A real-time monitoring and scheduling system for energy consumption of emulsion paint production equipment

Abstract

This invention discloses a real-time energy consumption monitoring and scheduling system for latex paint production equipment, belonging to the technical field of energy consumption monitoring and scheduling for latex paint production equipment. The system includes a data acquisition module for synchronously acquiring voltage, current, speed, and temperature signals of the production equipment, generating a timestamped raw energy consumption dataset; an energy consumption feature extraction module for dividing the steady-state and transient intervals, extracting power baseline values ​​and power mutation features, forming an energy consumption feature vector; a scheduling strategy generation module for inputting the energy consumption feature vector into a preset rule chain, outputting a scheduling scheme through hierarchical matching of constraints; and an execution module for converting the scheduling scheme into control commands for issuance and receiving feedback. This system achieves precise control of the production process by dynamically identifying energy consumption features and matching them with hierarchical rules, avoiding the lag of manual intervention and improving the scheduling response speed and the accuracy of energy consumption control.

Description

Technical Field

[0001] This invention relates to the field of energy consumption monitoring and scheduling technology for latex paint production equipment, specifically to a real-time energy consumption monitoring and scheduling system for latex paint production equipment. Background Technology

[0002] The production of latex paint involves multiple processes such as dispersion, grinding, and paint mixing. The power characteristics of each piece of equipment vary significantly, and equipment starts, stops, and load changes frequently, resulting in high overall energy consumption management complexity. Existing energy monitoring and scheduling solutions often rely on manual experience to set equipment operating levels and sequences, or employ automated control methods based on static metrics. This involves monitoring only threshold parameters such as voltage and current, triggering simple alarms or shutdowns when limits are exceeded. These methods fail to accurately distinguish and quantify dynamic energy consumption behaviors such as steady-state power consumption baselines and transient power surges during equipment operation, leading to scheduling decisions often based on incomplete or unrepresentative energy consumption information. A further deficiency in existing technologies lies in their reliance on comparing overall averages or instantaneous values ​​for energy consumption data, without considering the equipment's own operational status changes to define energy consumption analysis intervals. This results in a mixture of high-energy-consuming transient processes and stable operating phases, making it difficult for scheduling systems to accurately capture critical periods with the greatest impact on power lines or the greatest potential for energy-saving regulation. Meanwhile, traditional scheduling strategies are mostly single-condition triggering modes. When multiple constraints occur concurrently, there is a lack of a mechanism for orderly and rapid trade-offs between multi-level safety conditions and energy-saving goals. This can easily lead to strategy conflicts during peak load periods, affecting system response stability and control effectiveness.

[0003] Focusing on the field of energy consumption monitoring and scheduling of latex paint production equipment, how to accurately segment and extract features of the dynamically changing energy consumption patterns during equipment operation, and how to construct a scheduling decision structure that can orderly coordinate multiple objectives such as safety protection, process interlocking, and energy-saving optimization, are technical problems that require continuous research and development. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time monitoring and scheduling system for energy consumption of latex paint production equipment. This system can divide energy consumption data into intervals and extract fractal features based on the dynamic changes in equipment operating status. At the same time, it can perform step-by-step matching of energy consumption features through a rule chain structure with priority order to generate an equipment scheduling scheme that takes into account both safety and energy efficiency.

[0005] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides a real-time energy consumption monitoring and scheduling system for latex paint production equipment, including a data acquisition module, an energy consumption feature extraction module, a scheduling strategy generation module, and an execution module. The data acquisition module is used to synchronously acquire voltage, current, speed, and temperature signals from each production device in the latex paint production line, and to de-mark the signals according to the device type, generating a raw energy consumption dataset with timestamps. The energy consumption feature extraction module is used to segment the raw energy consumption dataset, dividing it into steady-state and transient intervals based on the operating status identifiers of each production device, extracting a power baseline value from each steady-state interval, and extracting the power fluctuation amplitude and duration from each transient interval to form an energy consumption feature vector. The scheduling strategy generation module is used to input the energy consumption feature vector into a preset rule chain, which contains several energy consumption constraints and device interlocking conditions ordered by priority. By matching the energy consumption feature vector with the constraints level by level, a scheduling scheme containing the start-stop sequence and operating level of each device is output. The execution module converts the scheduling scheme into control commands and sends them to the programmable logic controllers (PLCs) of each production device. It also receives execution feedback signals and updates the original energy consumption dataset. This enables real-time monitoring and automatic scheduling of energy consumption for each device in the latex paint production line, effectively reducing overall energy consumption while ensuring production safety and process interlocking.

[0006] Preferably, the data acquisition module deploys multiple high-speed sampling terminals on each production line. Each high-speed sampling terminal is synchronously triggered using the same clock source. The acquired analog signals are converted from analog to digital and packaged into data frames according to device number and signal type. These frames are then transmitted via industrial Ethernet to a central data aggregation unit, which reassembles them into the original energy consumption dataset according to timestamp order. Synchronous acquisition and unified time synchronization ensure strict alignment of electrical parameters and operating status signals of multiple devices in the time dimension, providing a reliable data foundation for accurate extraction of subsequent energy consumption characteristics.

