Energy-saving regulation and control system for industrial park

Abstract

The invention discloses an energy-saving regulation and control system for an industrial park, and relates to the technical field of industrial energy-saving optimization, and the system comprises an energy efficiency evaluation module which is used for calculating a process energy consumption ratio based on a process energy consumption time sequence matrix, obtaining an energy efficiency entropy in combination with a production line thermodynamic constant, and extracting an energy efficiency entropy threshold from a historical optimal working condition database, when the energy efficiency entropy is greater than an energy efficiency entropy threshold value, analyzing a process pair waiting for timeout, and generating a process recombination scheme; the quantum optimization module is used for mapping an equipment operation state in the process recombination scheme into quantum bits, fitting an inter-process coupling coefficient based on equipment historical energy consumption data, and constructing a quantum Hamiltonian; and the energy-saving regulation module is used for executing quantum annealing through a D-Wave Advantage quantum processor based on the quantum Hamiltonian, generating an optimal ground state solution and carrying out industrial park multi-equipment collaborative energy-saving regulation through the optimal ground state solution. According to the invention, the accuracy of energy efficiency evaluation is improved, and the overall performance of energy-saving regulation and control is also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial energy-saving optimization, and in particular to an energy-saving control system for an industrial park. Background Art

[0002] As the manufacturing industry develops towards intelligence and greenness, energy-saving and control technologies have become a key means for industrial parks to optimize resource allocation, reduce energy costs, and improve environmental friendliness. In recent years, data-driven energy efficiency evaluation and process scheduling optimization methods have been widely used in industrial production. A typical energy-saving and control system usually includes: collecting production line equipment operating status and energy consumption data, constructing an energy consumption characteristic matrix through data preprocessing; analyzing the energy consumption distribution of each process based on this matrix, and setting thresholds based on historical optimal operating conditions for energy efficiency monitoring; when abnormal energy consumption fluctuations are detected, heuristic algorithms or linear programming methods are used to generate process reorganization strategies to optimize production line operating efficiency. In addition, some systems also introduce thermodynamic parameters to more accurately model the energy flow process, thereby improving the scientificity and practicality of energy efficiency evaluation.

[0003] Although the above technologies have improved the energy management capabilities of the industrial park to a certain extent, the following problems still need to be solved in the actual application process. First, the traditional energy efficiency evaluation model mainly relies on static threshold settings or local energy consumption indicators, which makes it difficult to dynamically adapt to the energy efficiency fluctuations caused by the coordinated operation of multiple processes in complex production lines, resulting in low accuracy in identifying non-optimal working conditions. Secondly, most of the existing scheduling optimization methods are based on classical optimization algorithms (such as genetic algorithms, simulated annealing, etc.). When faced with high-dimensional, strongly coupled, and nonlinear process relationships, they are prone to falling into local optimal solutions and cannot achieve global optimization in the true sense. Especially in the scenario of large-scale equipment collaborative control, the solution efficiency and stability are difficult to meet the real-time control requirements, which limits the overall performance improvement of the energy-saving control system. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an energy-saving control system for an industrial park to solve the problems in the prior art of inaccurate identification of non-optimal working conditions and difficulty in achieving efficient global search for scheduling optimization.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an energy-saving control system for an industrial park, which includes a data acquisition module for collecting raw data from the industrial park production line, preprocessing the raw data, and constructing a process energy consumption time series matrix;

[0008] The energy efficiency evaluation module calculates the energy consumption proportion of each process based on the process energy consumption time series matrix, obtains the energy efficiency entropy based on the production line thermodynamic constant, and extracts the energy efficiency entropy threshold from the historical optimal operating condition database. When the energy efficiency entropy exceeds the energy efficiency entropy threshold, it analyzes the process pairs that have timed out and generates a process reorganization plan.

[0009] The quantum optimization module is used to map the equipment operating status in the process reorganization plan into quantum bits, fit the inter-process coupling coefficient based on the equipment's historical energy consumption data, and construct the quantum Hamiltonian;

[0010] The energy-saving regulation module is used to perform quantum annealing based on the quantum Hamiltonian through the D-Wave Advantage quantum processor to generate the optimal ground state solution, and use the optimal ground state solution to perform collaborative energy-saving regulation of multiple devices in the industrial park.

[0011] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the original data includes equipment power consumption waveforms, workpiece flow timestamps and idling status identifiers.

