A comprehensive energy efficiency evaluation method and system for a smart park

Abstract

The application provides a comprehensive energy efficiency evaluation method and system for a smart park, comprising: constructing a nonlinear metabolic matrix reflecting the energy correlation relationship between devices, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; defining an interaction rule library of the metabolic network, dynamically triggering the rules and adjusting the real-time metabolic flux according to real-time sensor data; calculating the quantitative values of each energy efficiency index according to the real-time metabolic flux, quantifying the uncertainty through fuzzy entropy theory, dynamically allocating the multi-objective weight, and calculating the comprehensive energy efficiency score; calculating the quantitative values of each energy efficiency index under the optimal state according to the optimal metabolic flux distribution, comparing the fuzzy entropy, determining the energy efficiency index to be optimized, identifying the high-risk and potential risk, and formulating the optimization strategy. The application considers various devices and energy flow conditions in the park, accurately reflects the energy efficiency level of the system, dynamically adjusts the evaluation according to real-time data, identifies and solves the uncertainty factors of the energy system, and ensures the efficient and stable operation of the system.

Description

Technical Field

[0001] This invention relates to the field of energy efficiency assessment technology for smart parks, and in particular to a comprehensive energy efficiency assessment method and system for smart parks. Background Technology

[0002] Smart parks encompass multiple sectors, including industrial production, commercial offices, and residential living. Their energy systems are complex and diverse, involving various energy sources such as electricity, heat, and natural gas, with intricate energy interactions between equipment. Efficient energy utilization and rational allocation are crucial for the operation of smart parks. On the one hand, energy costs are a significant component of park operating costs; reducing energy consumption and waste can effectively improve the park's economic efficiency. On the other hand, reducing energy consumption and pollutant emissions is of great importance for achieving environmental protection and sustainable development goals.

[0003] However, current traditional energy efficiency assessment methods often focus on using single energy efficiency indicators, such as energy consumption per unit of output or energy consumption per unit area, to measure the energy utilization efficiency of a park. These indicators are usually obtained through simple statistical calculations, considering only the relationship between energy consumption and a specific output. Assessments using a single indicator cannot fully reflect the complexity and diversity of a park's energy system, ignoring the interactions and correlations between different equipment and different energy forms. For example, focusing only on energy consumption per unit of output may fail to detect energy waste problems in the operation of certain equipment, because the energy consumption of these devices may be masked by the overall output value.

[0004] Current energy efficiency assessment methods may employ simple linear models to describe the energy system of a park, predicting and evaluating energy efficiency levels by establishing a linear relationship between energy consumption and various influencing factors. These models are typically based on empirical formulas or simple statistical analysis, assuming a linear relationship between energy consumption and influencing factors. However, the energy system of a smart park is a complex nonlinear system, and the relationship between energy consumption and various influencing factors often involves complex nonlinear relationships. Simple linear models cannot accurately describe these nonlinear relationships, leading to significant errors in prediction and assessment results. Furthermore, the park's energy system is affected by various uncertainties, such as equipment failure, energy supply fluctuations, and environmental changes. Simple linear models cannot effectively handle these uncertainties and cannot accurately assess the system's energy efficiency level under uncertain conditions. Summary of the Invention

[0005] Therefore, the purpose of this invention is to propose a comprehensive energy efficiency assessment method and system for smart parks to solve the problems mentioned above.

[0006] According to the present invention, a comprehensive energy efficiency assessment method for smart parks is proposed, the method comprising:

[0007] A nonlinear metabolic matrix reflecting the energy correlation between devices is constructed, and the optimal metabolic flux distribution is solved by mixed integer nonlinear programming.

[0008] Define an interaction rule base with the metabolic network, dynamically trigger rules and adjust the real-time metabolic flux of each device based on real-time sensor data from each device;

[0009] Based on the real-time metabolic flux of each device, the quantitative values ​​of each energy efficiency index under the current state are calculated, and the uncertainty of the energy efficiency index under the current state is quantified by fuzzy entropy theory. Multi-objective weights are dynamically allocated, and the comprehensive energy efficiency score is calculated.

[0010] Based on the optimal metabolic flux distribution, the quantitative values ​​of each energy efficiency index under the optimal state are calculated. The fuzzy entropy of the energy efficiency index under the current state and the optimal state is compared to determine the energy efficiency index to be optimized. High-risk and potential risks are identified from the energy efficiency index to be optimized, and targeted optimization strategies are formulated.

[0011] Furthermore, the construction of a nonlinear metabolic matrix reflecting the energy correlation between devices, and the solution of the optimal metabolic flux distribution through mixed-integer nonlinear programming, includes:

[0012] Based on the energy flow relationships of equipment in the park, the equipment is defined as metabolic enzymes and the energy flow as metabolites, and a nonlinear metabolic matrix is ​​constructed. Where n is the total number of devices in the park. , It is a nonlinear function used to represent the energy correlation between the i-th device and the j-th device;

[0013] Define the energy flow rate vector of the equipment as the metabolic flux vector. , Let be the metabolic flux of the i-th device, and represent the energy flow rate of the i-th device. Let be the metabolic flux of the j-th device, and let represent the energy flow rate of the j-th device.

[0014] Construct a nonlinear metabolic flux balance equation: , , where 0 is the zero vector, representing the overall balance between energy input and output in the park;

[0015] Introducing dynamic constraints, including constraints on energy supply and demand fluctuations and constraints on the number of equipment start-ups and shutdowns:

[0016] Using energy efficiency as the optimization objective and incorporating dynamic constraints, the optimal metabolic flux distribution is obtained through mixed-integer nonlinear programming. This reflects the energy flow rate of each device under optimal energy efficiency, among which, Let be the metabolic flux of the i-th device under the optimal metabolic flux distribution.

[0017] Furthermore, the optimal metabolic flux distribution is obtained by using energy efficiency as the optimization objective function, combined with dynamic constraints, and solved through mixed-integer nonlinear programming. ,include:

[0018] Based on energy flow costs and differences in energy flow between equipment, the energy efficiency objective function F(v) is constructed as follows:

[0019] ,in, Let i be the energy flow cost coefficient for the i-th device. The penalty coefficient for the difference in energy flow between the i-th device and the j-th device;

[0020] Minimizing the energy efficiency objective function F(v) is taken as the objective of optimizing the mixed-integer nonlinear programming problem. The optimization objective formula is:

[0021] ,

[0022] ;

[0023] The optimal metabolic flux distribution is obtained by solving the optimization objective of the mixed-integer nonlinear programming problem. .

[0024] Furthermore, the defined interaction rule base of the metabolic network dynamically triggers rules and adjusts the real-time metabolic flux of each device based on real-time sensor data from each device, including:

[0025] Build a rule base that includes basic rules, preference rules, and exception rules;

[0026] For each device, a rule base is matched in real time based on its sensor data. If a rule is triggered, the real-time metabolic flux is adjusted using the following formula: ,in, Let be the metabolic flux of the i-th device before adjustment. For the i-th device, trigger the flux change of the basic rule. For the i-th device, trigger the flux change of the preference rule. The flux change that triggers the abnormal rule for the i-th device;

[0027] The adjusted real-time metabolic flux of each device is fed back to the nonlinear metabolic matrix S, and the energy efficiency objective function is recalculated.