[0007] The energy consumption feature extraction module reads the operating status identifiers from the status registers of each device. These identifiers include four states: startup, stable operation, stop, and fault. The original energy consumption dataset is segmented into continuous time segments, with the change times of these operating status identifiers as boundaries. A sliding window variance is calculated for the power signal within each time segment. If the variance is less than a preset threshold, it is considered a steady-state interval; otherwise, it is considered a transient interval. From each steady-state interval, the arithmetic mean of all power sampling points within that interval is calculated as the power baseline. From each transient interval, the difference between the maximum power value in that interval and the power baseline of the previous steady-state interval is taken as the power fluctuation amplitude, and the duration of that interval is taken as the duration. The resulting energy consumption feature vector can accurately distinguish the energy consumption characteristics of the device during its stable operation phase and transition phase, facilitating targeted processing by scheduling strategies.

[0008] In the scheduling strategy generation module, the rule chain consists of multiple rules arranged in descending order of priority. Each rule includes a condition part and an action part. The condition part is determined by judging the numerical range of a specific component in the energy consumption feature vector, and the action part specifies the corresponding device start-up / stop sequence and operating level. The scheduling strategy generation module checks the energy consumption feature vector sequentially, starting with the highest priority rule, to see if it satisfies the condition part of that rule. Once satisfied, it immediately outputs the action part of that rule and stops checking subsequent rules, thus enabling a deterministic priority mechanism to quickly respond to safety events and energy consumption optimization needs. As a technical solution of this invention, a power coupling coefficient between devices is introduced in the condition part of each rule. This power coupling coefficient is defined as the ratio of the total power of two devices operating simultaneously to the sum of their individual operating power. When the power base value in the energy consumption feature vector, divided by the power coupling coefficient, falls within a preset energy-saving range, the condition part of the rule is deemed satisfied. This takes into account the nonlinear effects of power superposition between devices in the scheduling decision, improving the accuracy of energy-saving ranking. The rules in the rule chain, ranked from highest to lowest priority, specifically include: overload protection rules, the condition of which is that the power fluctuation of any device exceeds the rated power limit of that device; equipment interlock rules, the condition of which is that the operating status identifiers of two devices are activated simultaneously and there is a sequential dependency relationship between them; and energy-saving ranking rules, the condition of which is that the current total power base value exceeds the lower limit of the overall line's power supply capacity. This priority hierarchy ensures that any scheduling scheme is optimized for energy saving only after satisfying the hard constraints of overload protection and process interlocks, thus guaranteeing the safety and process rationality of the production system.

[0009] When converting control commands, the execution module maps the device start-stop sequence and operating level in the scheduling scheme to the register addresses and values ​​of the corresponding programmable logic controllers (PLCs). It generates a command queue according to the order in which the devices start and broadcasts the command queue via the industrial fieldbus. It reads the response status of each PLC one by one; if a response times out, the command is resent and the exception is recorded. This execution mechanism ensures the accurate delivery of scheduling commands to field devices and forms a closed-loop confirmation through response feedback, improving execution reliability and anomaly traceability.

[0010] In a preferred embodiment of the present invention, the system further includes an energy consumption baseline generation module, used to filter data from multiple complete production cycles of the same product batch and under the same ambient temperature from historical raw energy consumption datasets, calculate the weighted average of the power base values ​​of each steady-state interval within each production cycle, and use the weighted average as the energy consumption baseline value of the production equipment. Specifically, the data of each production cycle is divided into several sub-intervals according to time periods, and each sub-interval is assigned a weight coefficient, which is determined based on the closeness of the equipment load rate within the sub-interval to the standard load rate. The energy consumption baseline value is obtained by multiplying the weight coefficient by the power base value of the corresponding sub-interval, summing the results, and then dividing by the sum of the weight coefficients. When generating a scheduling scheme, the scheduling strategy generation module also compares the energy consumption baseline value with the power base value in the current energy consumption feature vector. When the deviation exceeds a preset deviation threshold, a temporary adjustment to the rule priority in the rule chain is triggered. This enables the scheduling system to dynamically change the priority of scheduling rules based on the deviation between the actual operating status of the equipment and the baseline energy consumption level, thereby making adaptive adjustments to factors such as equipment performance degradation and operating condition fluctuations, and continuously maintaining the effectiveness of energy consumption monitoring and scheduling.

[0011] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0012] The original energy consumption dataset is segmented into continuous time segments by changing the operating state identifier. A sliding window variance calculation is performed on the power signal within each time segment, and whether the variance is less than a preset threshold is used as the basis for dividing the steady-state and transient intervals. This interval division method, combining the dual boundaries of device operating state changes and signal statistical characteristics, ensures that the power baseline value extracted from the steady-state interval accurately reflects the device's power consumption level under stable load, while the power fluctuation amplitude and duration extracted from the transient interval accurately quantifies the energy consumption intensity and duration of the start-up impact or load change process. The scheduling strategy algorithm thus obtains feature inputs that strictly correspond to the physical operating stage of the device, avoiding the smoothing and masking of transient impact effects by traditional averaging processing. This allows subsequent scheduling schemes to control start-stop timing and speed based on the actual impact intensity rather than the fuzzy overall power consumption. The energy consumption feature vector is input into a rule chain composed of multiple rules arranged from high to low priority. The condition is checked level by level, and the corresponding action is output immediately. This priority-driven rule chain execution logic, which stops upon the first match, fundamentally solves the policy conflict problem in multi-constraint concurrent scenarios. Overload protection rules are given the highest priority, ensuring an immediate response when any device's power surge exceeds its rated power limit. Equipment interlock rules handle process dependencies, and energy-saving prioritization rules adjust the total power base value. Each level is evaluated in an orderly manner according to a predetermined sequence. The scheduling process does not require complex multi-objective optimization iterative calculations. Under the premise of meeting safety and process constraints, energy-saving scheduling is automatically achieved, significantly shortening the decision-making time from energy consumption characteristic input to scheduling scheme output, and enhancing the system's response determinism under high-load dynamic changes. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0014] Figure 1 This is a schematic diagram of the structure of a real-time monitoring and scheduling system for energy consumption of latex paint production equipment;

