Energy consumption real-time monitoring and optimal distribution system in plastic bottle production process

Abstract

The invention belongs to the technical field of plastic bottle production control, and particularly discloses an energy consumption real-time monitoring and optimal distribution system in a plastic bottle production process, which constructs a standard process parameter curve and an energy consumption reference of energy consumption equipment based on historical batch data of optimal energy efficiency of plastic bottles. The technical parameters and energy consumption data of the equipment are collected in real time and dynamically compared in the actual production process, the parameter mismatch time period is recognized, quantitative perception of hidden energy efficiency degradation is achieved, meanwhile, after the parameter mismatch time period of each piece of equipment is recognized, the parameter mismatch time period is mapped to a unified global time axis, and through time sequence alignment and overlay analysis, the hidden energy efficiency degradation of the equipment is obtained. According to the method, the concurrent high-energy-consumption events are accurately identified, finally, after the concurrent high-energy-consumption events occur, based on the preset process coupling relation between the devices and the real-time production task state, the adjustable devices are accurately screened out of the devices involved in the events, process sensing type refined screening of the adjustment objects is achieved, and the takt time stability of a production line is maintained to the maximum extent.

Description

Technical Field

[0001] This invention belongs to the field of plastic bottle production control technology, and specifically discloses a real-time monitoring and optimized allocation system for energy consumption in the plastic bottle production process. Background Technology

[0002] The production of plastic bottles is a complex process involving the coordinated operation of multiple high-energy-consuming machines. Its total energy consumption is an important component of the company's production costs. In order to reduce energy costs, it is necessary to optimize and control the energy consumption behavior in the production process.

[0003] Currently, there are the following limitations in the energy consumption management of plastic bottle production: Existing energy consumption monitoring multi-focus devices measure total power consumption or instantaneous power. However, high power consumption alone does not necessarily equate to low energy efficiency. Equipment may still operate but become inefficient when process parameters deviate from optimal settings, resulting in hidden energy losses. These losses cannot be effectively captured by traditional power consumption monitoring methods, leading to the overlooking of significant energy-saving potential. Traditional energy-saving measures often target individual devices, such as optimizing their control logic or replacing them. However, peak energy consumption on a production line is often caused by the combined effect of multiple devices operating at high loads or inefficiently at the same time. Existing energy management systems struggle to identify and address this system-level problem of concurrent high energy consumption, resulting in limited effectiveness in reducing overall demand-based electricity costs and achieving peak load shaving and valley filling.

[0004] When excessive energy consumption or peak energy risk is identified, selecting the appropriate adjustment targets is crucial. Existing methods typically rely on experience or simple rules, such as selecting non-core production equipment, failing to fully consider the tight process coupling between equipment in the production flow and the real-time production task status. This crude selection may lead to adjustment commands disrupting the normal production cycle, or even causing production interruptions or quality fluctuations, making it difficult to continuously and safely implement energy-saving optimization measures in actual production. Summary of the Invention

[0005] In view of this, the present invention aims to propose a real-time energy consumption monitoring and optimization allocation system for the plastic bottle production process. By integrating process operation data and energy consumption data in the plastic bottle production process, it realizes a solution from single-equipment anomaly diagnosis to multi-equipment collaborative optimization, effectively solving the problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solution: a real-time energy consumption monitoring and optimized allocation system for the production process of plastic bottles, comprising: The benchmark library construction module extracts curves showing how equipment process parameters change during the production process from historical plastic bottle production data as standard parameter curves, and records the unit product energy consumption value corresponding to each standard parameter curve.

[0007] Synchronous acquisition module: Synchronously acquires real-time process parameter data and energy consumption data of each piece of equipment during the production process.

[0008] Parameter mismatch identification module: compares real-time process parameter data with the standard parameter curves of the corresponding equipment, and combines energy consumption data to identify the period of parameter mismatch.

[0009] High-energy-consuming event analysis module: Maps the parameter mismatch periods identified by each device to a unified time axis for alignment and overlay analysis to identify concurrent high-energy-consuming events.

[0010] Adjustable equipment screening module: When a concurrent high-energy-consuming event is detected, the adjustable equipment is screened from the equipment involved in the event based on the preset process coupling relationship between equipment and the production task status of the equipment.

