An electrical energy-saving control system based on industrial parks

Abstract

This invention relates to the field of electrical energy-saving automation control, specifically disclosing an electrical energy-saving control system for industrial parks, comprising: a data acquisition unit for acquiring time-of-use electricity price data from the power grid; a data processing unit for determining the importance level of tasks and for acquiring historical energy consumption-temperature rise data curves of each electrical device based on continuously acquired historical states, historical power, historical internal temperatures, and historical thermal inertia coefficients; a dynamic optimization scheduling unit for constructing a global optimization model with the optimization objectives of minimizing comprehensive electricity costs and maximizing production efficiency; a calibration unit for periodically calling up the historical energy consumption-temperature rise data curves, inputting them into the global optimization model for optimization calibration, generating calibration parameters, and updating the global optimization strategy; and a feedback unit for executing control commands according to the updated global optimization strategy and feeding them back to each electrical device, thereby realizing dynamic adjustment of the operation control of multiple electrical devices.

Description

Technical Field

[0001] This invention relates to the field of electrical energy-saving automation control, and specifically to an electrical energy-saving control system based on industrial parks. Background Technology

[0002] Existing industrial energy-saving control systems mostly remain at the stage of extensive management of individual equipment, mainly relying on fixed timing control or simple temperature threshold start-stop logic, which is difficult to meet the needs of modern industrial parks for refined energy management. Specifically, current technologies have the following three significant limitations: Lack of global scheduling: It is impossible to coordinate the start-up and shutdown sequence of multiple devices according to the priority of production tasks or the peak and off-peak electricity prices of the power grid, resulting in auxiliary energy consumption (such as centralized heat dissipation) being concentrated during peak electricity consumption periods, causing energy cost waste; Fixed parameters: The system relies on manually entered initial temperature resistance thresholds. As the equipment ages, its heating characteristics and optimal operating range drift. Fixed thresholds can easily lead to misjudgments or decreased energy efficiency.

[0003] Passive response: It can only deal with temperature anomalies after they occur, lacks the ability to use historical data for trend prediction, and cannot realize the transformation from reactive maintenance to preventive maintenance; It should be noted that existing systems still generally rely on traditional industrial Ethernet or wireless communication technologies across the entire system, which is insufficient to meet the stringent requirements of industrial parks for communication security and reliability. Traditional Ethernet is susceptible to "broadcast storms" and has poor resistance to electromagnetic interference; wireless communication is vulnerable to radio frequency interference generated by the start-up and shutdown of large electromechanical equipment in the factory, leading to data packet loss, command delays, or even data loss. In industrial environments with strong electromagnetic interference, the instability of the communication link directly threatens the real-time performance and security of the control system, resulting in inaccurate execution of energy-saving strategies and even the risk of equipment malfunction. Therefore, there is an urgent need for a communication architecture with high reliability, strong anti-interference capabilities, and redundant self-healing mechanisms to provide secure and stable data transmission guarantees for global energy-saving control. Summary of the Invention

[0004] The purpose of this invention is to provide an electrical energy-saving control system for industrial parks, which solves the following technical problems: How to achieve adaptive intelligent equipment aging and predictive control based on production task priorities and peak-valley electricity prices, and realize the construction of a new type of global energy-saving control system for industrial parks; How to build a highly reliable and secure transmission channel through global ring network fiber optic communication technology to ensure zero data loss and accurate delivery of instructions in industrial environments with strong electromagnetic interference, and to realize the construction of a global, highly secure new energy-saving control system for industrial parks.

[0005] The objective of this invention can be achieved through the following technical solutions: An electrical energy-saving control system for industrial parks includes: The data acquisition unit is used to read the real-time status, real-time power, real-time internal temperature and real-time thermal inertia coefficient of each intelligent electrical device in the industrial park in real time via the ring network fiber optic link, and to obtain the time-of-use electricity price data of the power grid. The data processing unit is used to connect to the MES (Manufacturing Execution System) to obtain production scheduling information, parse and extract the task type, estimated operation time and delivery urgency of each production task, and determine the task importance level based on the delivery urgency. It is also used to obtain the energy consumption-temperature rise historical data curves of each electrical device based on continuously collected historical status, historical power, historical internal temperature and historical thermal inertia coefficient; The dynamic optimization scheduling unit is used to construct a global optimization model with the optimization objectives of minimizing comprehensive electricity costs and maximizing production efficiency, based on real-time power, real-time thermal inertia coefficient and grid time-of-use electricity price data obtained by the data acquisition unit and combined with the task importance level output by the data processing unit. The calibration unit is used to periodically call up historical energy consumption-temperature rise data curves, input them into the global optimization model for optimization and calibration, generate calibration parameters, and update the global optimization strategy. The feedback unit is used to execute control commands according to the updated global optimization strategy and feed them back to each electrical device, so as to realize the dynamic adjustment of the operation control of multiple electrical devices.

[0006] Preferably, the process by which the data processing unit determines the task importance level includes: Map the types of production tasks to energy efficiency requirements, and establish the correlation between production tasks, equipment energy consumption, and temperature control requirements. Based on the expected operation time and delivery urgency, the priority of each production task is analyzed and determined, and this priority is used as a constraint condition for the global optimization model.

[0007] Preferably, the process of constructing a global optimization model by the dynamic optimization scheduling unit specifically includes the following steps: S11. Obtain time-of-use electricity price data and demand-based electricity cost information from the power grid, and define the electricity cost function; S12. Obtain the task evaluation results and real-time equipment status output by the data processing unit, and define the production efficiency objective function; S13. Introduce the equipment thermal inertia coefficient and process temperature control constraints to establish a multivariable constraint equation set; S14. Using a genetic algorithm, with the goal of minimizing the weighted sum of the electricity cost function and the production efficiency objective function, we find the optimal solution that satisfies all constraints and generate an initial global optimization strategy.

[0008] Preferably, the calibration unit performs optimized calibration and the calculation process for generating calibration parameters is as follows: The energy consumption-temperature rise historical data curves output by the data processing unit are periodically called and input into the global optimization model. The recursive least squares method is used to identify the equipment aging characteristic parameters in the model online. By calculating the deviation between the actual temperature rise rate of the equipment and the reference model, the temperature control threshold and thermal inertia coefficient are dynamically corrected to generate calibration parameters. The calculation formula is:

[0009] in, This represents the current real-time temperature rise rate of the equipment. The reference temperature rise rate is set as the baseline model. This is due to the additional energy loss caused by aging. This is the temperature rise weighting coefficient. Energy consumption weighting coefficient; Using calibration parameters The objective function and constraint boundaries of the global optimization model are updated to achieve adaptive adjustment of the global optimization strategy.

[0010] Preferably, the data processing unit further includes a task evaluation subunit: The task evaluation subunit is used to comprehensively evaluate the current production task range based on the task importance level, and outputs the task's adjustable space parameters for the dynamic optimization scheduling unit to call. The task's adjustable space parameters include the temperature control flexibility coefficient and the time elasticity coefficient.