[0028] Furthermore, based on the real-time metabolic flux of each device, the quantitative values ​​of each energy efficiency index under the current state are calculated using the following formula:

[0029] ,in, This represents the quantified value of the a-th energy efficiency indicator under the current state, reflecting the energy efficiency level of the system under the current actual operating conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0030] Furthermore, the process of quantifying the uncertainty of energy efficiency indicators under the current state using fuzzy entropy theory, dynamically allocating multi-objective weights, and calculating a comprehensive energy efficiency score includes:

[0031] For each energy efficiency index under the current state, its fuzzy entropy is calculated using the following formula:

[0032] ,

[0033] in, Let be the fuzzy entropy of the a-th energy efficiency index under the current state, representing the degree of uncertainty of the a-th energy efficiency index under the current state. Let k be the membership degree of the quantified value of the a-th energy efficiency index under the current state in energy efficiency state b, and k be the number of levels of energy efficiency state division by the membership function.

[0034] Dynamic weights are assigned based on fuzzy entropy, using the following formula:

[0035] ,in, This reflects the adjustment of the importance of the a-th energy efficiency indicator under uncertainty in the current state, where A is the total number of energy efficiency indicators and λ is the temperature coefficient used to adjust the sensitivity of weight allocation.

[0036] The overall energy efficiency score (Score) is obtained by weighting and summing the dynamic weighted energy efficiency indicators. The formula is as follows:

[0037] ,in, This is the normalized value of the quantified value of the a-th energy efficiency index under the current state. This represents the quantified value of the a-th energy efficiency index under the current state. This represents the minimum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation. It represents the maximum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation.

[0038] Furthermore, based on the optimal metabolic flux distribution, the quantitative values ​​of each energy efficiency index under optimal conditions are calculated using the following formula:

[0039] ,in, This represents the quantified value of the a-th energy efficiency index under optimal conditions, reflecting the ideal energy efficiency level of the system under ideal conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device under the optimal metabolic flux distribution. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0040] Furthermore, by comparing the fuzzy entropy of the energy efficiency indicators in the current state and the optimal state, the energy efficiency indicators to be optimized are determined, and high-risk and potential risks are identified from the energy efficiency indicators to be optimized, and targeted optimization strategies are formulated, including:

[0041] For each energy efficiency index under optimal conditions, its fuzzy entropy is calculated using the following formula:

[0042] ,

[0043] in, Let be the fuzzy entropy of the a-th energy efficiency index under optimal conditions, representing the degree of uncertainty of the a-th energy efficiency index under optimal conditions. Let be the membership degree of the quantified value of the a-th energy efficiency index under the optimal state in energy efficiency state b, and k be the number of levels of the membership function for energy efficiency state.

[0044] Calculate the difference in fuzzy entropy between the energy efficiency index in the current state and the optimal state. The result is compared with a preset threshold ΔH, where... Let be the fuzzy entropy of the a-th energy efficiency index under the current state;

[0045] like If the a-th energy efficiency index is defined as the energy efficiency index to be optimized, it indicates that there is a significant difference between the current state and the optimal state in terms of the a-th energy efficiency index, which needs to be focused on and optimized.

[0046] For energy efficiency indicators marked as needing optimization, comparison and Size:

[0047] like If the a-th energy efficiency indicator is identified as a high-risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is significantly higher than that under the optimal state. The factors affecting its uncertainty are analyzed, and corresponding optimization measures are formulated.

[0048] like If the a-th energy efficiency indicator is identified as a potential risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is lower than that under the optimal state, and therefore attention should be paid to it and preventive measures should be formulated in advance.

[0049] This invention also proposes a comprehensive energy efficiency assessment system for smart parks, used to implement the aforementioned comprehensive energy efficiency assessment method for smart parks, the system comprising:

[0050] Metabolic flux module: used to construct a nonlinear metabolic matrix that reflects the energy correlation between devices, and to solve for the optimal metabolic flux distribution through mixed integer nonlinear programming;

[0051] Real-time metabolic flux module: used to define the rule base for interaction with the metabolic network, dynamically trigger rules and adjust the real-time metabolic flux of each device based on real-time sensor data from each device;

[0052] The comprehensive energy efficiency assessment module is used to calculate the quantitative values ​​of each energy efficiency index under the current state based on the real-time metabolic flux of each device, and to quantify the uncertainty of the energy efficiency index under the current state through fuzzy entropy theory, dynamically allocate multi-objective weights, and calculate the comprehensive energy efficiency score.

[0053] Energy efficiency optimization module: It is used to calculate the quantitative values ​​of each energy efficiency index under the optimal state based on the optimal metabolic flux distribution, compare the fuzzy entropy of the energy efficiency index under the current state and the optimal state, determine the energy efficiency index to be optimized, identify high-risk and potential risks from the energy efficiency index to be optimized, and formulate targeted optimization strategies.

[0054] In summary, the comprehensive energy efficiency assessment method for smart parks of the present invention first constructs a nonlinear metabolic matrix reflecting the energy correlation between devices to accurately capture the complex energy interaction characteristics between devices in the park. Then, mixed-integer nonlinear programming is used to solve for the optimal metabolic flux distribution, achieving dynamic optimization and balance of system energy flow, thereby effectively improving energy utilization efficiency and reducing energy waste. Next, an interaction rule base with the metabolic network is defined, and the real-time metabolic flux of each device is dynamically adjusted based on real-time sensor data. This ensures that the metabolic flux can accurately reflect the real-time changes in the park's energy demand and abnormal fluctuations in the current operating status of devices, guaranteeing the stability and reliability of energy supply. Then, the quantitative values ​​of each energy efficiency index are calculated based on the real-time metabolic flux, reflecting the energy efficiency level of the system under the current actual operating state. Fuzzy entropy theory is used to quantify the uncertainty of each energy efficiency index under the current state, reflecting the degree of uncertainty of that energy efficiency index in the current state. Dynamic weights are assigned according to the fuzzy entropy of the energy efficiency index under the current state, i.e., the weights are automatically adjusted according to the degree of uncertainty of the energy efficiency index, allowing energy efficiency indicators with lower uncertainty to play a greater role in the comprehensive assessment, thereby improving the accuracy and reliability of energy efficiency assessment. Finally, based on the optimal metabolic flux distribution, the quantitative values ​​of each energy efficiency index under the optimal state are calculated to reflect the energy efficiency level of the park's energy system under ideal operating conditions. Then, the fuzzy entropy of each energy efficiency index under the optimal state is calculated using fuzzy entropy theory to quantify the uncertainty of each energy efficiency index under the optimal state and provide a comparison benchmark. By comparing the fuzzy entropy of the energy efficiency index with that under the current state, the degree of difference between the current state and the optimal state in terms of the uncertainty of the energy efficiency index is quantified, the indicators to be optimized are determined, and high-risk and potential risks are identified from the indicators to be optimized. Targeted optimization is then carried out to solve the uncertainty factors of the park's energy system, improve the energy efficiency level, and thus ensure the efficient and stable operation of the park's energy system.

