Laboratory equipment operation system with self-adaptive energy management

Abstract

The invention relates to the technical field of automatic control, particularly discloses a laboratory equipment operation system for adaptive energy management, and aims to solve the problems that equipment energy consumption dynamic characteristic capture is insufficient, a management strategy lacks adaptability, and power grid impact control capability is lacked during multi-equipment cooperative operation. The system comprises an equipment working condition fine sensing module, an experiment task intention analysis engine, a multi-time-scale energy consumption prediction unit, an equipment cluster collaborative dispatcher and a dynamic electricity price response control module, and the energy consumption is predicted through millisecond-level power consumption sensing, task intention analysis, multi-scale energy consumption prediction, cluster collaborative dispatching and electricity price response control. Accurate prediction of laboratory equipment energy consumption, operation cost optimization and power grid impact minimization are realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automatic control, and particularly relates to a laboratory equipment operation system with adaptive energy management. BACKGROUND

[0002] Laboratory equipment operation management is a key technical field for ensuring the efficiency of scientific experiments and energy consumption control, involving the coordinated scheduling and state monitoring of various precision instruments, environmental control devices and auxiliary equipment. Among them, the adaptive energy management system dynamically adjusts the equipment operation strategy by real-time sensing of the equipment operation state, experimental task demand and external energy supply conditions, and has become an important research direction for realizing the green and intelligent operation of the laboratory.

[0003] In the prior art, equipment energy consumption data usually depends on periodic manual transcription or simple independent electric metering, which is difficult to capture the dynamic power consumption characteristics of the equipment under different working conditions such as standby, no-load and peak operation in real time. Traditional energy management strategies are mostly based on fixed time tables or preset simple start-stop rules, and lack adaptability to dynamic changes in experimental progress, energy consumption coupling relationship between equipment and external factors such as time-of-use electricity price, resulting in high energy waste and running cost. In addition, when multiple devices are running cooperatively, the start and stop of high-power units such as refrigeration equipment and vacuum systems will produce significant power grid impact, and the existing system lacks the ability to predict and smooth control of such transient impact.

[0004] The burstiness of experimental tasks, equipment aging and changes in environmental temperature and humidity further exacerbate the complexity of energy management. For example, the insertion of urgent experimental tasks may disrupt the original energy consumption budget plan, and the traditional static allocation mode cannot quickly re-optimize resources. At the same time, the existing system generally lacks long-term tracking and degradation warning of equipment energy efficiency state, and it is difficult to implement preventive energy consumption maintenance while ensuring experimental continuity. Therefore, there is an urgent need for an intelligent energy management scheme that can deeply integrate multi-source real-time data, adapt to changes in experimental tasks and external environment, and has the ability of fine control at the equipment level and energy efficiency optimization at the system level. SUMMARY

[0005] The present application aims to provide a laboratory equipment operation system with adaptive energy management, to solve the problems of insufficient capture of equipment energy consumption dynamic characteristics, lack of adaptability of management strategies to experimental processes and external factors, and lack of control ability of power grid impact when multiple devices are running cooperatively. To achieve the above-mentioned purpose, the technical solution adopted by the present application is to construct a complete system integrating a device working condition fine perception module, an experimental task intention analysis engine, a multi-time scale energy consumption prediction unit, a device cluster cooperative scheduler, and a dynamic price response control module. The system realizes millisecond-level capture and feature analysis of device transient and steady-state power consumption through a multi-dimensional sensor array deployed in the device power supply circuit; through natural language processing technology and an experimental process knowledge base, unstructured experimental task descriptions are converted into structured execution plans with clear device requirements, time constraints, and energy efficiency characteristics; through the fusion of real-time data and task plans, a hybrid energy consumption prediction model is constructed for short-term impact, medium-term benchmark, and long-term trend; through the establishment of an optimization model targeting economic optimization and minimum power grid impact, the device start-stop timing and running power are cooperatively planned; finally, through the response to time-of-use electricity price signals, dynamic scheduling of translatable loads is realized, achieving optimization of total laboratory operation cost and continuous improvement of energy efficiency level.

[0006] The device working condition fine perception module is composed of intelligent measurement and control units deployed at the front end of the power supply circuit of each key laboratory equipment. Each intelligent measurement and control unit integrates a high-precision wide-band current sensor, a voltage sensor, a power factor detection chip, and a temperature sensing probe. The current sensor adopts a closed-loop Hall principle, with a bandwidth covering 0HZ to 10KHZ, which can accurately measure the impact current peak value generated at the start of the device, the current effective value during steady-state operation, and the small leakage current in standby state. The voltage sensor adopts a combined circuit based on a precision resistance voltage division network and a high linearity isolation operational amplifier, which monitors the effective value and fluctuation range of the supply voltage in real time. The power factor detection chip calculates the phase difference between voltage and current signals in real time through high-speed sampling and digital signal processing technology, and outputs the instantaneous power factor value of the device with a resolution of 0.01. The temperature sensing probe selects a platinum resistance element, which is installed close to the heat sink or ventilation port of the main power device of the equipment, for monitoring the temperature rise curve during equipment operation. All intelligent measurement and control units upload the collected voltage instantaneous value, current instantaneous value, power factor value, and temperature value to the central processing platform in real time through the industrial Ethernet or high-reliability wireless communication network deployed inside the laboratory at a sampling frequency of not less than 100HZ.

