A laboratory-oriented energy consumption and environmental protection data integrated supervision method and system and medium

Abstract

The application relates to a laboratory-oriented energy consumption and environmental protection data integrated supervision method and system and a medium, and relates to the technical field of energy consumption and environmental protection supervision. The energy consumption and environmental protection data integrated supervision method comprises the following steps: collecting and cleaning equipment energy consumption data and waste discharge data of each functional area of a laboratory, and constructing a global energy consumption and discharge data chain; according to historical energy consumption and discharge data, a multi-dimensional statistical analysis is performed on the global energy consumption and discharge data chain, an intelligent early warning mechanism is combined, abnormal data chains are identified and traced, and an abnormal data source is determined; the abnormal data source is optimized and adjusted according to an experiment schedule, energy saving control tasks and environmental protection control tasks are generated; the energy saving control tasks and the environmental protection control tasks are analyzed, are converted into corresponding equipment control instructions, are distributed to corresponding experimental equipment and discharge treatment equipment, and are connected with external systems; and through a collaborative control mechanism, the abnormal early warning response time is shortened, and the disposal efficiency of energy consumption waste and environmental protection over-standard is improved.

Description

Technical Field

[0001] This application relates to the field of energy consumption and environmental protection monitoring technology, and in particular to a method, system and medium for integrated monitoring of energy consumption and environmental protection data for laboratories. Background Technology

[0002] Laboratory environmental conditions are a key factor affecting the accuracy of experimental or testing results in fields such as medicine, biochemistry, instrument calibration, agriculture, construction, and electrical engineering. All experiments rely on monitoring instruments to provide accurate and reliable environmental parameters to ensure the scientific validity and repeatability of experimental data.

[0003] Temperature and humidity are core indicators for laboratory environmental control. In addition, indoor microclimate factors, such as airflow velocity and air distribution, also significantly impact the experimental environment. For most physicochemical experiments, the impact of temperature and humidity on experimental results is limited. However, in scenarios such as precision instrument analysis and micro-weighing, the stability of environmental conditions directly affects the reliability of data. Therefore, for functional areas such as balance rooms and precision instrument rooms, corresponding temperature and humidity control strategies should be developed based on the operational requirements of the instruments and equipment and experimental method specifications. The control range of environmental parameters for each area should be clearly defined to achieve refined and differentiated management of the laboratory environment.

[0004] Modern laboratories, due to their dense equipment (such as analytical instruments, HVAC systems, and wastewater and exhaust gas treatment devices) and continuous experimental processes, face the dual management challenges of "high total energy consumption and fragmented data" and "strict environmental regulations and decentralized data."

[0005] Existing patents disclose an adaptive energy consumption monitoring and adjustment system for smart laboratories, comprising a dedicated energy consumption data acquisition module, an intelligent data processing and analysis module, and an adaptive energy consumption adjustment module. The dedicated energy consumption data acquisition module includes a nested structure of a continuous database, a relational database, and middleware software. The middleware software is used for device adaptation, the relational database is used to correlate and match the acquired data, and the continuous database is used to store the matched data. The dedicated energy consumption data acquisition module also has built-in sensors for data acquisition and real-time transmission of data to the central processing unit via a wireless network. The intelligent data processing and analysis module optimizes and mines the data acquired in the central processing unit using big data processing and machine learning, dynamically analyzes the laboratory energy consumption data, and outputs the optimization results and dynamic analysis results in real-time after multimodal analysis.

[0006] The existing technical solutions mentioned above have the following defects: 1. Abnormal laboratory energy consumption and environmental protection exceedances mostly rely on manual periodic data verification or environmental protection department spot checks, which have a lag time of up to several days. The warning is only a simple prompt that the data exceeds the threshold, which cannot locate the source of the abnormality, resulting in delays in risk handling and may lead to environmental penalties or increased energy waste. 2. Wastewater and waste gas emission data are collected separately by environmental monitoring equipment and stored in an independent system; there is no correlation analysis between energy consumption data and emission data: it is impossible to determine whether the increase in energy consumption corresponds to the compliance of emissions, nor is it possible to indirectly improve environmental protection effects through energy consumption optimization. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the purpose of this application is to provide a method, system, and medium for integrated monitoring of energy consumption and environmental protection data in laboratories. Through a collaborative management and control mechanism, the response time for anomaly warnings is shortened from several days to minutes, and the source of anomalies can be located, thereby improving the efficiency of handling energy waste and environmental violations, and reducing the risks of environmental penalties and energy waste.

[0008] This was achieved using the following technical solutions: Firstly, this application provides an integrated monitoring method for energy consumption and environmental protection data in laboratories, including: Collect and clean equipment energy consumption data and waste emission data from various functional areas of the laboratory, and construct a global energy consumption and emission data chain. Based on multi-dimensional statistical analysis of historical energy consumption and pollution discharge data, the global energy consumption and emission data chain is analyzed, and combined with an intelligent early warning mechanism, abnormal data chains are identified and traced to determine abnormal data sources. Based on the experimental plan, optimize and adjust abnormal data sources to generate energy-saving control tasks and environmental protection control tasks. The system analyzes energy conservation and environmental protection control tasks, transforms them into corresponding equipment control instructions, distributes them to the corresponding experimental equipment and sewage treatment equipment, and interfaces with external systems.

[0009] By adopting the above technical solutions, a global data chain for laboratory energy consumption and emissions is constructed based on multi-source data fusion and statistical analysis algorithms. An intelligent early warning mechanism identifies anomalies and traces their sources. Combined with the experimental plan, energy-saving and environmental protection control tasks are dynamically generated, which are then transformed into equipment instructions and distributed for execution. At the same time, it connects with external systems, realizing the closed-loop, intelligent and collaborative management of laboratory energy and emissions, improving resource utilization, environmental compliance and operational efficiency.

