A plant operation data-based equipment energy efficiency optimization method and system

Abstract

The application discloses a kind of equipment energy efficiency optimization method and system based on plant data, it is related to the technical field of artificial intelligence.The method comprises: real-time acquisition equipment operating parameter, vibration energy data and user behavior information, constructs three-dimensional feature matrix;Vibration energy data and user behavior information are analyzed, vibration energy potential value and user behavior compliance degree are obtained respectively;Vibration energy potential value and user behavior compliance degree are included in energy efficiency optimization objective function, and equipment coordination strategy is dynamically generated;Obtain equipment failure parameter and external environment parameter, quantify the influence of equipment health status and external environment change on energy efficiency, dynamically adjust energy efficiency optimization objective function, generate equipment coordination optimization strategy, for equipment energy efficiency optimization management.The application realizes the whole process closed loop from data perception to intelligent decision, and finally realizes the intellectualization and sustainability of equipment energy efficiency management in complex dynamic environment.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method and system for optimizing equipment energy efficiency based on plant data. Background Technology

[0002] Plant data refers to a collection of various data related to the operation, maintenance, energy, environment, and safety management of a factory or production facility. It is used to ensure a stable production environment, optimize resource allocation, and improve operational efficiency. Optimizing equipment energy efficiency based on plant data is a key path for modern industry to improve energy utilization efficiency, reduce operating costs, and achieve green manufacturing. Its core objective is to achieve intelligent, green, and safe management of the factory.

[0003] In related technologies, traditional equipment energy efficiency optimization methods focus only on a single parameter, ignoring the synergistic effects of external factors, resulting in data silos and a lack of multi-dimensional correlations. Furthermore, the generated energy efficiency optimization objective functions are usually based on historical data or fixed rules, making it difficult to adapt to dynamic environmental changes, thus requiring improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing equipment energy efficiency based on plant data, so as to solve the problems mentioned in the background art.

[0005] Firstly, this application provides a method for optimizing equipment energy efficiency based on plant data, which adopts the following technical solution:

[0006] Real-time acquisition of equipment operating parameters, vibration energy data, and user behavior information; alignment with timestamps; construction of a three-dimensional feature matrix.

[0007] By analyzing vibration energy data and user behavior information, vibration energy potential value and user behavior compliance are obtained respectively.

[0008] By incorporating vibration energy potential and user behavior compliance into the energy efficiency optimization objective function, a device collaboration strategy is dynamically generated to obtain the optimal device collaboration path.

[0009] Acquire equipment fault parameters and external environmental parameters, quantify the impact of equipment health status and external environmental changes on energy efficiency, dynamically adjust the energy efficiency optimization objective function, and generate equipment collaborative optimization strategies for equipment energy efficiency optimization management.

[0010] Preferably, the steps of analyzing vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance, respectively, are as follows:

[0011] The vibration energy data is preprocessed to obtain standard vibration energy data, which includes angular frequency ω and acceleration amplitude A.

[0012] Through formula The vibration energy density E is calculated, where m is the mass of the vibrating equipment;

[0013] Based on the vibration energy density E, using the formula The potential value P of the recoverable electrical energy per unit time, i.e., the vibration energy, is calculated, where, This refers to energy conversion efficiency, also known as electromagnetic conversion efficiency.

[0014] The vibration energy potential value is compared with the preset vibration energy threshold. If the vibration energy potential value exceeds the preset vibration energy threshold, the equipment will experience abnormal vibration, triggering a fault alarm.

[0015] Analyze user behavior information to determine user behavior compliance.

[0016] Preferably, the steps for analyzing user behavior information to obtain user behavior compliance are as follows:

[0017] The user behavior information includes the number of device operations F1 performed by the user per unit time, the parameter adjustment range A1 for each operation, and the difference between the device task priority set by the user and the system recommended priority B1.

[0018] Define the safe operation frequency threshold F max The formula F = 1 - (F1 / F) max The compliance degree of operation frequency, F, is calculated by multiplying n1 by n1, where n1 is a weighting coefficient and is not equal to zero.

[0019] Define parameter adjustment range threshold A max The formula A = 1 - (A1 / A) is used to... max The compliance degree of parameter adjustment range, A, is calculated by multiplying n2 by n2, where n2 is a weighting coefficient and is not equal to zero.

[0020] The matching degree of the device task priority set by the user is calculated by the formula B=1-B1×n3, where n3 is the weight coefficient and is not equal to zero.

[0021] The user behavior compliance is calculated by combining the compliance of operation frequency (F), parameter adjustment range (A), and device task priority matching degree (B).

[0022] Preferably, the steps for calculating user behavior compliance based on the comprehensive compliance of operation frequency (F), parameter adjustment range (A), and device task priority matching degree (B) are as follows:

[0023] The user behavior compliance degree D is calculated by weighting and combining the operation frequency compliance degree F, the parameter adjustment range compliance degree A, and the device task priority matching degree B, and then using the formula D=F×n4+A×n5+B×n6.