[0015] Figure 2 This is a flowchart of the scheduling strategy generation process based on rule chains and power coupling coefficient correction;

[0016] Figure 3 It is the synchronous acquisition curve of the voltage signal of the production equipment;

[0017] Figure 4 This is a schematic diagram showing the division of the steady-state and transient intervals of the energy consumption power signal of production equipment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0019] See Figure 1 This invention provides a real-time energy consumption monitoring and scheduling system for latex paint production equipment, including a data acquisition module, an energy consumption feature extraction module, a scheduling strategy generation module, and an execution module. The data acquisition module synchronously collects voltage, current, speed, and temperature signals from each production device on the latex paint production line, and segments and marks the signals according to the device type to generate a raw energy consumption dataset with timestamps. The energy consumption feature extraction module segments the raw energy consumption dataset, dividing it into steady-state and transient intervals based on the operating status identifiers of each production device. It extracts a power baseline value from each steady-state interval and extracts the power fluctuation amplitude and duration from each transient interval, forming an energy consumption feature vector. The scheduling strategy generation module inputs the energy consumption feature vector into a preset rule chain. The rule chain contains several priority-ordered energy consumption constraints and device interlocking conditions. By matching the energy consumption feature vector with the constraints level by level, it outputs a scheduling scheme containing the start-stop sequence and operating level of each device. The execution module converts the scheduling scheme into control commands and sends them to the programmable logic controllers of each production device, while simultaneously receiving execution feedback signals and updating the raw energy consumption dataset.

[0020] Example 1:

[0021] In practice, the data acquisition module synchronously acquires voltage, current, speed, and temperature signals through high-speed sampling terminals distributed across various production equipment on the latex paint production line. Each production line has multiple high-speed sampling terminals, each corresponding to at least one production device and connected to the corresponding device's voltage, current, speed, and temperature sensors.

[0022] All high-speed sampling terminals are synchronously triggered using the same clock source. This same clock source is provided by a master clock generator within the central data aggregation unit. The clock signal generated by the master clock generator is transmitted to each high-speed sampling terminal via a switch in the industrial Ethernet network that supports a precise time synchronization protocol. The time synchronization unit built into each high-speed sampling terminal receives the clock signal and calibrates its local clock, ensuring that the clock deviation between all high-speed sampling terminals is less than one microsecond. At the arrival of each synchronization trigger cycle, the high-speed sampling terminal simultaneously latches the analog signals output by the voltage sensor, current sensor, speed sensor, and temperature sensor, thereby ensuring strict consistency in the acquisition timing of the voltage, current, speed, and temperature signals.

[0023] The analog signals acquired by the high-speed sampling terminal enter the analog-to-digital converter (ADC) unit inside the terminal. The ADC unit performs analog-to-digital conversion on the voltage, current, speed, and temperature analog signals, generating digital values ​​for voltage, current, speed, and temperature, respectively. The ADC resolution is 16 bits, and the conversion rate is matched to the synchronous trigger cycle.

[0024] After analog-to-digital conversion, the high-speed sampling terminal packages the digital voltage, digital current, digital speed, and digital temperature values ​​into data frames according to the preset device number and signal type. The data frame structure includes a frame header, device number field, signal type field, timestamp field, data field, and checksum field. The device number field contains the unique device number corresponding to the production equipment of the high-speed sampling terminal; the signal type field identifies the combination of signal types contained in the data field; the timestamp field contains the timestamp recorded by the local clock of the high-speed sampling terminal at the synchronization trigger moment; the data field sequentially stores the digital voltage, digital current, digital speed, and digital temperature values; and the checksum field uses a cyclic redundancy check value to ensure data transmission integrity.

[0025] The data frames generated by the high-speed sampling terminal are transmitted to the central data aggregation unit via industrial Ethernet. Industrial Ethernet uses the EtherCAT protocol, with the high-speed sampling terminal acting as an EtherCAT slave and the central data aggregation unit as an EtherCAT master. The high-speed sampling terminal encapsulates the data frames into EtherCAT data packets and uploads them to the central data aggregation unit in each communication cycle.

[0026] After receiving data frames from all high-speed sampling terminals, the central data aggregation unit extracts the timestamp from each frame and reassembles the data frames into a raw energy consumption dataset according to the timestamp order. The raw energy consumption dataset is organized using timestamps as indexes; data frames from different production equipment corresponding to the same timestamp are merged into a single record. This record contains the equipment number, voltage, current, rotational speed, and temperature values ​​of the production equipment. The central data aggregation unit stores the reassembled raw energy consumption dataset in its internal memory and continuously updates it.