[0010] This application further specifies: collecting and cleaning equipment energy consumption data and waste emission data from various functional areas of the laboratory, and constructing a global energy consumption and emission data chain, including: Energy consumption of experimental equipment in each functional area of ​​the laboratory was measured to obtain raw energy consumption data. Wastewater treatment equipment in the laboratory was tested for emissions to obtain raw emission data; Based on the data acquisition timestamp, the raw energy consumption data and raw emission data are filtered, denoised, and time-series aligned to obtain synchronous energy consumption data and synchronous emission data. Based on the functional areas, the synchronous energy consumption data and synchronous emission data are grouped to generate equipment energy consumption data and waste emission data; Based on the equipment code, the energy consumption data and waste emission data of the equipment are correlated to obtain the functional area-level data chain; Based on the laboratory layout, the functional area-level data chains are aggregated to generate a global energy consumption and emission data chain.

[0011] By adopting the above technical solution, based on filtering and noise reduction and time series alignment algorithms, the original energy consumption and emission data are calibrated into synchronous data. A partition-level data chain is formed by grouping functional areas and associating them with equipment codes. Then, the data is aggregated into a global data chain according to the laboratory layout, which improves the data synchronization, accuracy and management granularity.

[0012] This application further specifies: based on multi-dimensional statistical analysis of historical energy consumption and pollution discharge data, the overall energy consumption and emission data chain is analyzed, and combined with an intelligent early warning mechanism, abnormal data chains are identified and traced to determine abnormal data sources, including: Based on the functional zones, the global energy consumption and emission data chain is spatially aggregated to calculate the regional total energy consumption ratio, energy intensity per unit area, emission outlet compliance rate, and pollutant concentration ranking table. The energy consumption time series curve is constructed by integrating the proportion of total regional energy consumption and the energy intensity per unit area. The emission outlet compliance rate and pollutant concentration ranking table are merged to generate a time series curve of emission concentration. Statistical calculations are performed on historical energy consumption and pollution discharge data based on data dimensions to generate historical data baselines; Based on the monitoring cycle, the historical data baseline is separated by time zone to generate time-separated benchmark energy consumption curves and benchmark emission concentration curves. Based on the intelligent early warning mechanism, the time-sharing benchmark energy consumption curve and benchmark emission concentration curve are separated by time period to generate corresponding differentiated dynamic threshold ranges; The energy consumption time-series curves and emission concentration time-series curves were compared item by item with the corresponding differentiated dynamic threshold intervals. If any item is not within the corresponding differentiated dynamic threshold range, then based on the data type, it is determined to be either an energy consumption anomaly or an emission anomaly, and an anomaly event identifier is generated. If none of the combined items are within the corresponding differentiated dynamic threshold range, it is determined to be an association anomaly, and an anomaly event identifier is generated. Parse the abnormal event identifier and extract the abnormal trigger timestamp and device code; Based on the anomaly trigger timestamp, the global energy consumption and emission data chain is traced upstream and downstream to extract the abnormal data chain; Based on the device code, perform causality checks and anomaly cross-referencing on the abnormal data chain to locate the abnormal data source, including: The abnormal data chains are classified according to the equipment codes, and the abnormal change rate of equipment energy consumption and the change rate of sewage concentration are calculated. If the abnormal rate of change in equipment energy consumption exceeds the preset energy consumption limit threshold, but the rate of change in wastewater concentration is less than the preset concentration change threshold, then the abnormal data source is determined to be equipment mechanical failure or pipeline leakage. If the abnormal change rate of equipment energy consumption is greater than the preset energy consumption limit threshold, and the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be excessive process load or insufficient treatment equipment efficiency. If the abnormal change rate of equipment energy consumption is less than or equal to the preset energy consumption limit threshold, but the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be a violation of front-end experimental operation or abnormal fluctuation of raw water concentration. If at least one device in the same functional area experiences the above-mentioned anomaly, the timestamps of all the abnormal devices are aligned to determine the first abnormal data source to be triggered.

[0013] By adopting the above technical solutions, a global data chain for energy consumption and emissions is constructed through multi-dimensional data aggregation. By combining historical baseline separation and dynamic threshold comparison, energy consumption, emissions and related anomalies are intelligently identified and event identifiers are generated. Furthermore, based on cross-comparison of equipment codes and change rates, the sources of anomalies such as equipment failure, process over-limit, or operational violations are located, thereby improving the accuracy of anomaly identification, the efficiency of source tracing, and the level of intelligence in operation and maintenance decision-making.

[0014] This application is further configured to: optimize and adjust abnormal data sources according to the experimental plan, and generate energy-saving control tasks and environmental protection control tasks, including: The experimental plan is parsed to extract the start and end times of the experiment, the experimental functional areas, and the equipment codes. The start and end times of the experiment and the experimental functional areas are divided into time segments to determine the peak period, the low period, the non-working period and the standby idle period, and corresponding spatiotemporal labels are generated. The correlation between experimental equipment is determined by overlapping and matching the occurrence time window of the abnormal data source with the spatiotemporal label. If the time window coincides with the peak of the experiment, the correlation of the experimental equipment is determined to be a load-related anomaly. If the time window occurs during non-working hours or standby idle periods, the correlation of the experimental equipment is determined to be a non-load-related anomaly. Based on the relevance of the experimental equipment and its coding, corresponding equipment control notices and emission anomaly handling notices are generated, including: If the anomaly is load-related, a notification for floating adjustment of the temperature setpoint during the off-peak period will be generated for the HVAC load abnormality area during the off-peak period, and a notification for staggered pre-start or load balancing distribution will be generated for the equipment with abnormal energy efficiency ratio during the peak period. If the abnormality is not related to the load, then for abnormal energy consumption equipment during non-working periods or standby idle periods, generate an automatic shutdown notification for non-working periods or an idle timed hibernation notification. If the filter cartridge or adsorbent material of the laboratory's processing equipment becomes saturated, a consumable replacement reminder will be generated. If a violation of experimental procedures leads to an abnormal concentration of the stock solution, an experimental procedure review notice will be generated, and the corresponding laboratory personnel will be identified by combining the personnel code. Based on equipment management notices and emission anomaly handling notices, the abnormal data chain is simulated and corrected to generate energy-saving management tasks and environmental protection control tasks.

