An intelligent operation-based mechatronic system linkage and intelligent control system

Abstract

This invention provides a smart operation-based electromechanical system linkage and intelligent control system, belonging to the field of intelligent control technology for electromechanical systems. This invention synchronously collects multi-source data on building thermal status, air quality status, power load status, and dynamic fluctuations in equipment operation. It constructs a cross-system coupled evaluation mechanism to determine the coordinated status between systems such as air conditioning, fresh air, power, and water pumps, and triggers a tiered linkage adjustment strategy in case of anomalies. Simultaneously, it performs a global operational optimization evaluation based on overall energy consumption levels and equipment operating intensity to achieve energy saving and load mitigation control; and conducts control stability evaluation based on operational fluctuation characteristics to suppress the risks of oscillation and disturbance transmission. By constructing a multi-layered closed-loop regulation system integrating coupling coordination, operational optimization, and stability control, the invention improves the collaborative efficiency, operational stability, and energy utilization level of building electromechanical systems.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for electromechanical systems, specifically to an intelligent control system for the linkage and control of electromechanical systems based on smart operation. Background Technology

[0002] With the rapid development of smart building and digital operation and maintenance technologies, building electromechanical systems are gradually evolving from traditional decentralized control to centralized monitoring and intelligent scheduling. Air conditioning systems, fresh air systems, power distribution systems, water pump systems, and lighting systems, as core components of building operation, directly affect indoor environmental quality, energy consumption levels, and equipment lifespan. While existing Building Automation Systems (BAS) can achieve single-system parameter adjustment and status monitoring, they are mostly based on local control logic and lack cross-system collaborative analysis and comprehensive optimization mechanisms.

[0003] In actual operation, there is a significant coupling relationship between air conditioning load regulation, fresh air volume configuration, power load distribution, and water pump delivery capacity. When one subsystem adjusts, it often causes a chain reaction of changes in other systems. For example, adjusting the temperature setting may lead to changes in compressor load, which in turn affects peak power and transmission energy consumption. Without unified evaluation indicators and linkage control strategies, problems such as asynchronous regulation, increased energy consumption fluctuations, and frequent equipment start-ups and shutdowns can easily occur. At the same time, there is a dynamic balance between the overall building energy consumption level and the intensity of equipment operation. Simply relying on empirical parameters or fixed thresholds for control is difficult to adapt to complex scenarios such as changes in population density, fluctuations in the external environment, and random load disturbances.

[0004] In addition, existing systems typically focus on energy consumption statistics or single performance evaluations, lacking a multi-dimensional indicator system that comprehensively reflects the degree of cross-system coupling, global operational efficiency, and control stability. This makes it impossible to classify and optimize the system's operating status, leading to lagging or excessive intervention in adjustment strategies, and increasing energy consumption and equipment fatigue risks. Summary of the Invention

[0005] The purpose of this invention is to provide an electromechanical system linkage and intelligent control system based on intelligent operation, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An electromechanical system linkage and intelligent control system based on intelligent operation, comprising: The data acquisition module is used for multi-source synchronous acquisition of thermal status, air quality status, power load status, and dynamic fluctuation status of equipment operation of building electromechanical systems; The cross-system coupling assessment and coordination control module is used to calculate the cross-system coupling index and compare it with the coupling coordination judgment threshold to determine whether the electromechanical system coupling coordination meets the standard. If it meets the standard, a coupling correction state dataset is generated; if it does not meet the standard, a device linkage adjustment strategy is given. The global operation optimization evaluation module is used to calculate the global operation optimization index and compare it with the global operation judgment threshold to determine whether the operation status is qualified. If qualified, a global operation stability dataset is established; if unqualified, a global energy saving and equipment load reduction adjustment strategy is given. The control stability assessment module is used to calculate the control stability index and compare it with the control stability judgment threshold to determine whether the electromechanical system linkage control state is qualified. If it is not qualified, control damping and disturbance suppression strategies are applied.

[0007] Furthermore, the data acquisition module includes a cross-system coupled acquisition unit, a global operational energy consumption acquisition unit, and an electromechanical operating status acquisition unit; The cross-system coupling acquisition unit is used to monitor in real time the thermal coupling status, air quality coupling status, power load coupling status, and personnel density status between building electromechanical systems. It collects return air temperature by installing temperature sensors at the air conditioning return air duct location; reads the set temperature by connecting to the air conditioning controller communication interface; collects indoor carbon dioxide concentration by installing carbon dioxide concentration sensors in personnel activity areas and reads the air quality standard concentration from the system standard database; collects real-time fresh air volume by installing air volume sensors in the main fresh air duct; retrieves the optimal fresh air volume for the scenario from historical operating data; collects real-time load power by installing smart meters on the building's main power distribution line; and reads the maximum allowable load from the power capacity configuration database. The global operation energy consumption acquisition unit is used to monitor the overall energy consumption level of the building and the operating intensity of equipment in real time. It collects the operating power of the water pump by installing a three-phase power meter in the water pump control cabinet, collects the lighting power by installing a rail-mounted smart power meter in the lighting power distribution circuit, retrieves the building area through the building asset management database, and counts the number of times the equipment starts and stops per unit time by accessing the operation log interface of each subsystem controller and reads the maximum allowable number of starts and stops from the equipment technical specification database. The electromechanical operation status acquisition unit is used to continuously monitor the dynamic fluctuation status of the electromechanical system during operation. It acquires return air temperature time series data by installing a platinum resistance temperature sensor at the air conditioning return air duct; and acquires load power time series data by installing a multi-functional power quality analyzer in the low-voltage power distribution main circuit.