[0011] Optimize the allocation and execution module: issue adjustment instructions to each adjustable device and synchronize the instructions with the production batch information to the production execution system.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs standard process parameter curves and energy consumption benchmarks for energy-consuming equipment based on historical batch data of optimal energy efficiency of plastic bottles. In actual production, it collects and dynamically compares the process parameters and energy consumption data of the equipment in real time, identifies the periods of parameter mismatch, and breaks through the limitations of traditional methods that rely solely on power or electricity monitoring. This enables quantitative perception of the hidden performance degradation caused by process deviations, providing a highly reliable data foundation for subsequent tracing of high-energy-consuming events and collaborative optimization and control.

[0013] 2. After identifying the parameter mismatch periods of each device, this invention maps them to a unified global time axis. Through time-series alignment and overlay analysis, it accurately identifies concurrent high-energy-consuming events in which multiple devices simultaneously or overlap, resulting in energy efficiency degradation. This reveals the causes of system-level energy consumption peaks, breaks through the limitations of a single-device perspective, and achieves a cognitive leap from local anomalies to global load coupling. It provides event triggering basis and decision support for carrying out collaborative performance efficiency regulation.

[0014] 3. After a concurrent high-energy-consuming event occurs, this invention performs a multi-dimensional collaborative evaluation of the equipment involved in the event based on the preset process coupling relationship between equipment and the real-time production task status, and accurately selects adjustable equipment. This selection mechanism abandons the isolated, experience-based equipment selection method and realizes process-aware, refined identification of the adjustment object. Under the premise of ensuring that the critical production path is not disrupted, it maximizes the stability of the production line cycle time, thereby ensuring that energy-saving optimization measures can be implemented safely, reliably, and continuously in the actual production environment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system composition in this invention.

[0017] Figure 2 This is a flowchart illustrating the implementation of the high-energy-consuming event analysis module in this invention.

[0018] Figure 3 This is a schematic diagram illustrating the instruction issuance for the adjustable device in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] This invention proposes a real-time energy consumption monitoring and optimization allocation system for the plastic bottle production process, including a benchmark library construction module, a synchronous acquisition module, a parameter mismatch identification module, a high energy consumption event analysis module, an adjustable equipment screening module, and an optimization allocation execution module.

[0021] See Figure 1 As shown, the benchmark library construction module and the synchronous acquisition module are both connected to the parameter mismatch identification module. The parameter mismatch identification module is connected to the high energy consumption event analysis module. The high energy consumption event analysis module is connected to the adjustable device screening module. The adjustable device screening module is connected to the optimized allocation execution module.

[0022] The benchmark library construction module is used to extract curves of equipment process parameters changing with the production process from historical production data of plastic bottles as standard parameter curves, and to associate and record the unit product energy consumption value corresponding to each standard parameter curve.

[0023] Given that the energy consumption of equipment in the plastic bottle production process is closely related to its process parameters, in order to effectively identify the hidden energy consumption loss caused by the deviation of parameters from the optimal settings, it is necessary to construct a standard process parameter curve of the equipment under efficient and stable operating conditions as a reference for assessing whether the current operating conditions deviate from the energy efficiency benchmark.

[0024] The benchmark was constructed using historical production data because it truly reflects the typical operating conditions of the production line in actual operation, where qualified products are produced and unit energy consumption is low. By selecting the production batches marked as having the best energy efficiency, we can ensure that the benchmark fits the actual on-site capabilities and avoid the problem of pure theoretical modeling being out of touch with engineering practice.

[0025] Furthermore, by associating each standard parameter curve with its corresponding unit product energy consumption value, the linkage between process parameters and energy efficiency performance can be achieved: when real-time parameters deviate from the standard range, the deviation can be compared with the benchmark energy consumption to determine whether the deviation has indeed caused energy efficiency degradation, thereby providing reliable data support for attribution and optimization intervention of high energy consumption events.

[0026] In a preferred embodiment of the present invention, the benchmark library construction module contains the following: Retrieve historical records of multiple production batches marked as having the highest energy efficiency from the database of the plastic bottle production management system.

[0027] It should be noted that optimal energy efficiency refers to the historical production batch with the lowest energy consumption per unit product, i.e., the least amount of electricity consumed to produce one qualified plastic bottle, while meeting product quality, production cycle and process specifications.

[0028] Based on the plastic bottle production process, identify the energy-consuming equipment involved in each batch, including injection molding machines, blow molding machines, and dryers.