[0015] By adopting the above technical solution, spatiotemporal tags are generated based on the analysis of the experimental plan. The correlation of equipment load is determined by the overlap and matching of abnormal occurrence time with tags such as peak period and idle period. Based on this, differentiated notifications such as temperature fluctuation, off-peak pre-start, automatic shutdown, consumable replacement or operation review are generated. After simulation correction, these are transformed into energy-saving and environmental protection control tasks, realizing precise time-period matching and graded response of laboratory energy consumption and emissions, which significantly improves the pertinence and execution efficiency of control.

[0016] Secondly, this application also provides an integrated monitoring system for energy consumption and environmental protection data in laboratories, employing the following technical solution: An integrated energy consumption and environmental protection data monitoring system for laboratories, comprising a method for implementing integrated energy consumption and environmental protection data monitoring, including: The real-time data acquisition module is used to collect and clean the energy consumption data and waste emission data of equipment in various functional areas of the laboratory, and to build a global energy consumption and emission data chain. The integrated monitoring platform module is used to perform multi-dimensional statistical analysis of the global energy consumption and emission data chain based on historical energy consumption and pollution discharge data, and combined with an intelligent early warning mechanism, identify and trace abnormal data chains to determine abnormal data sources. The intelligent control module is used to optimize and adjust abnormal data sources according to the experimental plan, and generate energy-saving control tasks and environmental protection control tasks. The external collaboration module is used to parse energy-saving management and control tasks and environmental protection control tasks, convert them into corresponding equipment control instructions, distribute them to the corresponding experimental equipment and sewage treatment equipment, and interface with external systems.

[0017] By adopting the above technical solutions, a global data chain for laboratory energy consumption and emissions is constructed based on multi-source data fusion and historical statistical analysis. Through intelligent early warning, abnormal sources are accurately identified, and energy-saving and environmental protection control tasks are generated in combination with experimental plans. Finally, these tasks are transformed into equipment instructions for distribution and execution and are linked with external systems. This achieves a closed-loop intelligent control of laboratory energy and environmental management, significantly improving anomaly response efficiency, resource utilization, and environmental compliance.

[0018] Thirdly, this application also provides an electronic device, comprising: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by one or more processors, the one or more processors implement any of the methods in the above scheme.

[0019] Fourthly, this application also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to realize the integrated monitoring method for energy consumption and environmental protection data in the laboratory as described above.

[0020] In summary, the beneficial technical effects of this application are as follows: Through a collaborative management and control mechanism, the response time for abnormal early warnings has been shortened from several days to minutes, and the source of abnormalities can be located, which improves the efficiency of handling energy waste and environmental violations, and reduces the risk of environmental penalties and energy waste. Based on multi-source data fusion and statistical analysis algorithms, a global data chain for laboratory energy consumption and emissions is constructed. An intelligent early warning mechanism identifies anomalies and traces their sources. Combined with experimental plans, energy-saving and environmental protection control tasks are dynamically generated, which are then transformed into equipment instructions and distributed for execution. At the same time, it connects with external systems, realizing the closed-loop, intelligent and collaborative management of laboratory energy and emissions. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the integrated monitoring method for energy consumption and environmental protection data in this application; Figure 2 This is a flowchart illustrating step S1 in the integrated monitoring method for energy consumption and environmental protection data in this application. Figure 3 This is a schematic diagram of the regulatory deployment of each functional area of ​​the laboratory in this application; Figure 4 This is a flowchart illustrating step S2 in the integrated monitoring method for energy consumption and environmental protection data in this application. Figure 5 This is a schematic diagram of the integrated monitoring system for energy consumption and environmental protection data in this application. Detailed Implementation

[0022] The present application will be further described in detail below with reference to the accompanying drawings.

[0023] Reference Figure 1 This application discloses an integrated monitoring method for energy consumption and environmental protection data in laboratories, comprising: S1: Collect and clean equipment energy consumption data and waste emission data in each functional area of ​​the laboratory, and build a global energy consumption and emission data chain; S2: Based on multi-dimensional statistical analysis of historical energy consumption and pollution discharge data, identify and trace abnormal data chains and determine abnormal data sources by combining intelligent early warning mechanisms; S3: Optimize and adjust abnormal data sources according to the experimental plan, and generate energy-saving control tasks and environmental protection control tasks; S4: Analyze energy-saving management and environmental control tasks, convert them into corresponding equipment control instructions, distribute them to the corresponding experimental equipment and sewage treatment equipment, and interface with external systems.