[0007] The experimental task intention analysis engine receives structured task data from the laboratory information management system or natural language description text input by the experimental personnel through the human-computer interaction interface. The natural language processing model built in the engine adopts the BERT architecture, is pre-trained and fine-tuned using the laboratory historical task log data set, and is trained with a learning rate of 0.001 and a batch size of 32; the knowledge base is constructed by analyzing the ISO standard experimental operation documents, is stored in a graph database, and an automatic updating mechanism is set up every week. The model first performs word segmentation and part-of-speech tagging on the input text, and then performs named entity recognition to extract key elements such as device names, experimental parameter setting values, runtime requirements, and start time constraints involved in the task description. The engine is also connected to a continuously maintained experimental process knowledge base. The knowledge base stores a large number of standard experimental operation procedures, divides each experimental type into a series of device operation step sequences with strict time sequence logic, and labels each step with the specific device involved, the typical power consumption characteristic curve of the device in the step, the estimated execution time of the step, the dependency relationship between steps, and the sensitivity level to power supply quality. The engine calculates the semantic similarity between the entities extracted in the input task and the standard operation procedures in the knowledge base, and combines the time logic constraints to perform consistency checking, and finally generates a structured experimental task execution plan. The plan clearly lists all the devices required to complete the task, the specific operation mode of each device, the expected start time, the running duration, the shutdown time, and the power demand profile during the running period.

[0008] The multi-time scale energy consumption prediction unit receives real-time high-frequency data stream from the device working condition fine perception module and structured task plan from the experimental task intention analysis engine. The unit adopts a hybrid prediction model architecture, and performs energy consumption prediction for three time scales: short-term, medium-term, and long-term. The short-term prediction is for the future 5 minutes to 4 hours, and its model core relies on the real-time power sequence provided by the device working condition fine perception module. The model first down-samples and extracts features from the high-frequency data, extracts the average power, power change rate, and energy distribution features in a specific frequency band within the sliding window. Then a deep long short-term memory network is used to learn the short-term dynamic pattern of the device power sequence. The network is trained on a large amount of historical data and can accurately predict the total power consumption curve of the laboratory in the future several hours, and warn of the power impact peak value that may be caused by the start and stop of high-power devices in advance several minutes. The medium-term prediction is for the future 4 hours to 24 hours, and its model mainly inputs the structured execution plan of all scheduled tasks within the next 24 hours output by the experimental task intention analysis engine. The model generates a baseline load curve for the next 24 hours by superimposing the activation time, running time, and standard power consumption curve of each device in the plan, and fuses the fine-tuned correction results output by the short-term prediction model. The long-term prediction is for the future 1 day to 7 days, and its model focuses on macro trend analysis, mainly based on historical same period energy consumption data, known device preventive maintenance plan, and macro experimental arrangement calendar, and uses a seasonal autoregressive integrated moving average model for prediction. All prediction results are attached with confidence intervals calculated based on historical error statistics, and are uniformly output to the device cluster collaborative scheduler.

[0009] The cluster co-scheduler is the core optimization decision unit of the system. It receives the energy consumption prediction curves from the multi-time scale energy consumption prediction unit, the detailed task plan from the experimental task intent analysis engine, and the time-of-use electricity price information from the external data interface. Inside the scheduler, a large-scale multi-objective optimization problem is constructed and solved. The decision variables of this problem are the running states and power levels of each controllable device in the future 24 hours at 15-minute intervals, i.e., 96 time slices. The optimization objective function is mainly composed of two components weighted and summed. The first target component is the total running economic cost, which specifically includes energy electricity charges and demand electricity charges. Energy electricity charges are calculated by multiplying the predicted total power of each time slice with the electricity price of the corresponding time period and integrating. Demand electricity charges are represented as a penalty term for peak power. The second target component is the grid impact degree, which is represented by quantifying the variance of the total load curve or the integral of the absolute value of the load change rate, and the goal is to minimize it to achieve load smoothing. The constraint conditions of the optimization problem include that each experimental task must be completed within its specified time window, the running logic constraints between devices within a task must be met, the running power of a single device must not exceed its allowed range, and the total power consumption of the laboratory at any time must not exceed the upper limit of the safe capacity of the power supply line. The scheduler uses an improved genetic algorithm or a mixed integer linear programming solver to efficiently solve this complex optimization problem, and finally outputs a detailed cluster co-running schedule. This schedule precisely specifies when each device starts, runs at what power, and when it stops, and optimally adjusts the execution time of translatable tasks. The optimization objective function is: minimize α × total running economic cost + β × grid impact degree, where the weights α and β are determined by historical data regression analysis, α = 0.7, β = 0.3, and can be dynamically adjusted according to the electricity price fluctuations.