[0024] Set a compliance threshold range [d1, d2], and compare the user behavior compliance level D with the compliance threshold range;

[0025] If the user's behavior compliance level D is in [d1, d2], then the user's behavior constitutes a minor violation, triggering a warning and a minor penalty;

[0026] If the user's behavior compliance level D is in [0, d1], then the user's behavior constitutes a serious violation, triggering mandatory penalties and permission restrictions.

[0027] Preferably, the steps of incorporating vibration energy potential and user behavior compliance into the energy efficiency optimization objective function to dynamically generate equipment coordination strategies are as follows:

[0028] Normalize the vibration energy potential value P. The standard value of vibration energy potential P0 is obtained;

[0029] Construct the objective function for optimizing equipment energy consumption , where P i This represents the real-time energy consumption of device i. This refers to energy conversion efficiency, also known as electromagnetic conversion efficiency.

[0030] Based on the user behavior compliance, the user behavior compliance penalty item D0 is calculated using the formula D0=1-D.

[0031] Extract the vibration energy density E and construct a comprehensive objective function for optimizing equipment energy efficiency. , where w1 and w2 are weighting coefficients and neither is equal to zero;

[0032] Based on the comprehensive objective function, the comprehensive objective function f is selected. 总 The path with the smallest value is the globally optimal path, thus obtaining the device collaborative optimization path.

[0033] Preferably, the steps for acquiring equipment fault parameters and external environmental parameters, quantifying the impact of equipment health status and external environmental changes on energy efficiency, dynamically adjusting the energy efficiency optimization objective function, and generating equipment collaborative optimization strategies for equipment energy efficiency optimization management are as follows:

[0034] Obtain equipment fault parameters, including the number of equipment faults g and the equipment running time t, and calculate the equipment health K using the formula K=1-g / t;

[0035] Obtain external environmental parameters, including ambient temperature parameter T and energy price parameter J;

[0036] Establish the mapping relationship between equipment fault parameters, external environmental parameters and equipment energy efficiency to obtain the energy efficiency degradation model. ,in The baseline energy efficiency is given by k, which is the influencing factor, and v1 and v2 are weighting coefficients, both of which are not equal to zero.

[0037] Based on the energy efficiency degradation model, the comprehensive objective function for equipment energy efficiency optimization is dynamically adjusted to generate a collaborative optimization strategy for equipment energy efficiency optimization management.

[0038] Preferably, the steps for dynamically adjusting the comprehensive objective function for equipment energy efficiency optimization based on the energy efficiency degradation model, and generating a collaborative optimization strategy for equipment energy efficiency optimization management, are as follows:

[0039] Obtain the predicted temperature parameters and predicted electricity price parameters for a future set time period, input the predicted temperature parameters and predicted electricity price parameters into the energy efficiency degradation model, and generate the predicted energy efficiency curve;

[0040] Set an energy efficiency degradation threshold, extract the period in the predicted energy efficiency curve where the energy efficiency decline exceeds the set energy efficiency degradation threshold as the energy efficiency high-risk period, and trigger an early warning signal;

[0041] Based on the warning signal, identify non-critical task production equipment in the equipment collaborative optimization path and shut down the operation of non-critical task production equipment;

[0042] Simultaneously, the comprehensive objective function for optimizing equipment energy efficiency is dynamically adjusted to generate a collaborative optimization strategy for equipment energy efficiency management.

[0043] Preferably, the steps for simultaneously and dynamically adjusting the comprehensive objective function for equipment energy efficiency optimization and generating a collaborative optimization strategy for equipment energy efficiency optimization management are as follows:

[0044] Obtain the carbon emission cost parameter C, extract the ambient temperature parameter T and energy price parameter J, and define the environmental impact factor H = v3×T + v4×J + v5×C, where v3, v4, and v5 are all weighting coefficients and none of them are equal to zero.

[0045] Extracting benchmark performance The vibration energy density E and the user behavior compliance penalty term D0 are used to dynamically adjust the comprehensive objective function for equipment energy efficiency optimization, resulting in the standard objective function for equipment energy efficiency optimization. ;

[0046] Based on the standard objective function, the standard objective function f is selected. 标The path with the largest value is the globally optimal path, which yields the standard path for device collaboration, used for device energy efficiency optimization and management.

[0047] Secondly, this application provides an equipment energy efficiency optimization system based on plant data, which adopts the following technical solution:

[0048] A plant data-based equipment energy efficiency optimization system includes:

[0049] The multi-source data acquisition module collects equipment operating parameters, vibration energy data, and user behavior information in real time, aligns the timestamps, and constructs a three-dimensional feature matrix.

[0050] The vibration energy and user behavior analysis module analyzes vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance, respectively.

[0051] The device collaboration strategy generation module incorporates vibration energy potential and user behavior compliance into the energy efficiency optimization objective function, dynamically generates device collaboration strategies, and obtains the optimal path for device collaboration.