[0024] The implementation principle of this embodiment is as follows: Smart meters, water meters, gas meters, and online waste gas and wastewater monitoring instruments collect energy consumption data (such as fume hoods, ultra-low temperature freezers, and pure water machines) and waste emission data (such as COD, ammonia nitrogen, and VOCs) from various functional areas such as chemical, biological, and physical laboratories at a frequency of once per minute. After protocol parsing and outlier removal, a three-dimensional global energy consumption and emission data chain covering the entire building is constructed. Subsequently, based on historical energy consumption and pollution discharge data from the past two years, time series decomposition and clustering algorithms are used to perform multi-dimensional statistical analysis on the data chain. When energy consumption suddenly increases by 15% during a certain period (such as nighttime non-experimental periods), the system triggers an intelligent early warning mechanism. By comparing the equipment operation logs and duty records from the same period, the source is traced back to the abnormal data source of the fume hood not being closed at night, and the responsible equipment and specific laboratory are identified.

[0025] Further analysis of the next day's experimental schedule revealed that a cell culture experiment requiring a clean bench was scheduled for 9:00 AM. The optimized adjustment strategy was to switch the continuously running fume hood to standby mode and delay its automatic startup until one hour before the experiment. Simultaneously, the RO water purifier's flushing cycle was shortened from twice daily to once. This generated energy-saving control tasks including "equipment standby instructions" and "flushing cycle shortening instructions," as well as environmental control tasks for the waste gas treatment facility that adjusted the activated carbon adsorption airflow in real-time based on VOC concentration. Finally, the system translated these tasks into Modbus instructions executable by the corresponding fume hood controller, water purifier PLC, and waste gas treatment system. These instructions were distributed to each device via the Modbus / TCP protocol and integrated with the building automation system and the park's energy efficiency management platform, forming a closed-loop management system of "monitoring-early warning-optimization-execution."

[0026] Reference Figure 2 Preferably, step S1 includes: S11: Perform energy consumption measurement on experimental equipment in each functional area of ​​the laboratory to obtain raw energy consumption data; S12: Conduct wastewater discharge testing on the wastewater treatment equipment in the laboratory to obtain raw discharge data; S13: Based on the data acquisition timestamp, filter and denoise the raw energy consumption data and raw emission data and align the time sequence to obtain synchronous energy consumption data and synchronous emission data; S14: Group synchronous energy consumption data and synchronous emission data according to functional areas to generate equipment energy consumption data and waste emission data; S15: Correlate equipment energy consumption data and waste emission data based on equipment codes to obtain functional area-level data chains; S16: Aggregate the functional area-level data chains according to the laboratory layout to generate a global energy consumption and emission data chain.

[0027] In this embodiment, refer to Figure 3 Metering points were determined one by one according to functional areas: smart meters were installed at the power distribution cabinet inlets of the experimental operation area, sample storage area, and equipment room; smart water meters and gas meters were installed on the main water and gas supply pipes and major branches; and dedicated meters were installed separately at the front end of key equipment such as HVAC units, wastewater treatment devices, and large analytical instruments. Each metering point was recorded with its installation location code, a list of equipment under its jurisdiction, and the type of metering medium.

[0028] COD, pH, and heavy metal concentration sensors are installed at the outlet of the wastewater treatment unit; VOC and particulate matter concentration sensors are installed at the exhaust outlet of the waste gas treatment unit; and temperature and airflow sensors are installed at the main discharge outlet of the laboratory. All sensors are corrosion-resistant and in direct contact with the measured medium.

[0029] Assign a unique device code to each energy meter and environmental sensor. Logically bind the environmental sensor to the corresponding processing device, and simultaneously associate the processing device with the smart meter on its power supply circuit, forming a three-way association of "sensor—processing device—energy meter" in the system registry.

[0030] Smart meters and environmental sensors push data to the regional edge gateway via wired or wireless means according to a set sampling period. Energy consumption data includes instantaneous power / flow, cumulative consumption value, and time distribution; environmental data includes real-time concentration values ​​and estimated emission values ​​of various pollutants.

[0031] The raw data is filtered using a moving average to eliminate instantaneous disturbances. Abnormal operating conditions are automatically marked: a sudden drop in meter reading to 0 is marked as "meter malfunction," a reading exceeding the rated value by several times is marked as "sensor drift," and a significant fluctuation in wastewater COD concentration outside the normal range is marked as "treatment anomaly." Abnormal data points are retained but carry an anomaly flag, without affecting subsequent continuity.

[0032] Meter data from different manufacturers and using different protocols are uniformly converted into a standard JSON structure. Each data entry includes: device code, data type, collected value, unit, timestamp, and anomaly flag. The standardized data is then pushed to the system server via the local area network.

[0033] The system server's master clock is used as a reference to synchronize the time of all edge gateways. Each received data item is written to the time-series database with a unified timestamp format.

[0034] Energy consumption meters typically have sampling periods ranging from seconds to minutes, while environmental sensors may have sampling periods in milliseconds. In the database module, alignment is performed with minutes as the uniform granularity: high-frequency data is averaged over time periods, while low-frequency data is filled using linear interpolation, ensuring comparability of data across dimensions at the same time coordinate.

[0035] Based on the equipment code and installation location mapping table, each data entry is labeled with its corresponding functional area. The data is stored using a two-dimensional index of "functional area - time" for easy and rapid retrieval by spatial dimension later.

[0036] Using the bound ID as the association key, the real-time power consumption data of the wastewater treatment machine is paired with the COD and pH sensor data of the wastewater outlet line by line on the time axis to generate "energy consumption-emission" data pairs for the equipment in each time window. Similarly, the power consumption of the exhaust gas adsorption tower is associated with the VOC and particulate matter data of the exhaust gas outlet.

[0037] By using correlated data pairs, the energy consumption per unit processing capacity of each processing device is calculated in real time. This indicator is stored synchronously as a derived field of the data chain.

[0038] The completed "equipment-energy consumption-emission" correlation data is written into the database in a structured form. Each record includes a timestamp, functional area code, equipment code, energy consumption type, energy consumption value, emission type, emission concentration, emission amount, and correlation data quality marker.