[0012] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the preprocessing includes time alignment and outlier elimination.

[0013] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the specific steps of constructing the process energy consumption time series matrix are as follows:

[0014] Based on the equipment power consumption waveform, the fundamental power value is extracted through FFT transformation, and the process duration is obtained based on the workpiece flow timestamp;

[0015] The fundamental power value, process duration and idling status identifier are arranged into a process energy consumption time series matrix.

[0016] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the specific steps of calculating the process energy consumption ratio based on the process energy consumption time series matrix are as follows:

[0017] By filtering the idling data, the idling state is marked as idling, and the corresponding fundamental power value is reset to zero to generate the effective fundamental power value. The actual energy consumption of the process is calculated using the trapezoidal integration method to generate the total power consumption of the process;

[0018] According to the total power consumption of the process and the actual energy consumption of the process, the energy consumption ratio vector of the process is obtained.

[0019] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, wherein: the energy efficiency entropy is obtained by combining the thermodynamic constants of the production line, and the specific steps are as follows:

[0020] Collect equipment temperature distribution data, obtain the maximum temperature rise, and measure the equipment heat capacity in combination with the effective fundamental power value of the corresponding process;

[0021] Read the rated power of the equipment's scattering system, extract the production line's thermodynamic constants based on the maximum temperature rise and process energy consumption ratio vector, and generate energy efficiency entropy through weighted summation.

[0022] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, wherein: the analysis of the process pairs waiting for timeout and the generation of the process reorganization plan are specifically performed as follows:

[0023] Use overtime idling detection to mark overdue process pairs, and determine the process priority weight through critical path identification;

[0024] Arrange processes based on priority weights, calculate expected energy saving rates, and generate process reorganization plans.

[0025] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the specific steps of mapping the equipment operating status in the process reorganization scheme into quantum bits, fitting the inter-process coupling coefficient based on the historical energy consumption data of the equipment, and constructing the quantum Hamiltonian are as follows:

[0026] Extract the device ID set of the process in the process reorganization plan, map the device operation status to quantum bits, and generate the initial quantum state vector according to the process arrangement of the process reorganization plan;

[0027] Based on historical equipment energy consumption data, the equipment energy consumption matrix is ​​constructed, the energy consumption covariance between equipment is extracted, and the coupling coefficient between processes is fitted using the least squares method;

[0028] Real-time time-of-use electricity prices are collected, combined with the initial quantum state vector, the coupling coefficient between processes, and the energy efficiency entropy, to construct the electricity price cost term and entropy constraint term and generate the quantum Hamiltonian.

[0029] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, wherein: quantum annealing is performed by the D-Wave Advantage quantum processor, and the specific steps are as follows:

[0030] Initialize the quantum bit to a uniform superposition state, apply a transverse magnetic field, and linearly adjust the quantum Hamiltonian from the initial value to the target value;

[0031] During the linear adjustment process, the quantum tunneling effect is used to penetrate the energy barrier, and quantum state measurement is performed at the end of annealing. The ground state with the lowest energy is selected from multiple measurement results as the optimal ground state solution.

[0032] As a preferred solution of the energy-saving control system for industrial parks described in the present invention, the specific steps of performing collaborative energy-saving control of multiple devices in the industrial park through the optimal base state solution are as follows:

[0033] According to the optimal ground state solution, the quantum bit state is analyzed and the set of activated devices is extracted;

[0034] Obtain the optimal startup time for the device through time window optimization, set power limits, and generate device control instructions;

[0035] Collaborative energy-saving regulation of multiple devices in the industrial park is carried out through equipment control instructions.

[0036] The beneficial effects of this invention are as follows: by collecting raw production line data and constructing a process energy consumption time series matrix, combined with thermodynamic parameters to calculate energy efficiency entropy, a dynamic, global assessment of the production line's energy efficiency status is achieved. When non-optimal operating conditions are identified, a process reorganization plan is generated, and the equipment operating status is further mapped to quantum bits. A quantum Hamiltonian containing electricity price and energy efficiency constraints is constructed, and a global optimization solution is performed using the quantum tunneling effect, thereby achieving coordinated energy-saving control of multiple devices. This not only improves the accuracy and adaptability of energy efficiency assessment, but also overcomes the bottleneck of traditional scheduling optimization, which is prone to falling into local optimality, and improves the intelligence level and overall performance of energy-saving control. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a schematic diagram of the energy-saving control system used in the industrial park.