[0029] This invention selects injection molding machines, blow molding machines, and dryers as the key targets for energy efficiency monitoring and optimization because these three devices account for the highest proportion of energy consumption and have the most direct impact on the process in the production of plastic bottles. Moreover, their operating status directly determines product quality and production cycle.

[0030] The injection molding machine is responsible for melting plastic particles and injecting them into preforms. Its heating system and hydraulic / servo drive system consume a lot of power, and parameters such as temperature, pressure, and screw speed are sensitive to energy consumption.

[0031] Blow molding machines heat preforms and inflate them into the final bottle shape. This involves infrared heating furnaces, stretching and high-pressure blowing systems. Energy consumption is concentrated during operation and is easily affected by process fluctuations.

[0032] Dryers are used to dehumidify and dry plastic raw materials before injection molding. They require a continuous supply of high-temperature hot air and are high-energy-consuming auxiliary equipment that operates continuously for long periods of time.

[0033] For injection molding machines, the temperature change curve of the heating zone, screw speed curve, and clamping force curve are extracted from the production batch records as standard parameter curves.

[0034] For blow molding machines, the temperature curves of each temperature zone of the heating furnace, the speed curve of the stretching rod, and the air pressure curve are extracted as standard parameter curves.

[0035] For the dryer, its hot air temperature curve and fan speed curve are extracted as its standard parameter curves.

[0036] Each extracted standard parameter curve is associated with the unit product energy consumption value obtained by dividing the total power consumption of the equipment during the production process of that batch by the total output of that batch, and then linked to form a benchmark data entry.

[0037] All equipment baseline data entries are integrated and stored to form a process parameter baseline library.

[0038] The synchronous acquisition module is used to synchronously acquire real-time process parameter data and real-time energy consumption data of each device during the production process.

[0039] In the implementation method of this invention, the synchronous acquisition module contains the following: During the plastic bottle production process, real-time process parameter data is read through the communication interface connected to the equipment at fixed sampling intervals.

[0040] Specifically, the process parameters collected for the injection molding machine include the temperature of the heating zone, screw speed, and clamping force.

[0041] The process parameters collected for the blow molding machine include the temperature of each zone of the heating furnace, the speed of the stretching rod, and the blowing pressure.

[0042] The process parameters collected for the dryer include the drying hot air temperature and the fan speed.

[0043] By installing smart meters or power sensors in the main power supply circuit of the equipment, real-time current and voltage data are collected with the same sampling period, and the real-time power is calculated as real-time energy consumption data. A unified timestamp is applied to the process parameter data and energy consumption data at each sampling moment to ensure that the two are synchronized in time.

[0044] The parameter mismatch identification module is used to compare real-time process parameter data with the standard parameter curves of the corresponding equipment, identify the parameter mismatch period, and extract the energy consumption information of the corresponding period from the real-time energy consumption data.

[0045] After completing the construction of the standard curve of equipment process parameters and energy consumption benchmark, and simultaneously acquiring the real-time process parameter data of each piece of equipment in the plastic bottle production process, parameter mismatch analysis can be carried out. By dynamically comparing the real-time data with the parameter standard, abnormal periods of process deviation accompanied by energy efficiency degradation can be identified.

[0046] In a preferred embodiment of the present invention, the specific process for identifying the parameter mismatch period is as follows: Based on the time series data of equipment process parameters collected in real time during the plastic bottle production process, the standard parameter curves of the corresponding equipment are retrieved from the benchmark library; For each process parameter in the standard curve, extract its maximum and minimum values ​​throughout the entire production cycle to construct the corresponding allowable fluctuation range.

[0047] Understandably, in the batch with optimal energy efficiency, the natural fluctuation range of process parameters reflects the reasonable tolerance of the equipment during stable and efficient operation. This range can represent the efficient operation envelope, and exceeding it means process deviation.

[0048] The real-time collected process parameter data is compared point by point with the allowable fluctuation range. When the parameter value at a certain time point exceeds the range, monitoring continues from that moment until the parameter returns to the range, thus defining the initial mismatch period.

[0049] Furthermore, when process parameters exceed the allowable fluctuation range at a certain moment, it is not immediately determined to be a parameter mismatch. This is because such instantaneous deviations may originate from normal process disturbances, such as equipment start-up and shutdown transitions, rather than system performance degradation. Directly treating such instantaneous out-of-bounds deviations as mismatch events and triggering energy-saving interventions may not only lead to misjudgments but also generate unnecessary adjustment commands, disrupting normal production cycles.