[0027] See Figure 3 In the graph, the horizontal axis represents time in milliseconds (ms), and the vertical axis represents the digital voltage in volts (V). The legend identifies the three curves as corresponding to the voltage signals of production equipment 01, 02, and 03, respectively. All three voltage curves exhibit near-stationary performance within the time range of 0 to 1000 ms, with minimal fluctuations, indicating that the signals are in a steady-state range.

[0028] Specifically, the voltage values ​​of production equipment 01 and 03 are both stably distributed at approximately 375V, and their curves highly overlap, indicating that the voltages of the two devices are synchronized and without significant differences. The voltage value of production equipment 02 remains at approximately 220V, significantly lower than that of production equipment 01 and 03, which meets the requirements for different voltage level markings for different equipment types. None of the three curves show significant abrupt changes or trend shifts, and the variance is extremely low, indicating that the voltage signals synchronously acquired by the data acquisition module are consistent at all times, and the equipment is operating in a stable state.

[0029] Example 2:

[0030] In practical implementation, when the energy consumption feature extraction module segments the original energy consumption dataset, it retrieves the data records corresponding to each production device from the dataset. For each production device, the module accesses its status register via industrial Ethernet or fieldbus and reads the operating status identifier. The operating status identifier is an enumerated variable with four possible values: start-up, stable operation, stop, and fault. The status register is updated in real time by the production device's programmable logic controller (PLC). Whenever the operating status of the production device changes, the PLC immediately writes the new operating status identifier into the status register.

[0031] The energy consumption feature extraction module continuously monitors changes in the operating status identifier of each production device. When the module detects a change in the operating status identifier of a production device from one state to another, it records the timestamp of the change, using this moment as a boundary moment. The module then uses all boundary moments across all production devices within the monitoring period to segment the data for each device in the original energy consumption dataset into multiple consecutive time segments. Each time segment corresponds to a single operating status identifier, and this identifier remains constant within each time segment.

[0032] For each time segment obtained, the energy consumption feature extraction module extracts digital voltage and current values ​​from the original energy consumption dataset and calculates the power value at each sampling point within the time segment. The power value is calculated as the product of the digital voltage and digital current values. The obtained power values ​​are arranged in chronological order to form a power signal sequence.

[0033] The energy consumption feature extraction module performs sliding window variance calculation on the power signal sequence within each time segment. The length of the sliding window is set to ten sampling points, and the sliding step size is set to one sampling point. The calculation process of the sliding window variance is as follows: starting from the beginning of the power signal sequence, ten consecutive power values ​​are taken to form a window, and the variance of the ten power values ​​within the window is calculated. Then, the window is moved forward by one sampling point, and a new set of ten power values ​​is taken to continue calculating the variance, until the window covers the end of the power signal sequence. The variance of the power values ​​within each window is calculated according to the following formula:

[0034]

[0035] in, Indicates the first The variance of power values ​​within a sliding window; The number is the sequence number of the sliding window. Take positive integers, from Start with sequential numbering; Indicates the first Within the first sliding window Power value, The index of the power value in the window. Pick to Integers; Indicates the first The arithmetic mean of ten power values ​​within a sliding window .

[0036] The preset threshold is set based on the rated power of the production equipment, specifically one percent of the squared rated power. The rated power is obtained from the nameplate parameters of the production equipment and pre-stored in the configuration table of the energy consumption feature extraction module. The variance of the power values ​​calculated by all sliding windows within a time segment is then considered. When all values ​​are less than a preset threshold, the energy consumption feature extraction module classifies the time segment as a steady-state interval. Conversely, when the power variance exists within any sliding window of the time segment... When the value is greater than or equal to a preset threshold, the energy consumption feature extraction module determines that time segment as a transient interval.

[0037] After dividing the steady-state and transient intervals, the energy consumption feature extraction module extracts the power baseline value from each steady-state interval. The power baseline value is extracted by calculating the arithmetic mean of all power sampling points within the steady-state interval. The energy consumption feature extraction module iterates through all power values ​​in the power signal sequence corresponding to the steady-state interval, sums all power values, and then divides by the total number of power sampling points within the steady-state interval. The resulting arithmetic mean is the power baseline value.

[0038] The energy consumption feature extraction module extracts the power fluctuation amplitude and duration from each transient interval. When extracting the power fluctuation amplitude, the module first locates the immediate preceding steady-state interval on the time axis. If there is no preceding steady-state interval, processing of that transient interval is skipped. The module reads the extracted power baseline value from the previous steady-state interval, then iterates through all power values ​​within the current transient interval to find the maximum power value. The maximum power value is subtracted from the power baseline value of the previous steady-state interval; the difference is taken as the power fluctuation amplitude. When extracting the duration, the module uses the length of the transient interval as the duration, which is the difference between the end and start timestamps of the transient interval.

[0039] The energy consumption feature extraction module combines the extracted power baseline values, power fluctuation amplitudes, and durations of each production device according to the device number and interval information to form an energy consumption feature vector. This energy consumption feature vector is then transmitted to the scheduling strategy generation module for subsequent use.

[0040] See Figure 4 In the graph, the horizontal axis represents time in seconds (s), and the vertical axis represents power in kilowatts (kW). The solid curves represent the power signal, and the dashed curves represent the corresponding power baseline values. The background color blocks distinguish between the steady-state interval (light blue area) and the transient interval (light red area), based on whether the variance of the power signal within the sliding window exceeds a preset threshold.