[0052] The equipment collaborative optimization strategy generation module acquires equipment fault parameters and external environment parameters, quantifies the impact of equipment health status and external environment changes on energy efficiency, dynamically adjusts the energy efficiency optimization objective function, and generates equipment collaborative optimization strategies for equipment energy efficiency optimization management.

[0053] In summary, this application includes at least one of the following beneficial technical effects:

[0054] 1. Integrating equipment status, vibration energy, and user behavior into a unified multi-dimensional data structure facilitates subsequent analysis and modeling, breaking the limitations of traditional single-dimensional analysis. It comprehensively considers equipment, environmental, and human factors, providing complete data support for energy efficiency optimization. Vibration energy data identifies energy loss points during equipment operation, quantifying energy utilization efficiency under different operating conditions. User behavior analysis uses a rule engine to determine whether user operations comply with energy-saving standards, providing feedback to encourage users to adjust their operating habits to reduce energy consumption. This achieves a dual energy efficiency improvement path of "equipment optimization" and "user guidance," avoiding global efficiency losses caused by independent optimization of single equipment. Fault parameters assess equipment health, predicting the impact of potential faults on energy efficiency. When equipment health declines, priority is given to ensuring equipment lifespan rather than simply pursuing energy efficiency. Environmental prediction anticipates future impacts, allowing for proactive strategy adjustments. This enables the system to adapt to uncertainties such as equipment aging and environmental changes, preventing energy efficiency optimization failures due to unforeseen factors. It also balances short-term energy efficiency improvements with long-term equipment health, extending equipment lifespan and reducing maintenance costs. This achieves a closed-loop process from data perception to intelligent decision-making, ultimately enabling optimal energy efficiency management of equipment in complex and dynamic environments.

[0055] 2. Vibration energy density is calculated using formulas, converting vibration signals into energy indicators that intuitively reflect the intensity of equipment vibration energy, providing a quantitative basis for energy recovery or fault diagnosis. Abnormal high-frequency or low-frequency energy peaks may correspond to specific faults. Formulas are used to calculate the recoverable electrical energy per unit time, quantifying the potential of equipment vibration energy to be converted into electrical energy, guiding the deployment of energy recovery systems, and installing vibration power generation devices in high-potential areas. The calculated P is compared with a preset threshold to quickly identify abnormal vibrations, preventing further equipment damage and allowing for intervention before the fault escalates, reducing maintenance costs and the risk of production interruption. A rules engine analyzes user operation records to quantify whether user behavior complies with energy-saving standards, reducing energy waste caused by human error. Compliance scoring provides feedback, encouraging users to optimize their operating habits and improve overall energy efficiency.

[0056] 3. Obtain predicted temperature and electricity price parameters for a future time period through meteorological forecasts and the electricity market. Input these parameters into the energy efficiency degradation model to obtain the predicted energy efficiency curve for the future period. This predictive data allows for early identification of energy efficiency decline trends, preventing energy losses due to sudden environmental changes or electricity price fluctuations. Set energy efficiency degradation thresholds based on historical data or business needs to identify periods of significant energy efficiency decline in advance, avoiding reactive responses and reducing the impact of sudden risks on production. Differentiate between critical and non-critical production equipment (i.e., core production line equipment, auxiliary equipment, and backup equipment). Shut down non-critical equipment during high-risk periods to ensure the stable operation of critical equipment, while reducing unnecessary energy consumption and significantly lowering operating costs. Add constraints for high-risk energy efficiency periods to the original comprehensive objective function to generate a new objective function. Achieve global energy efficiency optimization through iterative optimization. Attached Figure Description

[0057] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of the equipment energy efficiency optimization method based on plant data according to the present invention.

[0058] Figure 2 This is a schematic diagram of the module connection of an embodiment of an equipment energy efficiency optimization system based on plant data according to the present invention. Detailed Implementation

[0059] The following examples and... Figures 1-2 The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0060] This invention discloses a method for optimizing equipment energy efficiency based on plant data, specifically including the following steps:

[0061] Step S1: Collect equipment operating parameters, vibration energy data and user behavior information in real time, align timestamps, and construct a three-dimensional feature matrix;

[0062] Step S2: Analyze the vibration energy data and user behavior information to obtain the vibration energy potential value and user behavior compliance, respectively.

[0063] Step S3: Incorporate the vibration energy potential value and user behavior compliance into the energy efficiency optimization objective function, dynamically generate the equipment collaboration strategy, and obtain the optimal path for equipment collaboration;

[0064] Step S4: Obtain equipment fault parameters and external environment parameters, quantify the impact of equipment health status and external environment changes on energy efficiency, dynamically adjust the energy efficiency optimization objective function, and generate equipment collaborative optimization strategies for equipment energy efficiency optimization management.

