitoring system, the conference hall reservation monitoring system is specifically used for reservation management of the conference hall in the building, and the building Internet of Things monitoring system specifically arranges a plurality of sensors in the building to realize online monitoring of the building.

[0022] The conference hall reservation response module is configured to respond to the reservation of the conference hall in the building, and collect the reservation period, target temperature and participant ID set of the conference hall.

[0023] The conference hall pre-cooling time prediction module is configured to set a participant confirmation interval based on the mapping relationship table and feed back to the participants, dynamically predict the pre-cooling time point of the conference hall, and until the interval between the pre-cooling time point and the current time point is less than or equal to the preset compensation analysis interval.

[0024] In specific embodiments of the present application, the participant confirmation interval is set based on the mapping relationship table and fed back to the participants, and the specific content includes: based on the mapping relationship table, extracting the participant ID set of each operation event and the historical response data with a mapping relationship, obtaining the actual participant ID set based on the historical response data, and thereby constructing a corresponding participant data set, the participant data set including the participant ID set and the actual participant ID set.

[0025] The similarity between the participant ID set of each operation event and the preset conference type template corresponding feature is calculated, the conference type template with the largest similarity result is selected as the recognition result, and thereby the conference type of each operation event is recognized.

[0026] It should be noted that the conference hall type template specifically refers to department meetings, group meetings, cross-department collaboration meetings, decision-making layer meetings and training meetings, etc.

[0027] Specifically, the similarity between the participant ID set of each operation event and the preset conference type template corresponding feature can be calculated by the Jaccard similarity formula J(A, B) = |A∩B| / |A∪B|, or the similarity between the two can be calculated by the Cosine similarity, which is relatively mature in the prior art and will not be described here.

[0028] The operation event record numbers corresponding to the same conference type are summarized, and the participant ID set and the actual participant ID set in the participant data set are extracted, and the deviation degree between the two is calculated.

[0029] For example, the deviation degree between the participant ID set and the actual participant ID set of the same conference type is calculated, which can be calculated by first calculating the similarity, and then taking the reciprocal or inverse to obtain the deviation degree between the two.

[0030] In specific embodiments of the present application, the conference type template corresponding feature specifically refers to the participant ID set.

[0031] According to the worker ID set of the conference hall, the conference type of the conference hall is identified and the corresponding deviation degree PX_0 is extracted, and the conference hall attendance interval duration is quantified according to the deviation degree.

[0032] In this embodiment: it is assumed that the deviation degree interval of the conference hall is (PX_min, PX_max), wherein PX_min represents the minimum deviation degree of the conference hall, and PX_max represents the maximum deviation degree of the conference hall, and the corresponding initial confirmation interval duration interval is (T_min, T_max), T_min represents the minimum confirmation interval duration of the conference hall, and T_max represents the maximum confirmation interval duration of the conference hall, and it is assumed that there is a linear relationship between the deviation degree and the confirmation interval duration, so the mapping can be performed by the following formula: T=T_max-(PX-PX_min) / PX_max-PX_min)×(T_max-T_min), wherein the initial confirmation interval duration interval is determined according to the conference type, and is determined by the conference manager.

[0033] In this way, each value of the deviation degree PX corresponds to an interval duration T of the conference hall.

[0034] If there is a nonlinear relationship between the deviation degree and the confirmation interval duration, a polynomial function can be used for mapping, and the specific formula is T=T_min+a*(PX-PX_min) 2 +b*(PX-PX_min), wherein the coefficients a and b satisfy the boundary conditions PX=PX_min and T=T_min, and PX=PX_max and T=T_max.

[0035] In a specific embodiment of the application, the dynamic prediction of the precooling time point of the conference hall is based on the conference hall attendance confirmation interval duration, extracts the worker ID set of the pre-arranged conference hall in the building, and sends an attendance confirmation instruction to each attendee, dynamically counts the time series of the worker ID set of the attendees, and summarizes the total number of attendees in the time series.

[0036] Based on the conference type of the conference hall, the corresponding operation event set is extracted, and the historical environment data is mapped from the mapping relationship table, wherein the historical environment data includes the per capita area demand refrigeration capacity and the average ventilation frequency (per person per square meter required refrigeration power).