[0011] Preferably, the task elastic weight coefficient is input as a modulation factor into the dynamic optimization scheduling unit, and the specific process is as follows: The modulation factor is converted into a task elasticity weighting coefficient through a weighted fusion of the temperature control flexibility coefficient and the time elasticity coefficient. :

[0012] in, The temperature control flexibility coefficient has a range of [value missing]. , The time elasticity coefficient, with a range of [missing information]. ; As a weighting factor for the temperature control flexibility coefficient, This is a weighting factor for the time elasticity coefficient, and ; Calibration parameters after being invoked based on task-tunable space parameters The formula is:

[0013] in, Let be the Sigmoid function, and let Used to limit the output to interval, The sensitivity gain constant is... This is the task elasticity weighting coefficient.

[0014] Preferably, the global optimization strategy specifically includes: The start-stop sequence table based on peak and off-peak electricity price periods is used to guide various intelligent electrical devices to perform preheating or precooling operations during off-peak electricity price periods. The auxiliary energy load distribution matrix is ​​used to dynamically adjust the operating power and duty cycle of multiple equipment heat dissipation or heating devices to achieve cluster load balance. The temperature control flexibility range setting is used to dynamically widen or tighten the allowable fluctuation range of the internal temperature of the equipment according to the importance level of the task, so as to maximize energy saving while ensuring production quality.

[0015] Preferably, the feedback unit executes control instructions according to the updated global optimization strategy in the following ways: The updated global optimization strategy is transformed into standard industrial control protocol commands and sent to the corresponding smart electrical devices through the edge computing gateway. By employing a closed-loop PID control algorithm and combining real-time feedback of internal temperature and power data from the data acquisition unit, the speed of the cooling fan or the power of the heating element are finely adjusted to ensure that the actual operating status of the equipment closely follows the trajectory of the global optimization strategy.

[0016] Preferably, it further includes a task execution prediction unit; The task execution prediction unit is used to simulate and predict the temperature change curve, energy consumption curve and completion time during the task execution process based on the global optimization model and the current state of the intelligent electrical equipment before executing the updated global optimization strategy. The system predicts and outputs task execution results, including estimated electricity costs, estimated production efficiency achievement rate, and temperature control compliance probability. The prediction results are then fed back to the dynamic optimization scheduling unit for verification and risk assessment before executing updated global optimization strategies.

[0017] Preferably, the step of obtaining the historical energy consumption-temperature rise data curves of each electrical device includes: S21. Continuously collect data from various intelligent electrical devices at historical moments. Historical state data, historical power data, historical internal temperature data, and historical thermal inertia coefficients are collected, and the multi-source data are time-axis aligned using timestamps as indexes. S22. Clean the aligned data, removing outliers and noisy data, and set the initial ambient temperature or equipment cold start temperature as the reference temperature. ; S23. Traverse the cleaned historical temperature data and calculate each historical moment point by point. Relative to reference temperature temperature rise value The calculation formula is:

[0018] in, for Historical internal temperature data collected at all times; S24. Based on the time sequence, construct the first curve of "historical power-time" and the second curve of "temperature rise-time" respectively; at the same time, construct a scatter plot of "energy consumption-temperature rise" relationship to characterize the thermal characteristics of the equipment with historical power as the horizontal axis and the temperature rise value at the corresponding time as the vertical axis. S25. Analyze the distribution pattern of the scatter plot of the "energy consumption-temperature rise" relationship, calculate the average temperature rise rate and steady-state temperature rise gain of the equipment, and mark the data in combination with the historical thermal inertia coefficient. Finally, generate the energy consumption-temperature rise historical data curve containing the aging characteristics and thermal response characteristics of the equipment, and output it to the dynamic optimization scheduling unit and calibration unit.

[0019] The beneficial effects of this invention are: (1) By using the real-time power, real-time thermal inertia coefficient and time-of-use electricity price data obtained by the data acquisition unit and the task importance level output by the data processing unit, the dynamic optimization scheduling unit constructs a global optimization model with the optimization objectives of minimizing comprehensive electricity costs and maximizing production efficiency. Through a multi-objective optimization algorithm, the global optimal strategy (including start-stop timing table and load allocation matrix) that satisfies both production delivery time and achieves the lowest electricity cost is solved in the global optimization model, thus realizing the system-level optimal configuration of park-level resources.

[0020] (2) By introducing the task elasticity weight coefficient, which is generated by the fusion of the temperature control flexibility coefficient and the time elasticity coefficient, the tolerance of the current production task to the control accuracy is quantified; and the task elasticity weight coefficient is used as the core modulation factor and embedded in the calculation formula of the calibration parameters. The Sigmoid function is used for nonlinear mapping. In high-precision tasks, the temperature control threshold is strictly corrected to ensure quality; in low-precision tasks, the task adjustable space is used to accommodate aging deviations in exchange for energy saving; on-demand calibration is realized, and the contradiction between equipment aging and fixed parameters is resolved.

[0021] (3) The present invention also sets up a task execution prediction unit. Before the physical command is issued, the task execution prediction unit simulates and deduces the entire process of task execution based on the global optimization model, predicts the temperature change curve, energy consumption curve and temperature control compliance probability; and feeds back the prediction results (expected electricity cost and production efficiency achievement rate) to the dynamic optimization scheduling unit, which greatly improves the safety and reliability of energy-saving control under complex working conditions.

[0022] (4) This invention builds a high-security and reliable foundation through full-domain ring network optical fiber communication, abandoning the traditional Ethernet and wireless communication solutions. It adopts ring network optical fiber communication technology to build the transmission backbone. Through the three-level redundant topology architecture of "device layer - edge layer - cloud platform layer", combined with the dual-ring self-healing technology and the inherent insulation and anti-interference characteristics of optical fiber, it completely solves the problems of data packet loss and command delay in industrial strong electromagnetic environment. This not only ensures the real-time performance of data acquisition and command issuance, but also improves the network security level of the system through physical isolation, providing an unbreakable secure channel for the transmission of high-value industrial data, and ensuring the stable operation of the entire energy-saving control system under extreme working conditions.