[0039] Figure 2 Flowchart generated for energy efficiency assessment and process reorganization plan.

[0040] Figure 3 Flowchart of quantum optimization and energy-saving control.

[0041] Figure 4 Flowchart for generating and issuing control instructions. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0045] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides an energy-saving control system for an industrial park, comprising the following steps:

[0046] Data acquisition module: collects raw data from the industrial park production line, pre-processes the raw data, and constructs a process energy consumption time series matrix;

[0047] Collect raw data from the industrial park production line, including equipment power consumption waveforms, workpiece flow timestamps, and idling status identification;

[0048] Furthermore, the power consumption waveform of the equipment is collected in real time through a high-frequency current transformer, and the fundamental power value is extracted using the fast Fourier transform method. The entry and exit time points of the workpiece are recorded through a UHF RFID (ultra-high frequency radio frequency identification) reader to obtain a timestamp, and the process duration is determined based on the time interval. The changes in the vibration signal during the operation of the equipment are analyzed, and the idling state is marked when the main frequency amplitude of the vibration signal is lower than <0.1g.

[0049] Preprocessing of raw data includes time alignment and outlier removal;

[0050] Furthermore, through NTP (Network Time Protocol) synchronization, the timestamps of the equipment power consumption waveform collected by the high-frequency current transformer, the timestamps of the workpiece flow recording, and the timestamps of the vibration spectrum collected by the triaxial piezoelectric accelerometer are aligned to a unified clock reference; the equipment power consumption waveform data, workpiece flow timestamp data, and vibration spectrum data are time-synchronized and arranged using a time index with millisecond-level precision;

[0051] Scan the data sequence of fundamental power values ​​and identify the data of continuous sampling points of fundamental power values. When it is detected that the fundamental power value between adjacent sampling points drops by more than 90% and lasts for more than two seconds, it is marked as an abnormal data segment, and all fundamental power value sampling points in the abnormal data segment are deleted. The fundamental power value sampling points of the unmarked abnormal data segment are retained, forming a fundamental power value with strictly aligned timestamps and no abnormal sudden drop, a workpiece flow timestamp, and an idling status identifier.

[0052] The fundamental power value, process duration, and idling state flag are combined into a process energy consumption time series matrix, which is expressed as follows:

[0053]

[0054] Among them, M t is the process energy consumption time series matrix at time t, P n (t) is the fundamental power value of the nth process at time t, τ n is the process duration of the nth process, δ n ∈{0, 1} is the idle state identifier of the nth process (0 means running, 1 means idling).

[0055] Energy efficiency evaluation module: Calculates the energy consumption proportion of each process based on the process energy consumption time series matrix, combines it with the production line thermodynamic constants to obtain energy efficiency entropy, and extracts the energy efficiency entropy threshold from the historical optimal operating condition database. When the energy efficiency entropy exceeds the energy efficiency entropy threshold, analyzes the process pairs that have timed out and generates a process reorganization plan;

[0056] By filtering the idling data, the fundamental power value corresponding to the idling state is marked as idling and reset to zero, retaining the effective fundamental power value;

[0057] Furthermore, when the idle state flag is 1, the fundamental wave power value of the corresponding process is corrected to zero; when the idle state flag is 0, the fundamental wave power value of the corresponding process is kept unchanged; and the corrected fundamental wave power values ​​of all processes are saved as effective fundamental wave power values;

[0058] It should be noted that the criteria for determining valid fundamental power values ​​requires not only zeroing the idle state flag but also ensuring that all corrected fundamental power values ​​are data points with strictly aligned timestamps and complete continuity. Specifically, when the idle state flag is 1, the fundamental power value of the corresponding process is corrected to zero. When the idle state flag is 0, the fundamental power value of the corresponding process remains unchanged. The corrected fundamental power values ​​are stored in the order of process index and timestamp, generating a complete corrected fundamental power value.