[0037] A precooling time calculation model is constructed:

[0038] ST1: Calculate the total heat load of the conference hall, Q_total=Q_person+Q_equipment+Q_enclosure+Q_infiltration, wherein Q_person is the personnel heat dissipation, Q_equipment is the total equipment heat dissipation, Q_enclosure is the conference hall heat transfer, and Q_infiltration is the outdoor air infiltration heat.

[0039] Specifically, according to the total number of participants and the heat dissipation of each person, the per capita area demand refrigeration capacity of the conference hall of the conference type is multiplied by the total number of participants to obtain the personnel heat dissipation Q_personnel, and due to the differences in the body shape and age of the participants in the conference type, the corresponding demand refrigeration capacity also has differences, and through the per capita area demand refrigeration capacity corresponding to the conference type of the conference hall, the accuracy of the precooling time point prediction can be improved.

[0040] Specifically, the power of various equipment in the conference hall, such as projectors, lights, etc., is counted, and is converted into heat dissipation, and exemplarily, the power consumption on the general equipment label is marked as heat dissipation, and it is assumed that the total power of the equipment is P_equipment, then Q_equipment=P_equipment.

[0041] Specifically, the heat transfer quantity of the conference hall Q_enclosure=k*A*(T_out-T_in) is calculated according to the area of the conference hall, the heat transfer coefficient of the enclosure and the temperature difference between indoor and outdoor, wherein k is the heat transfer coefficient of the enclosure, A is the area of the enclosure, T_out and T_in are the real-time outdoor temperature and real-time indoor temperature respectively.

[0042] Specifically, the outdoor air infiltration heat Q_infiltration=n*V*c*ρ*(T_out-T_in) is calculated according to the air change frequency of the conference hall and the specific heat capacity of air, wherein n is the average air change frequency, the volume of the conference hall is V, the specific heat capacity of air is c, and the air density is p.

[0043] ST2: Determine the refrigeration capacity of the refrigeration equipment, and the refrigeration capacity Q_refrigeration of the refrigeration equipment is Q_rating*gamma, wherein Q_rating is the rated refrigeration capacity of the refrigeration equipment, and gamma is a model parameter, and specifically, the model parameter can be understood as a load rate.

[0044] ST3: Calculate the precooling duration Wherein c is the specific heat capacity of air, m is the mass of air in the conference hall, and T_goal is the target temperature of the conference hall.

[0045] Based on the starting time point in the reservation period of the conference hall and the precooling duration T, the total number of participants in the dynamically updated time sequence is dynamically updated, and the precooling time point of the conference hall is dynamically updated.

[0046] The application first integrates the operation data, response data, physical data of environmental sensors and time sequence data of equipment operation of the conference reservation system into a unified analysis framework, constructs a "man-machine-environment" collaborative optimization model, and combines the number of participants and the indoor and outdoor temperature to dynamically predict the precooling time point, which makes up for the deficiencies in the prior art, replaces the traditional static rules, has high precooling efficiency, strong real-time performance, and realizes accurate matching of cold supply and real demand.

[0047] The digital twin simulation and optimization module is configured to construct a conference hall 3D model, define a digital twin model of a refrigeration system, input a predicted pre-cooling time point of the conference hall, operating parameters of the refrigeration equipment and real-time indoor and outdoor temperatures into the digital twin model, obtain real-time response event records, determine whether the predicted pre-cooling time point of the conference hall meets the reservation requirement, and perform corresponding adaptive adjustment based on the determination result.

[0048] In this embodiment, a conference hall 3D model is established by using the pre-acquired physical parameters such as the spatial volume, the building thermal conductivity, the door and window position and thermal insulation performance, the air conditioner outlet layout, etc., and a digital twin model of the refrigeration system is defined based on the equipment operation logic, the cold quantity transmission path and the energy consumption model.

[0049] In specific embodiments of the present application, the real-time response event records include an indoor temperature trend graph in the pre-cooling phase, an indoor temperature trend graph in the conference phase, and refrigeration equipment operation characteristics, wherein the refrigeration equipment operation characteristics include an average refrigeration efficiency ratio COP_ac, total energy consumption E_ac, a standard deviation σ_P of a power time sequence, and an average value P_avg of the power time sequence.

[0050] Specifically, the COP_ac is specifically the refrigeration capacity / input power, and the unit is kW.