[0065] In practical applications, equipment status, vibration energy, and user behavior are integrated into a unified multi-dimensional data structure, facilitating subsequent analysis and modeling. This breaks the limitations of traditional single-dimensional analysis, comprehensively considering equipment, environmental, and human factors to provide complete data support for energy efficiency optimization. Vibration energy data identifies energy loss points during equipment operation, quantifying the energy utilization efficiency of equipment under different operating conditions. User behavior analysis uses a rule engine to determine whether user operations comply with energy-saving standards, providing feedback to users and encouraging them to adjust their operating habits to reduce energy consumption, achieving a dual energy efficiency improvement path of "equipment optimization" and "user guidance." Vibration energy potential and user behavior compliance are used as constraints in the objective function. Optimization algorithms are used to calculate equipment collaboration strategies in real time, generating optimal collaborative paths and avoiding global efficiency losses caused by independent optimization of single equipment, achieving system-level energy efficiency improvement. Fault parameters assess equipment health, predicting the impact of potential faults on energy efficiency. When equipment health declines, priority is given to ensuring equipment lifespan rather than simply pursuing energy efficiency. Environmental factors affect equipment efficiency, requiring dynamic modeling of their impact. Environmental prediction forecasts anticipate future impacts and adjust strategies in advance. This enables the system to adapt to uncertainties such as equipment aging and environmental changes, preventing energy efficiency optimization from failing due to unforeseen factors. It also balances short-term energy efficiency improvements with long-term equipment health, extending equipment lifespan and reducing maintenance costs. It achieves a closed-loop process from data perception to intelligent decision-making, ultimately enabling optimal energy efficiency management in complex and dynamic environments.

[0066] The steps for analyzing vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance are as follows:

[0067] Step S21: Preprocess the vibration energy data to obtain standard vibration energy data, which includes angular frequency ω and acceleration amplitude A.

[0068] Step S22, using the formula The vibration energy density E is calculated, where m is the mass of the vibrating equipment;

[0069] Step S23, based on the vibration energy density E, using the formula The potential value P of the recoverable electrical energy per unit time, i.e., the vibration energy, is calculated, where, This refers to energy conversion efficiency, also known as electromagnetic conversion efficiency.

[0070] Step S24: Compare the vibration energy potential value with the preset vibration energy threshold. If the vibration energy potential value exceeds the preset vibration energy threshold, the equipment will experience abnormal vibration, triggering a fault alarm.

[0071] Step S25: Analyze user behavior information to obtain user behavior compliance.

[0072] In practical applications, the vibration energy density E is calculated using a formula, converting the vibration signal into an energy index that intuitively reflects the intensity of equipment vibration energy, providing a quantitative basis for energy recovery or fault diagnosis. Abnormal high-frequency or low-frequency energy peaks may correspond to specific faults, such as increased high-frequency energy in bearing faults or concentrated low-frequency energy due to imbalance. The formula calculates the recoverable electrical energy per unit time, quantifying the potential of equipment vibration energy to be converted into electrical energy, guiding the deployment of energy recovery systems, and installing vibration power generation devices in high-potential areas. The calculated P is compared with a preset threshold, allowing for rapid identification of abnormal vibrations, preventing further equipment damage, and enabling intervention before the fault escalates, reducing maintenance costs and production interruption risks. A rule engine analyzes user operation records, quantifying whether user behavior complies with energy-saving standards, reducing energy waste caused by human error, and providing feedback through compliance scoring to encourage users to optimize their operating habits and improve overall energy efficiency.

[0073] The steps for analyzing user behavior information to determine user behavior compliance are as follows:

[0074] Step S251, the user behavior information includes the number of device operations F1 performed by the user within a unit of time, the parameter adjustment range A1 for each operation, and the difference B1 between the device task priority set by the user and the system recommended priority;

[0075] Step S252, define the safe operation frequency threshold F max The formula F = 1 - (F1 / F) max The compliance degree of operation frequency, F, is calculated by multiplying n1 by n1, where n1 is a weighting coefficient and is not equal to zero.

[0076] Step S253, define parameter adjustment range threshold A max The formula A = 1 - (A1 / A) is used to... maxThe compliance degree of parameter adjustment range, A, is calculated by multiplying n2 by n2, where n2 is a weighting coefficient and is not equal to zero.

[0077] Step S254: The matching degree B of the device task priority set by the user is calculated by the formula B=1-B1×n3, where n3 is the weight coefficient and is not equal to zero.

[0078] Step S255: Calculate the user behavior compliance by combining the compliance of operation frequency (F), parameter adjustment range (A), and device task priority matching degree (B).