[0039] The energy consumption and emission data of each device are summed and aggregated according to functional area and time window to generate the total electricity consumption, total water consumption, total gas consumption and total emissions of various pollutants for each functional area.

[0040] For each total emission record, add its corresponding energy consumption data details and equipment emission details to achieve a complete data traceability path from total emissions down to specific equipment and then down to specific time periods.

[0041] A global energy consumption and emission data chain snapshot is generated at fixed intervals, including the energy consumption distribution of functional areas, emission concentration trends, equipment energy efficiency ratio rankings, and year-on-year and month-on-month changes for the current period.

[0042] Reference Figure 4 Preferably, step S2 includes: Based on the functional zones, the global energy consumption and emission data chain is spatially aggregated to calculate the regional total energy consumption ratio, energy intensity per unit area, emission outlet compliance rate, and pollutant concentration ranking table. The energy consumption time series curve is constructed by integrating the proportion of total regional energy consumption and the energy intensity per unit area. The emission outlet compliance rate and pollutant concentration ranking table are merged to generate a time-series curve of emission concentration. Statistical calculations are performed on historical energy consumption and pollution discharge data based on data dimensions to generate historical data baselines; Based on the monitoring cycle, the historical data baseline is separated by time zone to generate time-separated benchmark energy consumption curves and benchmark emission concentration curves. Based on the intelligent early warning mechanism, the time-sharing benchmark energy consumption curve and benchmark emission concentration curve are separated by time period to generate corresponding differentiated dynamic threshold ranges; The energy consumption time-series curves and emission concentration time-series curves were compared item by item with the corresponding differentiated dynamic threshold intervals. If any item is not within the corresponding differentiated dynamic threshold range, then based on the data type, it is determined to be either an energy consumption anomaly or an emission anomaly, and an anomaly event identifier is generated. If none of the combined items are within the corresponding differentiated dynamic threshold range, it is determined to be an association anomaly, and an anomaly event identifier is generated. Parse the abnormal event identifier and extract the abnormal trigger timestamp and device code; Based on the anomaly trigger timestamp, the global energy consumption and emission data chain is traced upstream and downstream to extract the abnormal data chain; Based on the device code, perform causality checks and anomaly cross-referencing on the abnormal data chain to locate the abnormal data source, including: The abnormal data chains are classified according to the equipment codes, and the abnormal change rate of equipment energy consumption and the change rate of sewage concentration are calculated. If the abnormal rate of change in equipment energy consumption exceeds the preset energy consumption limit threshold, but the rate of change in wastewater concentration is less than the preset concentration change threshold, then the abnormal data source is determined to be equipment mechanical failure or pipeline leakage. If the abnormal change rate of equipment energy consumption is greater than the preset energy consumption limit threshold, and the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be excessive process load or insufficient treatment equipment efficiency. If the abnormal change rate of equipment energy consumption is less than or equal to the preset energy consumption limit threshold, but the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be a violation of front-end experimental operation or abnormal fluctuation of raw water concentration. If at least one device in the same functional area experiences the above-mentioned anomaly, the timestamps of all the abnormal devices are aligned to determine the first abnormal data source to be triggered.

[0043] In this embodiment, energy consumption data chains and emission data chains from a continuous period of the past are retrieved, and statistical characteristics of each monitoring indicator are calculated according to functional area, equipment type, and time period, including mean, variance, peak and valley values, and periodic fluctuation patterns.

[0044] For each functional area and key equipment, time-of-use baseline energy consumption curves for weekdays and non-weekdays, as well as baseline emission concentration curves for each emission outlet, are generated. These curves reflect the expected data range under normal experimental activities.

[0045] Establish a normal correlation model between energy consumption and corresponding emissions in functional areas, and record the typical range of energy efficiency ratios of each treatment device under different experimental loads.

[0046] By spatially aggregating equipment energy consumption and environmental sensor data by region, the total energy consumption ratio, energy intensity per unit area, emission compliance rate, and major pollutant concentration ranking of each region are calculated to identify hotspots with high energy consumption or high emissions.

[0047] For each key piece of equipment, plot its energy consumption time-series curve and the corresponding emission concentration overlay, and analyze the synchronicity and correlation between the two. Equipment whose energy efficiency ratio deviates from the normal range is marked as a key focus.

[0048] By comparing and statistically analyzing the total energy consumption and emission compliance rate during peak and off-peak periods, as well as during working and non-working periods, the differences in environmental protection efficiency under different loads are revealed.

[0049] Users can configure differentiated warning thresholds in the warning rule engine based on the importance of functional areas and devices. For energy consumption, users can set conditions such as "instantaneous power surge exceeding 20% ​​of the historical average for the same period," while for environmental protection, users can set composite conditions such as "COD concentration exceeding emission permit limits" or "VOC concentration increasing continuously over multiple periods."

[0050] For indicators significantly affected by experimental activity patterns, the system automatically generates dynamic threshold bands for different time periods based on historical statistical baselines. Thresholds are appropriately relaxed during weekdays to reduce false alarms, while thresholds are tightened during off-peak hours to more sensitively detect equipment anomalies under unattended conditions.

[0051] The configured early warning rules are mounted to the real-time data stream processing engine to achieve millisecond-level conditional judgments on each piece of the latest data.

[0052] The data stream processing engine scans each standardized energy consumption and emission data point against specific rules. If a value exceeds a threshold, or if a combination of multiple values ​​meets a compound rule, an anomaly event identifier is immediately generated.