[0041] From the overall trend of the image, the power signal went through several stages. In the period from 0 to approximately 1 second, the power signal rose from zero, entering the transient range. Then, between approximately 1 and 3 seconds, the power signal rapidly rose to approximately 15kW, also in the transient range, indicating a sudden power surge during equipment startup. The period from 3 to 10 seconds was the steady-state range, with the power signal fluctuating little and closely hovering around the dotted power base value, indicating stable equipment operation. Between 10 and 12 seconds, the power signal rose significantly again, forming a second transient range, reflecting a switch in equipment operating status or load adjustment. Between 12 and 17 seconds was another steady-state range, with a power base value of approximately 23kW; the power signal fluctuated little and remained stable near the power base value. Between 17 and 19 seconds, the power signal dropped sharply to zero, entering the transient range, corresponding to the equipment shutdown process. After 19 seconds, the power signal remained at zero, indicating that the equipment had stopped operating.

[0042] Example 3:

[0043] In specific implementation, please refer to Figure 2 The scheduling strategy generation module receives the energy consumption feature vector output by the energy consumption feature extraction module and uses it as input data to a preset rule chain for matching. The rule chain consists of multiple rules arranged in descending order of priority and stored in the non-volatile memory of the scheduling strategy generation module. Each rule includes a condition part and an action part. The condition part determines the numerical range of a specific component in the energy consumption feature vector, and the action part specifies the corresponding start-up and shutdown sequence of the production equipment and the operating level of the production equipment.

[0044] The scheduling strategy generation module starts with the highest priority rule, extracts the condition part of each rule sequentially, and compares the actual values ​​of the energy consumption feature vector components referenced in the condition part with the defined numerical ranges in the condition part item by item. The components referenced in the condition part are the power base value, the magnitude of power fluctuations, or the duration. The numerical range is determined by methods including greater than, less than, equal to, and within an interval.

[0045] When the scheduling strategy generation module checks a rule, if the values ​​of all referenced components in the energy consumption feature vector meet the value range specified in the condition part, then the condition part is considered satisfied. The scheduling strategy generation module immediately extracts the action part of the rule, generates a scheduling scheme including the start-up and shutdown sequence of production equipment and the operating levels of the production equipment, and stops checking the remaining low-priority rules. If the condition part of the current rule is not satisfied, the scheduling strategy generation module continues to check the rules of the next lower priority until a rule whose condition part is satisfied is found.

[0046] In the conditional judgment process of each rule, the scheduling strategy generation module introduces a power coupling coefficient. The power coupling coefficient is defined as the ratio of the total power of two production devices operating simultaneously to the sum of their power when operating independently. The power coupling coefficient is obtained as follows: the scheduling strategy generation module extracts the base power values ​​of the two coupled production devices from the energy consumption feature vector; it then queries the pre-existing production device coupling relationship table in the scheduling strategy generation module to determine the production device pairs for which the power coupling coefficient needs to be calculated; finally, the scheduling strategy generation module reads the total power of the two production devices recorded under historical simultaneous operating conditions from the coupling relationship table, as well as the sum of their power when operating independently, and calculates the power coupling coefficient using the following formula:

[0047]

[0048] in, Indicates production equipment With production equipment The power coupling coefficient between them The range of values ​​is greater than and less than or equal to real numbers; Indicates production equipment With production equipment The total power of the two production devices when they are running simultaneously is expressed in kilowatts. Indicates production equipment Power base value when operating independently, in kilowatts; Indicates production equipment The power baseline value during independent operation is expressed in kilowatts. The production equipment coupling relationship table is generated from historical operating data and updated periodically. Each update selects steady-state interval data from the most recent thirty production cycles for recalculation and writes it to non-volatile memory.

[0049] In the condition section of the rule, when the scheduling policy generation module divides the power base value in the energy consumption feature vector by the power coupling coefficient... Then, the corrected power value is obtained. The scheduling strategy generation module compares the corrected power value with the preset energy-saving range. The preset energy-saving range is a set of power value ranges divided according to the overall power supply capacity of the production line and the electricity price period, and is stored in the configuration table of the scheduling strategy generation module. When the corrected power value falls into the preset energy-saving range, the condition part of the rule is determined to be satisfied.

[0050] In practice, the rules in the rule chain are arranged in order of priority from high to low, including safety overload protection rules, equipment interlock rules, and energy-saving sorting rules.

[0051] The safety overload protection rule has the highest priority. The condition portion of the safety overload protection rule is that the power fluctuation of any production equipment read from the energy consumption feature vector exceeds the rated power limit of that production equipment. The rated power limit is read from the nameplate parameter database of the production equipment. When the condition portion of the safety overload protection rule is met, the action portion of the rule specifies that the operating state of the production equipment whose power fluctuation exceeds the limit should be immediately switched to stop, and the startup sequence of downstream production equipment with process dependencies should be delayed until the power fluctuation of the exceeding production equipment recovers to below the rated power limit.

[0052] Equipment interlock rules have intermediate priority, second only to overload protection rules. The condition part of an equipment interlock rule requires that the operating status identifiers of two production devices in the energy consumption feature vector are simultaneously "started," and that there is a sequential process dependency between the two production devices. The sequential process dependency is read from the production process configuration table, which stores the identifiers of each pair of production devices with process dependencies. When the condition part of the equipment interlock rule is met, the action part of the equipment interlock rule specifies adjusting the startup sequence of the two production devices, so that the preceding process production device starts first, and the subsequent process production device starts only after the preceding process production device has completed startup and entered a stable operating range. Simultaneously, the operating gears of the two production devices are matched to the same gear.