[0050] During the initial mismatch period, real-time power data of the corresponding device is extracted synchronously and compared with the unit product energy consumption value associated with the device in the benchmark library, which is converted into an equivalent power reference value.

[0051] Considering that the energy consumption benchmarks associated with standard process parameter curves in the benchmark library are stored as cumulative energy consumption indicators in the form of unit product energy consumption, while the instantaneous power of the equipment is collected in real time during production, due to the difference in their time scales—the former being a normalized average energy consumption intensity and the latter being an instantaneous energy consumption rate—they cannot be directly compared numerically.

[0052] Therefore, the unit product energy consumption benchmark needs to be converted into a corresponding equivalent power reference value based on the current production cycle and actual output rate. The specific conversion process is as follows: Based on the average production cycle time of the historical production batches corresponding to the standard parameter curve, the theoretical output per unit time (e.g., per minute) is calculated. Multiplying the unit product energy consumption value by the theoretical output per unit time yields the equivalent average power reference value that the equipment should maintain under this efficient operating condition.

[0053] This conversion achieves a reasonable mapping from energy consumption benchmarks to the power dimension, allowing for comparison with real-time power data on the same scale. Only when process parameters deviate from the allowable range and real-time power consistently exceeds this equivalent reference value is it considered a genuine energy efficiency degradation event, thus completing the dual verification of abnormal parameters and increased energy consumption.

[0054] If the real-time power at a certain moment is higher than the equivalent reference value, then the length of time that the system is in a high-energy-consumption state is recorded from that moment onwards, and this is recorded as the high-energy-consumption duration.

[0055] Calculate the ratio of the duration of high energy consumption to the total duration of the initial mismatch period, and compare this ratio with the mismatch confirmation limit. If the ratio reaches or exceeds the mismatch confirmation limit, the initial mismatch period is confirmed as the parameter mismatch period, and the start and end times of this period are recorded.

[0056] In the example implementation of the above operation, the mismatch confirmation limit refers to the percentage of high-energy-consuming time used to determine whether a preliminary mismatch period should be formally confirmed as a parameter mismatch period, expressed as a percentage. This limit is typically set at a proportion exceeding 50%, with a typical range of 60% to 90%.

[0057] The duration of high energy consumption in the above operation reflects the length of time during which the actual operating power of the equipment is continuously higher than the equivalent power reference value corresponding to the energy efficiency benchmark during the initial mismatch period, and characterizes the persistence of the true energy efficiency degradation state.

[0058] The ratio of the duration of high energy consumption to the total duration of the initial mismatch period quantifies the proportion of time with high energy consumption during the parameter deviation period, used to measure whether the deviation is systematic rather than a transient disturbance. The higher the ratio, the stronger and more stable the correlation between abnormal process parameters and increased energy consumption. When the ratio reaches or exceeds the preset mismatch confirmation limit, it indicates that the equipment is indeed in an inefficient operating state for most of the parameter mismatch period, meeting the dual criteria of parameter deviation and increased energy consumption.

[0059] Therefore, this period can be identified as a parameter mismatch period. This period not only accurately marks the time window in which hidden energy consumption occurs, but also reveals its root cause, namely the energy efficiency decline caused by the deviation of process parameters from the optimal setting. Such periods are the targets for subsequent energy-saving optimization.

[0060] The high-energy-consuming event analysis module is used to align and overlay the parameter mismatch periods marked by each device on the same global time axis to identify concurrent high-energy-consuming events.

[0061] Because the equipment in a plastic bottle production line is tightly coupled in terms of process and operates with highly coordinated rhythms, the periods of parameter mismatch between different equipment may partially overlap or occur concentratedly within the same production window. This spatiotemporal concurrency means that multiple pieces of equipment may be in a high-energy-consuming state simultaneously during the same period, thus superimposing to form a system-level peak power load.

[0062] Therefore, implementing energy-saving optimization independently only for the parameter mismatch period of a single device, while ignoring the operating status of other devices during the same period, may disrupt process synchronization between devices, leading to cycle time imbalance, buffer overflow, or waiting accumulation, which in turn reduces production line efficiency and may even affect product quality. This invention identifies concurrent high-energy-consuming events by performing alignment and overlay analysis on a global time axis during parameter mismatch periods. It aims to achieve safe, efficient, and collaborative energy efficiency optimization by comprehensively scheduling adjustable resources at the system level.