[0059] Obtain batch start and end signals from the production line PLC (Programmable Logic Controller) to obtain the production cycle and query all effective fundamental power values ​​within the time period;

[0060] Furthermore, the production cycle start timestamp is recorded according to the batch start signal, and the production cycle end timestamp is recorded according to the batch end signal; using the start timestamp and end timestamp as time interval conditions, a time range query request is submitted to the time series database storing the effective fundamental power values; the time series database performs a timestamp index scan operation and returns all effective fundamental power value records within the interval from the start timestamp to the end timestamp;

[0061] It should be noted that the valid fundamental power value records in the time series database are sorted in ascending timestamp order, and a timestamp index structure is established based on the timestamp field. The timestamp index structure is a general sorted array-based structure. When a time range query request is submitted, the time series database directly locates the data location corresponding to the production cycle start timestamp based on the timestamp index structure, scans to the data location corresponding to the production cycle end timestamp, and returns all valid fundamental power value records between the production cycle start timestamp and the production cycle end timestamp.

[0062] Based on the effective fundamental power value, the trapezoidal integration method is used to calculate the actual energy consumption of the process. The expression is:

[0063]

[0064] Among them, E i is the actual energy consumption of the i-th process, Δt is the sampling time interval, N is the total number of sampling points, k is the sampling point index, is the i-th process at t k The effective fundamental power value at the moment, is the i-th process at t k+1 The effective fundamental power value at the moment;

[0065] Obtain the total power consumption based on the actual energy consumption of each process, calculate the energy consumption proportion of each process, and generate the process energy consumption proportion vector;

[0066] Furthermore, the actual energy consumption of all processes is traversed and accumulated to obtain the total power consumption; the actual energy consumption of each process is processed in order of process index, and the actual energy consumption of the current process is divided by the total power consumption to obtain the energy consumption proportion of the current process; the energy consumption proportions of all processes are stored in order of process index, and a process energy consumption proportion vector is generated, with the number of elements being the same as the total number of processes and the value range of each element being a closed interval between zero and one;

[0067] For example, the actual energy consumption of process 1 is 1500, the actual energy consumption of process 2 is 3000, and the actual energy consumption of process 2 is 4500;

[0068] The actual energy consumption of the process is 1500+3000+4500=9000;

[0069] The process energy consumption ratio vector is [0.1667, 0.3333, 0.5000].

[0070] Use a FLIR T860 infrared thermal imager to scan the surface of the equipment, collect equipment temperature distribution data, extract the temperature rise curve from the temperature distribution data, and obtain the maximum temperature rise;

[0071] It should be noted that the FLIR T860 infrared thermal imager performs a device surface scanning operation, capturing the temperature of each spatial location point on the device surface to form device temperature distribution data; extracts the temperature change sequence of each spatial location point according to the time dimension from the device temperature distribution data; calculates the ambient temperature baseline difference of the temperature change sequence of each spatial location point to generate a temperature rise value sequence for the corresponding location point; integrates the temperature rise value sequences of all spatial location points according to time points and plots them into multiple temperature rise curves, scans all data points of the temperature rise curves one by one, and records and outputs the highest temperature occurring during the detection process as the maximum temperature rise.

[0072] Extract the effective fundamental power value of the process corresponding to the maximum temperature rise and measure the heat capacity of the equipment;

[0073] Specifically, the specific production period where the maximum temperature rise occurs is located; the process number corresponding to the production period is determined; the stored time series record of the effective fundamental power value is retrieved according to the process number; all effective fundamental power value sampling points within the process production period are extracted to form a power value set; a time integration operation is performed on the power value set to obtain the total energy consumption; and the total energy consumption is divided by the thermal capacity of the maximum temperature rise output device.

[0074] Read the cooling fan PLC data through the Modbus TCP protocol (Modbus industrial communication protocol based on TCP protocol) to obtain the fan rated power. Combined with the equipment heat capacity, the production line thermodynamic constants are extracted.

[0075] It should be noted that a data communication channel with the cooling fan PLC is established through the Modbus TCP protocol, the content of the cooling fan's device parameter register is read, and the cooling fan's rated power is parsed from the content of the device parameter register; the cooling fan's rated power and the equipment's heat capacity are input into the production line thermodynamic constant calculation formula, and the coefficient calculation operation is performed to obtain the production line thermodynamic constant.