[0055] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by means of embodiments of the invention. Attached Figure Description

[0056] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0057] Figure 1 This is a flowchart of the comprehensive energy efficiency assessment method for smart parks according to Embodiment 1 of the present invention;

[0058] Figure 2 This is a system block diagram of the comprehensive energy efficiency assessment system for smart parks according to Embodiment 2 of the present invention. Detailed Implementation

[0059] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0060] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0062] Example 1

[0063] Please see Figure 1 This invention proposes a comprehensive energy efficiency assessment method for smart parks, the method comprising steps S101 to S104:

[0064] S101, construct a nonlinear metabolic matrix that reflects the energy correlation between devices, and solve for the optimal metabolic flux distribution through mixed integer nonlinear programming.

[0065] Further optionally, the construction of a nonlinear metabolic matrix reflecting the energy correlation between devices, and the solution of the optimal metabolic flux distribution through mixed-integer nonlinear programming, includes:

[0066] Based on the energy flow relationships of equipment in the park, the equipment is defined as metabolic enzymes and the energy flow as metabolites, and a nonlinear metabolic matrix is ​​constructed. Where n is the total number of devices in the park. , It is a nonlinear function used to represent the energy correlation between the i-th device and the j-th device;

[0067] Define the energy flow rate vector of the equipment as the metabolic flux vector. , Let be the metabolic flux of the i-th device, and let represent the energy flow rate of the i-th device.

[0068] Construct a nonlinear metabolic flux balance equation: , , where 0 is the zero vector, representing the overall balance between energy input and output in the park;

[0069] Introducing dynamic constraints, including constraints on energy supply and demand fluctuations and constraints on the number of equipment start-ups and shutdowns:

[0070] Using energy efficiency as the optimization objective and incorporating dynamic constraints, the optimal metabolic flux distribution is obtained through mixed-integer nonlinear programming. This reflects the energy flow rate of each device under optimal energy efficiency.

[0071] Understandably, in biological metabolic systems, metabolic enzymes catalyze chemical reactions in metabolites, achieving the conversion and transfer of matter and energy. Similarly, in the energy system of an industrial park, equipment acts like metabolic enzymes, undertaking the functions of energy conversion, transmission, and utilization; energy flow is analogous to metabolites, being transferred and converted between different devices. For example, in an industrial park, boiler equipment is like a type of metabolic enzyme, converting fuels such as coal (energy input, similar to raw materials for metabolites) into heat energy (energy output, similar to metabolite products). This heat energy is then transmitted through pipelines to other equipment that requires heat energy, such as dryers, which then use the heat energy to dry the materials, completing the further conversion and utilization of energy.

[0072] In this embodiment, park equipment is defined as metabolic enzymes, energy flow is defined as metabolites, and a nonlinear metabolic matrix of the park energy system is constructed. ,in , As a nonlinear function, it is used to describe the complex energy relationships between devices (i.e., between the i-th device and the j-th device). This nonlinear relationship can reflect actual conditions such as efficiency degradation and load dependence. For example, there are two devices A and B in a park, and device A transmits electrical energy to device B. When the output power of device A (i.e., ...) is... When the output power of device A is relatively low, the transmission efficiency is high due to relatively fixed factors such as line loss; however, as the output power of device A increases, the line loss increases non-linearly, resulting in a decrease in the actual power transmitted to device B (compared to the output power of device B). The increase in efficiency (related to the rate of increase) gradually decreases, which is a nonlinear relationship of efficiency decay. This can be addressed by using a suitable nonlinear function. To describe.

[0073] Specifically, in constructing nonlinear functions In this case, the energy flow rate between equipment can be used as the independent variable, and the index reflecting the energy correlation between equipment can be used as the dependent variable to draw a scatter plot. Based on the scatter plot distribution trend, the type of nonlinear relationship can be preliminarily determined, and f can be assumed. ijThe possible forms are then determined. The parameters of the nonlinear function are estimated by minimizing the sum of squared errors using the least squares method. The least squares problem is then solved using iterative algorithms (such as gradient descent) to finally determine the... The specific form provides an accurate functional basis for constructing nonlinear metabolic matrices.

[0074] In the smart park, representative equipment combinations are selected, such as production equipment (CNC machine tools, industrial robots, etc.), energy conversion equipment (air compressors, boilers, etc.), and auxiliary equipment (ventilation and air conditioning systems, lighting, etc.). Energy input / output rates and load status information are collected at regular time intervals (e.g., every 15 minutes) using energy monitoring sensors and load sensors. Simultaneously, energy efficiency indicators such as park energy consumption, carbon emissions, and costs are recorded. The collected data then undergoes preprocessing operations such as cleaning (removing outliers and missing values) and normalization (eliminating the influence of dimensions) to ensure data quality.

[0075] Define the energy flow rate vector of the equipment as the metabolic flux vector. ,in Let be the metabolic flux of the i-th device, and let represent the energy flow rate (including input or output rate) of the i-th device. This is used to intuitively reflect the activity level of the device in the energy system and the energy transfer efficiency. Indicates the output. It represents the input and provides a clear quantitative representation of energy flow.

[0076] Constructing a nonlinear metabolic flux balance equation , where 0 is the zero vector. This equation ensures the overall balance between energy input and output in the park and is an important constraint for the stable operation of the energy system.

[0077] At the same time, constraints on energy supply and demand fluctuations are introduced (to ensure that metabolic flux meets the balance within the prediction error range) and constraints on the number of equipment start-ups and shutdowns (to limit the number of equipment start-ups and shutdowns to avoid damage to equipment and increased costs caused by frequent start-ups and shutdowns), so that the optimization process is more in line with the actual operation needs of the park's energy system.

[0078] Then, using the energy efficiency objective function as the optimization objective and incorporating dynamic constraints, the optimal metabolic flux distribution is obtained through mixed-integer nonlinear programming. The optimal metabolic flux distribution is the energy flow rate of each device under optimal energy efficiency (i.e. ideal energy efficiency state). The optimal metabolic flux distribution obtained by optimization can realize the dynamic balance and energy efficiency maximization of the park's energy system, and provide optimization guidance for subsequent energy efficiency management.

[0079] Optionally, the optimal metabolic flux distribution is obtained by using a mixed-integer nonlinear programming approach, with the energy efficiency objective function as the optimization target and incorporating dynamic constraints. ,include:

[0080] Based on energy flow costs and differences in energy flow between equipment, the energy efficiency objective function F(v) is constructed as follows:

[0081] ,in, Let i be the energy flow cost coefficient for the i-th device. The penalty coefficient for the difference in energy flow between the i-th device and the j-th device;

[0082] Minimizing the energy efficiency objective function F(v) is taken as the objective of optimizing the mixed-integer nonlinear programming problem. The optimization objective formula is:

[0083] ,

[0084] ;

[0085] The optimal metabolic flux distribution is obtained by solving the optimization objective of the mixed-integer nonlinear programming problem. .

[0086] Understandably, constructing an energy efficiency objective function It consists of two parts. Part One This reflects the economics of energy flow in equipment. By considering the energy flow cost coefficient for each piece of equipment, the optimization process can balance the relationship between energy flow volume and cost, avoiding unnecessary energy consumption and cost increases. Part Two As a penalty term for differences in energy flow between devices, it is used to balance the energy flow between devices. This is achieved by introducing a penalty coefficient. When the energy flow difference between devices is too large, the value of the penalty term will increase, thereby promoting the energy flow between devices to tend to be balanced during the optimization process, avoiding excessive or insufficient energy flow for some devices, and ensuring the stable operation of the park's energy system.