[0053] The energy-saving ranking rule has the lowest priority. The condition part of the energy-saving ranking rule is that the current total power base value exceeds the lower limit of the overall line's power supply capacity. The current total power base value is the sum of the power base values ​​of all operating production equipment in the energy consumption feature vector. The lower limit of the overall line's power supply capacity is read from the power supply system parameter table. When the condition part of the energy-saving ranking rule is met, the action part of the energy-saving ranking rule specifies that the production equipment is ranked from high to low according to its power base value. The operating level of the production equipment with the highest power base value is lowered by one level, and the total power base value is recalculated. If it still exceeds the lower limit of the power supply capacity, the operating level of the production equipment with the second highest power base value is lowered by one level, until the total power base value is less than or equal to the lower limit of the power supply capacity.

[0054] Example 4:

[0055] In practice, the execution module obtains the scheduling scheme from the scheduling strategy generation module. The scheduling scheme records the start-up and shutdown sequence of each production device and its operating speed. The execution module internally maintains a register mapping table, which stores the communication address of the corresponding programmable logic controller (PLC), the register address for controlling start-up and shutdown, and the register address for controlling the operating speed for each production device. The register mapping table is generated during system initialization by importing the production line configuration list and is stored in the execution module's non-volatile memory.

[0056] When the execution module converts the scheduling scheme into control instructions, it iterates through the equipment start-stop sequence and operating gear entries for each production device listed in the scheduling scheme. For each production device, the execution module retrieves the communication address, start-stop register address, and gear register address of the programmable logic controller corresponding to the production device from the register mapping table. The execution module maps the start action in the equipment start-stop sequence to the register value 0x0001 corresponding to the start-stop register address, and maps the stop action to the register value 0x0000 corresponding to the start-stop register address; it maps the operating gear of the production device to the register value corresponding to the gear register address. The operating gears are divided into five levels, corresponding to register values ​​0x0001, 0x0002, 0x0003, 0x0004, and 0x0005 respectively. The execution module combines the retrieved register addresses and the mapped register values ​​into a write register instruction frame according to the ModbusTCP protocol format.

[0057] The execution module generates an instruction queue according to the startup order of production equipment specified in the scheduling scheme. The instruction queue is a first-in, first-out (FIFO) data structure. The execution module places the write register instruction frame corresponding to the first production equipment to be started at the head of the instruction queue, followed by the write register instruction frame corresponding to the next production equipment to be started, and so on, until all write register instruction frames for all production equipment to be started are enqueued. For production equipment that needs to perform a stop action, its write register instruction frame is appended after the start instruction frame.

[0058] After the instruction queue is generated, the execution module broadcasts the instruction queue via the industrial fieldbus. The industrial fieldbus uses the EtherNet / IP protocol. The execution module sequentially encapsulates each write register instruction frame in the instruction queue into an EtherNet / IP message, filling the destination address field of the EtherNet / IP message with the broadcast address. The encapsulated EtherNet / IP messages are simultaneously forwarded by the industrial Ethernet switch to all programmable logic controllers (PLCs) connected to the industrial fieldbus. Each PLC receives an EtherNet / IP message, parses the Modbus TCP unit identifier contained in the message, and if the unit identifier matches the PLC's own device address, the PLC performs a write register operation; otherwise, the PLC discards the message.

[0059] After sending a write register instruction frame, the execution module immediately starts an acknowledgment timeout timer with a timeout duration of 200 milliseconds. During this timeout period, the execution module waits for an acknowledgment frame from the corresponding programmable logic controller (PLC). The acknowledgment frame also follows the Modbus TCP protocol and contains the function code and the address of the register to be written. If the execution module receives an acknowledgment frame from the PLC within the timeout period, and the function code and register address in the acknowledgment frame match the sent write register instruction frame, the instruction is considered successfully executed. The execution module records the production equipment number of the corresponding PLC, the time of acknowledgment reception, and the execution success status. If the execution module does not receive an acknowledgment frame from the corresponding PLC after the timeout period expires, or if the function code or register address in the received acknowledgment frame does not match, an acknowledgment timeout is determined. The execution module immediately retransmits the same write register instruction frame and records the timestamp of this timeout event, the production equipment number, and the reason for the timeout in the exception log. The maximum number of retransmissions is set to 3. If no valid response frame is received after three consecutive retransmissions, the execution module will mark the corresponding production equipment as having a communication failure status and generate an alarm message containing the production equipment number and the failure type, and report it to the superior monitoring system.

[0060] Example 5:

[0061] In its implementation, this system also includes an energy consumption baseline generation module, which is connected to the historical raw energy consumption dataset storage unit. The historical raw energy consumption dataset storage unit stores raw energy consumption datasets for multiple complete production cycles. Each record includes the production equipment number, timestamp, product batch number, ambient temperature value, voltage digital value, current digital value, speed digital value, and temperature digital value.