[0008] Furthermore, the cross-system coupling evaluation and coordination control module includes a coupling parameter extraction unit, a first calculation unit, and a first analysis unit; The coupling parameter extraction unit is used to extract return air temperature and set temperature data, employ a three-times standard difference anomaly removal method for anomaly identification and filtering, and perform heat load deviation analysis using a difference calculation method to obtain the heat load deviation; it also extracts indoor carbon dioxide concentration and air quality standard concentration data, employs a three-times standard difference anomaly removal method for anomaly identification and filtering, and performs air quality deviation analysis using a difference calculation method to obtain the air quality deviation; and it extracts real-time load power and maximum allowable load data, employs a three-times standard difference anomaly removal method for anomaly identification and filtering, and performs load rate analysis using a ratio calculation method to obtain the power load rate.

[0009] Furthermore, the first calculation unit is used to calculate the cross-system coupling index by acquiring the heat load deviation, air quality deviation, and power load rate, combined with the real-time fresh air volume and the scenario-optimal fresh air volume, and after dimensionless processing.

[0010] Furthermore, the first analysis unit is used to obtain a first evaluation result by comparing the cross-system coupling index with the coupling coordination judgment threshold through a preset coupling coordination judgment threshold, including: When the cross-system coupling index is less than or equal to the coupling coordination judgment threshold, it indicates that the electromechanical system coupling coordination meets the standard. Maintain the current operating parameters of air conditioning, fresh air, power and water pump, record the current operating parameter group and the corresponding cross-system coupling index; generate a coupling correction state dataset and continuously monitor it. When the cross-system coupling index exceeds the coupling coordination judgment threshold, it indicates that the electromechanical system coupling coordination is not up to standard, and there are risks such as asynchronous heat load and air quality adjustment, mismatch between fresh air volume configuration and load demand, abnormal power load rate, and lag in multi-system linkage. This triggers the first warning instruction and generates the first strategy: reduce non-critical power load by 10%, reduce zone lighting power by 10% to reduce the overall system load intensity and release power capacity; adjust the compressor operating frequency by 5% to correct indoor heat load deviation and restore temperature stability; reduce water pump operating flow by 5% to simultaneously reduce energy consumption on the delivery side and alleviate power load pressure; after adjustment, recalculate. If the system coupling coordination is still not up to standard, then carry out phased progressive adjustment until the system coupling coordination meets the standard.

[0011] Furthermore, the global operation optimization evaluation module includes an operation intensity parameter extraction unit, a second calculation unit, and a second analysis unit; The operational intensity parameter extraction unit is used to extract pump operating power, lighting power, real-time load power, and building area data based on the operating parameters of the coupled modified state dataset. It uses a three-fold standard difference anomaly removal method to identify and filter abnormal data, and calculates the intensity of energy consumption per unit area using a unit area energy consumption construction method. It also extracts the number of equipment start-ups and shutdowns per unit time and the maximum allowable number of start-ups and shutdowns, and uses a normalized ratio calculation method to analyze equipment fatigue and obtain the equipment fatigue index.

[0012] Furthermore, the second calculation unit is used to calculate the global operation optimization index by combining the obtained unit area energy consumption intensity and equipment fatigue index with the heat load deviation and power load rate, after dimensionless processing.

[0013] Furthermore, the second analysis unit is used to obtain a second evaluation result by setting a global operation judgment threshold and comparing the global operation optimization index with the global operation judgment threshold, including: When the global operation optimization index is less than or equal to the global operation judgment threshold, it indicates that the operation status is qualified, the overall energy consumption level of the electromechanical system and the equipment load intensity are within a controllable range, the current operation parameters and operation status information are recorded, a global operation stability dataset is established, and continuous monitoring is performed. When the global operation optimization index exceeds the global operation judgment threshold, it indicates that the operation status is unqualified, with risks of excessive energy consumption and accelerated equipment fatigue. This triggers a second warning instruction and generates a second strategy: reduce the water pump operating frequency by 5% to reduce transmission energy consumption and alleviate equipment load intensity; increase the air conditioner set temperature by 0.5℃ to reduce the continuous operation ratio of the compressor and suppress peak power fluctuations; delay the start-up and shutdown time of non-core equipment by 10% of the cycle to reduce the number of start-ups and shutdowns per unit time and slow down the growth rate of the equipment fatigue index; after the strategy is executed, it is recalculated until the global operation optimization index is less than or equal to the global operation judgment threshold.

[0014] Furthermore, the control stability assessment module includes a dynamic fluctuation parameter extraction unit, a third calculation unit, and a third analysis unit; The dynamic fluctuation parameter extraction unit is used to extract return air temperature time series data, load power time series data, and equipment start-up and shutdown frequency data per unit time based on the global operational stability dataset. It uses a sliding time window statistical analysis method to perform discrete fluctuation calculations on the return air temperature time series data to obtain the standard deviation of temperature fluctuation per unit time; it also uses a sliding time window statistical analysis method to perform discrete fluctuation calculations on the load power time series data to obtain the standard deviation of load fluctuation per unit time; and based on the equipment start-up and shutdown frequency data per unit time and the maximum allowable start-up and shutdown frequency data, it uses a normalized ratio calculation method for dimensionless processing to obtain the normalized start-up and shutdown frequency. The third calculation unit is used to calculate the control stability index by obtaining the standard deviation of temperature fluctuation per unit time, the standard deviation of load fluctuation per unit time, and the normalized number of start-stop cycles, after dimensionless processing.