[0023] Of course, any product implementing this invention does not necessarily need to achieve all the advantages described above at the same time. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a unit diagram of an electrical energy-saving control system for industrial parks according to the present invention; Figure 2 A flowchart illustrating the process of constructing a global optimization model for the dynamic optimization scheduling unit of this invention; Figure 3 This is a flowchart illustrating the steps of obtaining historical energy consumption-temperature rise data curves for various electrical devices in this invention. Figure 4 This invention relates to an electrical energy-saving control system for industrial parks. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 As shown, this invention is an electrical energy-saving control system for industrial parks, comprising: a ring network fiber optic communication layer (as the underlying transmission backbone), a data acquisition unit, a data processing unit, a dynamic optimization scheduling unit, a calibration unit, and a feedback unit; wherein, the ring network fiber optic communication layer adopts a three-level redundant topology of "equipment layer-edge layer-cloud platform layer", the purpose of which is to comprehensively improve the communication security and reliability of the system and resist strong electromagnetic interference in industrial sites. Equipment layer ring network: connects sensors and actuators of intelligent electrical equipment (CNC machine tools, robots, etc.), adopts industrial-grade fiber optic transceivers (such as IEEE 802.3ah standard), supports wide temperature range of -40℃ to 85℃, and adapts to harsh industrial environments; Edge layer ring network: connects the edge computing gateway and the device layer ring network, and adopts dual-ring self-healing technology (such as RapidSpanning Tree Protocol, RSTP). In case of failure, it automatically switches paths (≤50ms) to ensure the continuity of data processing and command issuance on the edge side. Cloud platform layer ring network: Connecting the edge layer and the cloud platform data center, it adopts a 10 Gigabit fiber optic link to carry high bandwidth requirements such as MES system integration, power grid price API calls, and global optimization model calculations, ensuring real-time synchronization of multi-source data; The data acquisition unit is used to read the real-time status, real-time power, real-time internal temperature and real-time thermal inertia coefficient of each intelligent electrical device in the industrial park in real time via the ring network fiber optic link, and to obtain the time-of-use electricity price data of the power grid. The data processing unit is used to connect to the MES (Manufacturing Execution System) to obtain production scheduling information, parse and extract the task type, estimated operation time and delivery urgency of each production task, and determine the task importance level based on the delivery urgency. It is also used to obtain the energy consumption-temperature rise historical data curves of each electrical device based on continuously collected historical status, historical power, historical internal temperature and historical thermal inertia coefficient; The dynamic optimization scheduling unit is used to construct a global optimization model with the optimization objectives of minimizing comprehensive electricity costs and maximizing production efficiency, based on real-time power, real-time thermal inertia coefficient and grid time-of-use electricity price data obtained by the data acquisition unit and combined with the task importance level output by the data processing unit. The calibration unit is used to periodically call up historical energy consumption-temperature rise data curves, input them into the global optimization model for optimization and calibration, generate calibration parameters, and update the global optimization strategy. The feedback unit is used to execute control commands according to the updated global optimization strategy and feed them back to each electrical device, so as to realize the dynamic adjustment of the operation control of multiple electrical devices.

[0028] In the above technical solution, an energy-saving control system based on an industrial park is designed. This system includes a data acquisition unit, a data processing unit, a dynamic optimization scheduling unit, a calibration unit, and a feedback unit. By introducing time-of-use pricing and task priority, the energy-saving control system breaks through the limitations of single-machine control, realizes multi-device collaborative scheduling and peak shaving and valley filling at the park level, significantly reduces the overall electricity cost, ensures that the energy-saving control system can adapt to parameter adjustment after equipment aging, and extends the equipment life cycle.

[0029] Specifically, the data acquisition unit reads the real-time status, power, internal temperature, and thermal inertia coefficient of each intelligent electrical device in the industrial park via an industrial ring network fiber optic link. Sensors deployed on intelligent electrical devices (such as CNC machine tools, industrial robots, and HVAC systems) collect the device's operating status (running / standby / fault), real-time power (kW), real-time internal temperature (°C), and real-time thermal inertia coefficient (a parameter reflecting the insulation and heat dissipation capabilities of intelligent devices) in real time via the industrial ring network fiber optic communication link. The ring network fiber optic uses a redundant topology (such as a dual-ring self-healing type) to support millisecond-level fault switching, ensuring zero data loss and improving security in industrial environments with strong electromagnetic interference. At the same time, the ring network fiber optic interface connects to the power grid company's API to obtain time-of-use electricity price data (including unit price and demand charge information for peak, valley, and flat periods), providing a highly reliable and low-latency real-time basis for cost calculation. The data processing unit connects production management and the physical characteristics of intelligent equipment. Specifically, firstly, production tasks are analyzed: primarily by interfacing with the MES (Manufacturing Execution System), the latest production scheduling information is obtained. Using natural language processing or a rule engine, key attributes of each production task are analyzed and extracted, including task type, warning operation duration, and delivery urgency. Based on delivery urgency, a weighted calculation is performed to determine the task importance level, which is then used as a hard constraint for subsequent optimization models. Next, dynamic changes in historical equipment data are acquired. By continuously storing historical status, historical power, historical internal temperature, and historical thermal inertia coefficient reported by the data acquisition unit, the energy consumption-temperature rise historical data curves of each electrical device are reconstructed. This curve not only records the heating characteristics of the equipment under different loads but also implies the aging trend of the equipment (such as the energy consumption required to reach the same temperature increasing year by year), providing core input for the calibration unit. A global optimization model is constructed by a dynamic optimization scheduling unit to further optimize and make decisions on the data processed by the data processing unit. The model construction process is as follows: First, the objective function is set. The dual objective function is constructed by minimizing the comprehensive power cost and maximizing the production efficiency. No constraints are set for the model. The constraints include production constraints, physical constraints and process constraints. Among them, the production constraints must meet the task importance level output by the data processing unit, and the physical constraints need to be limited by the real-time thermal inertia coefficient of the construction equipment. The temperature change cannot be infinitely fast. The process constraints are different allowable temperature fluctuation ranges corresponding to different task types. The calibration unit enables the system to achieve adaptive evolution based on the dynamic optimization scheduling unit, thus solving the model mismatch problem caused by equipment aging. Specifically, periodic calibration is achieved by setting the response period based on the energy consumption-temperature rise historical data curve generated in the data processing unit. The calibration also includes inputting the energy consumption-temperature rise historical data curve into the model of the dynamic optimization scheduling unit. During parameter identification, the recursive least squares method is used to identify the equipment aging characteristic parameters in the model online. Specifically, dynamic calibration parameters are calculated and generated by combining the task-adjustable space parameters. These dynamic calibration parameters are then used to correct the temperature control threshold and thermal inertia coefficient in the global optimization model, generating an updated global optimization strategy to ensure that the model formula closely reflects the actual physical state of the intelligent device. Based on the tracking results, the system automatically corrects the highest temperature threshold, lowest temperature threshold, and normal operating temperature range of the corresponding device stored in the database, achieving dynamic updates of the calibration parameters.

[0030] The feedback unit transforms cloud-generated strategies into physical actions, forming a closed-loop control system to achieve actual energy-saving control of electrical systems in industrial parks. Specifically, this involves converting the updated global optimization strategy into standard industrial protocol commands. This requires an edge computing gateway supporting ring network fiber optic access to convert the updated global optimization strategy into standard industrial control protocol commands (such as Modbus TCP / IP over Fiber, OPC UA over TSN), which are then sent to the corresponding intelligent electrical devices via the ring network fiber optic link. During this closed-loop execution control process, a closed-loop PID control algorithm is used, combined with real-time feedback of internal temperature and power data from the data acquisition unit via the ring network fiber optic link. This allows for fine-tuning of the cooling fan speed or heating element power. The high bandwidth (≥1Gbps) and low latency (≤10ms) characteristics of the ring network fiber optic link, along with its inherent data security transmission advantages, ensure the synchronization of control commands and feedback data, avoiding the "broadcast storm" problems of traditional Ethernet or the "packet loss delay" problems of wireless communication. This ensures that the actual operating status of the equipment closely follows the trajectory of the optimization strategy, guaranteeing system stability even with minor disturbances in the on-site environment.