[0062] When the energy consumption baseline generation module filters data from the historical raw energy consumption dataset storage unit, it first determines the target product batch and the current ambient temperature. The target product batch is obtained from the production work order data in the production execution system, and the current ambient temperature is obtained from the values ​​collected and stored by ambient temperature sensors deployed around the latex paint production line. The energy consumption baseline generation module searches the historical raw energy consumption dataset for all complete production cycle data records where the product batch number is the same as the target product batch and the ambient temperature value deviates from the current ambient temperature value within ±2 degrees Celsius. A complete production cycle refers to all data records from the first data record corresponding to the start of material feeding for a production batch to the last data record corresponding to the end of finished product filling for the same production batch.

[0063] After filtering out data from multiple complete production cycles that meet the criteria, the energy consumption baseline generation module extracts the production equipment number and timestamp from each data record. Using the method described by the energy consumption feature extraction module, it processes the raw energy consumption dataset for each complete production cycle, identifies each steady-state interval, and calculates the power baseline value for each interval. Within each complete production cycle, there may be multiple steady-state intervals for the same production equipment, and each steady-state interval corresponds to a power baseline value.

[0064] When the energy consumption baseline generation module calculates the weighted average of the power baseline values ​​for each steady-state interval within each production cycle, it divides the data for each production cycle into several sub-intervals according to time periods. The time period is divided as follows: starting from the beginning of the production cycle, a sub-interval is formed every 1500 seconds, and if the last sub-interval is less than 1500 seconds, it is treated as the actual length. Each sub-interval is defined by a start timestamp and an end timestamp.

[0065] For each sub-interval, the energy consumption baseline generation module calculates the equipment load rate of the production equipment within that sub-interval. The equipment load rate is the ratio of the actual operating power baseline of the production equipment within the sub-interval to the rated power of the production equipment. The rated power of the production equipment is extracted from the equipment parameter database. The energy consumption baseline generation module reads the standard load rate of the production equipment. The standard load rate is the load rate value of the production equipment operating under optimal conditions. The standard load rate is stored in the equipment parameter database and is provided by the equipment manufacturer or determined through process debugging.

[0066] The energy consumption baseline generation module assigns a weighting coefficient to each sub-interval. This weighting coefficient is determined based on how closely the device load rate within the sub-interval approaches the standard load rate. The weighting coefficient is calculated using the following formula:

[0067]

[0068] in, Indicates the first In the production cycle, the first The weight coefficients corresponding to each sub-interval The range of values ​​is greater than or equal to and less than or equal to real numbers; Indicates the first In the production cycle, the first Equipment load rate of production equipment within each sub-range It is a dimensionless ratio; Indicates the production equipment in the first The standard load rate under the operating conditions corresponding to each sub-range It is a dimensionless ratio; The sequence number is the production cycle number, taken as a positive integer, starting from... Start sequentially numbering up to the total number of complete production cycles selected; Let the sub-interval be the sequence number within a production cycle, taking a positive integer, starting from... Start with sequential numbering. When the device load rate... Compared with standard load rate When completely identical, the weighting coefficient Values When the equipment load rate Compared with standard load rate The absolute value of the deviation is equal to the standard load rate. At that time, weighting coefficient Values .

[0069] The energy consumption baseline generation module calculates the energy consumption baseline value by multiplying the weighted coefficients by the power base values ​​of the corresponding sub-intervals and summing the results, then dividing by the sum of the weighted coefficients. The specific calculation process is as follows: For a given production equipment, the weighted coefficients of each sub-interval in all selected complete production cycles are multiplied by the corresponding power base values ​​within that sub-interval, and all multiplication terms are accumulated. Simultaneously, the weighted coefficients of all sub-intervals are directly added together to obtain the total weighted coefficients. The sum is then divided by the total weighted coefficients, and the quotient is the energy consumption baseline value for that production equipment. The energy consumption baseline value is stored in the internal memory of the energy consumption baseline generation module and is bound to the production equipment number.

[0070] During the process of generating a scheduling scheme, the scheduling strategy generation module also calls the energy consumption baseline value stored in the internal memory of the energy consumption baseline generation module. The scheduling strategy generation module reads the current power baseline value of the production equipment from the energy consumption feature vector, compares this current power baseline value with the energy consumption baseline value, and calculates the deviation value. The deviation value is the percentage obtained by dividing the absolute value of the difference between the current power baseline value and the energy consumption baseline value by the energy consumption baseline value.

[0071] The scheduling strategy generation module has a preset deviation threshold set at 15%. When the deviation exceeds 15%, the scheduling strategy generation module triggers a temporary adjustment to the priority of rules in the rule chain. The temporary adjustment works as follows: the scheduling strategy generation module identifies the production equipment type corresponding to the deviation value and elevates the priority of the energy-saving sorting rule related to that production equipment type in the rule chain to a middle priority position, after the equipment interlock rule and before the original energy-saving sorting rule. The temporary adjustment remains in effect until the end of the current production batch. At the end of the production batch, the scheduling strategy generation module reads the initial rule priority configuration table from its internal memory and restores the priority of each rule in the rule chain to its initial configuration.