[0079] In practical applications, abstract user operations are transformed into quantifiable metrics. High equipment operation frequency can lead to equipment overload or fatigue damage, such as frequent motor start-stop; excessive parameter adjustments can cause equipment malfunctions, such as sudden load changes causing increased vibration; and a significant discrepancy between user-defined equipment task priorities and system-recommended priorities can reduce overall system efficiency. By comprehensively considering the compliance of operation frequency, parameter adjustment magnitude, and equipment task priority matching, user behavior compliance is calculated, balancing the combined impact of operation frequency, parameter adjustments, and task priorities to avoid bias from a single metric. Through threshold setting and compliance calculation, overclocking operations and abnormal parameter adjustments are identified, preventing equipment overload or malfunctions. Task priority matching analysis helps users optimize their operation processes and reduce resource waste. Simultaneously, by combining user behavior data with system strategies, a dynamic balance between "user behavior compliance" and "system efficiency" is achieved.

[0080] The steps for calculating user behavior compliance based on the comprehensive compliance scores of operation frequency (F), parameter adjustment range (A), and device task priority (B) are as follows:

[0081] Step S2551: The compliance degree of operation frequency F, the compliance degree of parameter adjustment range A, and the matching degree of device task priority B are weighted and combined, and the user behavior compliance degree D is calculated by formula D=F×n4+A×n5+B×n6.

[0082] Step S2552: Set the compliance threshold range [d1, d2] and compare the user behavior compliance D with the compliance threshold range;

[0083] Step S2553: If the user's behavior compliance level D is in [d1, d2], then the user's behavior has a minor violation, triggering a warning and a minor penalty;

[0084] Step S2554: If the user behavior compliance level D is in [0, d1], then the user behavior has serious violations, triggering mandatory penalties and permission restrictions.

[0085] In practical applications, scattered compliance indicators are transformed into a unified comprehensive score (D), facilitating rapid assessment of the overall compliance of user behavior. Weights are allocated based on scenario requirements; for example, parameter adjustments have higher weight in high-risk equipment, highlighting key risk indicators and enabling dynamic risk assessment. Compliance threshold ranges are set to accurately identify minor and serious violations, allowing for differentiated measures based on the severity of the violation, avoiding user resistance caused by a "one-size-fits-all" approach. Simultaneously, the system ensures automatic intervention within a controllable risk range, reducing human error. Warning and penalty mechanisms create positive incentives; for example, improved compliance can restore privileges, encouraging users to proactively adjust their behavior to improve compliance. At the same time, high-risk operation privileges are restricted to reduce the probability of equipment damage, energy waste, or safety incidents.

[0086] The steps for incorporating vibration energy potential and user behavior compliance into the energy efficiency optimization objective function to dynamically generate equipment coordination strategies are as follows:

[0087] Step S31: Normalize the vibration energy potential value P. The standard value of vibration energy potential P0 is obtained;

[0088] Step S32: Construct the objective function for optimizing equipment energy consumption. , where P i This represents the real-time energy consumption of device i. This refers to energy conversion efficiency, also known as electromagnetic conversion efficiency.

[0089] Step S33: Based on the user behavior compliance, calculate the user behavior compliance penalty item D0 using the formula D0=1-D;

[0090] Step S34: Extract the vibration energy density E and construct a comprehensive objective function for optimizing equipment energy efficiency. , where w1 and w2 are weighting coefficients and neither is equal to zero;

[0091] Step S35: Based on the comprehensive objective function, select the comprehensive objective function f. 总 The path with the smallest value is the globally optimal path, thus obtaining the device collaborative optimization path.

[0092] In practical applications, the vibration energy potential value is normalized to eliminate the influence of extreme values ​​on the objective function. For example, when a device malfunctions and P suddenly increases, normalization still maintains a reasonable weight. Constructing an objective function for device energy consumption optimization directly quantifies the energy cost of device operation and is the fundamental objective of energy efficiency optimization. Real-time energy consumption can be dynamically adjusted according to device status to ensure the timeliness of the strategy. User behavior compliance is transformed into a penalty, ensuring that the optimization strategy not only focuses on energy efficiency but also enforces compliance with user behavior. For example, when frequent device starts and stops cause increased vibration, D decreases and D0 increases, and the system will prioritize more compliant paths. The optimization algorithm searches all possible device coordination paths and selects the one that maximizes f. 综 The shortest path is the globally optimal path, adapting to changes in equipment status, user behavior, and environment. At the same time, redundant operation is reduced through equipment coordination strategies, achieving intelligent management of equipment energy efficiency.

[0093] The steps involved in acquiring equipment fault parameters and external environmental parameters, quantifying the impact of equipment health status and external environmental changes on energy efficiency, dynamically adjusting the energy efficiency optimization objective function, and generating equipment collaborative optimization strategies for equipment energy efficiency optimization management are as follows:

[0094] Step S41: Obtain equipment fault parameters, including the number of equipment faults g and the equipment running time t. The equipment health K is calculated using the formula K=1-g / t.

[0095] Step S42: Obtain external environmental parameters, including ambient temperature parameter T and energy price parameter J;

[0096] Step S43: Establish the mapping relationship between equipment fault parameters, external environmental parameters, and equipment energy efficiency to obtain the energy efficiency degradation model. ,in The baseline energy efficiency is given by k, which is the influencing factor, and v1 and v2 are weighting coefficients, both of which are not equal to zero.