[0031] The above-mentioned data acquisition unit, data processing unit, dynamic optimization scheduling unit, calibration unit, and feedback unit ensure that the real-time capture of equipment status and grid electricity price is completed before MES task analysis, realizing the mining of historical data and the construction of energy consumption-temperature rise curves. The data processing unit analyzes the MES task, evaluates its importance, and mines historical data to construct energy consumption-temperature rise curves. The dynamic optimization scheduling unit calculates the global strategy with the lowest cost and highest efficiency. The calibration unit uses historical curves and the task's adjustable space to calculate calibration parameters and dynamically correct the strategy. The feedback unit issues instructions to control equipment actions and sends back the execution results to complete the closed loop.

[0032] As one embodiment of the present invention, the process by which the data processing unit determines the task importance level includes: Map the types of production tasks to energy efficiency requirements, and establish the correlation between production tasks, equipment energy consumption, and temperature control requirements. Based on the expected operation time and delivery urgency, the priority of each production task is analyzed and determined, and this priority is used as a constraint condition for the global optimization model.

[0033] In the above technical solution, by converting unstructured task types into physical constraint indicators that the system can recognize, the system has a built-in task type-energy efficiency requirement mapping table. For example, precision grinding tasks are mapped to high temperature control rigid requirements (temperature fluctuations are not allowed), and workpiece preheating tasks are mapped to low temperature control rigid requirements (large temperature fluctuations are allowed). Through this mapping, a three-element correlation relationship between production tasks, equipment energy consumption, and temperature control requirements is constructed, so that when performing a task, not only is the task information known, but also the power consumption and temperature control range information of the intelligent equipment performing the task can be determined. Furthermore, a dynamic scoring process is completed based on the priority of task duration and delivery urgency. This quantifies the urgency of management into an algorithmic priority coefficient. The system's calculation process includes delivery urgency analysis, comparing the current time with the task's set delivery date. The closer to the delivery date, the higher the delivery urgency. By analyzing the estimated task duration, long-cycle tasks have higher scheduling flexibility, while short-cycle tasks tend to be more time-sensitive. By comprehensively analyzing delivery urgency and task duration, long-cycle tasks have higher scheduling flexibility, while short-cycle tasks tend to be more time-sensitive. A weighted algorithm is used to calculate a task importance level score, which is then directly applied. This reflects the order of tasks in the production schedule; as a constraint input to the global optimization model, the model, in solving for the optimal solution, strictly adheres to its stringent temperature control range and continuous operation requirements, which means achieving maximum energy saving during that period. When the dynamic optimization scheduling unit constructs the global optimization model, the task importance level generated above is no longer an isolated data point, but is directly used as a constraint input to the model; and when solving for the optimal solution, it strictly adheres to its stringent temperature control range and continuous operation requirements, even if maximum energy saving cannot be achieved during that period, allowing the model to obtain the maximum scheduling freedom, and can operate during periods of low electricity prices, utilizing a wider temperature control flexibility range for energy-saving adjustment.

[0034] Through the above process, the task importance level is no longer an isolated data point, but is directly used as a constraint input for the model. When the model solves for the optimal solution, it must strictly adhere to its stringent temperature control range and continuous operation requirements. Even if maximum energy saving cannot be achieved during this period, the model gains greater scheduling freedom and can operate during periods of low electricity prices, or use a wider temperature control flexibility range to adjust the above process for energy saving. Therefore, the data processing unit successfully transforms the production scheduling semantics in the MES system into mathematical constraints that drive the global optimization algorithm.

[0035] Please see Figure 2 As shown, in one embodiment of the present invention, the process of the dynamic optimization scheduling unit constructing a global optimization model specifically includes the following steps: S11. Obtain time-of-use electricity price data and demand-based electricity cost information from the power grid, and define the electricity cost function; S12. Obtain the task evaluation results and real-time equipment status output by the data processing unit, and define the production efficiency objective function; S13. Introduce the equipment thermal inertia coefficient and process temperature control constraints to establish a multivariable constraint equation set; S14. Using a genetic algorithm, with the goal of minimizing the weighted sum of the electricity cost function and the production efficiency objective function, we find the optimal solution that satisfies all constraints and generate an initial global optimization strategy.

[0036] In the above technical solution, firstly, the system accesses the time-of-use electricity price data and demand charge information from the power grid in real time, and constructs a comprehensive cost function that includes both electricity consumption charge and demand charge based on the billing of the maximum demand for the month. :

[0037] in, for Total grid power draw at any given time for The time-of-use electricity price corresponding to each moment, This is the demand-based electricity price. For the maximum instantaneous load occurring within the scheduling cycle, this function not only penalizes electricity consumption during periods of high electricity prices, but also penalizes drastic fluctuations in grid load through demand terms, guiding the system to execute peak shaving and valley filling strategies; it transforms production management objectives into optimization objectives in mathematical models, ensuring that energy conservation does not come at the expense of production capacity; and it receives task evaluation results (including task importance level and temperature control flexibility range) and real-time equipment status (running, idle, fault) output from the data processing unit. Then, the constructed objective function is defined by the system as a function that maximizes production efficiency. :

[0038] in, This refers to the set of all production tasks within the current scheduling period. Assigning task importance weights to data processing units The task completion rate (0 or 1). The quality factor depends on whether the equipment temperature is stable within the temperature control range required by the process (if the temperature is too high or too low, this factor is less than 1); high priority tasks ( If the objective function is not completed or the target is not met, it will have a significant negative impact, forcing the optimization algorithm to prioritize the smooth execution of such tasks. Next, the thermal inertia coefficient of the equipment is established. This describes the rate of temperature rise and fall of the equipment, constituting a thermodynamic constraint:

[0039] Constraints limit the rate of temperature change in the equipment, preventing the model from generating unrealistic instantaneous temperature control commands. For example, process temperature control constraints are introduced, and based on the "production task - equipment energy consumption - temperature control requirement" relationship established by the data processing unit, allowable upper and lower temperature limits are set for each task. : ≤ ≤

[0040] For high-priority tasks, this range is narrower (rigid constraint); for low-priority tasks, this range is wider (flexible range, conducive to energy saving); in addition, it includes other physical and logical constraints, power balance constraints, task timing constraints, and device state constraints.