[0063] See Figure 2 As shown, the specific process for identifying concurrent high-energy-consuming events is as follows: Establish a unified timeline covering all production equipment.

[0064] Map all parameter mismatch periods reported by all devices onto a unified timeline according to their recorded start and end times.

[0065] A sliding scan is performed on the timeline with a fixed time window, and the percentage of devices that are simultaneously in the parameter mismatch period within each sliding window is counted.

[0066] When the proportion of devices in the parameter mismatch period exceeds the preset concurrency proportion, it is determined that a concurrent high-energy-consuming event has occurred within that time window.

[0067] Record the time window and list of devices involved in concurrent high-energy-consuming events.

[0068] The aforementioned concurrency ratio refers to the lower limit of the proportion of the number of devices simultaneously in a parameter mismatch state within a certain sliding time window to the total number of high-energy-consuming devices participating in the monitoring.

[0069] Specifically, the system will only identify the current time window as a concurrent high-energy-consuming event when the proportion reaches or exceeds the concurrency ratio threshold. For example, if the concurrency ratio is set to more than half, such as >50%, it means that at least half of the high-energy-consuming devices must be in a parameter mismatch state at the same time in order to trigger energy consumption collaborative optimization.

[0070] The adjustable device screening module is used to screen adjustable devices from the devices involved in the event when a concurrent high-energy-consuming event is identified, based on the preset process coupling relationship between devices and the production task status of the devices.

[0071] After identifying concurrent high-energy-consuming events, energy consumption optimization needs to be implemented on the relevant equipment. However, not all the equipment involved is suitable for adjustment: some equipment may be in a strongly time-coupled process, and arbitrarily interfering with its operation can easily lead to production interruptions, cycle time imbalances, or product quality fluctuations.

[0072] Therefore, it is essential to accurately select adjustable equipment from those involved in the event based on the equipment's process coupling relationships and real-time task status. This selection mechanism ensures that energy-saving optimization measures are effectively implemented while guaranteeing production safety and stability. The preset equipment process coupling relationship and production task status involved in the above operation are as follows: The preset equipment process coupling relationship is represented by a directed graph. In the graph, the nodes represent production equipment, and the directed edges represent the sequential dependencies in the process flow. Each directed edge is given an attribute to identify whether there is a continuity constraint between adjacent equipment, that is, whether the output of the preceding equipment must be transmitted to the following equipment without interruption.

[0073] The production task status refers to the process stage of each device's current task and the estimated remaining running time of the task, which are obtained in real time from the manufacturing execution system. The process stage includes the standby stage, the preheating and start-up stage, and the production operation stage.

[0074] The aforementioned intermediate process stages refer to the operational status categories of production equipment within its current production task lifecycle. These stages characterize whether the equipment is performing substantive processing, is in a preparation state, or is temporarily idle. Because the adjustment risk of equipment depends on its current operational status, simply knowing that the equipment is running is insufficient to determine whether adjustment is possible; it is essential to clarify whether it is in preparation or production. Introducing the division into process stages is to accurately characterize the adjustment feasibility of equipment over time, thereby providing crucial contextual basis for energy consumption optimization decisions.

[0075] Specifically, the standby phase indicates that the equipment has completed the previous batch of tasks and has not yet received new task instructions. It is in an idle waiting state, which means that there is no material processing. The energy consumption is mainly used to maintain basic standby. It has a high degree of freedom in delay adjustment and can usually safely postpone the start-up without affecting the overall cycle time.

[0076] The preheating start-up phase indicates that the equipment has received the task instruction and is preparing for the process, but has not yet entered actual product processing. This means that although there is energy consumption, no product is produced. If there is no urgent demand downstream and the process buffer conditions are met, the preheating can be delayed within a certain range, which is an adjustable window.

[0077] The production operation phase indicates that the equipment is carrying out substantial processing of materials, which is directly related to product quality and cycle stability; this phase generally cannot be interrupted or delayed.

[0078] In a preferred embodiment, the process for selecting adjustable devices is as follows: Upon receiving a notification of a concurrent high-power consumption event, retrieve the list of devices involved in the event.

[0079] The system invokes the preset equipment process coupling relationship and, based on the list of equipment involved in the concurrent high-energy-consuming event, extracts the process dependency subgraph between the involved equipment from the equipment process coupling relationship, including their upstream and downstream dependencies and whether there are continuity constraints.