[0087] Minimizing the energy efficiency objective function F(v) is taken as the objective of optimizing the mixed-integer nonlinear programming problem, and the optimization formula is: Simultaneously, it must satisfy the nonlinear metabolic flux balance equation S(v)⋅v=0 and dynamic constraints (such as energy supply and demand fluctuation constraints and equipment start-up and shutdown frequency limits). These constraints ensure that the metabolic flux distribution obtained by the optimization solution not only considers energy efficiency targets, but also meets the actual operation requirements of the park's energy system, including energy supply and demand balance and equipment operation stability.

[0088] By solving the optimization objective of this mixed-integer nonlinear programming problem, the optimal metabolic flux distribution can be obtained. The optimal metabolic flux distribution is the energy flow rate of each device under optimal energy efficiency (i.e., ideal energy efficiency state). By optimizing the solution to obtain the optimal metabolic flux distribution, the dynamic balance and energy efficiency of the park's energy system can be achieved. That is, under the premise of meeting the energy supply and demand balance and equipment operation stability, by optimizing the energy flow between devices, the energy flow cost can be reduced, the differences in energy flow between devices can be balanced, and thus the energy efficiency level of the entire park's energy system can be improved. This provides a scientific basis and optimization direction for energy management and decision-making in smart parks.

[0089] Further optionally, the introduction of dynamic constraints includes:

[0090] To ensure that metabolic flux remains in balance within the prediction error range, an energy supply and demand fluctuation constraint is introduced. The formula for the energy supply and demand fluctuation constraint is:

[0091] ,in, Let m be the energy demand of the j-th user, and m be the total number of users. The allowable proportion of energy supply and demand fluctuations;

[0092] Introducing a constraint on the number of start and stop times, the formula is:

[0093] ,in, Let be an integer variable representing the start / stop status of the i-th device in time slot t. , M represents the upper limit for the number of times the device can be started and stopped, and M is a sufficiently large constant. To evaluate the total number of time slots within a time period.

[0094] Understandably, by introducing constraints on energy supply and demand fluctuations, we ensure that metabolic flux remains balanced within the prediction error range. In actual smart park energy systems, energy demand exhibits a certain degree of uncertainty. Introducing this constraint allows for a certain range of fluctuations between actual energy supply and demand and the predicted values ​​(as determined by...). The control mechanism ensures that the metabolic flux distribution obtained from the optimization solution is more in line with the actual situation, avoiding the problem of not being able to achieve the desired balance in actual operation due to excessive pursuit of precise balance, and ensuring the stability and reliability of the park's energy supply.

[0095] Equipment start-stop frequency constraints limit the number of times equipment can be started and stopped within the evaluation period. Frequent start-stop operations not only damage the equipment and shorten its lifespan but also increase energy consumption and operating costs. Setting an upper limit on the number of equipment start-stop operations helps to mitigate this limitation. and combined This condition (ensuring that energy flows only when the equipment is turned on) allows the optimization process to consider both energy efficiency targets and the operational stability and cost factors of the equipment.

[0096] The formula for constraining the number of equipment start-stop cycles is as follows: .in, Let be an integer variable representing the start / stop status of the i-th device in time slot t. ( This indicates that the device is turned on. (Indicates device is off); This represents the upper limit for the number of times the equipment can be started and stopped; M is a sufficiently large constant. To evaluate the total number of time slots within a given time period, within a preset evaluation time period... Within this time period, the result is obtained by dividing the time period into certain time intervals Δt. , To evaluate the start time of the time period To assess the end time of the time period, For a pre-set time interval (e.g., It can be set to 1 hour, 15 minutes, etc., and the specific value is determined according to the required accuracy of the evaluation.

[0097] S102 defines the interaction rule base with the metabolic network, which dynamically triggers rules and adjusts the real-time metabolic flux of each device based on real-time sensor data from each device.

[0098] Further optionally, the defined interaction rule base with the metabolic network dynamically triggers rules and adjusts the real-time metabolic flux of each device based on real-time sensor data from each device, including:

[0099] Build a rule base that includes basic rules, preference rules, and exception rules;

[0100] For each device, a rule base is matched in real time based on its sensor data (such as electricity price, temperature, and user feedback). If a rule is triggered, the real-time metabolic flux is adjusted using the following formula: ,in, Let be the metabolic flux of the i-th device before adjustment. For the i-th device, trigger the flux change of the basic rule. For the i-th device, trigger the flux change of the preference rule. The flux change that triggers the abnormal rule for the i-th device;

[0101] The adjusted real-time metabolic flux of each device is fed back to the nonlinear metabolic matrix S, and the energy efficiency objective function is recalculated.

[0102] Understandably, a rule base is constructed, encompassing basic rules (such as electricity price / temperature triggers), preference rules (such as user comfort priority), and exception rules (such as equipment failure). These rules comprehensively cover various situations that may arise during the operation of the park's energy system, providing a rich basis for dynamically adjusting metabolic flux. For example:

[0103] Basic rule example: When the real-time electricity price P exceeds the set threshold At that time, the metabolic flux of the lighting equipment will be... The rate has been adjusted to 0.7 times the original rate. This rule takes into account electricity prices; reducing the energy consumption of lighting equipment when electricity prices are high helps lower energy costs.

[0104] Preference rule example: When users provide feedback Below the set minimum value At that time, the metabolic flux of the air conditioning equipment will be... Adjust to the current value and 0.8⋅ The larger value in (here it is assumed) (This refers to the upper limit of the metabolic flux of the air conditioning equipment). This rule prioritizes user comfort; when user feedback indicates insufficient comfort, the energy supply to the air conditioning equipment is appropriately increased to improve user satisfaction.

[0105] Example of an exception rule: When the i-th device fails, the metabolic flux of the backup device of the i-th device will be... The operating load has been adjusted to double the original level (i.e., full load operation). This rule ensures that backup equipment can be put into use in a timely manner in the event of equipment failure, thus guaranteeing the normal operation of the park's energy system.

[0106] For each device, by adjusting the formula The flux changes triggered by different types of rules are superimposed to obtain the adjusted metabolic flux. This enables dynamic adjustment of metabolic flux. Let be the metabolic flux of the i-th device before adjustment. For the i-th device, trigger the flux change of the basic rule. For the i-th device, trigger the flux change of the preference rule. The flux change that triggers the abnormal rule for the i-th device.

[0107] The adjusted real-time metabolic flux of each device is fed back to the nonlinear metabolic matrix S, ensuring that the real-time adjustment of metabolic flux can reflect the current operating status of the system in a timely and accurate manner. The energy efficiency objective function is recalculated, so that the system can re-optimize energy efficiency according to the new metabolic flux distribution, ensuring that the park's energy system is always in or close to the optimal operating state.

[0108] S103 calculates the quantitative values ​​of each energy efficiency index under the current state based on the real-time metabolic flux of each device, and quantifies the uncertainty of the energy efficiency index under the current state through fuzzy entropy theory, dynamically allocates multi-objective weights, and calculates the comprehensive energy efficiency score.