[0010] The dynamic price response control module is specifically designed to interact with the time-of-use electricity price policy. This module receives or predicts the electricity price curve for the next 24 hours in real time. It analyzes the initial running schedule generated by the cluster co-scheduler and identifies flexible load tasks that have no strict execution time requirements and can be shifted within a wide time range. Such tasks usually include non-urgent sample preprocessing, device preheating, data backup processing, and cleaning and disinfection programs. Inside the module, a cost sensitivity analysis algorithm is run to iterate through all identified flexible load tasks, calculating the economic benefits that can be brought by shifting them from high-price periods to low-price periods. Under the premise of ensuring that no urgent or rigid experimental tasks are affected, the module generates a specific load shifting recommendation scheme, which lists the recommended shifted tasks, the recommended new execution time window, and the expected cost savings. This recommendation scheme is fed back to the cluster co-scheduler and can be used as an input condition to trigger a new round of optimization calculation, thereby generating an updated running schedule with better economic efficiency.

[0011] As a preferred embodiment of the present application, the system is further integrated with a device energy efficiency state assessment and early warning module. The module continuously collects and stores long-term operation data from the device working condition fine perception module, including cumulative operation time, historical power consumption curve, efficiency value under typical working condition, and variation trend of key temperature points. The module establishes an independent energy efficiency baseline model for each device, which learns the mapping relationship between power consumption and output performance of the device in a healthy state through machine learning methods such as support vector regression or Gaussian process regression. The module regularly compares the real-time operation data of the device with the energy efficiency baseline model, and calculates the energy efficiency deviation. The energy efficiency deviation is defined as the absolute difference between actual power consumption and model predicted power consumption divided by predicted power consumption multiplied by 100%. When the energy efficiency deviation of a device continuously exceeds the preset threshold of 15% for a certain period of time, the module generates a device energy efficiency degradation warning information, prompting that maintenance or calibration may be needed. At the same time, the module also statistically analyzes the standby power consumption of the device, identifies devices with abnormally high standby power consumption, and provides data support for energy saving renovation of the laboratory.

[0012] Further, the system operates in a hierarchical control architecture, which includes a strategic planning layer, a tactical scheduling layer, and an operation execution layer. The strategic planning layer has a cycle of weeks or months, and formulates macro energy consumption budget and energy efficiency targets for key experimental tasks based on long-term prediction and overall laboratory planning. The tactical scheduling layer has a cycle of days, and performs the core function of the device cluster collaborative scheduler, generating a detailed device operation plan for the next 24 hours. The operation execution layer has a cycle of minutes or seconds, and is responsible for receiving and strictly executing the control instructions issued by the tactical scheduling layer, while performing real-time monitoring and closed-loop feedback through the device working condition fine perception module, to ensure that the system operates stably according to the plan, and responds quickly to sudden abnormalities.

[0013] Compared with the prior art, the present application has the advantages and positive effects that: 1. The present application deploys intelligent measurement and control units integrated with multi-dimensional sensors, achieving full-range fine perception of laboratory devices from transient impact current to steady-state power consumption, from active power to reactive power factor, completely changing the status quo of insufficient capture of device dynamic characteristics by traditional energy consumption metering methods, and providing a high-resolution, high-timeliness data basis for precise energy management.

[0014] 2. The present application introduces an experimental task intention analysis engine, which automatically converts unstructured experimental descriptions into structured task execution plans that can be understood and calculated by machines, enabling energy management strategies to deeply understand the specific needs, time constraints and energy efficiency characteristics of scientific research activities, achieving dynamic adaptation of management strategies and experimental progress, and significantly improving the intelligent level and decision-making relevance of the system.

[0015] 3. The application adopts a multi-time scale hybrid prediction model to realize comprehensive energy consumption prediction from minute-level transient impact to week-level trend change, especially short-term accurate prediction of device start-stop transient power, which provides a key time window and decision basis for the system to take load smoothing measures in advance, effectively enhancing the active inhibition capability of power grid impact.

[0016] 4. The application solves the optimization problem with the goal of economic optimization and minimum power grid impact through a device cluster collaborative scheduler, which considers device operation logic, task time constraints, infrastructure safety limits and external electricity price signals, realizes global optimal planning of device start-stop timing and running power, not only significantly reduces operation cost on the premise of ensuring smooth experiment, but also improves the friendliness to regional power quality.

[0017] 5. The application actively uses time-of-use electricity price signals through a dynamic electricity price response control module to identify and optimize the scheduling of flexible loads, further tapping the energy-saving and cost-reducing potential of demand-side response, and realizes the maximization of laboratory operation economic benefits.

[0018] 6. The integrated device energy efficiency state evaluation and early warning module realizes early detection and early warning of device energy efficiency degradation through machine learning analysis of long-term operation data, supports the implementation of predictive maintenance strategies, helps to maintain the device in an efficient operation state at all times, controls energy waste from the source, and prolongs the service life of the device. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is the overall technical scheme architecture diagram of the laboratory device operation system of the adaptive energy management proposed by the application; Figure 2 is the core principle framework diagram of the device cluster collaborative scheduler in the application; Figure 3 is the logic flow framework diagram of the experiment task intention analysis engine and the multi-time scale energy consumption prediction unit. DETAILED DESCRIPTION

[0020] Embodiment 1 Please refer to the attached Figure 1 This embodiment describes in detail the specific technical implementation of a laboratory device operation system of adaptive energy management. The system aims to realize fine, intelligent and cost-optimized management of laboratory device energy consumption through deep integration of device working condition perception, experiment task analysis, multi-scale energy consumption prediction, cluster collaborative scheduling and electricity price response control.