[0080] Meanwhile, based on the list of devices involved in concurrent high-energy-consuming events, the estimated remaining running time of the tasks currently being performed by the devices involved is extracted from the production task status.

[0081] For each device in the equipment list, determine whether it is a potential adjustable device by considering its role in the process dependency subgraph and the estimated remaining runtime of its current task.

[0082] Specifically, the process for determining whether a device is potentially adjustable is as follows: For each device in the device list, perform the following two-condition validation: a) The equipment has no upstream or downstream adjacent equipment in the process dependency subgraph, or there are upstream and downstream adjacent equipment but the process connection between the equipment and the downstream equipment is not marked as a continuity constraint.

[0083] Condition a) indicates that the device does not have strong temporal coupling constraints.

[0084] b) The estimated remaining runtime of the task currently being performed by the device is higher than the preset minimum adjustable time threshold.

[0085] Condition b) indicates that the device has sufficient time margin for the current task.

[0086] In condition b) above, the minimum adjustable time threshold means that the estimated remaining running time of the current task of the equipment must exceed this value in order to be considered to have the time margin for adjustment. This means that the current task of the equipment has not yet ended, and the current operating state has a certain buffer window in the time dimension. Adjustment within this window will not cause downstream equipment to wait for timeout or production line blockage.

[0087] Specifically, the minimum adjustable time threshold should be greater than the shortest time required for the device to complete one safe adjustment.

[0088] Example: The heating system of the injection molding machine has a large thermal inertia, and it takes 30 to 60 seconds to stabilize after adjustment. The threshold can be set to 60 seconds. The dryer fan has a fast frequency response, which can be adjusted within 10 seconds, and the threshold can be set to 15-20 seconds.

[0089] When a device meets all of the above conditions, it is judged to be a potentially adjustable device.

[0090] Understandably, condition b) ensures the engineering feasibility of the adjustment action from the perspective of time feasibility. Together with condition a) process coupling constraint, it constitutes a two-dimensional criterion for the process timing of the equipment's adjustment potential. Only when both conditions are met can the equipment be judged as potentially adjustable equipment.

[0091] For equipment identified as potentially adjustable, it is classified as adjustable depending on the current process stage. If the current process stage is the standby stage or the preheating start-up stage, it is classified as adjustable equipment.

[0092] The optimized allocation execution module is used to issue adjustment instructions to each adjustable device and synchronize the instructions with the production batch information to the production execution system.

[0093] See Figure 3 As shown, the specific contents of the above module are as follows: For adjustable devices in standby mode, generate and issue delayed start commands.

[0094] It should be noted that equipment in the standby phase has completed the previous batch of tasks and is in an idle waiting state, without having started any substantial energy-consuming operations. If the next task is started immediately, it will enter a high-energy-consuming preheating phase ahead of time, which may overlap with the high-load periods of other equipment, increasing the total system demand.

[0095] By delaying the start-up time, the high-energy-consuming phases of the equipment can be staggered from the current concurrent high-energy-consuming window, achieving load shifting, effectively peak shaving, reducing maximum demand, and thus reducing demand-related electricity costs. Simultaneously, there is no process output during the standby phase, so delayed start-up will not affect product quality, and within the allowable cycle time, it will not affect overall delivery.

[0096] For adjustable equipment in the preheating and start-up phase, generate and issue a command to pause heating.

[0097] It should be noted that the main energy consumption during the preheating and start-up phase comes from the heating system. At this time, the equipment has not yet come into contact with the material or entered the forming cycle; it is a pure energy storage process. If there is no urgent demand downstream, continuing heating will result in excess heat or ineffective heat preservation.

[0098] By pausing heating, the main energy source can be directly cut off, significantly reducing instantaneous power consumption. Heating can be resumed after the concurrent high-energy-consumption event subsides, and thermal inertia allows for a rapid return to the set point, avoiding greater energy consumption caused by prolonged reheating. This operation achieves short-term power reduction and effectively suppresses load peaks while ensuring that the final process temperature meets the target.

[0099] At the same time as each instruction is issued, the production equipment targeted by the instruction, the adjustment action, and the corresponding production batch number are encapsulated as a status update message and sent to the production execution system.

[0100] In the innovative implementation of the above scheme, the optimized allocation execution module also includes a closed-loop verification process for adjustment effect, as follows: after issuing adjustment instructions to the adjustable device, an effect monitoring cycle is started.