[0051] In specific embodiments of the present application, the determination of whether the predicted pre-cooling time point of the conference hall meets the reservation requirement is specifically performed by: based on the indoor temperature trend graph in the pre-cooling phase in the real-time response event records, extracting the final temperature in the pre-cooling phase, comparing the final temperature in the pre-cooling phase with a target temperature, when the final temperature in the pre-cooling phase is greater than the target temperature, determining that the predicted pre-cooling time point of the conference hall does not meet the reservation requirement, and otherwise, performing the following analysis.

[0052] Based on the indoor temperature trend graph in the conference phase in the real-time response event records, setting a temperature suitable fluctuation step length in the conference phase, defining a permitted minimum temperature T_min=T_goal-TI and a maximum temperature T_max=T_goal+TI according to the target temperature in the pre-cooling phase, checking whether all data points in the indoor temperature trend graph in the conference phase meet T_min≤T(t)≤T_max, wherein T(t) represents the temperature of a certain data point, if a certain data point does not meet the requirement, the data point is recorded as an out-of-limit point, the number of out-of-limit points T_ex and the number of data points T_t are counted, the proportion of out-of-limit points M(T_ex-T_t)=(T_ex) / (T_t) is obtained, if M(T_ex-T_t)>M’(T_ex-T_t), it is determined that the predicted pre-cooling time point of the conference hall does not meet the reservation requirement, wherein M’(T_ex-T_t) is a pre-set out-of-limit point proportion threshold, and otherwise, it is determined that the predicted pre-cooling time point of the conference hall meets the reservation requirement.

[0053] Specifically, a suitable fluctuation compensation step of the conference stage is set, which can be combined with the average fluctuation temperature and the complaint rate under the same conference type, if the complaint rate is less than the complaint rate threshold, the average fluctuation temperature under the same conference type is taken as the temperature suitable fluctuation step of the conference stage, otherwise, the temperature suitable fluctuation step can be set according to the complaint rate, wherein the complaint rate is inversely proportional to the temperature suitable fluctuation step.

[0054] In specific embodiments of the present application, the corresponding adaptive adjustment based on the judgment result specifically includes: if the predicted pre-cooling time point of the conference hall does not meet the reservation demand, adjusting the model parameters of the pre-cooling time calculation, recalculating the pre-cooling duration, to obtain the corresponding pre-cooling time point.

[0055] The pre-cooling time point, model parameters and real-time indoor and outdoor temperature are input into the digital twin model, the real-time response data of all model parameters are summarized, all model parameters meeting the reservation demand are counted through the real-time response data of all model parameters, and the optimal solution of the model parameters and the corresponding pre-cooling time point are determined.

[0056] The present application verifies the accuracy of the reservation time point through the digital twin model, and adaptively adjusts the model parameters through the equipment running quality score, re-predicts the reservation time point, ensures that the running quality of the refrigeration equipment reaches the optimal under the condition of meeting the refrigeration demand, and balances the efficiency, energy consumption and power performance of the refrigeration equipment.

[0057] In specific embodiments of the present application, the model parameters of the precooling time calculation are adjusted, and the specific adjustment method is: based on the real-time response event record of the refrigeration equipment operation characteristics, a refrigeration equipment operation quality quantification score model is quantified, which is specifically: Score = w1*COP_ac / COP_ra + w2*min(E_base / E_ac, 1) + w3*min[(1-σ_P / P_avg), 0], wherein COP_ra and E_base are the standard COP and reference benchmark energy consumption of the refrigeration equipment, w1, w2 and w3 are the weight influence factors corresponding to the efficiency index, energy consumption index and stability index, respectively, in the present application, the units of COP (dimensionless), energy consumption (kW) and power (kW) are different, and direct addition is meaningless, COP_ac / COP_ra, E_base / E_ac and 1-σ_P / P_avg are used to normalize COP, energy consumption and power, after normalization, all indexes become 0-1 value, which is convenient for weighted comprehensive, wherein the higher the average refrigeration efficiency ratio, the better the efficiency of the refrigeration equipment, generally COP_ac of the refrigeration equipment is less than or equal to COP_ra, the lower the energy consumption, the higher the score, when the actual total energy consumption is higher than the reference benchmark energy consumption, the score is proportionally reduced, ensuring that the energy consumption index is always in the range of 0-1, avoiding score overflow when energy saving performance is too good, the standard deviation of the power time sequence reflects the power fluctuation amplitude, and σ_P / P_avg is the ratio of the standard deviation to the average, which measures the degree of power change, for example, if the fluctuation rate is 0.1, the power fluctuates slightly, if the fluctuation rate is greater than 0.3, the stability score is 1-σ_P / P_avg, the lower the fluctuation rate, the higher the stability, if the fluctuation rate exceeds the average, the score is negative, and then it is forced to be 0.