[0021] The bottom layer data foundation of the system is provided by the equipment working condition fine perception module. The core component of the module is the intelligent measurement and control unit deployed in front of the power supply circuit of each key laboratory equipment. These intelligent measurement and control units are not simple power measurement devices, but comprehensive data acquisition terminals integrated with multiple high-precision sensors. The current sensor integrated in each unit adopts a closed-loop Hall principle, and its design bandwidth covers a wide frequency range of 0 Hz to 10 kHz. This bandwidth characteristic enables it to accurately capture the impact current waveform rich in high-order harmonics generated by the equipment at the start of the moment, and also can stably measure the effective value of the power frequency current of the equipment in the steady state. The measurement accuracy of the current sensor is calibrated strictly, and the standard current source is used to calibrate periodically in a constant temperature environment of 25°C. The calibration process follows the IEC 61869 standard to ensure that the error within the full range is not more than ±0.5%; the calibration of the voltage sensor uses a precision voltage reference source, and achieves ±0.2% accuracy through multi-point linear compensation technology. The power factor detection chip calculates the phase difference between the voltage and current signals in the same cycle in real time through high-speed sampling and digital signal processing technology, and outputs the instantaneous power factor value of the equipment with a resolution of 0.01. The temperature sensing probe usually selects a platinum resistance element, and its installation position is carefully selected, usually close to the heat sink or ventilation port of the main power device of the equipment, for monitoring the temperature rise curve of the equipment during operation, with a sampling frequency synchronized with the electrical parameters, not less than 100 Hz. All intelligent measurement and control units upload the raw voltage instantaneous value, current instantaneous value, power factor value and temperature value collected to the central processing platform of the system through the industrial-grade Ethernet network or high-reliability wireless communication module deployed in the laboratory at a rate of not less than 100 Hz. The data frame format follows the pre-defined industrial communication protocol, including device unique identifier, time stamp, channel sampling value and data check code.

[0022] Please refer to the attached Figure 1 with the attached Figure 3, the experimental task intent parsing engine is the key module for the system to understand the upper-layer experimental requirements. There are two main channels for the input source of the engine: one is to automatically obtain the scheduled experimental task description text through the standard application programming interface and the existing information management system of the laboratory; the other is to allow the experimental personnel to manually input or voice input the description of the temporary and unplanned experimental task through the specially designed human-computer interaction interface. A natural language processing model specially trained for the laboratory field is run inside the engine. The model first performs word segmentation processing on the input task description text, dividing continuous sentences into semantic units of individual words. Then, part-of-speech tagging is performed to identify the grammatical role of each word, such as noun, verb, and adjective. Next, the named entity recognition step is performed to extract entity information closely related to experimental operations from the text, including but not limited to device names such as high-speed centrifuge, polymerase chain reaction instrument, and biochemical incubator; experimental parameters such as temperature set value, rotation speed, and running time; time constraints such as start time and latest completion time; and task priority identification. After completing the preliminary text parsing, the engine starts its core semantic understanding and logical reasoning process. It accesses a continuously maintained and updated experimental process knowledge base. The knowledge base is essentially a structured database that stores a large number of standard experimental operation procedures reviewed by experts. Each standard experimental operation procedure breaks down a specific type of experiment into a series of device operation step sequences with strict timing logic and dependency relationships. For example, for a cell culture experiment, the standard operation procedure may include steps such as preheating the culture medium, thawing the cells, centrifugation and resuspension, inoculation, and microscopic observation. The knowledge base details each step, including the specific device involved, the typical power consumption characteristic curve of the device in that step, the estimated execution time of the step, the serial or parallel constraint relationship between the step and the predecessor and successor steps, and the sensitivity level of the step to power continuity or power quality. The experimental task intent parsing engine maps the unstructured text description into one or more structured, machine-readable experimental task execution plans by calculating the semantic similarity between the extracted entities in the input task and the standard operation procedures in the knowledge base, and performing consistency checking based on the time logic constraints. The plan is a complex data structure that explicitly lists all the devices required to complete the task, the specific operation mode of each device, the expected start time of each device, the expected running duration, the expected downtime, the power demand profile during operation, and the criticality level of the device operation to the success of the experiment.