[0101] During this period, the energy consumption data of relevant devices is continuously collected through the synchronous acquisition module, and the parameter mismatch identification module and the concurrent high energy consumption event analysis module are triggered to re-analyze the scope of the original event.

[0102] If the reanalysis results show that the original concurrent high-energy-consuming events have been eliminated, then record that this optimization allocation has been successfully completed.

[0103] If the reanalysis results show that the original event still exists or the equipment generates new parameter mismatches, an alarm message indicating that the optimization has not met expectations will be generated and submitted to the production management system along with the current equipment status data for manual decision-making.

[0104] This invention maximizes the effectiveness and safety of energy-saving measures by performing closed-loop verification of the adjustment effect after issuing the adjustment command.

[0105] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0106] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations 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. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0107] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time energy consumption monitoring and optimized allocation system for plastic bottle production, characterized in that, include: Benchmark library construction module: Extracts curves showing the changes in equipment process parameters with the production process from historical plastic bottle production data as standard parameter curves, and records the unit product energy consumption value corresponding to each standard parameter curve; Synchronous acquisition module: Synchronously acquires real-time process parameter data and energy consumption data of each piece of equipment during the production process; Parameter mismatch identification module: compares real-time process parameter data with the standard parameter curves of the corresponding equipment, and combines energy consumption data to identify the period of parameter mismatch; High-energy-consuming event analysis module: Maps the parameter mismatch periods identified by each device to a unified time axis for alignment and overlay analysis to identify concurrent high-energy-consuming events; Adjustable equipment screening module: When a concurrent high-energy-consuming event is detected, the adjustable equipment is screened from the equipment involved in the event based on the preset process coupling relationship between equipment and the production task status of the equipment. Optimize the allocation and execution module: issue adjustment instructions to each adjustable device and synchronize the instructions with the production batch information to the production execution system.

2. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 1, characterized in that: The contents of the benchmark library construction module are as follows: Retrieve historical production batch records marked as having the highest energy efficiency from the database of the plastic bottle production management system; Based on the plastic bottle production process, the energy-consuming equipment involved in the production is selected, including injection molding machines, blow molding machines, and dryers; For injection molding machines, the temperature change curve of the heating zone, screw speed curve, and clamping force curve are extracted from the production batch records as standard parameter curves. For blow molding machines, the temperature curves of each temperature zone of the heating furnace, the speed curve of the stretching rod, and the air pressure curve are extracted as standard parameter curves. For the dryer, its hot air temperature curve and fan speed curve are extracted as its standard parameter curves; Each extracted standard parameter curve is associated with the calculated value of the unit product energy consumption by dividing the total power consumption of the equipment during the production process of that batch by the total output of that batch, and then linked to form a benchmark data entry. The baseline data entries of all devices are integrated and stored to form a baseline library.

3. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 1, characterized in that: The contents of the synchronous acquisition module are as follows: During the plastic bottle production process, real-time process parameter data is read through the communication interface of the connected equipment at a fixed sampling period. By installing smart meters or power sensors in the main power supply circuit of the equipment, real-time current and voltage data are collected with the same sampling period, and the real-time power is calculated as real-time energy consumption data. A unified timestamp is applied to the process parameter data and energy consumption data at each sampling moment to ensure that the two are synchronized in time.

4. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 1, characterized in that: The specific process for identifying the parameter mismatch period is as follows: Based on the time series data of equipment process parameters collected in real time during the plastic bottle production process, the standard parameter curves of the corresponding equipment are retrieved from the benchmark library; For each process parameter in the standard curve, extract its maximum and minimum values ​​throughout the entire production cycle to construct the corresponding allowable fluctuation range; The real-time collected process parameter data is compared point by point with the allowable fluctuation range. When the parameter value at a certain time point exceeds the range, monitoring continues from that moment until the parameter returns to the range, thereby defining the initial mismatch period. During the initial mismatch period, the real-time power data of the corresponding device is extracted synchronously and compared with the unit product energy consumption value associated with the device in the benchmark library, which is converted into an equivalent power reference value. If the real-time power at a certain moment is higher than the equivalent reference value, then the length of time that the power is continuously in a high-energy-consumption state is recorded from that moment on, and is recorded as the high-energy-consumption duration. Calculate the ratio of the duration of high energy consumption to the total duration of the initial mismatch period, and compare this ratio with the mismatch confirmation limit. If the ratio reaches or exceeds the mismatch confirmation limit, the initial mismatch period is confirmed as the parameter mismatch period, and the start and end times of this period are recorded.