[0076] The energy consumption proportion vectors of each process are weighted and summed, and the energy efficiency entropy is generated by combining the thermodynamic constants of the production line. The expression is:

[0077]

[0078] Among them, S is the energy efficiency entropy, κ is the production line thermodynamic constant, v i is the energy consumption ratio of the i-th process, and I is the total number of processes;

[0079] Extract the energy efficiency entropy threshold from the historical optimal operating condition database. When the energy efficiency entropy is greater than the energy efficiency entropy threshold, analyze the process pairs with timeout idling waiting through timeout idling detection and generate an energy efficiency entropy status flag.

[0080] It should be noted that the historical optimal energy efficiency entropy is queried through the historical optimal working condition database interface; the historical optimal energy efficiency entropy is extracted as the energy efficiency entropy threshold; the relationship between the energy efficiency entropy and the energy efficiency entropy threshold is compared; when the energy efficiency entropy exceeds the energy efficiency entropy threshold, the timeout idling detection process is activated; the timeout idling detection process retrieves the workpiece flow timestamp data set; the waiting time interval between each pair of processes is obtained; when the waiting time interval is greater than 5 minutes, the process pair is marked as an idling waiting timeout state; and the energy efficiency entropy state flag is generated based on the idling waiting timeout state marking result.

[0081] It should also be noted that the historical optimal operating condition database is a time-series database storage structure that records the verified and verified set of optimal operating status parameters of the production line; it includes historical data points of process energy consumption ratio vectors, maximum value sequence of equipment temperature distribution data, set of calibration values ​​of production line thermodynamic constants and corresponding energy efficiency entropy threshold values; each data entry is associated with a specific timestamp, production line configuration identifier, and product model identification code; the historical optimal operating condition database uses a time index mechanism to achieve fast field retrieval, and the index fields include production batch number, energy efficiency entropy and time interval label; the stored process energy consumption ratio vector is derived from the optimal process energy consumption ratio vector of the historical production batch, the maximum temperature rise sequence is associated with the peak record of key equipment temperature distribution data, and the energy efficiency entropy threshold is the highest energy efficiency entropy historical value under each production line configuration identifier minus five percent.

[0082] Determine the priority weight of key processes through critical path identification;

[0083] Specifically, the forward and backward dependencies between processes are analyzed through the production BOM (Bill of Materials) table to construct a process dependency graph; the critical path method is used to traverse all paths in the process dependency graph to obtain the total cumulative process duration of each path; the path with the largest total cumulative process duration is identified as the critical path; all processes included in the critical path are marked as critical processes; high priority weights are assigned to critical processes, and low priority weights are assigned to non-critical processes.

[0084] Arrange processes in descending order according to priority weights, calculate expected energy saving rates, and generate process reorganization plans;

[0085] Furthermore, a sorting operation is performed according to the priority weight of the process, and a process sequence is generated by arranging the process sequence from high to low priority weight. The expected energy saving rate is calculated based on the historical energy consumption data and the process sequence. The expression is:

[0086]

[0087] Where η is the expected energy saving rate, is the actual average power of the i-th process, τ i is the standard process time of the i-th process, P′ jis the predicted power of the jth process after reorganization, τ j is the standard process duration of the j-th process;

[0088] It should be noted that the predicted power is based on the historical equipment energy consumption data and the arrangement order of the new process sequence in the process reorganization plan. The average power value of the same or similar processes in the historical equipment energy consumption data under similar process arrangements is used as the predicted power. During the statistical process, the historical energy consumption data is grouped and summarized according to the process number, process sequence and equipment operating status, and the average power value of each group is extracted as the predicted power of the corresponding process under the new process sequence in the process reorganization plan.

[0089] The process reorganization plan includes the new process sequence field, the expected energy saving rate field, and the adjustment process identification list field.

[0090] Quantum Optimization Module: This module maps the equipment operating status in the process reorganization plan to quantum bits, fits the inter-process coupling coefficient based on the equipment's historical energy consumption data, and constructs a quantum Hamiltonian.

[0091] Extract the device ID set involved in all processes in the process reorganization plan, map the device operating status to quantum bits, and assign a quantum bit index to each device;

[0092] Furthermore, the new process sequence field and the adjusted process identification list field contained in the process reorganization plan are traversed to extract all associated device IDs. The device IDs form a device ID set, and a correspondence between the device ID and the quantum bit is established. The device operation status is mapped to the quantum bit value: when the process reorganization plan requires the device to be running, the quantum bit is assigned a value of one, and when it is required to be shut down, the quantum bit is assigned a value of zero, and the quantum bit value state is generated; a unique quantum bit index number is assigned to each device ID in the device ID set; the quantum bit index numbers are assigned continuously in natural number order; a mapping table between the quantum bit index number and the device ID is formed; and an initial quantum state vector is generated based on the quantum bit value state.