[0023] Please continue to refer to the attached Figure 1 with the attached Figure 3, multi-time scale energy consumption prediction unit is the bridge connecting the present and the future, which is responsible for generating the total energy consumption prediction of the laboratory in different time spans in the future. This unit receives two continuously updated data streams simultaneously: one is the ultra-high frequency data stream from the device working condition fine perception module, reflecting the real-time running state of the device; the other is the relatively macro structured task plan from the experimental task intention analysis engine, describing the future experimental arrangement. Inside the prediction unit, a hybrid prediction model architecture is adopted, and different core prediction algorithms and different main input features are deployed for short-term, medium-term and long-term prediction scales. The short-term prediction faces the time window of 5 minutes to 4 hours in the future, and its core goal is to accurately predict the rapid energy consumption fluctuations caused by device start-stop and running mode switching. The main input of the short-term prediction model is the real-time power sequence data collected by the device working condition fine perception module in the last few hours at a sampling rate of 100HZ. The model first down-samples and extracts features from these high-frequency data, such as average power in sliding window, power change rate, and energy distribution in specific frequency band, etc. Then, the model uses a deep long short-term memory network to learn the short-term dynamic pattern of the device power sequence. The long short-term memory network can effectively capture the long-term dependence in time series due to its internal gating mechanism, and is particularly suitable for predicting energy consumption data with inertia and periodicity. After training on a large amount of historical data, the network can predict the total power consumption curve of the laboratory in the next few hours based on the recent power sequence, especially the power impact peak value that may be generated when high-power devices such as vacuum pumps and refrigerators start. The medium-term prediction faces the time window of 4 hours to 24 hours in the future, and its core goal is to generate the baseline energy load curve for the whole day in the future. The main input of the medium-term prediction model is the structured execution plan of all scheduled experimental tasks within the next 24 hours output by the experimental task intention analysis engine. The model generates a preliminary load curve for the next 24 hours by superimposing the start time, running time and standard power consumption curve of each device in the plan. This preliminary curve is then fused with the high-precision prediction result of the next 4 hours output by the short-term prediction model. The fusion process usually uses weighted average or Kalman filtering algorithm, so that the medium-term prediction relies more on the accuracy of the short-term model in the near future and relies more on the integrity of the task plan in the distant future. The long-term prediction faces the time window of 1 day to 7 days in the future, and its core goal is to grasp the macro trend and periodicity of energy consumption. The main input of the long-term prediction model is the historical energy consumption data of the same period, the known device preventive maintenance plan, and the macro experimental arrangement calendar. The model uses a seasonal autoregressive integrated moving average algorithm to capture the seasonal patterns such as daily and weekly cycles in the energy consumption data. This model can predict which days in the next week may have high energy consumption peaks due to the concentration of large-scale experiments, and which days may have energy consumption valleys due to holidays or device maintenance.All the prediction results, whether short-term, medium-term or long-term, are accompanied by a confidence interval calculated based on the historical prediction error statistics of the model when output, for example, a range of ±10% of the predicted value, to quantify the uncertainty of the prediction. These multi-scale prediction results are uniformly sent to the device cluster coordinator as key inputs for its optimization decision-making.

[0024] Please refer to the attached Figure 1 With the attached Figure 2, the device cluster coordination scheduler is the wisdom brain and decision center of the whole system. As a complex information gathering and processing node, it receives input information from multiple upstream modules: multi-scale energy consumption prediction curves and their confidence intervals for the next 24 hours from the multi-time scale energy consumption prediction unit; detailed structured execution plans for all experimental tasks within the next 24 hours, including device requirements, time constraints, and running logic, from the experimental task intent analysis engine; and the grid time-of-use electricity price prediction curve for the next 24 hours or real-time electricity price signals from the external data interface. The core task of the scheduler is to solve a large-scale, multi-objective, constrained optimization problem. The mathematical nature of this optimization problem can be described as follows: under the premise of meeting all experimental task hard constraints, find a set of optimal device running state sequences to minimize the total running cost of the system, while making the total load curve as smooth as possible to reduce the impact on the grid. The decision variable of the optimization problem is a high-dimensional vector, with dimensions equal to the number of controllable devices multiplied by the number of 15-minute time slices within the next 24 hours, i.e. 96 time points. Each decision variable defines the running state of a certain device in a certain time slice, such as shutdown, standby, low-power running, rated-power running, etc. For devices that support continuous power adjustment, the decision variable can also be a specific power setting. The optimization objective function is mainly composed of two parts weighted and summed. The first target component is the total running economic cost, which is further divided into two parts: one part is the energy electricity fee, which is calculated by multiplying the predicted total power of each time slice with the electricity price of the corresponding time period and integrating; the other part is the demand electricity fee, which is the fee charged by the power company according to the monthly or daily maximum demand, which is usually represented as a penalty term for peak power in optimization. The second target component is the grid impact degree, which is usually represented by a quantitative index, such as the variance of the total load curve or the integral of the absolute value of the load change rate, and the goal is to minimize this index to achieve load smoothing. The constraint conditions of the optimization problem are very strict and complex, mainly including the following categories: the first category is the task completion constraint, which ensures that each experimental task must be completed within its specified time window and all necessary device operation steps must be executed; the second category is the device running logic constraint, such as the start of a device must be after the completion of the preheating of another device, or some devices cannot run simultaneously to avoid circuit overload; the third category is the device capacity constraint, the running power of each device must be within its minimum and maximum allowed power range; the fourth category is the infrastructure safety constraint, the total power consumption of the laboratory at any time cannot exceed the upper limit of the safe capacity of the power distribution line or transformer. In order to efficiently solve this complex mixed integer programming or nonlinear programming problem, the scheduler integrates a high-performance mathematical programming solver internally, such as an improved genetic algorithm, particle swarm algorithm, or commercial mixed integer linear programming solver.The solution process may take several minutes or even longer, typically performed at a fixed time each day, such as midnight, to conduct a one-time rolling optimization calculation for the next 24 hours. Upon successful solution, the scheduler outputs a detailed schedule for the coordinated operation of the device cluster. This schedule is a two-dimensional matrix, with rows representing devices and columns representing time segments. Each cell precisely specifies the operating status and power level of the corresponding device within that time segment. This schedule serves as the direct basis for subsequent system execution and control.