[0041] Finally, the optimal solution and strategy generation are the core steps in solving the model. Heuristic algorithms are used to find the optimal balance point in multi-objective conflicts. Since minimizing cost and maximizing production efficiency are usually in conflict, the system uses a weighted sum method to merge the two objectives into a single optimization objective. :

[0042] in, Preference coefficients set for managers The larger the system is, the more it tends to save money. The smaller the size, the more it prioritizes maintaining production; It should be noted that in the genetic algorithm solution, the start / stop status and heat dissipation / heating power setting of each device are encoded as chromosomes. A large number of possible scheduling schemes are randomly generated as the initial population, and the fitness value of each individual is calculated. The algorithm simulates biological evolution, retaining individuals with good fitness and generating a new generation of population through crossover and mutation, gradually approaching the optimal solution until the maximum number of iterations or solution quality convergence is achieved. After the algorithm converges, the optimal solution is output, and this solution is parsed into a specific set of control instructions, namely the initial global optimization strategy, the start / stop timing table (specifying when each device starts or stops), the auxiliary energy load allocation matrix (specifying the specific values ​​of cooling fan speed and heating tube power at each time), and the temperature control setpoint (dynamically adjusted temperature control target point).

[0043] As one embodiment of the present invention, the method for optimizing calibration by the calibration unit and the calculation process for generating calibration parameters are as follows: The energy consumption-temperature rise historical data curves output by the data processing unit are periodically called and input into the global optimization model. The recursive least squares method is used to identify the equipment aging characteristic parameters in the model online. By calculating the deviation between the actual temperature rise rate of the equipment and the reference model, the temperature control threshold and thermal inertia coefficient are dynamically corrected to generate calibration parameters. The calculation formula is:

[0044] in, This represents the current real-time temperature rise rate of the equipment. The reference temperature rise rate is set as the baseline model. This is due to the additional energy loss caused by aging. This is the temperature rise weighting coefficient. Energy consumption weighting coefficient; Using calibration parameters The objective function and constraint boundaries of the global optimization model are updated to achieve adaptive adjustment of the global optimization strategy.

[0045] In the above technical solution, the standard unit performs the optimization calibration and generates calibration parameters, reflecting the core characteristics of system adaptability and intelligence. This process not only corrects model mismatch caused by equipment aging, but also combines the actual situation of the current production task to ensure the dynamic accuracy of the optimization strategy. The specific technical process is as follows: The calibration unit does not run in real time, but is set with a fixed calibration cycle (e.g., once every morning or once a week) to avoid frequent adjustments interfering with system stability; the energy consumption-temperature rise historical data curve generated by the data processing unit is periodically called, which contains the equipment's operating trajectory over a long period of time and is a key basis for identifying the health status of the equipment. The system adopts the recursive least squares method to input the historical data curve into the current global optimization model. By utilizing its characteristics of fast convergence and forgetting old data, it realizes online tracking of slow time-varying parameters, enabling the system to identify the equipment aging characteristic parameters in the model online. These parameters quantify the degree of equipment performance degradation over time. After completing parameter identification, the system calculates the core calibration parameters. The calculation formula is:

[0046] in, This refers to the temperature rise deviation rate. The current real-time temperature rise rate of the equipment is obtained directly from the slope of the energy consumption-temperature rise historical data curve. The temperature rise rate serves as the reference temperature rise rate for the preset benchmark model. The temperature rise deviation rate reflects the relative rate of change of the equipment's temperature rise characteristics. When equipment aging leads to poor heat dissipation, the actual temperature rise rate will be higher than the benchmark value. An increase in this ratio represents additional energy loss due to aging. An aging motor requires more electrical energy to achieve the same output power or temperature rise. This ratio is used to adjust the weight of temperature characteristic changes in the final calibration decision. For precision equipment with extremely high temperature control accuracy requirements, the temperature rise weighting coefficient... The value is set too high, and the energy consumption weighting coefficient is high. Used to adjust the weight given to energy efficiency degradation in decision-making, for high-energy-consuming equipment. The value is set too high; adjust accordingly. and In practical applications, the system can flexibly adapt to the calibration needs of different types of equipment, such as determining whether temperature control stability is emphasized (increasing...). (Value) or focus on energy efficiency and economy (increase) value); The calculated calibration parameters Instead of being directly issued as control commands, it acts as a regulator within the global optimization model itself, thereby achieving adaptive adjustment of the strategy. This is achieved by incorporating a new mechanism into the original dual objective function of minimizing electricity costs and maximizing production efficiency. As a regulatory factor, when When a larger value indicates severe equipment aging, the system will automatically increase the weight of equipment health maintenance or temperature control stability in the objective function, so that even if energy costs increase slightly in the short term, equipment safety and lifespan will be prioritized.

[0047] use Adjust the equipment's allowable operating temperature range, if When the heat dissipation capacity of the display device decreases, the system automatically lowers the maximum allowable temperature threshold to prevent malfunctions caused by overheating. Based on the identified aging characteristic parameters, the thermal inertia coefficient in the device model is updated. This makes the model's prediction of device temperature change trends more accurate, avoiding control lag or oscillations caused by model distortion.

[0048] In one embodiment of the present invention, the data processing unit further includes a task evaluation subunit: The task evaluation subunit is used to comprehensively evaluate the current production task range based on the task importance level, and outputs the task's adjustable space parameters for the dynamic optimization scheduling unit to call. The task's adjustable space parameters include the temperature control flexibility coefficient and the time elasticity coefficient.

[0049] In the aforementioned technical solution, the task evaluation subunit is a crucial link in the data processing unit for transforming production management intentions into physical control strategies. It receives the task importance level output from the upstream task parsing subunit and performs a deep engineering interpretation of it. It doesn't simply pass high and low priority as binary labels to the downstream; based on this level, it further analyzes the extent to which the task can be adjusted during execution without affecting the final output quality and delivery time in the current production environment. The task adjustment space parameters output by the task evaluation subunit mainly include the temperature control flexibility coefficient and the time flexibility coefficient. The temperature control flexibility coefficient is used to quantitatively characterize the degree to which the device can tolerate internal temperature fluctuations. The closer it is to 1, the higher the rigidity, corresponding to high priority or precision machining tasks.

[0050] As one embodiment of the present invention, the task elastic weight coefficient is input as a modulation factor into the dynamic optimization scheduling unit, and the specific process is as follows: The modulation factor is converted into a task elasticity weighting coefficient through a weighted fusion of the temperature control flexibility coefficient and the time elasticity coefficient. :

[0051] in, The temperature control flexibility coefficient has a range of [value missing]. , The time elasticity coefficient, with a range of [missing information]. ; As a weighting factor for the temperature control flexibility coefficient, This is a weighting factor for the time elasticity coefficient, and ; Calibration parameters after being invoked based on task-tunable space parameters The formula is:

[0052] in, Let be the Sigmoid function, and let Used to limit the output to interval, The sensitivity gain constant is... This is the task elasticity weighting coefficient.