[0093] Based on historical equipment energy consumption data, the equipment energy consumption matrix is ​​constructed, the energy consumption covariance between equipment is extracted, and the coupling coefficient between processes is fitted using the least squares method;

[0094] It should be noted that the time series energy consumption record set is retrieved from the historical equipment energy consumption data; the energy consumption data of different equipment are aligned according to the timestamps to form an equipment energy consumption matrix with a complete row and column structure; the fluctuation correlation between every two equipment sequences in the equipment energy consumption matrix is ​​calculated to obtain the energy consumption covariance matrix between equipment; the energy consumption covariance matrix between equipment is fitted by the least squares method to obtain the coupling coefficient between production processes.

[0095] Collect the real-time time-of-use electricity price, combine the initial quantum state vector, the coupling coefficient between processes, and the energy efficiency entropy, construct the electricity price cost term and the entropy constraint term, and generate the quantum Hamiltonian, which is expressed as follows:

[0096]

[0097] Where H is the quantum Hamiltonian, m is the total number of devices, and J pq is the coupling coefficient between the processes of equipment p and equipment q, σ p is the quantum bit state of device p, σ q is the quantum bit state of device q, h p is the electricity price cost factor of equipment p, λ is the energy efficiency entropy constraint weight coefficient;

[0098] It should be noted that the real-time time-of-use electricity price is obtained, combined with the rated power of the equipment, and the real-time time-of-use electricity price is applied to the rated power of the equipment to form the electricity price cost base; a negative transformation is applied to the electricity price cost base to generate an electricity price cost item; the entropy weight coefficient is read, and the entropy weight coefficient is integrated with the energy efficiency entropy to generate an entropy constraint item; the quantum bit index sequence of the initial quantum state vector is traversed, the coupling coefficient of the corresponding position of the coupling coefficient between processes is retrieved, and the coupling coefficient is associated with the quantum bit state value to construct a basic coupling unit; all basic coupling units are integrated and a negative transformation is applied to form a coupling item; the coupling item, the electricity price cost item and the entropy constraint item are combined to generate a quantum Hamiltonian.

[0099] Energy-saving adjustment module: Based on the quantum Hamiltonian, quantum annealing is performed through the D-Wave Advantage quantum processor to generate the optimal ground state solution. The optimal ground state solution is then used to coordinate energy-saving control of multiple devices in the industrial park.

[0100] Quantum annealing performed via the D-Wave Advantage quantum processor;

[0101] Specifically, the quantum bit is initialized to a uniform superposition state; a transverse magnetic field is applied; the Hamiltonian parameters are linearly adjusted from the initial value to the target value; the quantum tunneling effect is used to penetrate the energy barrier; a quantum state measurement is performed at the end of annealing; and the lowest energy ground state is selected from multiple measurement results as the optimal ground state solution.

[0102] Furthermore, during quantum annealing performed by the D-Wave Advantage quantum processor, initialization of the qubits to a uniform superposition state is accomplished based on the standard qubit initialization instruction set provided in the quantum processor control interface. By applying a superconducting current pulse of equal amplitude and phase to each qubit, all qubit states are simultaneously in an equiprobable linear superposition of the zero and one states, forming a uniform superposition state probability amplitude distribution. When applying a transverse magnetic field, the D-Wave Advantage quantum processor's transverse field regulation circuit applies a fixed-direction, controllable-intensity transverse magnetic field to each qubit. The regulation process is achieved by controlling the current intensity and current direction parameters in the circuit. The current intensity corresponds one-to-one with the qubit coupling position. The magnetic field is applied uniformly and remains within the processor's designated quantum annealing channel throughout the process. During the linear parameter adjustment process, the D-Wave Advantage quantum processor calculates the transverse magnetic field strength and target Hamiltonian weight at the current point in time in real time based on a pre-set linear interpolation function on the annealing timeline. The transverse magnetic field strength decreases in equal steps with the annealing time, while the target Hamiltonian weight increases in equal steps with the annealing time, maintaining a strict linear relationship between the two over time. The utilization of quantum tunneling physics relies entirely on the inherent quantum tunneling properties of superconducting qubits within the D-Wave Advantage quantum processor's quantum annealing physics architecture. This property occurs naturally, driven by the combined effects of the transverse magnetic field and the gradual strengthening of the target Hamiltonian, without the need for external intervention. The D-Wave Advantage quantum processor control interface implements a loop execution instruction to repeat a thousand independent annealing and measurement cycles. The annealing parameters remain consistent for each annealing and measurement process. The ground state energy values ​​and qubit state combinations are automatically recorded through measurement normalization. The measurement data are written to a data buffer in chronological order for subsequent selection of the qubit state combination with the lowest ground state energy value as the optimal ground state solution.