[0053] Based on the triggering rule type, abnormal events are categorized into "energy consumption anomalies," "emission anomalies," or "related anomalies." Emission anomalies are further subdivided by pollutant type, and energy consumption anomalies are subdivided by energy medium.

[0054] When an abnormal event is generated, context information is encapsulated synchronously, including the trigger time, abnormal data value, threshold boundary, functional area, associated device code, and current experimental load status, forming a structured early warning information package.

[0055] Abnormal energy consumption triggers a yellow alert, which notifies equipment managers via system pop-ups and push notifications; exceeding environmental standards triggers a red alert, which is simultaneously pushed to the safety and environmental protection manager and highlighted on the platform interface.

[0056] The audible and visual alarms in the corresponding functional areas will automatically activate according to the warning level. Areas with abnormal energy consumption will light up with a yellow light, and areas with excessive environmental protection standards will light up with a red light, intuitively guiding on-site personnel to pay attention to abnormal locations.

[0057] Upon triggering the warning, the platform automatically retrieves a snapshot of the complete data chain related to the period before and after the abnormal period in the background, including the energy consumption history of the abnormal equipment, the corresponding emission data, and the operating status of other equipment in the same area, to prepare data for subsequent source tracing analysis.

[0058] Starting with the timestamp and device code of the abnormal event, trace upstream along the data chain to the power supply, water supply, and gas supply energy consumption input data of the device, and trace downstream to its corresponding wastewater and exhaust gas emission output data, extracting the complete data chain segment within the event window.

[0059] Causal relationship elimination analysis: Examine possible causes of anomalies one by one. If the energy consumption of the equipment increases abnormally but the emission concentration is normal, it is likely due to mechanical failure of the equipment or pipeline leakage; if the energy consumption and emission increase abnormally at the same time, it is likely due to excessive process load or decreased efficiency of the treatment device; if only the emission exceeds the standard while the energy consumption is normal, it may be due to violation of front-end experimental operation or abnormal fluctuation of raw water concentration.

[0060] Multi-point anomaly cross-comparison: If multiple devices in the same area trigger anomalies at the same time, the system determines the anomaly propagation path by aligning timestamps: the device that first experiences an anomaly is marked as the possible source of the accident, and the rest are determined to be affected devices.

[0061] The system visually marks the most likely root cause of the anomaly identified through source tracing analysis on a data map, while also displaying the related chain of evidence. After confirmation by management, the device or area is officially marked as the "root source data source" for this anomaly.

[0062] Preferably, step S3 includes: The experimental plan is parsed to extract the start and end times of the experiment, the experimental functional areas, and the equipment codes. The start and end times of the experiment and the experimental functional areas are divided into time segments to determine the peak period, the low period, the non-working period and the standby idle period, and corresponding spatiotemporal labels are generated. The correlation between experimental equipment is determined by overlapping and matching the occurrence time window of the abnormal data source with the spatiotemporal label. If the time window coincides with the peak of the experiment, the correlation of the experimental equipment is determined to be a load-related anomaly. If the time window occurs during non-working hours or standby idle periods, the correlation of the experimental equipment is determined to be a non-load-related anomaly. Based on the relevance of the experimental equipment and its coding, corresponding equipment control notices and emission anomaly handling notices are generated, including: If the anomaly is load-related, a notification for floating adjustment of the temperature setpoint during the off-peak period will be generated for the HVAC load abnormality area during the off-peak period, and a notification for staggered pre-start or load balancing distribution will be generated for the equipment with abnormal energy efficiency ratio during the peak period. If the abnormality is not related to the load, then for abnormal energy consumption equipment during non-working periods or standby idle periods, generate an automatic shutdown notification for non-working periods or an idle timed hibernation notification. If the filter cartridge or adsorbent material of the laboratory's processing equipment becomes saturated, a consumable replacement reminder will be generated. If a violation of experimental procedures leads to an abnormal concentration of the stock solution, an experimental procedure review notice will be generated, and the corresponding laboratory personnel will be identified by combining the personnel code. Based on equipment management notices and emission anomaly handling notices, the abnormal data chain is simulated and corrected to generate energy-saving management tasks and environmental protection control tasks.

[0063] In this embodiment, users enter or import experimental schedules for a future period through the platform interface, including the experiment name, start and end times, involved functional areas, expected participants, and a list of key equipment. The system automatically parses the time span and spatial scope of each plan.

[0064] According to the experimental plan, each time period and functional area will be assigned a status label: peak experimental period, off-peak experimental period, non-working period, and standby idle period. These labels will serve as the activation conditions for energy consumption optimization strategies and the basis for segmenting environmental protection reports.

[0065] If the experimental plan is temporarily adjusted, the system will update the spatiotemporal label mapping table in real time to ensure that the subsequent generated control tasks and report divisions are always consistent with the latest plan.

[0066] Extract the time window of the abnormal event and perform overlap analysis with the spatiotemporal labels of the experiment plan. If the abnormality occurs during the peak period of the experiment, it is judged as "load-related abnormality"; if it occurs during non-working periods or idle periods, it is judged as "non-load-related abnormality". The latter often points to hidden faults when the equipment is not in operation or when it is unattended.

[0067] If the abnormal equipment belongs to the list of critical equipment in a certain experimental plan, the system will automatically associate the experimental number and the person in charge information to determine the responsible person for subsequent task assignments.

[0068] A correlation determination conclusion is generated for each abnormal data source to be processed, clarifying the relationship between the abnormality and the experimental plan, and guiding the selection of subsequent optimization strategies.

[0069] For equipment marked as having abnormally high energy consumption during non-working hours, generate a "automatic shutdown during non-working hours" or "idle timed hibernation" scheme; for areas with abnormally high HVAC loads during off-peak periods, generate a "floating adjustment of temperature setpoint during off-peak periods" scheme; for processing equipment with low energy efficiency ratios during peak periods, generate a "peak-shifting pre-start or load balancing distribution" scheme.