[0015] Furthermore, the third analysis unit is used to obtain a third evaluation result by setting a preset control stability judgment threshold and comparing the control stability index with the control stability judgment threshold. When the control stability index is less than or equal to the control stability judgment threshold, it indicates that the electromechanical system linkage control status is qualified and should be continuously monitored. When the control stability index exceeds the control stability judgment threshold, it indicates that the electromechanical system linkage control state is unqualified, posing risks of temperature oscillation, amplified power load fluctuations, and equipment mechanical fatigue. This triggers a third early warning command and generates a third strategy: The adjustment range of the compressor is limited to within 2% based on the current compressor operating frequency parameter to suppress the spread of temperature fluctuations; the adjustment time window is extended by 10% based on the current linkage control time parameter to increase the electromechanical system response buffer time and reduce oscillation frequency; and large-amplitude linkage commands are suspended based on the current cross-system linkage control command strength parameter to weaken the transmission of coupling disturbances between systems. The strategy is recalculated after execution until the control stability index exceeds the control stability judgment threshold.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the problems of decentralized control and lack of cross-system coordination in existing electromechanical systems. By comprehensively analyzing the operating status of the cold and heat source system, ventilation system, and power system, it achieves multi-system linkage regulation, avoids the asynchronous operation of heat load regulation and air quality control, and the imbalance of power capacity configuration, thereby improving the overall operational coordination. This invention addresses the problem that building energy consumption control relies on experience-based adjustments and struggles to balance energy conservation and equipment lifespan. By comprehensively optimizing equipment operating intensity and overall load levels, it reduces unnecessary energy consumption while meeting environmental requirements, lowers the risk of long-term high-load operation of equipment, and slows down the accumulation of mechanical fatigue. This invention addresses the problems of temperature oscillation and load fluctuation amplification that easily occur during linkage control. By limiting the adjustment range, extending the response buffer time, and suppressing the transmission of large-amplitude linkage commands, it effectively reduces the risk of system oscillation and improves the stability and safety of electromechanical system operation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system flow of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Example 1 Please see Figure 1 This invention provides a technical solution: a smart operation-based electromechanical system linkage and intelligent control system, comprising: The data acquisition module is used for multi-source synchronous acquisition of thermal status, air quality status, power load status, and dynamic fluctuation status of equipment operation of building electromechanical systems; The cross-system coupling assessment and coordination control module is used to calculate the cross-system coupling index and compare it with the coupling coordination judgment threshold to determine whether the electromechanical system coupling coordination meets the standard. If it meets the standard, a coupling correction state dataset is generated; if it does not meet the standard, a device linkage adjustment strategy is given. The global operation optimization evaluation module is used to calculate the global operation optimization index and compare it with the global operation judgment threshold to determine whether the operation status is qualified. If qualified, a global operation stability dataset is established; if unqualified, a global energy saving and equipment load reduction adjustment strategy is given. The control stability assessment module is used to calculate the control stability index and compare it with the control stability judgment threshold to determine whether the electromechanical system linkage control state is qualified. If it is not qualified, control damping and disturbance suppression strategies are applied.

[0021] In this embodiment, by constructing a hierarchical linkage control mechanism that includes cross-system coupling evaluation, global operation optimization evaluation, and control stability evaluation, the real-time perception and hierarchical adjustment of the building electromechanical system's operating status can be achieved. This can reduce the overall energy consumption level while ensuring environmental quality, reduce the risk of high-frequency fluctuations and overload operation of equipment, and thus improve the coordination, stability, and long-term operational reliability of the electromechanical system.

[0022] Example 2 Please see Figure 1 In the explanation of Embodiment 1, the data acquisition module specifically includes a cross-system coupled acquisition unit, a global operational energy consumption acquisition unit, and an electromechanical operation status acquisition unit. The cross-system coupling acquisition unit is used to monitor in real time the thermal coupling status, air quality coupling status, power load coupling status, and personnel density status between building electromechanical systems. It collects return air temperature (Tr) by installing a temperature sensor at the air conditioning return air duct location; reads the set temperature (Tset) by connecting to the air conditioning controller communication interface; collects indoor carbon dioxide concentration (Cnd) by installing a carbon dioxide concentration sensor in the personnel activity area, and reads the air quality standard concentration (Cstd) from the system standard database; collects real-time fresh air volume (Qv) by installing an air volume sensor in the main fresh air duct; retrieves the optimal fresh air volume for the scenario from historical operating data (Qvopt); collects real-time load power (Pe) by installing a smart meter on the building's main power distribution line; and reads the maximum allowable load (Pemax) from the power capacity configuration database. The global operation energy consumption acquisition unit is used to monitor the overall energy consumption level of the building and the operating intensity of equipment in real time. It collects the operating power of the water pump by installing a three-phase power meter in the water pump control cabinet, which is recorded as Wp; it collects the lighting power by installing a rail-mounted smart power meter in the lighting distribution circuit, which is recorded as Pl; it retrieves the building area from the building asset management database, which is recorded as A; it counts the number of times the equipment starts and stops per unit time by accessing the operation log interface of each subsystem controller, which is recorded as Ns; and it reads the maximum allowable number of starts and stops from the equipment technical specification database, which is recorded as Nsmax. The electromechanical operation status acquisition unit is used to continuously monitor the dynamic fluctuation status of the electromechanical system during operation. It collects return air temperature time series data by installing a platinum resistance temperature sensor at the air conditioning return air duct, which is denoted as Tr(t); and obtains load power time series data by installing a multi-functional power quality analyzer in the low-voltage power distribution main circuit, which is denoted as Pe(t).