[0058] It should be noted that the standard COP of the refrigeration equipment specifically refers to the nominal COP of the manufacturer under standard working conditions, and the reference benchmark energy consumption specifically refers to the ideal energy consumption estimated according to the rated power and running time of the equipment.

[0059] Again, it should be noted that the weight influence factors corresponding to the efficiency index, energy consumption index and stability index are not fixed, and can be changed according to the application scene and actual situation, for example, if the conference hall is important, the stability weight w3 can be increased, and if the electricity price increases, the energy consumption weight can be increased.

[0060] For example, assuming that the COP_ac of the refrigeration equipment is 3.5, COP_ra is 4.0, E_base is 5000 kWh, E_ac is 4000 kWh, σ_P is 0.5 kW, and P_avg is 0.5 kW, and w1, w2, and w3 are 0.5, 0.3, and 0.2 respectively, the calculated operation quality score of the refrigeration equipment is 0.5*0.875+0.3*1.0+0.2*0.9=0.9175.

[0061] Based on the operation quality score of the refrigeration equipment, the adjustment model parameter step of the refrigeration equipment is mapped, and according to the initial model parameters of the refrigeration equipment, the model parameter dataset of the refrigeration equipment is obtained.

[0062] In this embodiment: assuming that the operation quality score interval of the refrigeration equipment is (Score_min, Score_max), where Score_min represents the minimum operation quality score of the refrigeration equipment, and Score_max represents the maximum operation quality score of the refrigeration equipment, the corresponding adjustment model parameter step interval is (B_min, B_max), B_min represents the minimum step size of the adjustment model parameter, and B_max represents the maximum step size of the adjustment model parameter. Assuming that there is a linear relationship between the operation quality score and the adjustment model parameter step size, mapping can be performed using the following formula: B = B_min + (Score-Score_min) / Score_max-Score_min) × (B_max-B_min).

[0063] In this way, each value of the refrigeration equipment's operating quality score Score corresponds to a step size B of adjusting the model parameters.

[0064] For example, the model parameter data set of the refrigeration equipment is specifically (Q_Amount*(γ+B), Q_Refrigeration*(γ+2*B), Q_Refrigeration*(γ+3*B), ..., Q_Amount).

[0065] In a specific embodiment of the present invention, the optimal solution of the model parameters and the corresponding pre-cooling time point are determined by the following method: based on the real-time response data of all model parameters that meet the appointment requirements, the operation quality score of the refrigeration equipment corresponding to all model parameters that meet the appointment requirements is calculated through the refrigeration equipment operation quality quantitative scoring model, and the model parameter with the highest score is selected as the optimal solution of the model parameters, thereby extracting the corresponding pre-cooling time point.

[0066] The execution terminal is used to control the refrigeration system of the conference hall according to the adaptive adjustment results of the digital twin simulation and optimization module.

[0067] The above merely illustrates and describes the concept of the present application, and those skilled in the art can make various modifications or supplements to the described specific embodiments or adopt similar ways to replace, as long as the modifications or supplements do not deviate from the concept of the present application or exceed the defined scope of the present application, and should belong to the protection scope of the present application.