[0070] For environmental violations, if the cause is saturation of the filter element or adsorption material of the treatment equipment, a "Consumable Replacement Reminder" plan will be generated; if the cause is excessive concentration of the original solution due to violation of experimental operation, a "Review of Experimental Operation Specifications" plan will be generated and the responsible personnel will be associated.

[0071] Each plan includes specific quantitative control parameters, such as the adjustment range of the temperature setpoint, the equipment shutdown period, the recommended filter material type to be replaced, and the estimated percentage of energy saving or emission reduction effect.

[0072] Each optimization plan is encapsulated as a structured energy-saving management task, including task number, associated equipment or area, description of execution action, execution time window, expected effect indicators, and task priority.

[0073] Tasks are pushed to the administrator's interface as "To-Do Tasks". Administrators can review each task individually and approve its execution after confirmation. One-click batch review of regular tasks is supported, while tasks related to exceptions require individual confirmation.

[0074] Once approved, administrators can choose to manually trigger the process or have the system automatically issue execution commands to the corresponding device controllers within a specified time window to complete operations such as device shutdown and parameter adjustment. A countdown reminder is sent before automatic execution to avoid affecting ongoing experiments.

[0075] After the task is executed, the system continuously tracks the changes in energy consumption and emissions data of the equipment or area, generates an "optimization effect report", compares the differences in indicators before and after execution, and quantifies the energy saving or emission reduction results.

[0076] Report generation tasks are automatically triggered according to preset cycles such as daily, weekly, monthly, and quarterly. The time and space labels in the experiment plan table are used to segment and statistically analyze emission data for peak and off-peak periods in the report.

[0077] The system calls up the built-in environmental protection report template, extracts the raw data and statistical values ​​within the corresponding time window from the energy consumption and emission data chain, automatically calculates the instantaneous concentration, average concentration, compliance rate, total emissions and other indicators of each emission outlet, and fills them into the corresponding fields of the report.

[0078] If there are triggered environmental exceedance events within the report's coverage period, the system will automatically highlight the exceedance data rows in the report and insert an exceedance event summary, including the exceedance time, maximum concentration value, duration, and the operating status of related treatment equipment, to avoid using aggregated data to cover up the instantaneous exceedance facts.

[0079] For events marked as exceeding the standard in the report, the system automatically generates a corresponding "Exceedance Analysis Report", which links the detailed data chain of the period exceeding the standard, the conclusion of the anomaly tracing, the handling measures taken at that time, and the effect verification results, forming a complete event analysis closed loop.

[0080] Reference Figure 5 An integrated energy consumption and environmental protection data monitoring system for laboratories, applicable to integrated energy consumption and environmental protection data monitoring methods, including: The real-time data acquisition module is used to collect and clean the energy consumption data and waste emission data of equipment in various functional areas of the laboratory, and to build a global energy consumption and emission data chain. In this embodiment, the real-time data acquisition module serves as the system's "data entry point," enabling comprehensive and real-time collection of energy consumption and environmental protection data to ensure data accuracy and continuity. Energy consumption data acquisition unit: Deploy smart metering equipment: Install smart electricity meters, water meters, and gas meters (supporting wired or wireless communication) at the power supply / water supply / gas supply lines in various areas of the laboratory (such as experimental operation area, sample storage area, equipment room) and key equipment (HVAC unit, wastewater treatment device, large analytical instrument); the meters have real-time data transmission function and can collect data such as instantaneous energy consumption, cumulative energy consumption, and energy consumption time period distribution; Data transmission and preprocessing unit: Smart meters transmit data to the system through a gateway, completing "data noise reduction (eliminating meter reading errors), format unification (e.g., converting data streams from different meters into standard JSON format), and outlier marking (e.g., marking a sudden drop in meter reading to 0 as "meter failure")" to ensure data availability; Environmental data acquisition unit: Deployment of environmental sensors: Corresponding environmental sensors are deployed at the outlet of the wastewater treatment device (collecting COD, pH, and heavy metal concentration), the outlet of the exhaust gas treatment device (collecting VOC concentration and particulate matter concentration), and the laboratory emission outlet (collecting exhaust temperature and air volume); the sensors are waterproof and corrosion-resistant, directly contact the medium to be detected, and output detection data in real time. By binding IDs to environmental sensors and corresponding treatment equipment (such as "wastewater sensor - wastewater treatment machine" and "waste gas sensor - waste gas adsorption tower"), environmental data can be traced back to specific treatment equipment. At the same time, the energy consumption data of the equipment (such as the power consumption of the wastewater treatment machine) is linked to form a "equipment-energy consumption-emission" chain.