[0053] In the above technical solution, the task elasticity weighting coefficient is used. As a modulation factor input dynamic optimization scheduling unit, it achieves deep integration of production management intentions and physical equipment control, through task elastic weight coefficients. The generation mechanism provides a quantitative assessment of the task's tunability space and the temperature control flexibility coefficient. The range of values ​​is This characterizes the tolerance of production tasks to temperature fluctuations. A value closer to 1 indicates more rigid temperature control requirements (e.g., precision lithography) and a smaller room for optimization; a value closer to 0 indicates that larger temperature fluctuations are permissible (e.g., preheating of ordinary materials) and a larger room for optimization. The time flexibility coefficient... Range of values This characterizes the tolerance of a production task for completion time. A value closer to 1 indicates that the task must be completed on time (no flexibility); a value closer to 0 indicates that the task can be delayed (high flexibility). Weighting factor , According to the system administrator's settings based on the park's production strategy, and meeting the requirements... This dictates that temperature control stability will be given greater emphasis during the comprehensive evaluation. Larger or slower production timeliness Large, through fusion computing To achieve this when the task has extremely high temperature control requirements, and When it is close to 1, or when time is extremely tight Approximately 1, i.e., 1- When it is close to 0, The value approaches Setting it to a large value usually means that the task flexibility is extremely low, and stability and accuracy must be prioritized. This is especially true when temperature control requirements are less stringent. When it is close to 0, and there is enough time... Approaching 0, Its value approaches 0, indicating extremely high task flexibility and a large amount of room for optimization to save energy and reduce consumption.

[0054] Furthermore, the calibration parameters are invoked based on the task-tunable space parameters. The formula realizes the application of the modulation factor in the dynamic optimization scheduling unit, and generates It is not used directly as a control command, but rather as a core modulation factor that participates in the calibration parameters. In the calculation, this affects the decision-making of the global optimization model. The core calculation formula is: This is achieved by introducing the Sigmoid function as a non-linear activation function. Its function is to The output is smoothly mapped to Within the range, used to zoom in or out. The intensity of the effect, and by adjusting It can control the sensitivity of task flexibility to the final calibration force; It should be noted that for high-priority or low-elasticity tasks, i.e. →1: The value is very large, and after passing through the Sigmoid function... →1, Mainly affected by equipment aging deviation item Controlling the equipment will amplify the effects of aging and strictly enforce temperature control threshold corrections, even if it means consuming more energy to maintain high precision. For low-priority or highly flexible tasks, i.e. →0: The value is very small, and after passing through the Sigmoid function... →0.5, the aging deviation term is significantly reduced. The system allows the equipment's tunability margin (i.e., tolerating a certain degree of aging temperature rise or efficiency decline), thereby widening the temperature control range and implementing more aggressive energy-saving strategies; By adjusting It was used to update the global optimization model, thus enabling the use of Dynamically correct temperature control threshold and Ensure high The range tightened at low times. The range will be relaxed in time; and The feedback to the optimization algorithm dynamically adjusts the weight ratio of "energy consumption cost" and "equipment health / production quality" in the objective function, thus updating the objective function weights. This process goes beyond simply setting constraints on the task's adjustable space; it can be transformed into a continuous modulation factor. By introducing a Sigmoid nonlinear mapping, the system can intelligently balance equipment aging and production task tolerance, generating adaptive calibration parameters. This enables optimal global energy consumption control while ensuring production safety and quality.

[0055] As one embodiment of the present invention, the global optimization strategy specifically includes: The start-stop sequence table based on peak and off-peak electricity price periods is used to guide various intelligent electrical devices to perform preheating or precooling operations during off-peak electricity price periods. The auxiliary energy load distribution matrix is ​​used to dynamically adjust the operating power and duty cycle of multiple equipment heat dissipation or heating devices to achieve cluster load balance. The temperature control flexibility range setting is used to dynamically widen or tighten the allowable fluctuation range of the internal temperature of the equipment according to the importance level of the task, so as to maximize energy saving while ensuring production quality.

[0056] In the above technical solution, an energy-saving control scheme is achieved by dynamically optimizing the scheduling unit after comprehensively considering the grid electricity price, production task priority, and equipment physical characteristics. This realizes a systematic energy-saving control process at the park level, overcoming the limitations of traditional off-season control. Specifically, based on the start-stop sequence table of peak and off-peak electricity prices, and by intervening in grid trial data, the system identifies the peak and off-peak electricity price information of the day. For equipment that is not in immediate production (such as large injection molding machines, central air conditioning units, and heat treatment furnaces), the system automatically starts during off-peak hours, raising the internal temperature of the equipment to a "standby insulation zone" close to the operating temperature. Utilizing the thermal inertia coefficient (i.e., insulation capacity) of the equipment, the equipment does not need to undergo a high-energy-consuming heating phase again when production begins during the subsequent peak hours, thus avoiding high electricity prices. For non-critical equipment that can be delayed, the system schedules its start-stop sequence during off-peak hours, thereby directly reducing electricity costs. This achieves the transfer of high-energy-consuming heating or cooling processes from the safety-regulated peak hours to the cheaper off-peak hours, significantly reducing overall electroplating electricity costs. The strategy logic of the auxiliary energy load allocation matrix is ​​as follows: If multiple smart devices in the park are simultaneously operating at full load for heat dissipation or heating, a huge instantaneous power surge (demand) will be generated, leading to a surge in demand electricity costs. By dynamically adjusting the operating parameters of the auxiliary systems (cooling fans, heating pipes) of each device, load smoothing can be achieved, thereby avoiding grid impact caused by multiple devices operating at high power at the same time, realizing cluster load balance, effectively controlling basic electricity costs, and improving grid friendliness.

[0057] By setting a flexible temperature control range, different allowable temperature fluctuation ranges are dynamically set based on the task importance level, thus addressing different task types accordingly. Specifically, based on the allowable temperature fluctuation range: for high-priority or precision tasks, a narrow temperature control range (tightened) is set, for example, requiring the temperature to be maintained at (40±0.5)℃. At this point, the system control precision is high, but the auxiliary system may operate frequently, resulting in relatively high energy consumption, although product quality must be guaranteed. For low-priority or general tasks, a wide temperature control range (relaxed) is set. For example, the temperature is allowed to fluctuate within the range of (40±5)℃, which can utilize natural cooling or the equipment's own waste heat, reducing the number of times the cooling fan starts and its running time. This flexible temperature control range maximizes the utilization of the task's adjustable space (relaxed temperature control for low-priority tasks) while ensuring production quality (strict temperature control for high-priority tasks), tapping into deep energy-saving potential without affecting output.

[0058] As one embodiment of the present invention, the feedback unit executes control instructions according to the updated global optimization strategy in the following ways: The updated global optimization strategy is transformed into standard industrial control protocol commands and sent to the corresponding smart electrical devices through the edge computing gateway. By employing a closed-loop PID control algorithm and combining real-time feedback of internal temperature and power data from the data acquisition unit, the speed of the cooling fan or the power of the heating element are finely adjusted to ensure that the actual operating status of the equipment closely follows the trajectory of the global optimization strategy.

[0059] In the above technical solution, the standard industrial control protocol is an updated global optimization strategy generated by the dynamic optimization scheduling unit (such as "Equipment A starts at 02:00, maintains temperature of 45±2℃, and fan speed of 70%), which is essentially high-level logic data. The feedback unit first converts it into standard industrial control protocol instructions that can be recognized by industrial field equipment. Then, the standard industrial control protocol instructions are sent through the edge computing gateway to realize the coordination process of instructions such as protocol conversion, data caching and security, and execution at the nearest location. Through the closed-loop PID control algorithm, using the existing closed-loop PID (proportional-integral-derivative) control algorithm, smooth and precise adjustment can be achieved.