5. The real-time energy consumption monitoring and optimized allocation system in the plastic bottle production process as described in claim 4, characterized in that: The specific contents of the high-energy-consuming event analysis module are as follows: Establish a unified timeline covering all production equipment; Map all the parameter mismatch periods reported by all devices onto a unified timeline according to their recorded start and end times; Slide scans are performed on the timeline using a fixed time window, and the percentage of devices that are simultaneously in the parameter mismatch period within each sliding window is counted. When the proportion of devices exceeds the preset concurrency proportion, it is determined that a high-energy-consuming concurrent event has occurred within that time window. Record the time window and list of devices involved in concurrent high-energy-consuming events.

6. The real-time energy consumption monitoring and optimized allocation system in the plastic bottle production process as described in claim 5, characterized in that: The preset inter-equipment process coupling relationships and equipment production task status are as follows: The preset equipment process coupling relationship is represented by a directed graph. In the graph, nodes represent production equipment, and directed edges represent the sequential dependencies in the process flow. Each directed edge is assigned an attribute to identify whether there is a continuity constraint between adjacent equipment, that is, whether the output of the preceding equipment must be transmitted to the following equipment without interruption. The production task status refers to the process stage of each device's current task and the estimated remaining running time of the task, which are obtained in real time from the manufacturing execution system. The process stage includes the standby stage, the preheating and start-up stage, and the production operation stage.

7. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 6, characterized in that: The implementation process of the adjustable device screening module is as follows: Upon receiving a notification of a concurrent high-power consumption event, retrieve the list of devices involved in the event; Invoke the preset equipment process coupling relationship, and based on the list of equipment involved in the concurrent high energy consumption event, extract the process dependency subgraph between the equipment involved from the equipment process coupling relationship, including its upstream and downstream dependencies and whether there are continuity constraints; Meanwhile, based on the list of devices involved in concurrent high-energy-consuming events, the estimated remaining running time of the tasks currently being performed by the devices involved is extracted from the production task status; For each device in the equipment list, determine whether it is a potential adjustable device by considering its role in the process dependency subgraph and the estimated remaining runtime of its current task. For equipment identified as potentially adjustable, it is classified as adjustable depending on the current process stage. If the current process stage is the standby stage or the preheating start-up stage, it is classified as adjustable equipment.

8. The real-time energy consumption monitoring and optimized allocation system in the plastic bottle production process as described in claim 7, characterized in that: The determination of whether a device is a potential adjustable device is performed as follows: For each device in the device list, perform the following two-condition validation: a) The equipment has no upstream or downstream adjacent equipment in the process dependency subgraph, or it has upstream or downstream adjacent equipment but the process connection between it and the downstream equipment is not marked as a continuity constraint; b) The estimated remaining runtime of the task currently being performed by the equipment is higher than the preset minimum adjustable time threshold; When a device meets all of the above conditions, it is judged to be a potentially adjustable device.

9. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 1, characterized in that: The optimized allocation execution module includes the following: For adjustable devices in standby mode, generate and issue delayed start commands; For adjustable equipment in the preheating and start-up phase, generate and issue a command to pause heating; At the same time as each instruction is issued, the production equipment targeted by the instruction, the adjustment action, and the corresponding production batch number are encapsulated as a status update message and sent to the production execution system.

10. The real-time energy consumption monitoring and optimized allocation system for the plastic bottle production process as described in claim 1, characterized in that: The optimized allocation execution module also includes a closed-loop verification process for the adjustment effect, as detailed below: After issuing an adjustment command to the adjustable device, an effect monitoring cycle is initiated; During this period, the energy consumption data of relevant devices is continuously collected through the synchronous acquisition module, and the parameter mismatch identification module and the concurrent high energy consumption event analysis module are triggered to re-analyze the scope of the original event. If the reanalysis results show that the original concurrent high-energy-consuming events have been eliminated, then record that this optimization allocation has been successfully completed; If the reanalysis results show that the original event still exists or the equipment generates new parameter mismatches, an alarm message indicating that the optimization has not met expectations will be generated and submitted to the production management system along with the current equipment status data for manual decision-making.

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