[0103] According to the optimal ground state solution, the quantum bit state is analyzed and the set of activated devices is extracted;

[0104] Furthermore, all quantum bit state sequences contained in the optimal ground state solution are traversed; whether the quantum bit state is equal to the quantum bit activation state is determined; the quantum bit index number that satisfies the quantum bit state equal to the quantum bit activation state is recorded; the corresponding device ID is searched through the quantum bit index number to device ID mapping table; and all device IDs that meet the conditions are collected to form an activated device ID set.

[0105] Obtain the optimal startup time for the device through time window optimization, set power limits, and generate device control instructions;

[0106] It should be noted that the peak and valley electricity price period division information is retrieved through the real-time time-of-use electricity price; the device category identification corresponding to each device ID in the activated device ID set is traversed; the electricity price mapping operation is performed in combination with the device category identification and the time-of-use electricity price period division information, for example, only high-energy consumption equipment is allowed to operate in the valley electricity price period, intermittent operation equipment is allowed to operate in the valley or flat electricity price period, and continuous operation equipment is allowed to operate all the time; the time interval with the lowest equipment operation cost is determined and recorded as the optimal start-up time window; the equipment rated power is obtained; the power limit policy parameters are matched according to the equipment type identification, for example, the motor equipment is set to a limit of 90% of the rated power, the heating equipment is set to 85%, and the cooling equipment is set to 95%. The specific power limit is calculated in combination with the equipment rated power, and finally a set of equipment control instructions containing the equipment ID, the optimal start-up time window and the power limit is generated.

[0107] Use equipment control instructions to coordinate energy-saving control of multiple devices in the industrial park;

[0108] Specifically, the device control instruction set is transmitted to the intelligent circuit breaker array through the time-sensitive network communication protocol; the intelligent circuit breaker array connects or disconnects the circuit within the specified time period according to the optimal start time window field; the intelligent circuit breaker array monitors the device power measurement value in real time, and triggers the automatic power adjustment mechanism when the device power measurement value exceeds the power limit; at the same time, the process reorganization plan structure is transmitted to the production line programmable logic controller through the OPC UA industrial communication protocol (Unified Architecture Object Link and Embedded Communication Protocol); the programmable logic controller adjusts the production line equipment scheduling sequence according to the new process sequence field.

[0109] In summary, the present invention achieves a dynamic, global assessment of the production line's energy efficiency status by collecting raw production line data and constructing a process energy consumption time-series matrix, combining thermodynamic parameters to calculate energy efficiency entropy. When non-optimal operating conditions are identified, a process reorganization plan is generated. Furthermore, the equipment operating status is mapped to quantum bits, and a quantum Hamiltonian is constructed that incorporates electricity prices and energy efficiency constraints. This system then uses quantum tunneling to perform a global optimization solution, thereby achieving coordinated energy-saving control across multiple devices. This not only improves the accuracy and adaptability of energy-efficiency assessments, but also overcomes the bottleneck of traditional scheduling optimization, which is prone to falling into local optimality, thereby enhancing the intelligence and overall performance of energy-saving control.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Energy-saving control system for industrial parks, characterized by: include, The data acquisition module is used to collect the raw data of the industrial park production line, pre-process the raw data, and construct the process energy consumption time series matrix; The energy efficiency evaluation module calculates the energy consumption proportion of each process based on the process energy consumption time series matrix, obtains the energy efficiency entropy based on the production line thermodynamic constant, and extracts the energy efficiency entropy threshold from the historical optimal operating condition database. When the energy efficiency entropy exceeds the energy efficiency entropy threshold, it analyzes the process pairs that have timed out and generates a process reorganization plan. The quantum optimization module is used to map the equipment operating status in the process reorganization plan into quantum bits, fit the inter-process coupling coefficient based on the equipment's historical energy consumption data, and construct the quantum Hamiltonian; The energy-saving regulation module is used to perform quantum annealing based on the quantum Hamiltonian through the D-Wave Advantage quantum processor to generate the optimal ground state solution, and use the optimal ground state solution to perform collaborative energy-saving regulation of multiple devices in the industrial park.