[0109] Optionally, the quantitative values ​​of each energy efficiency index are calculated based on the real-time metabolic flux of each device under the current state, using the following formula:

[0110] ,in, This represents the quantified value of the a-th energy efficiency indicator under the current state, reflecting the energy efficiency level of the system under the current actual operating conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0111] Understandably, this formula By multiplying the absolute value of the real-time metabolic flux of each device by the unit energy flow coefficient of the corresponding energy efficiency index, and summing the results of all devices, the quantified value of each energy efficiency index under the current state is obtained. This comprehensively considers the contribution of all devices in the park to the energy efficiency index, accurately reflecting the actual operating state of the park's energy system under real-time metabolic flux distribution.

[0112] Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. These indicators are crucial for measuring the operational efficiency and environmental impact of a park's energy system. Total energy consumption reflects the overall energy consumption of the park's energy system and is a fundamental indicator for assessing energy utilization efficiency. Carbon emissions reflect the environmental impact of the park's energy system and align with current trends in energy conservation and emission reduction. Energy costs directly relate to the park's operating costs and significantly impact its economic benefits. By calculating the quantitative values ​​of these energy efficiency indicators, the performance of the park's energy system can be comprehensively evaluated.

[0113] Optionally, the step of quantifying the uncertainty of energy efficiency indicators under the current state using fuzzy entropy theory, dynamically allocating multi-objective weights, and calculating a comprehensive energy efficiency score includes:

[0114] For each energy efficiency index under the current state, its fuzzy entropy is calculated using the following formula:

[0115] ,

[0116] in, Let be the fuzzy entropy of the a-th energy efficiency index under the current state, representing the degree of uncertainty of the a-th energy efficiency index under the current state. Let k be the membership degree of the quantified value of the a-th energy efficiency index under the current state in energy efficiency state b, and k be the number of levels of energy efficiency state division by the membership function.

[0117] Dynamic weights are assigned based on fuzzy entropy, using the following formula:

[0118] ,in, This reflects the adjustment of the importance of the a-th energy efficiency indicator under uncertainty in the current state, fuzzy entropy. The larger the value (i.e., the higher the uncertainty), the higher the weight. The smaller the value, the less the impact of highly uncertain energy efficiency indicators on the overall assessment. A is the total number of energy efficiency indicators, and λ is the temperature coefficient used to adjust the sensitivity of weight allocation.

[0119] Based on dynamic weight w a The overall energy efficiency score (Score) is obtained by weighted summation of the energy efficiency indicators, using the following formula:

[0120] ,in, This is the normalized value of the quantified value of the a-th energy efficiency index under the current state. This represents the quantified value of the a-th energy efficiency index under the current state. This represents the minimum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation. It represents the maximum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation.

[0121] Understandably, since a smart park energy system is a complex system, the uncertainty of energy efficiency indicators is affected by a variety of factors. This embodiment utilizes the fuzzy entropy theory formula. This is used to quantify the uncertainty of each energy efficiency indicator under the current state. Fuzzy entropy is an indicator that measures the uncertainty of fuzzy sets and can well adapt to the complexity and uncertainty of the park's energy system. Here, the membership degree of the quantified energy efficiency indicator value under different energy efficiency states is calculated. This leads to the fuzzy entropy of the energy efficiency index under the current state. This is to reflect the degree of uncertainty of the energy efficiency index under the current conditions.

[0122] Then, based on the fuzzy entropy of the energy efficiency index under the current state... Assign dynamic weights, the formula is as follows: The temperature coefficient λ is used to adjust the sensitivity of weight allocation, and the fuzzy entropy... The larger the value (i.e., the higher the uncertainty), the higher the weight. The smaller the value, the less the impact of highly uncertain energy efficiency indicators on the overall assessment. Dynamic weight allocation can automatically adjust the weight of energy efficiency indicators according to their degree of uncertainty, allowing less uncertain energy efficiency indicators to play a greater role in the comprehensive assessment, thereby improving the accuracy and reliability of the assessment.

[0123] S104. Based on the optimal metabolic flux distribution, calculate the quantitative values ​​of each energy efficiency index under the optimal state, compare the fuzzy entropy of the energy efficiency index under the current state and the optimal state, determine the energy efficiency index to be optimized, identify high-risk and potential risks from the energy efficiency index to be optimized, and formulate targeted optimization strategies.

[0124] Optionally, the quantitative values ​​of each energy efficiency index under optimal conditions are calculated based on the optimal metabolic flux distribution, using the following formula:

[0125] ,in, This represents the quantified value of the a-th energy efficiency index under optimal conditions, reflecting the ideal energy efficiency level of the system under ideal conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device under the optimal metabolic flux distribution. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0126] Understandably, this formula By multiplying the absolute value of the metabolic flux of each device under the optimal metabolic flux distribution with the corresponding unit energy flow coefficient, and summing the results of all devices, the contribution of all devices in the park to the energy efficiency index is comprehensively considered, and the quantitative value of each energy efficiency index under the optimal state is obtained, which is used to reflect the ideal operating state of the park's energy system under the optimal metabolic flux distribution.

[0127] Since metabolic flux directly determines the energy efficiency indicators of each device. The degree of contribution. The greater the metabolic flux of a device, the greater its impact on energy efficiency indicators. For example, if the metabolic flux of a device increases, its energy consumption, carbon emissions, and cost will all increase. This will also increase accordingly. Therefore, by optimizing each metabolic flux, the optimal metabolic flux distribution can be solved. (e.g., minimizing total energy consumption or carbon emissions) to optimize various energy efficiency indicators. For example, in optimizing metabolic flux distribution, the metabolic flux of certain devices can be reduced by adjusting the operating status of the equipment, thereby reducing total energy consumption and carbon emissions, and lowering energy costs.

[0128] Further optionally, by comparing the fuzzy entropy of the energy efficiency indicators in the current state and the optimal state, the energy efficiency indicators to be optimized are determined, and high-risk and potential risks are identified from the energy efficiency indicators to be optimized, and targeted optimization strategies are formulated, including:

[0129] For each energy efficiency index under optimal conditions, its fuzzy entropy is calculated using the following formula:

[0130] ,

[0131] in, Let be the fuzzy entropy of the a-th energy efficiency index under optimal conditions, representing the degree of uncertainty of the a-th energy efficiency index under optimal conditions. Let be the membership degree of the quantified value of the a-th energy efficiency index under the optimal state in energy efficiency state b, and k be the number of levels of the membership function for energy efficiency state.

[0132] Calculate the difference in fuzzy entropy between the energy efficiency index in the current state and the optimal state. The result is compared with a preset threshold ΔH, where... Let be the fuzzy entropy of the a-th energy efficiency index under the current state;

[0133] like If the a-th energy efficiency index is defined as the energy efficiency index to be optimized, it indicates that there is a significant difference between the current state and the optimal state in terms of the a-th energy efficiency index, which needs to be focused on and optimized.

[0134] For energy efficiency indicators marked as needing optimization, comparison and Size:

[0135] like If the a-th energy efficiency indicator is identified as a high-risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is significantly higher than that under the optimal state. The factors affecting its uncertainty are analyzed, and corresponding optimization measures are formulated.