[0023] In this embodiment, by simultaneously acquiring data from multiple sources, including thermal status, air quality status, power load status, and equipment operation fluctuation status, and linking and reading database data, the full-dimensional integration and dynamic perception of electromechanical system operation data are achieved. This improves the accuracy and timeliness of system operation status identification, provides a reliable data foundation for subsequent linkage control and energy-saving optimization, and thus enhances the overall collaborative efficiency and operational safety of the building's electromechanical system.

[0024] Example 3 Please see Figure 1 In the explanation of Embodiment 2, the cross-system coupling evaluation and coordination control module specifically includes a coupling parameter extraction unit, a first calculation unit, and a first analysis unit. The coupling parameter extraction unit is used to extract return air temperature Tr and set temperature Tset data, and uses a three-times standard difference anomaly removal method to identify and filter abnormal data. It also uses a difference calculation method to analyze heat load deviation and obtain the heat load deviation, denoted as ΔT. Furthermore, it extracts indoor carbon dioxide concentration Cnd and air quality standard concentration Cstd data, uses a three-times standard difference anomaly removal method to identify and filter abnormal data, and uses a difference calculation method to analyze air quality deviation and obtain the air quality deviation, denoted as ΔC. Finally, it extracts real-time load power Pe and maximum allowable load Pemax data, uses a three-times standard difference anomaly removal method to identify and filter abnormal data, and uses a ratio calculation method to analyze load factor and obtain the power load factor, denoted as Re.

[0025] In this embodiment, by uniformly eliminating and quantitatively extracting anomalies in heat load deviation, air quality deviation, and power load rate, a standardized expression and comparability analysis of cross-system coupling states are achieved, improving the accuracy and consistency of electromechanical system coupling coordination assessment and enhancing the pertinence and effectiveness of multi-system linkage regulation.

[0026] Example 4 Please see Figure 1 In the explanation of Embodiment 3, specifically, the first calculation unit is used to calculate the cross-system coupling index, denoted as LCI, after dimensionless processing, by combining the obtained heat load deviation ΔT, air quality deviation ΔC, and power load rate Re with the real-time fresh air volume Qv and the scenario-optimal fresh air volume Qvopt. The formula is as follows:

[0027] In the formula, w1, w2, w3 and w4 represent weighting coefficients.

[0028] This characterizes the influence of heat load deviation ΔT on the coupling state, has a high weight, and reflects the dominant role of temperature regulation offset on the load coupling of air conditioning compressor and chilled water system. : Characterizes the influence of air quality deviation ΔC on the coupling state, accounting for a medium weight, and reflects the coupling relationship between the fresh air system and changes in personnel density; : Characterizes the impact of power load factor Re on coupling state, with medium weight, reflecting the constraint effect of the power side on the operating intensity of each subsystem; : Characterizes the impact of fresh air matching deviation on coupling state, accounts for auxiliary weight, and is used to reflect the degree of matching between fresh air volume configuration and load demand; By allocating the weights as described above, the heat load adjustment factor takes the lead, while also taking into account air quality, power constraints, and ventilation matching factors, thus achieving a comprehensive quantification of the degree of coupling among multiple systems.

[0029] In this embodiment, a comprehensive cross-system coupling index is constructed by performing a unified dimensionless fusion calculation on heat load deviation, air quality deviation, power load rate, and fresh air matching degree. This enables the coordinated quantitative evaluation of multi-source operating parameters, effectively improving the integrity and sensitivity of electromechanical system coupling imbalance identification, and providing a unified decision-making basis for linkage optimization control.

[0030] Example 5 Please see Figure 1 In the explanation of Embodiment 3, specifically, the first analysis unit is used to compare and analyze the cross-system coupling index LCI with the coupling coordination judgment threshold Lth through a preset coupling coordination judgment threshold to obtain a first evaluation result, including: When the cross-system coupling index LCI is less than or equal to the coupling coordination judgment threshold Lth, it indicates that the electromechanical system coupling coordination meets the standard. Maintain the current operating parameters of air conditioning, fresh air, power and water pump, record the current operating parameter group and the corresponding cross-system coupling index LCI; generate a coupling correction state dataset and continuously monitor it. When the cross-system coupling index LCI exceeds the coupling coordination judgment threshold Lth, it indicates that the electromechanical system coupling coordination is not up to standard, and there are risks such as asynchronous heat load and air quality adjustment, mismatch between fresh air volume configuration and load demand, abnormal power load rate, and lag in multi-system linkage. This triggers the first warning instruction and generates the first strategy: reduce non-critical power load by 10%, reduce zone lighting power by 10% to reduce the overall system load intensity and release power capacity; adjust the compressor operating frequency by 5% to correct indoor heat load deviation and restore temperature stability; reduce water pump operating flow by 5% to simultaneously reduce energy consumption on the delivery side and alleviate power load pressure. After adjustment, recalculate. If the system coupling coordination is still not up to standard, then carry out phased progressive adjustment until the system coupling coordination meets the standard.