2. The energy-saving control system for an industrial park according to claim 1, characterized in that: The specific steps of constructing the process energy consumption time series matrix are as follows: Based on the equipment power consumption waveform, the fundamental power value is extracted through FFT transformation, and the process duration is obtained based on the workpiece flow timestamp; The fundamental power value, process duration and idling status identifier are arranged into a process energy consumption time series matrix.

3. The energy-saving control system for an industrial park according to claim 2, characterized in that: The specific steps for calculating the process energy consumption ratio based on the process energy consumption time series matrix are as follows: By filtering the idling data, the fundamental power value corresponding to the idling state is reset to zero to generate the effective fundamental power value, and the actual energy consumption of the process is calculated using the trapezoidal integration method to generate the total power consumption of the process; According to the total power consumption of the process and the actual energy consumption of the process, the energy consumption ratio vector of the process is obtained.

4. The energy-saving control system for an industrial park according to claim 3, characterized in that: The specific steps for obtaining energy efficiency entropy by combining the thermodynamic constants of the production line are as follows: Collect equipment temperature distribution data, obtain the maximum temperature rise, and measure the equipment heat capacity in combination with the effective fundamental power value of the corresponding process; Read the rated power of the equipment's scattering system, extract the production line's thermodynamic constants based on the equipment's heat capacity and the process energy consumption ratio vector, and generate energy efficiency entropy through weighted summation.

5. The energy-saving control system for an industrial park according to claim 4, characterized in that: The analysis of the process pairs waiting for timeout generates a process reorganization plan. The specific steps are as follows: Use overtime idling detection to mark overdue process pairs, and determine the process priority weight through critical path identification; Arrange processes based on priority weights, calculate expected energy saving rates, and generate process reorganization plans.

6. The energy-saving control system for an industrial park according to claim 5, characterized in that: The specific steps of mapping the equipment operating status in the process reorganization scheme into quantum bits, fitting the inter-process coupling coefficient based on the equipment historical energy consumption data, and constructing the quantum Hamiltonian are as follows: Extract the device ID set of the process in the process reorganization plan, map the device operation status to quantum bits, and generate the initial quantum state vector according to the process arrangement of the process reorganization plan; Based on historical equipment energy consumption data, the equipment energy consumption matrix is ​​constructed, the energy consumption covariance between equipment is extracted, and the coupling coefficient between processes is fitted using the least squares method; Real-time time-of-use electricity prices are collected, combined with the initial quantum state vector, the coupling coefficient between processes, and the energy efficiency entropy, to construct the electricity price cost term and entropy constraint term and generate the quantum Hamiltonian.

7. The energy-saving control system for an industrial park according to claim 6, characterized in that: The quantum annealing is performed by the D-Wave Advantage quantum processor. The specific steps are as follows: Initialize the quantum bit to a uniform superposition state, apply a transverse magnetic field, and linearly adjust the quantum Hamiltonian from the initial value to the target value; During the linear adjustment process, the quantum tunneling effect is used to penetrate the energy barrier, and quantum state measurement is performed at the end of annealing. The ground state with the lowest energy is selected from multiple measurement results as the optimal ground state solution.

8. The energy-saving control system for an industrial park according to claim 7, characterized in that: The specific steps of using the optimal base state solution to coordinate energy-saving control of multiple devices in the industrial park are as follows: According to the optimal ground state solution, the quantum bit state is analyzed and the set of activated devices is extracted; Obtain the optimal startup time for the device through time window optimization, set power limits, and generate device control instructions; Collaborative energy-saving regulation of multiple devices in the industrial park is carried out through equipment control instructions.

9. The energy-saving control system for an industrial park according to claim 1, characterized in that: The raw data includes equipment power consumption waveform, workpiece flow timestamp and idling status identification.

10. The energy-saving control system for an industrial park according to claim 1, characterized in that: The preprocessing includes time alignment and outlier removal.

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