[0097] Step S44: Based on the energy efficiency degradation model, dynamically adjust the comprehensive objective function for equipment energy efficiency optimization, and generate a collaborative optimization strategy for equipment energy efficiency optimization management.

[0098] In practical applications, equipment health is calculated by the number of equipment failures and equipment uptime, transforming the abstract equipment status into quantifiable indicators. A higher K value indicates healthier equipment. If the K value is too low, a maintenance reminder is triggered to prevent energy efficiency degradation or downtime due to equipment failure. Temperature affects equipment efficiency; for example, high temperatures reduce motor efficiency, requiring dynamic adjustments to load distribution. Electricity prices determine economic efficiency; for instance, increasing electricity consumption during periods of low electricity prices can reduce operating costs. By dynamically adjusting optimization strategies using real-time data, the limitations of static models are avoided. Furthermore, by comprehensively considering energy efficiency, cost, and equipment health, suboptimal solutions caused by a single objective are avoided. Combining electricity price strategies to reduce operating costs and energy efficiency optimization to reduce carbon emissions achieves intelligent and dynamic energy efficiency management.

[0099] Based on the energy efficiency degradation model, the comprehensive objective function for optimizing equipment energy efficiency is dynamically adjusted to generate a collaborative optimization strategy for equipment energy efficiency management. The specific steps are as follows:

[0100] Step S441: Obtain the predicted temperature parameters and predicted electricity price parameters for a future set time period, input the predicted temperature parameters and predicted electricity price parameters into the energy efficiency degradation model, and generate the predicted energy efficiency curve;

[0101] Step S442: Set an energy efficiency degradation threshold, extract the period in the predicted energy efficiency curve where the energy efficiency decline exceeds the set energy efficiency degradation threshold as a high-risk period for energy efficiency, and trigger an early warning signal;

[0102] Step S443: Based on the warning signal, identify the non-critical task production equipment in the equipment collaborative optimization path and shut down the operation of the non-critical task production equipment;

[0103] Step S444: Simultaneously, dynamically adjust the comprehensive objective function for equipment energy efficiency optimization to generate a collaborative optimization strategy for equipment energy efficiency optimization management.

[0104] In practical applications, predicted temperature and electricity price parameters for a given future time period are obtained through meteorological forecasts and the electricity market. These parameters are then input into an energy efficiency degradation model to obtain a predicted energy efficiency curve for the future period. This predictive data allows for the early identification of energy efficiency decline trends, preventing energy losses due to sudden environmental changes or electricity price fluctuations. Energy efficiency degradation thresholds are set based on historical data or business needs to identify periods of significant energy efficiency decline, such as peak-temperature electricity price periods, thus avoiding reactive responses and reducing the impact of sudden risks on production. A distinction is made between critical and non-critical production equipment—that is, core production line equipment, auxiliary equipment, and backup equipment. During high-risk periods, non-critical equipment is shut down to ensure the stable operation of critical equipment, while simultaneously reducing unnecessary energy consumption and significantly lowering operating costs. Constraints related to high-risk energy efficiency periods are added to the original comprehensive objective function to generate a new objective function, which is then iteratively optimized to achieve global energy efficiency optimization.

[0105] Simultaneously, the comprehensive objective function for optimizing equipment energy efficiency is dynamically adjusted to generate a collaborative optimization strategy for equipment. The specific steps for managing equipment energy efficiency optimization are as follows:

[0106] Step S4441: Obtain carbon emission cost parameter C, extract ambient temperature parameter T and energy price parameter J, and define environmental impact factor H = v3×T + v4×J + v5×C, where v3, v4, and v5 are all weighting coefficients and none of them are equal to zero.

[0107] Step S4442, extract baseline performance The vibration energy density E and the user behavior compliance penalty term D0 are used to dynamically adjust the comprehensive objective function for equipment energy efficiency optimization, resulting in the standard objective function for equipment energy efficiency optimization. ;

[0108] Step S4443: Based on the standard objective function, select the standard objective function f. 标 The path with the largest value is the globally optimal path, which yields the standard path for device collaboration, used for device energy efficiency optimization and management.

[0109] In practical applications, temperature, electricity price, and carbon emission cost are unified into a single index H, which facilitates integration with the energy efficiency objective function. Carbon emission cost reflects policy constraints, prompting the system to prioritize low-carbon pathways. Electricity price reflects economic efficiency, balancing cost and energy efficiency. Through multi-objective optimization, the requirements for carbon emission reduction and cost control are achieved. The comprehensive objective function for equipment energy efficiency optimization is dynamically adjusted to obtain the standard objective function for equipment energy efficiency optimization. This is achieved by maximizing f. 标This approach comprehensively balances energy efficiency, environmental impact, and user behavior to achieve multi-objective collaborative optimization. Through optimization algorithms, it selects low-energy-consumption, low-carbon-emission equipment combinations while preventing user violations, ensuring that the equipment collaboration strategy achieves a balance among technology, economy, and environment. The entire process, through environmental factor quantification, multi-objective modeling, and global optimization, realizes intelligent and sustainable equipment energy efficiency management.