[0136] like If the a-th energy efficiency indicator is identified as a potential risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is lower than that under the optimal state, and therefore attention should be paid to it and preventive measures should be formulated in advance.

[0137] Understandably, using the fuzzy entropy theory formula Calculate the fuzzy entropy of each energy efficiency index under optimal conditions. Wherein, The fuzzy entropy of the a-th energy efficiency index under optimal conditions reflects its degree of uncertainty. Let be the membership degree of the quantified value of the 'a'-th energy efficiency index under the optimal state in energy efficiency state 'b', and let k be the number of levels of the membership function for energy efficiency states. By calculating the fuzzy entropy under the optimal state, the uncertainty of each energy efficiency index under that state can be scientifically quantified, providing a benchmark for subsequent comparisons with the current state.

[0138] Calculate the difference in fuzzy entropy of energy efficiency index between the current state and the optimal state. The difference in fuzzy entropy is then compared with a preset threshold ΔH to determine the energy efficiency index to be optimized. By calculating the difference in fuzzy entropy, the degree of difference between the current state and the optimal state in terms of the uncertainty of the energy efficiency index can be quantified, providing an objective basis for determining the index to be optimized. The preset threshold ΔH can be adjusted according to actual needs and evaluation standards.

[0139] For energy efficiency indicators marked as needing optimization, compare the fuzzy entropy of the energy efficiency indicators under the current state. Fuzzy entropy of energy efficiency index under optimal state The size of the energy efficiency index. If the fuzzy entropy of the energy efficiency index is under the current state... Fuzzy entropy greater than the energy efficiency index under optimal conditions This indicates that the uncertainty of the indicator is significantly higher in the current state than in the optimal state, thus classifying the energy efficiency indicator as a high-risk energy efficiency indicator. Factors affecting its uncertainty are analyzed, such as equipment malfunctions and energy supply fluctuations, and corresponding optimization measures are developed, such as repairing and maintaining malfunctioning equipment and optimizing energy supply strategies. Through in-depth analysis and targeted optimization of high-risk energy efficiency indicators, the uncertainty of the park's energy system can be rapidly reduced, and energy efficiency levels improved.

[0140] If the fuzzy entropy of the energy efficiency index under the current state Fuzzy entropy less than the energy efficiency index under optimal conditions If an energy efficiency indicator is deemed to have potential risks, it is identified as such. This indicates that the current system operation may be too rigid or overly constrained, resulting in stable energy efficiency performance (low uncertainty) but lacking resilience to changes. Once environmental or load fluctuations occur, the system's energy efficiency may experience a precipitous drop. For this energy efficiency indicator, preventative measures can be implemented in advance, such as establishing equipment operation early warning mechanisms and reserving emergency energy reserves, to push the park's energy system closer to its optimal state. Identifying potential risks in energy efficiency indicators allows for early detection of potential risks and the implementation of preventative measures, helping to avoid potential problems and ensuring the efficient and stable operation of the park's energy system.

[0141] In summary, the comprehensive energy efficiency assessment method for smart parks of the present invention first constructs a nonlinear metabolic matrix reflecting the energy correlation between devices to accurately capture the complex energy interaction characteristics between devices in the park. Then, mixed-integer nonlinear programming is used to solve for the optimal metabolic flux distribution, achieving dynamic optimization and balance of system energy flow, thereby effectively improving energy utilization efficiency and reducing energy waste. Next, an interaction rule base with the metabolic network is defined, and the real-time metabolic flux of each device is dynamically adjusted based on real-time sensor data. This ensures that the metabolic flux can accurately reflect the real-time changes in the park's energy demand and abnormal fluctuations in the current operating status of devices, guaranteeing the stability and reliability of energy supply. Then, the quantitative values ​​of each energy efficiency index are calculated based on the real-time metabolic flux, reflecting the energy efficiency level of the system under the current actual operating state. Fuzzy entropy theory is used to quantify the uncertainty of each energy efficiency index under the current state, reflecting the degree of uncertainty of that energy efficiency index in the current state. Dynamic weights are assigned according to the fuzzy entropy of the energy efficiency index under the current state, i.e., the weights are automatically adjusted according to the degree of uncertainty of the energy efficiency index, allowing energy efficiency indicators with lower uncertainty to play a greater role in the comprehensive assessment, thereby improving the accuracy and reliability of energy efficiency assessment. Finally, based on the optimal metabolic flux distribution, the quantitative values ​​of each energy efficiency index under the optimal state are calculated to reflect the energy efficiency level of the park's energy system under ideal operating conditions. Then, the fuzzy entropy of each energy efficiency index under the optimal state is calculated using fuzzy entropy theory to quantify the uncertainty of each energy efficiency index under the optimal state and provide a comparison benchmark. By comparing the fuzzy entropy of the energy efficiency index with that under the current state, the degree of difference between the current state and the optimal state in terms of the uncertainty of the energy efficiency index is quantified, the indicators to be optimized are determined, and high-risk and potential risks are identified from the indicators to be optimized. Targeted optimization is then carried out to solve the uncertainty factors of the park's energy system, improve the energy efficiency level, and thus ensure the efficient and stable operation of the park's energy system.

[0142] Example 2

[0143] Please see Figure 2 This invention proposes a comprehensive energy efficiency assessment system for smart parks, the system comprising:

[0144] Metabolic flux module: used to construct a nonlinear metabolic matrix that reflects the energy correlation between devices, and to solve for the optimal metabolic flux distribution through mixed integer nonlinear programming;

[0145] Real-time metabolic flux module: used to define the rule base for interaction with the metabolic network, dynamically trigger rules and adjust the real-time metabolic flux of each device based on real-time sensor data from each device;

[0146] The comprehensive energy efficiency assessment module is used to calculate the quantitative values ​​of each energy efficiency index under the current state based on the real-time metabolic flux of each device, and to quantify the uncertainty of the energy efficiency index under the current state through fuzzy entropy theory, dynamically allocate multi-objective weights, and calculate the comprehensive energy efficiency score.

[0147] Energy efficiency optimization module: It is used to calculate the quantitative values ​​of each energy efficiency index under the optimal state based on the optimal metabolic flux distribution, compare the fuzzy entropy of the energy efficiency index under the current state and the optimal state, determine the energy efficiency index to be optimized, identify high-risk and potential risks from the energy efficiency index to be optimized, and formulate targeted optimization strategies.

[0148] Further optionally, the metabolic flux module is also used for:

[0149] Based on the energy flow relationships of equipment in the park, the equipment is defined as metabolic enzymes and the energy flow as metabolites, and a nonlinear metabolic matrix is ​​constructed. Where n is the total number of devices in the park. , It is a nonlinear function used to represent the energy correlation between the i-th device and the j-th device;

[0150] Define the energy flow rate vector of the equipment as the metabolic flux vector. , Let be the metabolic flux of the i-th device, and represent the energy flow rate of the i-th device. Let be the metabolic flux of the j-th device, and let represent the energy flow rate of the j-th device.