[0060] It should be noted that the closed-loop control logic is as follows: Real-time feedback: The data acquisition unit continuously uploads the device's real-time internal temperature and real-time power to the feedback unit (or edge gateway). Error calculation: The system compares the "target temperature set by the global optimization strategy" with the "actual temperature fed back by the data acquisition unit" to obtain the deviation value; PID calculation: Proportional (P) term: Adjustments are made based on the current deviation. The larger the deviation, the stronger the adjustment (e.g., if the temperature far exceeds the target, cooling is immediately increased). Integral (I) term: Eliminate historical cumulative deviations and make cumulative adjustments for small deviations that have existed for a long time to prevent "static error" (i.e., the temperature is always just a little bit short of the target). Differential (D) term: predicts future deviation trends, makes advance adjustments based on the rate of temperature change, and suppresses overshoot (prevents temperature overshoot) and oscillation; Output control quantity: After PID calculation, a control quantity (such as 0-10V voltage signal or 4-20mA current signal) is output to directly drive the inverter of the cooling fan to change the speed, or to adjust the conduction angle / duty cycle of the solid-state relay of the heating element.

[0061] By fine-tuning the speed of the cooling fan or the power of the heating element, the actual operating state of the equipment closely follows the trajectory of the global optimization strategy. The PID output of the cooling fan controls the duty cycle of the PWM (Pulse Width Modulation) signal to achieve stepless speed regulation; it runs at full speed when a large amount of heat dissipation is needed, and runs at low speed when only temperature maintenance is required, avoiding start-stop shocks and noise. The PID output of the heating element controls the on-off frequency or thyristor conduction angle to achieve smooth power regulation and avoid grid surges.

[0062] Please see Figure 4 As shown, in one embodiment of the present invention, a task execution prediction unit is also included: The task execution prediction unit is used to simulate and predict the temperature change curve, energy consumption curve and completion time during the task execution process based on the global optimization model and the current state of the intelligent electrical equipment (the latest state data transmitted in real time via the ring network optical fiber) before executing the updated global optimization strategy. The system predicts and outputs task execution results (estimated electricity costs, production efficiency achievement rate, and temperature control compliance probability), and feeds back the prediction results to the dynamic optimization scheduling unit with low latency via a ring network fiber optic link. The high reliability of the ring network fiber optic link ensures the integrity of the prediction data, avoids "blind guessing strategies" caused by communication interruptions, and improves the accuracy of verification and risk assessment.

[0063] In the above technical solutions, traditional control systems often follow a reactive adjustment mode of "command-execution-feedback-correction," while this solution introduces a task execution prediction unit, upgrading it to a proactive prevention mode of "prediction-verification-execution." The triggering time is after the dynamic optimization scheduling unit generates the updated global optimization strategy, but before the feedback unit converts it into physical commands and sends them to the equipment. This enables full-cycle simulation and deduction of the strategy to be executed, assessing its potential risks (such as temperature control exceeding limits, cost overruns, and task delays), and feeding the assessment results back to the dynamic optimization scheduling unit to trigger the final strategy correction (verification and risk assessment). Specifically, the task execution prediction unit performs the following deduction steps based on the global optimization model and the current state of the intelligent electrical equipment: First, load and input the data: This includes loading the "start-stop timing table", "auxiliary energy consumption load allocation matrix" and "temperature control flexible range setting value" to be verified (strategy input); reading the current real-time power, real-time internal temperature and real-time thermal inertia coefficient of the current equipment obtained by the data acquisition unit (status input); and calling the time-of-use electricity price data and environmental temperature and humidity prediction data for future periods of the power grid (environmental input). Then, physical model simulation is performed, that is, using the same equipment thermodynamic model as the dynamic optimization scheduling unit (i.e., the core of the global optimization model), to extrapolate the strategy execution process; predict and obtain curves, specifically including: Based on the thermal inertia coefficient and the set heating / heating power, simulate the temperature change trajectory of the equipment over a period of time (until the end of the mission). Predict and obtain the temperature change curve; based on the start-stop sequence and power allocation matrix, calculate the power consumption at each moment to generate the predicted energy consumption curve. The total power consumption is predicted by integrating the data to obtain the energy consumption curve; combined with the production task load and equipment operating speed, the actual completion time of the task is predicted.

[0064] Please see Figure 3 As shown, in one embodiment of the present invention, the step of obtaining the historical data curves of energy consumption and temperature rise of each electrical device includes: S21. Continuously collect data from various intelligent electrical devices at historical moments. Historical state data, historical power data, historical internal temperature data, and historical thermal inertia coefficients are collected, and the multi-source data are time-axis aligned using timestamps as indexes. S22. Clean the aligned data, removing outliers and noisy data, and set the initial ambient temperature or equipment cold start temperature as the reference temperature. ; S23. Traverse the cleaned historical temperature data and calculate each historical moment point by point. Relative to reference temperature temperature rise value The calculation formula is:

[0065] in, for Historical internal temperature data collected at all times; S24. Based on the time sequence, construct the first curve of "historical power-time" and the second curve of "temperature rise-time" respectively; at the same time, construct a scatter plot of "energy consumption-temperature rise" relationship to characterize the thermal characteristics of the equipment with historical power as the horizontal axis and the temperature rise value at the corresponding time as the vertical axis. S25. Analyze the distribution pattern of the scatter plot of the "energy consumption-temperature rise" relationship, calculate the average temperature rise rate and steady-state temperature rise gain of the equipment, and mark the data in combination with the historical thermal inertia coefficient. Finally, generate the energy consumption-temperature rise historical data curve containing the aging characteristics and thermal response characteristics of the equipment, and output it to the dynamic optimization scheduling unit and calibration unit.

[0066] In the above technical solution, through the steps of S21-S25, the system successfully transforms the raw, discrete sensor data into high-value equipment thermal characteristic maps. This not only solves the problem that traditional systems cannot quantify the degree of equipment aging, but also provides a solid physical model foundation for subsequent global optimization strategies based on task priority and peak-valley electricity prices.

[0067] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, and therefore described more simply; relevant parts can be referred to the descriptions of the method embodiments.

[0068] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the protection scope of the present invention.