[0110] A plant data-based equipment energy efficiency optimization system, which applies the above-described plant data-based equipment energy efficiency optimization method, includes:

[0111] The multi-source data acquisition module collects equipment operating parameters, vibration energy data, and user behavior information in real time, aligns the timestamps, and constructs a three-dimensional feature matrix.

[0112] The vibration energy and user behavior analysis module analyzes vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance, respectively.

[0113] The device collaboration strategy generation module incorporates vibration energy potential and user behavior compliance into the energy efficiency optimization objective function, dynamically generates device collaboration strategies, and obtains the optimal path for device collaboration.

[0114] The equipment collaborative optimization strategy generation module acquires equipment fault parameters and external environment parameters, quantifies the impact of equipment health status and external environment changes on energy efficiency, dynamically adjusts the energy efficiency optimization objective function, and generates equipment collaborative optimization strategies for equipment energy efficiency optimization management.

[0115] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for optimizing equipment energy efficiency based on plant data, characterized in that, Includes the following steps: Real-time acquisition of equipment operating parameters, vibration energy data, and user behavior information; alignment with timestamps; construction of a three-dimensional feature matrix. By analyzing vibration energy data and user behavior information, vibration energy potential value and user behavior compliance are obtained respectively. By incorporating vibration energy potential and user behavior compliance into the energy efficiency optimization objective function, a device collaboration strategy is dynamically generated to obtain the optimal device collaboration path. The process involves acquiring equipment fault parameters and external environmental parameters, quantifying the impact of equipment health status and external environmental changes on energy efficiency, dynamically adjusting the energy efficiency optimization objective function, and generating a collaborative optimization strategy for equipment energy efficiency management. The specific steps are as follows: Obtain equipment fault parameters, including the number of equipment faults g and the equipment running time t, and calculate the equipment health K using the formula K=1-g / t; Obtain external environmental parameters, including ambient temperature parameter T and energy price parameter J; Establish the mapping relationship between equipment fault parameters, external environmental parameters and equipment energy efficiency to obtain the energy efficiency degradation model. in The baseline energy efficiency is given by k, which is the influencing factor, and v1 and v2 are weighting coefficients, both of which are not equal to zero. Obtain the predicted temperature parameters and predicted electricity price parameters for a future set time period, input the predicted temperature parameters and predicted electricity price parameters into the energy efficiency degradation model, and generate the predicted energy efficiency curve; Set an energy efficiency degradation threshold, extract the period in the predicted energy efficiency curve where the energy efficiency decline exceeds the set energy efficiency degradation threshold as the energy efficiency high-risk period, and trigger an early warning signal; Based on the warning signal, identify non-critical task production equipment in the equipment collaborative optimization path and shut down the operation of non-critical task production equipment; Obtain the carbon emission cost parameter C, extract the ambient temperature parameter T and energy price parameter J, and define the environmental impact factor H = v3×T + v4×J + v5×C, where v3, v4, and v5 are all weighting coefficients and none of them are equal to zero. Extracting benchmark performance The vibration energy density E and the user behavior compliance penalty term D0 are used to dynamically adjust the comprehensive objective function for equipment energy efficiency optimization, resulting in the standard objective function for equipment energy efficiency optimization. ; Based on the standard objective function, the standard objective function f is selected. 标 The path with the largest value is the globally optimal path, which yields the standard path for device collaboration, used for device energy efficiency optimization and management.

2. The equipment energy efficiency optimization method based on plant data according to claim 1, characterized in that, The steps of analyzing vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance, respectively, are as follows: The vibration energy data is preprocessed to obtain standard vibration energy data, which includes angular frequency ω and acceleration amplitude A. Through formula The vibration energy density E is calculated, where m is the mass of the vibrating equipment; Based on the vibration energy density E, using the formula The potential value P of the recoverable electrical energy per unit time, i.e., the vibration energy, is calculated, where, This refers to energy conversion efficiency, specifically electromagnetic conversion efficiency. The vibration energy potential value is compared with the preset vibration energy threshold. If the vibration energy potential value exceeds the preset vibration energy threshold, the equipment will experience abnormal vibration, triggering a fault alarm. Analyze user behavior information to determine user behavior compliance.

3. The equipment energy efficiency optimization method based on plant data according to claim 2, characterized in that, The steps for analyzing user behavior information to obtain user behavior compliance are as follows: The user behavior information includes the number of device operations F1 performed by the user per unit time, the parameter adjustment range A1 for each operation, and the difference between the device task priority set by the user and the system recommended priority B1. Define the safe operation frequency threshold F max The formula F = 1 - (F1 / F) max The compliance degree of operation frequency, F, is calculated by multiplying n1 by n1, where n1 is a weighting coefficient and is not equal to zero. Define parameter adjustment range threshold A max The formula A = 1 - (A1 / A) max The compliance degree of parameter adjustment range, A, is calculated by multiplying n2 by n2, where n2 is a weighting coefficient and is not equal to zero. The matching degree of the device task priority set by the user is calculated by the formula B=1-B1×n3, where n3 is the weight coefficient and is not equal to zero. The user behavior compliance is calculated by combining the compliance of operation frequency (F), parameter adjustment range (A), and device task priority matching degree (B).