[0025] Please refer to the attached document. Figure 1 The dynamic electricity price response control module is a crucial component for the system's economical operation. This module is specifically responsible for interacting with the grid's time-of-use pricing policy. It receives or predicts the electricity price curve for the next 24 hours in real time through a standard data interface. This curve clearly indicates the peak, flat, and off-peak electricity price periods of the day. The module's core function is to analyze the initial collaborative operation schedule generated by the equipment cluster collaborative scheduler and identify resilient loads that can be shifted over time. Resilient loads typically refer to tasks or equipment operations that are not strictly time-sensitive, and whose start and end times can be moved within a relatively wide time window without affecting the core experimental objectives. Typical resilient loads include: certain non-urgent sample pretreatment, equipment preheating or delayed shutdown, large-scale data backup and processing, and the execution of cleaning and disinfection procedures. The module internally runs a cost sensitivity analysis algorithm. This algorithm iterates through all identified resilient load tasks, and for each task, calculates the electricity cost savings if it were moved from the currently planned high-price period to the low-price period. The prerequisite for relocation is that it must meet the time elasticity window constraint of the task itself and must not conflict with any rigid tasks in terms of resources. The algorithm comprehensively considers the energy-saving benefits of relocation and other potential impacts, such as increased equipment idling losses. After a comprehensive evaluation, the dynamic electricity price response control module generates a specific load relocation proposal. This proposal lists the tasks to be relocated, the proposed new execution time window, and the expected cost savings in a structured data format. This proposal is not executed directly but is fed back to the equipment cluster collaborative scheduler. Upon receiving the relocation proposal, the scheduler uses it as a new input condition, which may trigger a new round of optimization calculations that take into account the benefits of reduced electricity prices, thereby generating an updated and more economical equipment collaborative operation schedule.

[0026] As a high-level functional component of this system, the Equipment Energy Efficiency Status Assessment and Early Warning module is responsible for monitoring and diagnosing the long-term operational health of equipment. This module continuously acquires and archives massive amounts of historical operational data from the Equipment Operating Condition Fine-Grained Sensing module. It establishes an independent digital profile for each key piece of equipment, recording its cumulative operating hours, historical power consumption curve database, typical operating efficiency under different load rates, and historical trend data for key temperature monitoring points. The core of the module is to establish a personalized energy efficiency baseline model for each piece of equipment. This baseline model describes the energy consumption characteristics of the equipment under healthy and normal conditions. The model is typically built using supervised machine learning methods, such as support vector regression or Gaussian process regression. Taking support vector regression as an example, its goal is to find a function that, given the equipment's operating state parameters, can predict its power consumption as accurately as possible.

[0027] Where x is the input feature vector, which may include the device's output power setting, ambient temperature, operating mode, etc. The kernel function maps features to a high-dimensional space, where w is the weight vector and b is the bias term. The model is trained using data from a historical period showing devices in known health conditions, learning the parameters of w and b. Once the energy efficiency baseline model is established, the module enters continuous monitoring mode. It periodically, for example daily, inputs the latest real-time operating data of the devices, including actual measured power consumption and output performance, into the trained baseline model, calculates the model's predicted expected power consumption, and compares it with the actual measured power consumption. The relative deviation between the two is calculated, i.e., the energy efficiency deviation. The energy efficiency deviation is equal to the absolute value of the actual power consumption minus the predicted power consumption, divided by the predicted power consumption, and then multiplied by 100%. The module sets an early warning threshold for energy efficiency deviation for each type of device, for example, 15%. When the system detects that the energy efficiency deviation of a device exceeds its early warning threshold for several consecutive monitoring periods, such as five consecutive days, it triggers an early warning mechanism. The system automatically generates an early warning message for equipment energy efficiency degradation. This message clearly indicates the equipment number, the current energy efficiency deviation, trend analysis, and suggests possible causes, such as equipment aging, the need for lubrication, sensor calibration drift, or internal dust accumulation, recommending corresponding inspections or preventative maintenance. Furthermore, this module pays special attention to the equipment's standby power consumption. It statistically analyzes the long-term average standby power consumption of all equipment during non-operating periods and compares it with the nominal standby power consumption of the same model or similar equipment in the laboratory. For equipment found to have consistently abnormally high standby power consumption, the module will also issue an alert, providing accurate data support for the laboratory to eliminate high standby power consumption equipment and carry out energy-saving renovations.

[0028] Furthermore, the system operates within a hierarchical control architecture, comprising a strategic planning layer, a tactical scheduling layer, and an operational execution layer. The strategic planning layer, operating on a weekly or monthly cycle, formulates macro-level energy consumption budgets and energy efficiency targets for key experimental tasks based on long-term forecasts and overall laboratory planning. The tactical scheduling layer, operating on a daily cycle, executes the core functions of the equipment cluster collaborative scheduler, generating detailed equipment operation plans for the next 24 hours. The operational execution layer, operating on a minute or second cycle, is responsible for receiving and strictly executing control commands issued by the tactical scheduling layer. Simultaneously, it performs real-time monitoring and closed-loop feedback through a fine-grained equipment condition sensing module, ensuring stable system operation according to plan and rapid response to unexpected anomalies. This hierarchical architecture ensures that the system can both conduct long-term strategic planning and address real-time operational needs, achieving an effective unification of macro-level objectives and micro-level execution.