[0151] Construct a nonlinear metabolic flux balance equation: , , where 0 is the zero vector, representing the overall balance between energy input and output in the park;

[0152] Introducing dynamic constraints, including constraints on energy supply and demand fluctuations and constraints on the number of equipment start-ups and shutdowns:

[0153] Using energy efficiency as the optimization objective and incorporating dynamic constraints, the optimal metabolic flux distribution is obtained through mixed-integer nonlinear programming. This reflects the energy flow rate of each device under optimal energy efficiency, among which, Let be the metabolic flux of the i-th device under the optimal metabolic flux distribution.

[0154] Further optionally, the metabolic flux module is also used for:

[0155] Based on energy flow costs and differences in energy flow between equipment, the energy efficiency objective function F(v) is constructed as follows:

[0156] ,in, Let i be the energy flow cost coefficient for the i-th device. The penalty coefficient for the difference in energy flow between the i-th device and the j-th device;

[0157] Minimizing the energy efficiency objective function F(v) is taken as the objective of optimizing the mixed-integer nonlinear programming problem. The optimization objective formula is:

[0158] ,

[0159] ;

[0160] The optimal metabolic flux distribution is obtained by solving the optimization objective of the mixed-integer nonlinear programming problem. .

[0161] Further optionally, the real-time metabolic flux module is also used for:

[0162] Build a rule base that includes basic rules, preference rules, and exception rules;

[0163] For each device, a rule base is matched in real time based on its sensor data. If a rule is triggered, the real-time metabolic flux is adjusted using the following formula: ,in, Let be the metabolic flux of the i-th device before adjustment. For the i-th device, trigger the flux change of the basic rule. For the i-th device, trigger the flux change of the preference rule. The flux change that triggers the abnormal rule for the i-th device;

[0164] The adjusted real-time metabolic flux of each device is fed back to the nonlinear metabolic matrix S, and the energy efficiency objective function is recalculated.

[0165] Further optionally, in the comprehensive energy efficiency assessment module, the formula for calculating the quantitative value of each energy efficiency index under the current state based on the real-time metabolic flux of each device is as follows:

[0166] ,in, This represents the quantified value of the a-th energy efficiency indicator under the current state, reflecting the energy efficiency level of the system under the current actual operating conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0167] Further optionally, the comprehensive energy efficiency assessment module is also used for:

[0168] For each energy efficiency index under the current state, its fuzzy entropy is calculated using the following formula:

[0169] ,

[0170] in, Let be the fuzzy entropy of the a-th energy efficiency index under the current state, representing the degree of uncertainty of the a-th energy efficiency index under the current state. Let k be the membership degree of the quantified value of the a-th energy efficiency index under the current state in energy efficiency state b, and k be the number of levels of energy efficiency state division by the membership function.

[0171] Dynamic weights are assigned based on fuzzy entropy, using the following formula:

[0172] ,in, This reflects the adjustment of the importance of the a-th energy efficiency indicator under uncertainty in the current state, where A is the total number of energy efficiency indicators and λ is the temperature coefficient used to adjust the sensitivity of weight allocation.

[0173] The overall energy efficiency score (Score) is obtained by weighting and summing the dynamic weighted energy efficiency indicators. The formula is as follows:

[0174] ,in, This is the normalized value of the quantified value of the a-th energy efficiency index under the current state. This represents the quantified value of the a-th energy efficiency index under the current state. This represents the minimum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation. It represents the maximum value among all quantified values ​​of the a-th energy efficiency indicator in actual operation.

[0175] Optionally, in the energy efficiency optimization module, the formula for calculating the quantified values ​​of each energy efficiency index under optimal conditions based on the optimal metabolic flux distribution is as follows:

[0176] ,in, This represents the quantified value of the a-th energy efficiency index under optimal conditions, reflecting the ideal energy efficiency level of the system under ideal conditions. Energy efficiency indicators include total energy consumption, carbon emissions, and energy costs. Let be the metabolic flux of the i-th device under the optimal metabolic flux distribution. Let be the unit energy flow coefficient of the a-th energy efficiency index of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n be the total number of devices in the park.

[0177] Further optionally, the energy efficiency optimization module is also used for:

[0178] For each energy efficiency index under optimal conditions, its fuzzy entropy is calculated using the following formula:

[0179] ,

[0180] in, Let be the fuzzy entropy of the a-th energy efficiency index under optimal conditions, representing the degree of uncertainty of the a-th energy efficiency index under optimal conditions. Let be the membership degree of the quantified value of the a-th energy efficiency index under the optimal state in energy efficiency state b, and k be the number of levels of the membership function for energy efficiency state.

[0181] Calculate the difference in fuzzy entropy between the energy efficiency index in the current state and the optimal state. The result is compared with a preset threshold ΔH, where... Let be the fuzzy entropy of the a-th energy efficiency index under the current state;

[0182] like If the a-th energy efficiency index is defined as the energy efficiency index to be optimized, it indicates that there is a significant difference between the current state and the optimal state in terms of the a-th energy efficiency index, which needs to be focused on and optimized.

[0183] For energy efficiency indicators marked as needing optimization, comparison and Size:

[0184] like If the a-th energy efficiency indicator is identified as a high-risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is significantly higher than that under the optimal state. The factors affecting its uncertainty are analyzed, and corresponding optimization measures are formulated.

[0185] like If the a-th energy efficiency indicator is identified as a potential risk energy efficiency indicator, it indicates that the uncertainty of the a-th energy efficiency indicator under the current state is lower than that under the optimal state, and therefore attention should be paid to it and preventive measures should be formulated in advance.

[0186] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A comprehensive energy efficiency evaluation method for a smart park, characterized in that, The method comprises: constructing a nonlinear metabolic matrix reflecting the energy correlation relationship between devices, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; defining an interaction rule library of the metabolic network, dynamically triggering the rules and adjusting the real-time metabolic flux of each device according to real-time sensor data of each device; calculating the quantitative value of each energy efficiency indicator in the current state according to the real-time metabolic flux of each device, quantifying the uncertainty of the energy efficiency indicator in the current state through fuzzy entropy theory, dynamically allocating multi-objective weights, and calculating the comprehensive energy efficiency score; comparing the fuzzy entropy of the energy efficiency indicators in the current state and the optimal state, determining the energy efficiency indicators to be optimized, identifying high-risk and potential risks from the energy efficiency indicators to be optimized, and formulating a targeted optimization strategy.

2. The method for comprehensive energy efficiency evaluation of a smart park according to claim 1, wherein, The construction of the nonlinear metabolic matrix reflecting the energy correlation relationship between devices, and the solving of the optimal metabolic flux distribution through mixed integer nonlinear programming, comprises: According to the energy flow relationship of the park equipment, the equipment is defined as a metabolic enzyme, the energy flow is defined as a metabolite, and a nonlinear metabolic matrix is constructed wherein n is the total number of park equipment, , is a nonlinear function for representing the energy correlation between the i th equipment and the j th equipment; The device energy flow rate vector is defined as the metabolic flux vector , is the metabolic flux of the ith device, and is the metabolic flux of the jth device, and represents the energy flow rate of the jth device. The nonlinear metabolic flux balance equation is constructed as follows: , where 0 is a zero vector, representing the overall balance of energy input and output of the park. introducing dynamic constraints, including energy supply and demand fluctuation constraints and device start-stop frequency limit constraints: With the energy efficiency objective function as the optimization objective, combined with dynamic constraints, the optimal metabolic flux distribution is obtained by solving the mixed integer nonlinear programming , reflecting the energy flow rate of each device under the optimal energy efficiency, wherein, is the metabolic flux of the i-th device under the optimal metabolic flux distribution.