[0031] The method for obtaining the coupling coordination judgment threshold Lth is as follows: By statistically analyzing a large amount of operational data of building electromechanical systems under different seasons, load rates, and personnel densities, the distribution range of heat load deviation, air quality deviation, power load rate, and fresh air matching degree under coordinated and unbalanced states is extracted. Combined with the system linkage response characteristics and equipment adjustment capabilities, a reasonable coupling coordination critical value is determined. At the same time, the HVAC operation evaluation standards, power load management specifications, and equipment manufacturer recommended control ranges are referenced, and calibration is performed in conjunction with engineering commissioning experience to accurately reflect the stability of multi-system collaborative operation and promptly identify the risks of asynchronous adjustment and linkage lag.

[0032] In this embodiment, by comparing the cross-system coupling state with the preset judgment criteria in a closed loop, and automatically triggering graded linkage adjustment measures when the criteria are not met, the thermal environment, power load and transmission energy consumption of the electromechanical system are coordinated and corrected. This effectively avoids the risk of energy consumption superposition and operational instability caused by asynchronous adjustment of multiple systems, and improves the overall coordination and safety of the building electromechanical system.

[0033] Example 6 Please see Figure 1 In the explanation of Embodiment 5, the global operation optimization evaluation module specifically includes an operation intensity parameter extraction unit, a second calculation unit, and a second analysis unit. The operational intensity parameter extraction unit is used to extract pump operating power Wp, lighting power Pl, real-time load power Pe, and building area A based on the operating parameters of the coupled modified state dataset. It uses a three-fold standard difference anomaly removal method to identify and filter abnormal data, and calculates the intensity of energy consumption per unit area using a unit area energy consumption construction method, denoted as Ei. It also extracts the number of equipment start-ups and shutdowns per unit time Ns and the maximum allowable number of start-ups and shutdowns Nsmax, and uses a normalized ratio calculation method to analyze the equipment fatigue degree, obtaining the equipment fatigue index, denoted as Fd.

[0034] In this embodiment, by collaboratively extracting and quantifying the energy consumption intensity per unit area and the frequency of equipment start-up and shutdown, the overall energy consumption level of the building and the operating intensity of the equipment are assessed simultaneously. This effectively avoids the one-sidedness caused by judging a single energy consumption indicator and improves the accuracy of operational status identification and the ability to warn of equipment lifespan risks.

[0035] Example 7 Please see Figure 1 In the explanation of Example 6, specifically, the second calculation unit is used to calculate the global operation optimization index, denoted as GOI, by combining the obtained unit area energy consumption intensity Ei and equipment fatigue index Fd with the heat load deviation ΔT and power load rate Re, after dimensionless processing. The formula is as follows:

[0036] In the formula, a1, a2, a3 and a4 represent weighting coefficients.

[0037] : Characterizes the impact of energy consumption intensity per unit area Ei on the overall operating status, has the highest weight, and directly reflects the building's energy utilization efficiency; : Characterizes the impact of heat load deviation ΔT on operating status, has a secondary weight, and is used to reflect the indirect impact of comfort deviation on energy consumption fluctuations; The fatigue index Fd characterizes the impact of equipment fatigue on its operating status and has a high weighting, reflecting the risks of long-term equipment reliability and maintenance costs. : Characterizes the impact of power load factor Re on operating status, has a secondary weight, and is used to reflect the impact of peak load on the safety margin of the power supply and distribution system; By integrating the above weights, a synergistic evaluation of energy efficiency and equipment health status can be achieved, providing a quantitative basis for energy-saving and load-mitigation strategies.

[0038] In this embodiment, a unified global operation optimization evaluation index is constructed by performing dimensionless fusion calculations of energy consumption intensity per unit area, equipment fatigue level, thermal load offset state, and power load level. This enables a comprehensive assessment of energy consumption risk and equipment operation risk, thereby improving the scientific and forward-looking nature of the overall operation decision-making of the building electromechanical system.

[0039] Example 8 Please see Figure 1 In the explanation of Embodiment Six, specifically, the second analysis unit is used to compare and analyze the global operation optimization index GOI with the global operation judgment threshold Gth by setting a preset global operation judgment threshold, denoted as Gth, to obtain a second evaluation result, including: When the Global Operation Optimization Index (GOI) is less than or equal to the Global Operation Judgment Thres (Gth), it indicates that the operation status is qualified, the overall energy consumption level of the electromechanical system and the equipment load intensity are within a controllable range, the current operation parameters and operating status information are recorded, a global operation stability dataset is established, and continuous monitoring is performed. When the Global Operation Optimization Index (GOI) exceeds the Global Operation Judgment Thres (Gth), it indicates that the operational status is unqualified, with risks of excessive energy consumption and accelerated equipment fatigue. This triggers a second warning instruction and generates a second strategy: reducing the water pump operating frequency by 5% to reduce transmission energy consumption and alleviate equipment load intensity; increasing the air conditioning set temperature by 0.5℃ to reduce the continuous operation ratio of the compressor and suppress peak power fluctuations; and delaying the start-up and shutdown time of non-core equipment by 10% of the cycle to reduce the number of start-ups and shutdowns per unit time and slow down the growth rate of the equipment fatigue index (Fd). After the strategy is executed, the calculation is repeated until the Global Operation Optimization Index (GOI) is less than or equal to the Global Operation Judgment Thres (Gth).