4. The equipment energy efficiency optimization method based on plant data according to claim 3, characterized in that, The steps for calculating user behavior compliance based on the comprehensive operation frequency compliance degree F, parameter adjustment range compliance degree A, and device task priority matching degree B are as follows: The user behavior compliance degree D is calculated by weighting and combining the operation frequency compliance degree F, the parameter adjustment range compliance degree A, and the device task priority matching degree B, and then using the formula D=F×n4+A×n5+B×n6. Set a compliance threshold range [d1, d2], and compare the user behavior compliance level D with the compliance threshold range; If the user's behavior compliance level D is in [d1, d2], then the user's behavior constitutes a minor violation, triggering a warning and a minor penalty; If the user's behavior compliance level D is in [0, d1], then the user's behavior constitutes a serious violation, triggering mandatory penalties and permission restrictions.

5. The equipment energy efficiency optimization method based on plant data according to claim 1, characterized in that, The step of incorporating vibration energy potential and user behavior compliance into the energy efficiency optimization objective function to dynamically generate equipment coordination strategies is as follows: The vibration energy potential value P was normalized. The standard value of vibration energy potential P0 is obtained; Construct the objective function for optimizing equipment energy consumption Where P i This represents the real-time energy consumption of device i. This refers to energy conversion efficiency, specifically electromagnetic conversion efficiency. Based on the user behavior compliance, the user behavior compliance penalty item D0 is calculated using the formula D0=1-D. Extract the vibration energy density E and construct a comprehensive objective function for optimizing equipment energy efficiency. , where w1 and w2 are weighting coefficients and neither is equal to zero; Based on the comprehensive objective function, the comprehensive objective function f is selected. 综 The path with the smallest value is the globally optimal path, thus obtaining the device collaborative optimization path.

6. A plant data-based equipment energy efficiency optimization system, characterized in that, The method for optimizing equipment energy efficiency based on plant data, as described in any one of claims 1-5, includes: The multi-source data acquisition module collects equipment operating parameters, vibration energy data, and user behavior information in real time, aligns the timestamps, and constructs a three-dimensional feature matrix. The vibration energy and user behavior analysis module analyzes vibration energy data and user behavior information to obtain vibration energy potential value and user behavior compliance, respectively. The device collaboration strategy generation module incorporates vibration energy potential and user behavior compliance into the energy efficiency optimization objective function, dynamically generates device collaboration strategies, and obtains the optimal path for device collaboration. The equipment collaborative optimization strategy generation module acquires equipment fault parameters and external environmental parameters, quantifies the impact of equipment health status and external environmental changes on energy efficiency, dynamically adjusts the energy efficiency optimization objective function, and generates equipment collaborative optimization strategies for equipment energy efficiency optimization management. The specific steps are as follows: Obtain equipment fault parameters, including the number of equipment faults g and the equipment running time t, and calculate the equipment health K using the formula K=1-g / t; Obtain external environmental parameters, including ambient temperature parameter T and energy price parameter J; Establish the mapping relationship between equipment fault parameters, external environmental parameters and equipment energy efficiency to obtain the energy efficiency degradation model. ,in The baseline energy efficiency is given by k, which is the influencing factor, and v1 and v2 are weighting coefficients, both of which are not equal to zero. Obtain the predicted temperature parameters and predicted electricity price parameters for a future set time period, input the predicted temperature parameters and predicted electricity price parameters into the energy efficiency degradation model, and generate the predicted energy efficiency curve; Set an energy efficiency degradation threshold, extract the period in the predicted energy efficiency curve where the energy efficiency decline exceeds the set energy efficiency degradation threshold as the energy efficiency high-risk period, and trigger an early warning signal; Based on the warning signal, identify non-critical task production equipment in the equipment collaborative optimization path and shut down the operation of non-critical task production equipment; Obtain the carbon emission cost parameter C, extract the ambient temperature parameter T and energy price parameter J, and define the environmental impact factor H = v3×T + v4×J + v5×C, where v3, v4, and v5 are all weighting coefficients and none of them are equal to zero. Extracting benchmark performance The vibration energy density E and the user behavior compliance penalty term D0 are used to dynamically adjust the comprehensive objective function for equipment energy efficiency optimization, resulting in the standard objective function for equipment energy efficiency optimization. ; Based on the standard objective function, the standard objective function f is selected. 标 The path with the largest value is the globally optimal path, which yields the standard path for device collaboration, used for device energy efficiency optimization and management.

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