Claims

1. The building operation and maintenance management system based on digital twin is characterized by: include: The data collection module is used to collect the historical operation data, historical response data and historical environment data of the conference room in the building, divide the historical response data and historical environment data based on the timestamp of the historical operation data, and establish a mapping relationship table between the three; The conference room reservation response module is used to respond to the reservation of the conference room in the building and collect the reservation time, target temperature, and the employee number of the participant; The conference room pre-cooling time prediction module is used to set the conference room meeting confirmation interval based on the mapping relationship table and feedback it to the participants, and dynamically predict the conference room pre-cooling time point until the interval between the pre-cooling time point and the current time point is less than or equal to the preset compensation analysis interval length; The digital twin simulation and optimization module is used to build a 3D model of the conference hall and define a digital twin model of the refrigeration system. The predicted pre-cooling time of the conference hall, the operating parameters of the refrigeration equipment, and the real-time indoor and outdoor temperatures are input into the digital twin model. Real-time response event records are obtained to determine whether the predicted pre-cooling time of the conference hall meets the reservation requirements, and corresponding adaptive adjustments are made based on the judgment results. The execution terminal is used to control the conference hall's cooling system based on the adaptive adjustment results of the digital twin simulation and optimization module.

2. The building operation and maintenance management system based on digital twin according to claim 1 is characterized in that: The historical response data and historical environment data are divided based on the timestamp of the historical operation data, and a mapping relationship table between the three is established, the specific contents of which include: Obtain the time interval corresponding to all operation events in the historical operation data, and for each operation event, extract the initial timestamp and the end timestamp of the operation event from the time interval, thereby obtaining the initial timestamp and the end timestamp of all operation events in the operation event record; Based on the initial timestamp and end timestamp of each operation event, they are compared with the time period of the historical response data in turn, and the historical response data corresponding to the same initial timestamp and end timestamp are intercepted, and the historical environment data corresponding to the same initial timestamp and end timestamp are intercepted, so as to establish a mapping relationship table of historical operation data, historical response data and historical environment data, and the order of the mapping relationships in the mapping relationship table is expressed as follows: operation event-historical response data-historical environment data.

3. The building operation and maintenance management system based on digital twin according to claim 2 is characterized in that: Based on the mapping relationship table, the conference room participation confirmation interval is set and fed back to the participants, and the specific contents include: Based on the mapping relationship table, extract the set of participant employee numbers for each operation event and the historical response data with the mapping relationship, obtain the set of actual participant employee numbers based on the historical response data, and thus construct a corresponding participant data set, which includes the set of participant employee numbers and the set of actual participant employee numbers; Calculate the similarity between the participant ID set of each operation event and the corresponding features of the preset meeting type template, and select the meeting type template with the largest similarity result as the recognition result, thereby identifying the meeting type of each operation event; Summarize the operation event record numbers corresponding to the same meeting type, extract the participant employee number set in the participant data set and the actual participant employee number set, and calculate the deviation between the two; According to the set of employee numbers of the conference hall's participants, the conference type of the conference hall is identified and the corresponding deviation degree PX_0 is extracted. The conference hall's meeting confirmation interval is quantified based on the deviation degree of the conference hall.

4. The building operation and maintenance management system based on digital twin according to claim 3 is characterized in that: The specific prediction method for dynamically predicting the pre-cooling time point of the conference hall is as follows: Based on the conference room confirmation interval, the employee ID set of the conference room in the reserved building is extracted, and a confirmation instruction is sent to each participant. The employee ID set of the participant in the time series is dynamically counted to summarize the total number of participants in the time series. Based on the conference type of the conference room, the corresponding operation event set is extracted and mapped to historical environmental data from the mapping relationship table. The historical environmental data includes the required cooling capacity per person and the average number of air changes. Construct a pre-cooling time calculation model: ST1: Calculate the total heat load of the conference room, Q_total = Q_people + Q_equipment + Q_enclosure + Q_infiltration, where Q_people is the heat dissipated by people, Q_equipment is the total heat dissipated by equipment, Q_enclosure is the heat transferred from the conference room, and Q_infiltration is the heat infiltrated by outdoor air. ST2: Determine the cooling capacity of the refrigeration equipment: the cooling capacity of the refrigeration equipment Q_cooling = Q_rated*γ, where Q_rated is the rated cooling capacity of the refrigeration equipment and γ is a model parameter; ST3: Calculate pre-cooling time Where c is the specific heat capacity of air, m is the mass of the air in the conference room, and T_goal is the target temperature of the conference room; Based on the starting time point and pre-cooling time length T in the reservation period of the conference room, and according to the total number of participants in the dynamically updated time series, the pre-cooling time point of the conference room is dynamically updated.