3. The integrated energy efficiency evaluation method for a smart park according to claim 2, wherein, The energy efficiency target function is used as an optimization target, dynamic constraints are combined, and optimal metabolic flux distribution is obtained by mixed integer nonlinear programming , comprising: constructing an energy efficiency objective function F(v) based on energy flow cost and energy flow difference between devices: wherein, is the energy flow cost coefficient of the i-th device, is the penalty coefficient of the energy flow difference between the i-th device and the j-th device; taking the minimization of the energy efficiency objective function F(v) as the target of the optimization of the mixed integer nonlinear programming problem, and the optimization target formula is: ， ； By solving the optimization objective of mixed integer nonlinear programming problem, the optimal metabolic flux distribution is obtained . 4.The method for comprehensive energy efficiency evaluation of a smart park according to claim 2, wherein, The definition of the interaction rule library of the metabolic network, the dynamic triggering of the rules and the adjustment of the real-time metabolic flux of each device according to the real-time sensor data of each device, comprises: constructing a rule library containing basic rules, preference rules and abnormal rules; For each device, the rule library is matched in real-time according to its sensor data, and if a rule is triggered, the real-time metabolic flux is adjusted, with the adjustment formula being: wherein, is the metabolic flux of the i-th device before adjustment, is the flux change of the i-th device triggered by the basic rule, is the flux change of the i-th device triggered by the preference rule, is the flux change of the i-th device triggered by the exception rule. feedback the adjusted real-time metabolic flux of each device to the nonlinear metabolic matrix S, and recalculate the energy efficiency objective function.

5. The method for integrated energy efficiency assessment of smart park according to claim 1, wherein, The calculation of the quantitative value of each energy efficiency indicator in the current state according to the real-time metabolic flux of each device, the formula is: wherein, represents the quantized value of the a-th energy efficiency indicator in the current state, reflecting the energy efficiency level of the system under the current actual operating state, and the energy efficiency indicators include total energy consumption, carbon emissions and energy cost, is the metabolic flux of the i-th device, is the unit energy flow coefficient of the a-th energy efficiency indicator of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n is the total number of park devices.

6. The integrated energy efficiency assessment method for a smart park according to claim 5, wherein, The quantification of the uncertainty of the energy efficiency indicator in the current state through fuzzy entropy theory, the dynamic allocation of multi-objective weights, and the calculation of the comprehensive energy efficiency score, comprises: for each energy efficiency indicator in the current state, calculate its fuzzy entropy, the formula is: ， wherein, is the fuzzy entropy of the a-th energy efficiency index in the current state, indicating the degree of uncertainty of the a-th energy efficiency index in the current state, is the membership degree of the a-th energy efficiency index quantitative value in the current state in the energy efficiency state b, and k is the number of partition levels of the membership function to the energy efficiency state. distributing dynamic weights according to the fuzzy entropy, the formula is: wherein, reflects the importance adjustment of the a-th energy efficiency indicator under uncertainty of the current state, A is the total number of energy efficiency indicators, and λ is a temperature coefficient for adjusting the sensitivity of the weight distribution; weighting and summing the energy efficiency indicators according to the dynamic weights to obtain the comprehensive energy efficiency score Score, the formula is: wherein, is a normalized value of the a-th energy efficiency index quantization value in the current state, is a quantization value of the a-th energy efficiency index in the current state, is a minimum value of all quantization values of the a-th energy efficiency index in the actual operation, is a maximum value of all quantization values of the a-th energy efficiency index in the actual operation.

7. The integrated energy efficiency assessment method for a smart park according to claim 2, wherein, The calculation of the quantitative value of each energy efficiency indicator in the optimal state according to the optimal metabolic flux distribution, the formula is: wherein, represents the quantized value of the a-th energy efficiency indicator under the optimal state, reflecting the ideal energy efficiency level under the ideal state of the system, and the energy efficiency indicators include total energy consumption, carbon emissions and energy cost, is the metabolic flux of the i-th device under the optimal metabolic flux distribution, is the unit energy flow coefficient of the a-th energy efficiency indicator of the i-th device, including unit energy consumption, unit carbon emission and unit cost, and n is the total number of park devices.

8. The integrated energy efficiency assessment method for a smart park according to claim 7, wherein, The comparison of the fuzzy entropy of the energy efficiency indicators in the current state and the optimal state, the determination of the energy efficiency indicators to be optimized, the identification of high-risk and potential risks from the energy efficiency indicators to be optimized, and the formulation of a targeted optimization strategy, comprises: for each energy efficiency indicator in the optimal state, calculate its fuzzy entropy, the formula is: ， wherein, is the fuzzy entropy of the a-th energy efficiency index in the optimal state, indicating the degree of uncertainty of the a-th energy efficiency index in the optimal state, is the membership degree of the quantitative value of the a-th energy efficiency index in the optimal state under the energy efficiency state b, and k is the number of division levels of the membership function to the energy efficiency state. The difference between the fuzzy entropy of the energy efficiency index in the current state and the optimal state is compared with a preset threshold value ΔH, wherein is the fuzzy entropy of the a-th energy efficiency index in the current state If , the a-th energy efficiency index is set as the energy efficiency index to be optimized, indicating that the current state and the optimal state have significant differences in the a-th energy efficiency index, which needs to be focused on and optimized. For the energy efficiency indicators marked to be optimized, compare the size of with If , the a-th energy efficiency index is determined as a high-risk energy efficiency index, indicating that the uncertainty of the a-th energy efficiency index under the current state is significantly higher than that under the optimal state, factors affecting the uncertainty are analyzed, and corresponding optimization measures are developed; If , the ath energy efficiency index is determined as a potential risk energy efficiency index, indicating that the uncertainty of the ath energy efficiency index under the current state is lower than that under the optimal state, and attention and preventive measures are made in advance.

9. A comprehensive energy efficiency evaluation system for a smart park, used to implement the comprehensive energy efficiency evaluation method for a smart park in any one of claims 1-8, characterized in that, The system comprises: a metabolic flux module for constructing a nonlinear metabolic matrix reflecting the energy correlation relationship between devices, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; a real-time metabolic flux module for defining an interaction rule library of the metabolic network, dynamically triggering the rules and adjusting the real-time metabolic flux of each device according to real-time sensor data of each device; The comprehensive energy efficiency evaluation module is configured to calculate quantitative values of each energy efficiency index in the current state according to actual metabolic fluxes of each device, quantify uncertainty of the energy efficiency index in the current state through fuzzy entropy theory, dynamically allocate multi-objective weights, and calculate a comprehensive energy efficiency score. The energy efficiency optimization module is configured to calculate quantitative values of each energy efficiency index in the optimal state according to the optimal metabolic flux distribution, compare fuzzy entropies of the energy efficiency index in the current state and the optimal state, determine energy efficiency indexes to be optimized, identify high risks and potential risks from the energy efficiency indexes to be optimized, and formulate a targeted optimization strategy.

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