[0040] The global operation judgment threshold Gth is obtained by conducting phased statistical analysis on data such as energy consumption per unit area, equipment start-up and shutdown frequency, and load intensity during long-term building operation, extracting the distribution boundaries between energy-saving compliance ranges and energy consumption abnormal ranges, and determining the global operation critical value by combining equipment fatigue characteristics and energy consumption control targets. At the same time, it is corrected by referring to building energy-saving design standards, energy consumption limit indicators, and operation and maintenance management specifications, and combining historical operation optimization results, so as to accurately reflect whether the overall energy consumption level of the building and the load intensity of equipment are within a safe and controllable range.

[0041] In this embodiment, by setting a global operation judgment mechanism and generating energy-saving and load-slowing adjustment strategies in conjunction with it, the system can proactively optimize parameters and adjust load balance when energy consumption increases or equipment fatigue intensifies. This effectively suppresses the risk of excessive energy consumption, delays the accumulation of equipment fatigue, and improves the long-term stable operation capability and overall operational efficiency of the building electromechanical system.

[0042] Example 9 Please see Figure 1 In the explanation of Embodiment 8, the control stability assessment module specifically includes a dynamic fluctuation parameter extraction unit, a third calculation unit, and a third analysis unit. The dynamic fluctuation parameter extraction unit is used to extract return air temperature time series data Tr(t), load power time series data Pe(t), and equipment start-up and shutdown frequency Ns per unit time based on the global operational stability dataset. It uses a sliding time window statistical analysis method to perform discrete fluctuation calculation on the return air temperature time series data Tr(t) to obtain the standard deviation of temperature fluctuation per unit time, denoted as σT; it also uses a sliding time window statistical analysis method to perform discrete fluctuation calculation on the load power time series data Pe(t) to obtain the standard deviation of load fluctuation per unit time, denoted as σPe; and based on the equipment start-up and shutdown frequency Ns and the maximum allowable start-up and shutdown frequency Nsmax data per unit time, it uses a normalized ratio calculation method for dimensionless processing to obtain the normalized start-up and shutdown frequency, denoted as Nsg. The third calculation unit is used to calculate the control stability index, denoted as CSI, by obtaining the standard deviation of temperature fluctuation per unit time σT, the standard deviation of load fluctuation per unit time σPe, and the normalized number of start-stop cycles Nsg, after dimensionless processing. The formula is as follows:

[0043] In the formula, s1, s2 and s3 represent weighting coefficients.

[0044] The standard deviation of temperature fluctuation σT represents the impact on control stability and has the highest weight, reflecting the direct impact of thermal regulation oscillations on comfort and frequent equipment response. : Characterizes the impact of load fluctuation standard deviation σPe on control stability, has a high weight, and reflects the amplification effect of power fluctuation on system stability; : Characterizes the impact of normalized start-stop frequency Nsg on control stability, and has an auxiliary weight to reflect the stability risks caused by mechanical shock to equipment and frequent control switching; By allocating the weights as described above, dynamic fluctuation factors become the dominant evaluation dimension, while also taking into account equipment start-up and shutdown behavior, thus achieving a physical quantitative characterization of the stability of the electromechanical system linkage control.

[0045] In this embodiment, by performing sliding window extraction and fusion calculation on the temperature fluctuation amplitude, load power fluctuation degree and equipment start-stop frequency, a quantitative assessment of the oscillation characteristics and control disturbance intensity of the electromechanical system can be achieved. This allows for the early identification of the amplification of temperature oscillations and the intensification of load fluctuations, thereby improving the stability and anti-disturbance capability of the system linkage control.

[0046] Example 10 Please see Figure 1 In the explanation of Embodiment Nine, specifically, the third analysis unit is used to compare and analyze the control stability index CSI with the control stability judgment threshold Cth by setting a preset control stability judgment threshold, and obtain the third evaluation result, including: When the control stability index CSI is less than or equal to the control stability judgment threshold Cth, it indicates that the electromechanical system linkage control status is qualified and should be continuously monitored. When the Control Stability Index (CSI) exceeds the control stability threshold (Cth), it indicates that the electromechanical system's linkage control state is unqualified, posing risks of temperature oscillation, amplified power load fluctuations, and equipment mechanical fatigue. This triggers a third warning command and generates a third strategy: The adjustment range of the compressor is limited to within 2% based on the current compressor operating frequency parameter to suppress the spread of temperature fluctuations; the adjustment time window is extended by 10% based on the current linkage control time parameter to increase the electromechanical system's response buffer time and reduce oscillation frequency; and large-amplitude linkage commands are suspended based on the current cross-system linkage control command strength parameter to weaken the transmission of coupling disturbances between systems. The strategy is recalculated after execution until the Control Stability Index (CSI) exceeds the control stability threshold (Cth).