5. The building operation and maintenance management system based on digital twin according to claim 1 is characterized in that: The real-time response event record includes an indoor temperature trend chart during the pre-cooling phase, a trend chart of the indoor temperature during the meeting phase, and refrigeration equipment operating characteristics. The refrigeration equipment operating characteristics include the average cooling efficiency ratio COP_ac, the total energy consumption E_ac, the standard deviation σ_P of the power time series, and the average value P_avg of the power time series.

6. The building operation and maintenance management system based on digital twin according to claim 5 is characterized in that: The specific method for judging whether the predicted pre-cooling time of the conference hall meets the reservation requirement is as follows: Based on the indoor temperature trend chart of the pre-cooling stage in the real-time response event record, the final temperature of the pre-cooling stage is extracted and compared with the target temperature. When the final temperature of the pre-cooling stage is greater than the target temperature, it is determined that the predicted pre-cooling time point of the conference hall does not meet the reservation requirements. Otherwise, the following analysis is performed Based on the trend graph of the indoor temperature during the conference phase in the real-time response event, an appropriate temperature fluctuation step size is set for the conference phase. According to the target temperature during the pre-cooling phase, the allowable minimum temperature T_min = T_goal - TI and the maximum temperature T_max = T_goal + TI are defined. All data points in the trend graph of the indoor temperature during the conference phase are checked to see whether they satisfy T_min ≤ T(t) ≤ T_maxT, where T(t) represents the temperature of a data point. If a data point does not meet the requirement, the data point is recorded as an out-of-limit point. The number of out-of-limit points T_ex and the number of data points T_t are counted to obtain the proportion of out-of-limit points M(T_ex - T_t) = (T_ex) / (T_t). If M(T_ex - T_t) > M'(T_ex - T_t), it is determined that the predicted pre-cooling time point of the conference hall does not meet the reservation requirement, where M'(T_ex - T_t) is the preset threshold for the proportion of out-of-limit points. Otherwise, it is determined that the predicted pre-cooling time point of the conference hall meets the reservation requirement.

7. The building operation and maintenance management system based on digital twin according to claim 5 is characterized in that: The specific contents of the adaptive adjustment based on the judgment result include: If the predicted pre-cooling time of the conference room does not meet the reservation requirements, the model parameters for the pre-cooling time calculation are adjusted and the pre-cooling duration is recalculated to obtain the corresponding pre-cooling time. The pre-cooling time point, model parameters and real-time indoor and outdoor temperatures are input into the digital twin model, and the real-time response data of all model parameters are summarized. All model parameters that meet the reservation requirements are counted through the real-time response data of all model parameters, and the optimal solution of the model parameters and its corresponding pre-cooling time point are determined.

8. The building operation and maintenance management system based on digital twin according to claim 7 is characterized in that: The specific adjustment method for adjusting the model parameters for pre-cooling time calculation is as follows: Based on the operation characteristics of refrigeration equipment recorded in real-time response events, a quantitative scoring model for the operation quality of refrigeration equipment is proposed, which is specifically: Score = w1*COP_ac / COP_ra+w2*min(E_base / E_ac,1)+w3*min[(1-σ_P / P_avg),0], where COP_ra and E_base are the standard COP and reference benchmark energy consumption of the refrigeration equipment, respectively; w1, w2, and w3 are the weighted influencing factors corresponding to the efficiency index, energy consumption index, and stability index, respectively. Based on the operation quality score of the refrigeration equipment, the adjustment model parameter step of the refrigeration equipment is mapped, and according to the initial model parameters of the refrigeration equipment, the model parameter dataset of the refrigeration equipment is obtained.

9. The building operation and maintenance management system based on digital twin according to claim 8 is characterized in that: The specific method for determining the optimal solution of the model parameters and the corresponding pre-cooling time point is as follows: Based on the real-time response data of all model parameters that meet the reservation requirements, the operation quality scores of the refrigeration equipment corresponding to all model parameters that meet the reservation requirements are calculated through the refrigeration equipment operation quality quantitative scoring model. The model parameter with the highest score is selected as the optimal solution of the model parameter, so as to extract the corresponding pre-cooling time point.

10. The building operation and maintenance management system based on digital twin according to claim 3 is characterized in that: The feature corresponding to the conference type template is specifically a set of employee numbers of participants.

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