Claims

1. An electrical energy-saving control system for industrial parks, characterized in that, include: The data acquisition unit is used to read the real-time status, real-time power, real-time internal temperature and real-time thermal inertia coefficient of each intelligent electrical device in the industrial park in real time via the ring network fiber optic link, and to obtain the time-of-use electricity price data of the power grid. The data processing unit is used to interface with the MES (Manufacturing Execution System) to obtain production scheduling information, parse and extract the task type, estimated operation time and delivery urgency of each production task, and determine the task importance level based on the delivery urgency. It is also used to obtain the energy consumption-temperature rise historical data curves of each electrical device based on continuously collected historical status, historical power, historical internal temperature and historical thermal inertia coefficient; The dynamic optimization scheduling unit is used to construct a global optimization model with the optimization objectives of minimizing comprehensive electricity costs and maximizing production efficiency, based on the real-time power, real-time thermal inertia coefficient and grid time-of-use electricity price data obtained by the data acquisition unit and combined with the task importance level output by the data processing unit. The calibration unit is used to periodically call the energy consumption-temperature rise historical data curve, input it into the global optimization model for optimization calibration, generate calibration parameters and update the global optimization strategy; The feedback unit is used to execute control commands according to the updated global optimization strategy and feed them back to each electrical device, so as to realize the dynamic adjustment of the operation control of multiple electrical devices.

2. The energy-saving control system for industrial parks according to claim 1, characterized in that, The process by which the data processing unit determines the importance level of a task includes: The task types of the production tasks are mapped to energy efficiency demand indicators, and the correlation between production tasks, equipment energy consumption, and temperature control requirements is established. Based on the estimated operation time and delivery urgency, the priority of each production task is analyzed and determined, and this priority is used as a constraint condition for the global optimization model.

3. The electrical energy-saving control system for industrial parks according to claim 1, characterized in that, The process by which the dynamic optimization scheduling unit constructs the global optimization model specifically includes the following steps: S11. Obtain time-of-use electricity price data and demand-based electricity cost information from the power grid, and define the electricity cost function; S12. Obtain the task evaluation results and real-time equipment status output by the data processing unit, and define the production efficiency objective function; S13. Introduce the equipment thermal inertia coefficient and process temperature control constraints to establish a multivariable constraint equation set; S14. Using a genetic algorithm, with the goal of minimizing the weighted sum of the electricity cost function and the production efficiency objective function, find the optimal solution that satisfies all constraints and generate an initial global optimization strategy.

4. The energy-saving control system for industrial parks according to claim 1, characterized in that, The calibration unit performs optimized calibration and the calculation process for generating calibration parameters is as follows: The energy consumption-temperature rise historical data curve output by the data processing unit is periodically called and input into the global optimization model. The recursive least squares method is used to identify the equipment aging characteristic parameters in the model online. By calculating the deviation between the actual temperature rise rate of the equipment and the reference model, the temperature control threshold and thermal inertia coefficient are dynamically corrected to generate calibration parameters. The calculation formula is: in, This represents the current real-time temperature rise rate of the equipment. The reference temperature rise rate is set for the preset baseline model. Additional energy loss due to aging, This is the temperature rise weighting coefficient. Energy consumption weighting coefficient; Using the calibration parameters The objective function and constraint boundaries of the global optimization model are updated to achieve adaptive adjustment of the global optimization strategy.

5. The electrical energy-saving control system for industrial parks according to claim 4, characterized in that, The data processing unit also includes a task evaluation subunit: The task evaluation subunit is used to comprehensively evaluate the current production task range based on the task importance level, and output the task optimization space parameters for the dynamic optimization scheduling unit to call. The task optimization space parameters include temperature control flexibility coefficient and time flexibility coefficient.

6. The energy-saving control system for industrial parks according to claim 5, characterized in that, The specific process involves inputting the task elasticity weight coefficient as a modulation factor into the dynamic optimization scheduling unit: The modulation factor is converted into a task elasticity weighting coefficient by weighted fusion of temperature control flexibility coefficient and time elasticity coefficient. : in, The temperature control flexibility coefficient has a range of [value missing]. , The time elasticity coefficient, with a range of [missing information]. ; As a weighting factor for the temperature control flexibility coefficient, This is a weighting factor for the time elasticity coefficient, and ; Calibration parameters after being invoked based on task-tunable space parameters The formula is: in, Let be the Sigmoid function, and let Used to limit the output to interval, The sensitivity gain constant is... This is the task elasticity weighting coefficient.

7. The electrical energy-saving control system for industrial parks according to claim 1, characterized in that, The global optimization strategy specifically includes: The start-stop sequence table based on peak and off-peak electricity price periods is used to guide various intelligent electrical devices to perform preheating or precooling operations during off-peak electricity price periods. The auxiliary energy load distribution matrix is ​​used to dynamically adjust the operating power and duty cycle of multiple equipment heat dissipation or heating devices to achieve cluster load balance. The temperature control flexibility range setting is used to dynamically widen or tighten the allowable fluctuation range of the internal temperature of the equipment according to the importance level of the task, so as to maximize energy saving while ensuring production quality.

8. The electrical energy-saving control system for industrial parks according to claim 1, characterized in that, The feedback unit executes control commands according to the updated global optimization strategy in the following ways: The updated global optimization strategy is transformed into standard industrial control protocol commands and sent to the corresponding smart electrical devices through the edge computing gateway. A closed-loop PID control algorithm is adopted, which, combined with the real-time feedback of the internal temperature and power data of the equipment from the data acquisition unit, fine-tunes the speed of the cooling fan or the power of the heating element to ensure that the actual operating status of the equipment closely follows the trajectory of the global optimization strategy.

9. A power energy-saving control system for industrial parks according to claim 1, characterized in that, It also includes a task execution prediction unit; The task execution prediction unit is used to simulate and predict the temperature change curve, energy consumption curve and completion time during the task execution process based on the global optimization model and the current state of the intelligent electrical equipment before executing the updated global optimization strategy. The system predicts and outputs the task execution results, including the expected electricity cost, the expected production efficiency achievement rate, and the probability of temperature control compliance. The prediction results are then fed back to the dynamic optimization scheduling unit for verification and risk assessment before updating the global optimization strategy.

10. A power energy-saving control system for industrial parks according to claim 9, characterized in that, The steps for obtaining the historical energy consumption-temperature rise data curves of each electrical device include: S21. Continuously collect data from various intelligent electrical devices at historical moments. The historical state data, historical power data, historical internal temperature data, and historical thermal inertia coefficient are collected, and the multi-source data are time-axis aligned using timestamps as indexes. S22. Clean the aligned data, removing outliers and noisy data, and set the initial ambient temperature or equipment cold start temperature as the reference temperature. ; S23. Traverse the cleaned historical temperature data and calculate each historical moment point by point. Relative to the reference temperature temperature rise value The calculation formula is: in, for Historical internal temperature data collected at all times; S24. Based on the time sequence, construct the first curve of "historical power-time" and the second curve of "temperature rise-time" respectively; at the same time, construct a scatter plot of "energy consumption-temperature rise" relationship to characterize the thermal characteristics of the equipment with historical power as the horizontal axis and the temperature rise value at the corresponding time as the vertical axis. S25. Analyze the distribution pattern of the "energy consumption-temperature rise" relationship scatter plot, calculate the average temperature rise rate and steady-state temperature rise gain of the equipment, and mark the data in combination with the historical thermal inertia coefficient. Finally, generate the energy consumption-temperature rise historical data curve containing the aging characteristics and thermal response characteristics of the equipment, and output it to the dynamic optimization scheduling unit and calibration unit.

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