[0047] The control stability threshold Cth is obtained by performing dynamic response analysis on temperature fluctuations, load fluctuations, and equipment start-stop frequency data of the electromechanical system under different regulation strategies and disturbance conditions. The fluctuation amplitude distribution range of the stable operating state and the oscillation amplification state is extracted. Combined with the system control response time and the mechanical bearing capacity of the equipment, the critical value of control stability is determined. At the same time, the system is calibrated with reference to the automatic control stability evaluation standard, equipment regulation performance indicators, and on-site commissioning experience to accurately reflect the sensitivity of the linkage control system to disturbances, identify the risk of oscillation propagation and coupling amplification in a timely manner, and ensure the safe and stable operation of the electromechanical system.

[0048] In this embodiment, by determining the linkage control status of the electromechanical system and implementing damping intervention measures such as limiting the compressor adjustment range, extending the control time window, and suppressing the linkage command intensity when an oscillation trend occurs, the disturbance coupling and oscillation transmission path between systems are effectively weakened, the risk of temperature and load fluctuation amplification is reduced, and the long-term stability and equipment reliability of the electromechanical system are improved.

[0049] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The algorithm of this invention is implemented as a Python script. Before executing the core logic, the program first executes a data loading module (e.g., using the widely used pandas library in Python) configured to read the aforementioned spreadsheet file and load its contents into the program's working memory (e.g., a DataFrame data structure). Subsequent algorithm steps will directly query and retrieve the required configuration parameters from this in-memory data structure.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A mechatronics system linkage and intelligent control system based on intelligent operation, characterized in that, include: The data acquisition module is used for multi-source synchronous acquisition of thermal status, air quality status, power load status, and dynamic fluctuation status of equipment operation of building electromechanical systems; The cross-system coupling assessment and coordination control module is used to calculate the cross-system coupling index and compare it with the coupling coordination judgment threshold to determine whether the electromechanical system coupling coordination meets the standard. If it meets the standard, a coupling correction state dataset is generated; if it does not meet the standard, a device linkage adjustment strategy is given. The global operation optimization evaluation module is used to calculate the global operation optimization index and compare it with the global operation judgment threshold to determine whether the operation status is qualified. If qualified, a global operation stability dataset is established; if unqualified, a global energy saving and equipment load reduction adjustment strategy is given. The control stability assessment module is used to calculate the control stability index and compare it with the control stability judgment threshold to determine whether the electromechanical system linkage control state is qualified. If it is not qualified, control damping and disturbance suppression strategies are applied.

2. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 1, characterized in that: The data acquisition module includes a cross-system coupled acquisition unit, a global operational energy consumption acquisition unit, and an electromechanical operation status acquisition unit; The cross-system coupling acquisition unit is used to monitor in real time the thermal coupling status, air quality coupling status, power load coupling status, and personnel density status between building electromechanical systems. It collects the return air temperature by installing a temperature sensor at the air conditioning return air duct; reads the set temperature by connecting to the air conditioning controller communication interface; collects the indoor carbon dioxide concentration by installing a carbon dioxide concentration sensor in the personnel activity area and reads the air quality standard concentration from the system standard database; and collects the real-time fresh air volume by installing an air volume sensor in the fresh air main duct. The optimal fresh air volume for a given scenario is retrieved from historical operational data, and real-time load power is collected by installing smart meters on the building's main power distribution line. Read the maximum allowable load from the power capacity configuration database; The global operation energy consumption acquisition unit is used to monitor the overall energy consumption level of the building and the operating intensity of equipment in real time. It collects the operating power of the water pump by installing a three-phase power meter in the water pump control cabinet, collects the lighting power by installing a rail-mounted smart power meter in the lighting power distribution circuit, retrieves the building area through the building asset management database, and counts the number of times the equipment starts and stops per unit time by accessing the operation log interface of each subsystem controller and reads the maximum allowable number of starts and stops from the equipment technical specification database. The electromechanical operation status acquisition unit is used to continuously monitor the dynamic fluctuation status of the electromechanical system during operation. It collects time series data of return air temperature by installing a platinum resistance temperature sensor at the air conditioning return air duct. By installing a multi-functional power quality analyzer in the low-voltage power distribution main circuit, load power time series data can be obtained.

3. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 2, characterized in that: The cross-system coupling evaluation and coordination control module includes a coupling parameter extraction unit, a first calculation unit, and a first analysis unit; The coupling parameter extraction unit is used to extract return air temperature and set temperature data, employ a three-times standard difference anomaly removal method for anomaly identification and filtering, and perform heat load deviation analysis using a difference calculation method to obtain the heat load deviation; it also extracts indoor carbon dioxide concentration and air quality standard concentration data, employs a three-times standard difference anomaly removal method for anomaly identification and filtering, and performs air quality deviation analysis using a difference calculation method to obtain the air quality deviation; and it extracts real-time load power and maximum allowable load data, employs a three-times standard difference anomaly removal method for anomaly identification and filtering, and performs load rate analysis using a ratio calculation method to obtain the power load rate.

4. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 3, characterized in that: The first calculation unit is used to calculate the cross-system coupling index by acquiring the heat load deviation, air quality deviation and power load rate, combined with the real-time fresh air volume and the optimal fresh air volume for the scenario, and after dimensionless processing.

5. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 3, characterized in that: The first analysis unit is used to obtain a first evaluation result by comparing the cross-system coupling index with the coupling coordination judgment threshold through a preset coupling coordination judgment threshold. When the cross-system coupling index is less than or equal to the coupling coordination judgment threshold, it indicates that the electromechanical system coupling coordination meets the standard. Maintain the current operating parameters of air conditioning, fresh air, power and water pump, record the current operating parameter group and the corresponding cross-system coupling index; generate a coupling correction state dataset and continuously monitor it. When the cross-system coupling index exceeds the coupling coordination judgment threshold, it indicates that the electromechanical system coupling coordination is not up to standard, and there are risks such as asynchronous heat load and air quality adjustment, mismatch between fresh air volume configuration and load demand, abnormal power load rate, and lag in multi-system linkage. This triggers the first warning instruction and generates the first strategy: reduce non-critical power load by 10%, reduce zone lighting power by 10% to reduce the overall system load intensity and release power capacity; adjust the compressor operating frequency by 5% to correct indoor heat load deviation and restore temperature stability; reduce water pump operating flow by 5% to simultaneously reduce energy consumption on the delivery side and alleviate power load pressure; after adjustment, recalculate. If the system coupling coordination is still not up to standard, then carry out phased progressive adjustment until the system coupling coordination meets the standard.

6. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 5, characterized in that: The global operation optimization evaluation module includes an operation intensity parameter extraction unit, a second calculation unit, and a second analysis unit. The operational intensity parameter extraction unit is used to extract pump operating power, lighting power, real-time load power and building area data based on the operating parameters of the coupled modified state dataset. It uses a three-fold standard difference anomaly removal method to identify and filter abnormal data, and calculates the intensity by constructing the energy consumption per unit area. Extract the data on the number of equipment starts and stops per unit time and the maximum allowable number of starts and stops, and use the normalized ratio calculation method to analyze the equipment fatigue level and obtain the equipment fatigue index.

7. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 6, characterized in that: The second calculation unit is used to calculate the global operation optimization index by combining the obtained unit area energy consumption intensity and equipment fatigue index with the heat load deviation and power load rate, after dimensionless processing.

8. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 6, characterized in that: The second analysis unit is used to obtain a second evaluation result by setting a global operation judgment threshold and comparing the global operation optimization index with the global operation judgment threshold. When the global operation optimization index is less than or equal to the global operation judgment threshold, it indicates that the operation status is qualified, the overall energy consumption level of the electromechanical system and the equipment load intensity are within a controllable range, the current operation parameters and operation status information are recorded, a global operation stability dataset is established, and continuous monitoring is performed. When the global operation optimization index exceeds the global operation judgment threshold, it indicates that the operation status is unqualified, with risks of excessive energy consumption and accelerated equipment fatigue. This triggers a second warning instruction and generates a second strategy: reduce the water pump operating frequency by 5% to reduce transmission energy consumption and alleviate equipment load intensity; increase the air conditioner set temperature by 0.5℃ to reduce the continuous operation ratio of the compressor and suppress peak power fluctuations; delay the start-up and shutdown time of non-core equipment by 10% of the cycle to reduce the number of start-ups and shutdowns per unit time and slow down the growth rate of the equipment fatigue index; after the strategy is executed, it is recalculated until the global operation optimization index is less than or equal to the global operation judgment threshold.

9. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 8, characterized in that: The control stability assessment module includes a dynamic fluctuation parameter extraction unit, a third calculation unit, and a third analysis unit; The dynamic fluctuation parameter extraction unit is used to extract return air temperature time series data, load power time series data and equipment start-up and shutdown times per unit time based on the global operation stability dataset. The sliding time window statistical analysis method is used to perform discrete fluctuation calculation on the return air temperature time series data to obtain the standard deviation of temperature fluctuation per unit time. The sliding time window statistical analysis method is used to perform discrete fluctuation calculation on the load power time series data to obtain the standard deviation of load fluctuation per unit time. Based on the data of the number of times the equipment starts and stops per unit time and the maximum allowable number of times it starts and stops, the normalized ratio calculation method is used to perform dimensionless processing to obtain the normalized number of times it starts and stops. The third calculation unit is used to calculate the control stability index by obtaining the standard deviation of temperature fluctuation per unit time, the standard deviation of load fluctuation per unit time, and the normalized number of start-stop cycles, after dimensionless processing.

10. The electromechanical system linkage and intelligent control system based on intelligent operation according to claim 9, characterized in that: The third analysis unit is used to obtain a third evaluation result by comparing the control stability index with a preset control stability judgment threshold, and then comparing the control stability index with the control stability judgment threshold. When the control stability index is less than or equal to the control stability judgment threshold, it indicates that the electromechanical system linkage control status is qualified and should be continuously monitored. When the control stability index exceeds the control stability judgment threshold, it indicates that the electromechanical system linkage control state is unqualified, posing risks of temperature oscillation, amplified power load fluctuations, and equipment mechanical fatigue. This triggers a third early warning command and generates a third strategy: The adjustment range of the compressor is limited to within 2% based on the current compressor operating frequency parameter to suppress the spread of temperature fluctuations; the adjustment time window is extended by 10% based on the current linkage control time parameter to increase the electromechanical system response buffer time and reduce oscillation frequency; and large-amplitude linkage commands are suspended based on the current cross-system linkage control command strength parameter to weaken the transmission of coupling disturbances between systems. The strategy is recalculated after execution until the control stability index exceeds the control stability judgment threshold.

Images