[0072] During the temporary adjustment performed by the scheduling strategy generation module, the energy consumption baseline generation module continues to periodically update the energy consumption baseline value from the historical raw energy consumption dataset. The update cycle is set to be performed once every thirty complete production cycles, and the updated energy consumption baseline value is immediately synchronized to the scheduling strategy generation module.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A real-time monitoring and scheduling system for energy consumption of latex paint production equipment, characterized in that, The system includes: The data acquisition module is used to synchronously acquire voltage, current, speed and temperature signals from each production equipment in the latex paint production line, and to delineate and mark the signals according to the equipment type to generate a raw energy consumption dataset with timestamps. The energy consumption feature extraction module is used to segment the original energy consumption dataset, divide the steady-state interval and transient interval according to the operating status identifier of each production equipment, extract the power base value from each steady-state interval, and extract the power change amplitude and duration from each transient interval to form an energy consumption feature vector. The scheduling strategy generation module is used to input the energy consumption feature vector into a preset rule chain. The rule chain contains several energy consumption constraints and equipment interlocking conditions ordered by priority. By matching the energy consumption feature vector with the constraints step by step, a scheduling scheme containing the start-stop sequence and operating level of each device is output. The execution module is used to convert the scheduling scheme into control instructions and send them to the programmable logic controllers of each production equipment, while receiving execution feedback signals and updating the original energy consumption dataset.

2. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 1, characterized in that, The data acquisition module synchronously acquires voltage, current, speed, and temperature signals, including: Multiple high-speed sampling terminals are deployed on each production line, and each high-speed sampling terminal is synchronously triggered using the same clock source; The acquired analog signals are then converted from analog to digital and packaged into data frames according to device number and signal type. The data is then transmitted to a central data aggregation unit via industrial Ethernet, where it is reassembled into the original energy consumption dataset according to timestamp order.

3. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 1, characterized in that, The energy consumption feature extraction module performs segmentation processing on the original energy consumption dataset, including: Read the operating status identifier from the status register of each device. The operating status identifier includes four states: startup, stable operation, stop, and fault. The original energy consumption dataset is divided into continuous time segments based on the change time of the running status identifier; The power signal within each time segment is subjected to a sliding window variance calculation. When the variance is less than a preset threshold, it is determined to be a steady-state interval; otherwise, it is determined to be a transient interval.

4. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 3, characterized in that, The energy consumption feature extraction module extracts a power baseline value from each steady-state interval, including: The arithmetic mean of all power sampling points within the steady-state interval is calculated as the power base value. When extracting the power fluctuation amplitude and duration from each transient interval, the difference between the maximum power value in the transient interval and the power base value of the previous steady-state interval is taken as the power fluctuation amplitude, and the time length of the transient interval is taken as the duration.

5. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 1, characterized in that, The scheduling strategy generation module inputs the energy consumption feature vector into a preset rule chain, including: The rule chain consists of multiple rules arranged in descending order of priority, and each rule contains a condition part and an action part; The condition part consists of judging the numerical range of a specific component in the energy consumption feature vector, and the action part specifies the corresponding device start-stop sequence and operating level; The scheduling strategy generation module checks the energy consumption feature vector sequentially, starting with the highest priority rule, to see if it meets the condition of the rule. Once it does, it immediately outputs the action part of the rule and stops checking subsequent rules.

6. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 5, characterized in that, The scheduling policy generation module includes the following in the condition section of each rule: A power coupling coefficient is introduced between devices, which is defined as the ratio of the total power of two devices when they are running simultaneously to the sum of their individual operating power. When the power base value in the energy consumption feature vector, after being divided by the power coupling coefficient, falls into the preset energy-saving range, the condition part of the rule is determined to be satisfied.

7. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 5, characterized in that, The rules in the rule chain, ranked from highest to lowest priority, include: The safety overload protection rule is conditional on the fact that the power fluctuation of any device exceeds the rated power limit of that device. The condition part of the equipment interlock rule is that the operating status identifiers of two pieces of equipment are both started and there is a sequential process dependency relationship between them. The energy-saving ranking rule is based on the condition that the current total power base value exceeds the lower limit of the overall line's power supply capacity.

8. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 1, characterized in that, When the execution module converts the scheduling scheme into control instructions, it includes: The device start / stop sequence and operating level in the scheduling scheme are mapped to the register addresses and values ​​of the corresponding programmable logic controllers; An instruction queue is generated according to the order in which the equipment is started, and the instruction queue is broadcast via industrial fieldbus. Read the response status of each programmable logic controller one by one. If the response times out, resend the instruction and record the exception.

9. The real-time energy consumption monitoring and scheduling system for latex paint production equipment according to claim 1, characterized in that, The system also includes: The energy consumption baseline generation module is used to filter data from multiple complete production cycles of the same product batch and the same ambient temperature from the historical raw energy consumption dataset, calculate the weighted average of the power base values ​​of each steady-state interval within each production cycle, and use the weighted average as the energy consumption baseline value of the production equipment. When generating a scheduling scheme, the scheduling strategy generation module also compares the energy consumption baseline value with the power base value in the current energy consumption feature vector. When the deviation exceeds a preset deviation threshold, it triggers a temporary adjustment to the priority of the rules in the rule chain.

10. A real-time monitoring and scheduling system for energy consumption of latex paint production equipment according to claim 9, characterized in that, When the energy consumption baseline generation module calculates the weighted average, it includes: The data for each production cycle is divided into several sub-intervals according to the time period, and a weight coefficient is assigned to each sub-interval. The weight coefficient is determined based on the degree of closeness between the equipment load rate and the standard load rate within the sub-interval. The energy consumption baseline value is obtained by multiplying the weighting coefficient by the power base value of the corresponding sub-interval, summing the results, and then dividing by the sum of the weighting coefficients.

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