[0029] This embodiment, through the detailed technical description of the equipment condition fine-sensing module, experimental task intent parsing engine, multi-timescale energy consumption prediction unit, equipment cluster collaborative scheduler, dynamic electricity price response control module, and equipment energy efficiency status assessment and early warning module, fully reveals the specific implementation method of this adaptive energy management laboratory equipment operation system. The system acquires dynamic equipment data through fine-sensing, understands experimental needs through intent parsing, anticipates energy consumption trends through multi-scale prediction, achieves global optimization through collaborative scheduling, explores economic potential through electricity price response, and ensures equipment health through energy efficiency assessment, ultimately constructing an efficient, intelligent, and economical closed-loop system for laboratory equipment energy management.

Claims

1. A laboratory equipment operation system with adaptive energy management, characterized in that, include: The equipment operating condition fine sensing module consists of intelligent measurement and control units deployed at the front end of the power supply circuit of each key laboratory equipment. Each intelligent measurement and control unit integrates a high-precision wideband current sensor, a voltage sensor, a power factor detection chip, and a temperature sensing probe. The current sensor adopts the closed-loop Hall principle, with a bandwidth covering 0Hz to 10kHz, and is used to measure the peak inrush current generated at the moment of equipment startup, the effective value of the current during steady-state operation, and the minute leakage current in standby mode. The voltage sensor adopts a combination circuit based on a precision resistor voltage divider network and a high linearity isolation operational amplifier, and is used to monitor the effective value of the supply voltage and its fluctuation range in real time. The power factor detection chip uses high-speed sampling and digital signal processing technology to calculate the phase difference between voltage and current signals in the same cycle in real time, and outputs the instantaneous power factor value of the device with a resolution of 0.

01. The temperature sensing probe uses a platinum resistance element, which is installed close to the heat sink of the main power device or the ventilation port of the housing to monitor the temperature rise curve during the operation of the device. All intelligent measurement and control units use an industrial Ethernet or high-reliability wireless communication network deployed in the laboratory to package the collected instantaneous voltage value, instantaneous current value, power factor value and temperature value into data frames and upload them to the central processing platform in real time at a sampling frequency of not less than 100HZ. The experimental task intent parsing engine receives structured task data from the laboratory information management system or natural language description text input by experimenters through the human-computer interaction interface. This engine incorporates a natural language processing model specifically trained for the laboratory domain. The model performs word segmentation and part-of-speech tagging on the input text, followed by named entity recognition, extracting key elements from the task description, such as equipment names, experimental parameter settings, runtime requirements, and startup time constraints. The engine also connects to a continuously maintained experimental procedure knowledge base, which stores a large number of standard experimental operating procedures, decomposing each experimental type into a series of settings with strict temporal logic. Prepare a sequence of operation steps, and label each step with the specific device involved, the typical power consumption characteristic curve of the device in that step, the estimated execution time of the step, the dependencies between steps, and the sensitivity level to power quality. The engine calculates the semantic similarity between the entities extracted from the input task and the standard operating procedures in the knowledge base, and performs consistency verification in combination with time logic constraints to generate a structured experimental task execution plan. The plan clearly lists all the devices required to complete the task, the specific operating mode of each device, the expected start time, the running duration, the downtime, and the power demand profile during the operation. A multi-timescale energy consumption prediction unit is used to simultaneously receive real-time high-frequency data streams from the equipment condition fine sensing module and structured task plans from the experimental task intent parsing engine. This unit employs a hybrid prediction model architecture, performing energy consumption predictions at three time scales: short-term, medium-term, and long-term. Short-term predictions cover the next 5 minutes to 4 hours, with the core model relying on real-time power sequences provided by a fine-grained equipment condition sensing module. The model first downsamples and extracts features from high-frequency data, extracting the average power, power change rate, and energy distribution characteristics of specific frequency bands within a sliding window. Subsequently, a deep long short-term memory network is used to learn the short-term dynamic patterns of the equipment power sequence. This network, trained on extensive historical data, can accurately predict the total power consumption curve of the laboratory over the next few hours and provide early warnings of potential power surges during the start-up and shutdown of high-power equipment, several minutes in advance. Medium-term predictions cover the next 4 to 24 hours. The model's main input is the structured execution plan of all scheduled tasks for the next 24 hours, output by the experimental task intent parsing engine. Based on the activation time, runtime, and standard power consumption curve of each device in the plan, the model generates a baseline load curve for the next 24 hours through superposition calculations, and integrates the refined correction results output from the short-term prediction model. Long-term predictions cover the next 1 to 7 days, focusing on macro-trend analysis. They are primarily based on historical energy consumption data for the same period, known equipment preventative maintenance plans, and the macro-experimental schedule calendar, using a seasonal autoregressive integral moving average model for prediction. All prediction results include confidence intervals calculated based on historical error statistics and are uniformly output to the device cluster collaborative scheduler. The equipment cluster collaborative scheduler receives energy consumption prediction curves from multi-timescale energy consumption prediction units, detailed task plans from the experimental task intent parsing engine, and grid time-of-use pricing information from external data interfaces. Internally, the scheduler constructs and solves a large-scale multi-objective optimization problem. The decision variables for this problem are the operating status and power level of each controllable device in 96 time segments at 15-minute intervals over the next 24 hours. The optimization objective function mainly consists of a weighted sum of two components. The first objective component is the total operating economic cost, specifically including energy costs and demand costs. Energy costs are determined through... The predicted total power for each time segment is calculated by multiplying the electricity price for the corresponding time period and integrating the result; the demand charge is reflected as a penalty term for peak power; the second objective component is the grid impact, which is characterized by the integral of the variance of the total load curve or the absolute value of the load change rate, with the goal of minimizing it to achieve load smoothing; the constraints of the optimization problem include that each experimental task must be completed within its specified time window, the operational logic constraints between devices within the task must be met, the operating power of a single device must not exceed its allowable range, and the total power consumption of the laboratory must not exceed the safe capacity limit of the power supply line at any time; The scheduler uses an improved genetic algorithm or a mixed-integer linear programming solver to efficiently solve this complex optimization problem and outputs a detailed timetable for the coordinated operation of the equipment cluster. This timetable precisely specifies when each piece of equipment should start, at what power it should operate, and when it should stop, and optimizes the execution time of translatable tasks. The dynamic electricity price response control module receives or predicts the electricity price curve for the next 24 hours in real time. This module analyzes the initial operating schedule generated by the equipment cluster coordinating scheduler, identifying flexible load tasks with less stringent execution time requirements that can be shifted over a wider time range. These tasks include non-urgent sample pretreatment, equipment preheating, data backup processing, and cleaning and disinfection procedures. The module's internal operating cost sensitivity analysis algorithm iterates through all identified flexible load tasks, calculating the economic benefits of shifting them from high-electricity-price periods to low-electricity-price periods. Without affecting any urgent or rigid experimental tasks, the module generates a specific load shifting proposal, listing the tasks to be shifted, the suggested new execution time window, and the expected cost savings. This proposed solution is fed back to the device cluster collaborative scheduler, serving as input to trigger a new round of optimization calculations, thereby generating a more economical updated runtime schedule.

2. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, In the intelligent measurement and control unit of the equipment operating condition fine sensing module, the measurement accuracy of the current sensor does not exceed ±0.5% within the full range; the measurement accuracy of the voltage sensor reaches ±0.2%; and the power factor detection chip outputs the instantaneous power factor value of the equipment with a resolution of 0.

01.

3. The laboratory equipment operation system with adaptive energy management according to claim 1, characterized in that, The natural language processing model of the experimental task intent parsing engine calculates the semantic similarity between entities extracted from the input task and standard operating procedures in the knowledge base, and performs consistency verification in conjunction with time logic constraints, ultimately mapping unstructured text descriptions into structured experimental task execution plans.

4. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, The short-term prediction model of the multi-timescale energy consumption prediction unit downsamples and extracts features from high-frequency data, extracts the average power, power change rate and energy distribution features of a specific frequency band within the sliding window, and uses a deep long short-term memory network to learn the short-term dynamic pattern of the device power sequence.

5. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, In the multi-objective optimization problem of the device cluster collaborative scheduler, the total operating economic cost includes energy cost and demand cost. Energy cost is calculated by multiplying the predicted total power for each time segment by the electricity price for the corresponding time segment and integrating the result. Demand cost is reflected as a penalty term for peak power.

6. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, The dynamic electricity price response control module identifies elastic load tasks including non-urgent sample pretreatment, equipment preheating, data backup processing, and cleaning and disinfection procedures; the cost sensitivity analysis algorithm traverses all identified elastic load tasks and calculates the economic benefits of moving each task from high-electricity-price periods to low-electricity-price periods.

7. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, It also includes an equipment energy efficiency status assessment and early warning module, which continuously collects and stores long-term operating data from the equipment operating condition fine sensing module; the module establishes an independent energy efficiency baseline model for each device, which learns the mapping relationship between power consumption and output performance of the device in a healthy state through support vector regression or Gaussian process regression machine learning methods; the module periodically compares the real-time operating data of the device with the energy efficiency baseline model and calculates the energy efficiency deviation.

8. The adaptive energy management laboratory equipment operation system according to claim 7, characterized in that, The energy efficiency deviation of the device energy efficiency status assessment and early warning module is defined as the absolute difference between the actual power consumption and the model predicted power consumption divided by the predicted power consumption and then multiplied by 100%. When the energy efficiency deviation of a device continuously exceeds the preset 15% threshold and continues for a certain period of time, the module generates device energy efficiency degradation early warning information.

9. The adaptive energy management laboratory equipment operation system according to claim 1, characterized in that, The system operates within a hierarchical control architecture, which includes a strategic planning layer, a tactical scheduling layer, and an operation execution layer. The strategic planning layer, operating on a weekly or monthly cycle, formulates macro-level energy consumption budgets and energy efficiency targets for key experimental tasks based on long-term forecasts and overall laboratory planning. The tactical scheduling layer, operating on a daily cycle, executes the core functions of the equipment cluster collaborative scheduler, generating detailed equipment operation plans for the next 24 hours. The operation execution layer, operating on a minute or second cycle, is responsible for receiving and strictly executing control commands issued by the tactical scheduling layer.

10. The laboratory equipment operation system with adaptive energy management according to claim 9, characterized in that, The operation execution layer of the hierarchical control architecture uses a fine-grained equipment condition sensing module for real-time monitoring and closed-loop feedback to ensure the system operates stably as planned and to respond quickly to sudden anomalies.

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