[0081] The integrated monitoring platform module is used to perform multi-dimensional statistical analysis of the global energy consumption and emission data chain based on historical energy consumption and pollution discharge data, and combined with an intelligent early warning mechanism, identify and trace abnormal data chains to determine abnormal data sources. In this embodiment, the integrated monitoring platform module constructs a centralized monitoring platform to achieve unified display, analysis, and early warning of energy consumption and environmental protection data, serving as the "central brain" of the system. Centralized Data Display Unit: Visual Dashboard: The platform homepage displays core data graphically: energy consumption dimension (total energy consumption, energy consumption ratio of each region, energy consumption ranking of major equipment) and environmental protection dimension (compliance rate of each emission outlet, real-time emission parameters, statistics of the number of times exceeding standards); it supports filtering data by "region (e.g., sample processing area, instrument area), equipment type (e.g., HVAC, laboratory instruments), time period (day / week / month)", and the data update frequency is synchronized with the collection frequency (e.g., energy consumption data is updated every 5 minutes, and environmental protection data is updated every 1 minute); Data Map: Based on the physical space map of the laboratory, the installation locations of various meters and environmental sensors are marked. Clicking on the location icon allows you to view the real-time energy consumption and emission data of the corresponding equipment, realizing an intuitive mapping between "physical location and data". Multi-dimensional statistical analysis unit: Supports statistical analysis of energy consumption distribution by "region" (e.g., "Energy consumption of instrument area accounts for 40%, and lighting accounts for 10%), statistical analysis of energy consumption trend of single equipment by "equipment" (e.g., "Energy consumption fluctuation curve of HVAC unit in the past 30 days"), and statistical analysis of peak and valley energy consumption by "time period" (e.g., "Energy consumption during working hours is 3 times that during non-working hours"). Compliance rates can be calculated by "discharge outlet" (e.g., "98% compliance rate for wastewater discharge outlets this month"), emission concentration trends can be calculated by "pollutant type" (e.g., "VOC concentration has remained stable within the safe threshold for the past week"), and the correlation between "energy consumption and emissions" can be calculated by "treatment equipment" (e.g., "for every 10% increase in the energy consumption of the wastewater treatment machine, the COD removal rate increases by 5%). It provides "energy consumption-environmental protection" linkage analysis functions, such as "whether the corresponding exhaust gas emissions exceed the standard when energy consumption in a certain area suddenly increases" and "comparison of emission compliance rates between peak and off-peak periods", helping to discover hidden management problems; Intelligent anomaly warning unit: Users can preset warning thresholds (such as "energy consumption surge exceeding 20% ​​triggers warning" or "VOC concentration exceeding threshold triggers warning"), and support setting differentiated thresholds by "region, device, and time period" (such as "relaxed energy consumption warning threshold during working hours and tightened during non-working hours"). When data exceeds the threshold, the system immediately triggers an early warning: ① On-site warning: The corresponding area's audible and visual alarms are activated (e.g., a yellow light illuminates in areas with abnormal energy consumption, and a red light illuminates in areas exceeding environmental standards); ② Remote warning: Warning information is pushed to management personnel via system pop-ups and SMS messages, including "abnormal type (energy consumption / environmental protection), abnormal location, abnormal data, and possible cause (e.g., "a sudden increase in energy consumption may be due to equipment failure or pipeline leakage")"; ③ Warning source tracing: The platform automatically retrieves relevant data from the abnormal period (e.g., historical energy consumption and corresponding emission data of abnormal equipment) to assist management personnel in quickly locating the cause; All early warning information (trigger time, person in charge, measures taken, and results) is automatically stored to form an early warning and handling ledger, which supports later query and auditing to ensure that risk handling is traceable.

[0082] The intelligent control module is used to optimize and adjust abnormal data sources according to the experimental plan, and generate energy-saving control tasks and environmental protection control tasks. In this embodiment, the intelligent control function module, based on data from the monitoring platform, integrates two core function modules: energy consumption optimization and environmental protection reports, to realize the implementation of "data-decision-action". Energy Consumption Optimization Unit: Based on historical energy consumption data (such as peak and valley energy consumption over the past 3 months and equipment energy consumption patterns) and experimental plans (experimental schedules entered by users in advance, such as "peak experimental period from 9:00 to 17:00 every Monday to Friday"), personalized energy-saving solutions are generated through algorithms: ① Equipment Management: Automatically shut down unnecessary equipment (such as idle refrigerators and backup lighting) during non-working hours, and reduce HVAC load during off-peak experimental periods (such as raising / lowering the temperature setpoint by 2℃); ② Area Management: Automatically cut off some power to low-usage areas (such as backup experimental areas); Energy-saving plans are pushed to managers as "tasks to be done". After approval, execution instructions can be manually or automatically issued to the corresponding equipment (such as remotely turning off the power of non-essential equipment or adjusting HVAC parameters through the platform). After the plan is implemented, the module tracks energy consumption changes and generates an "optimization effect report" (such as "After implementing the plan, energy consumption during non-working hours is reduced by 25%"). Environmental Reporting Unit: Built-in report templates required by environmental protection departments (such as "Monthly Report on Laboratory Wastewater Discharge" and "Quarterly Report on Waste Gas Discharge"). It automatically fills in the report content based on real-time collected environmental data and calculates statistical indicators such as compliance rate and average value, without the need for manual data entry. The reports can be exported to compliant formats such as PDF and Excel, and include data source and verification mark (to ensure that the data has not been tampered with); the reports can be automatically uploaded to the environmental supervision platform (if connected) or stored in the system database to meet the audit and filing needs of environmental protection departments; If there is data exceeding the standard in the report, the module will automatically associate detailed data for the period of exceeding the standard (such as the time of exceeding the standard, the concentration of exceeding the standard, and the operating status of the corresponding treatment equipment) and generate an "exceeding standard analysis report" to help the laboratory investigate the cause (such as "exceeding the standard because the filter element of the exhaust gas treatment machine is saturated and needs to be replaced").

[0083] The external collaboration module is used to parse energy-saving management and environmental protection control tasks, convert them into corresponding equipment control instructions, distribute them to the corresponding experimental equipment and sewage treatment equipment, and interface with external systems. In this embodiment, the external collaboration interface module supports interface with external systems to achieve data sharing and collaborative management, thereby expanding the application boundaries of the system. By connecting with the laboratory's existing management platform (such as LIMS system and equipment management system) through API interface, experimental plans and equipment usage status data are obtained, providing more accurate decision-making basis for the energy consumption optimization module (such as "According to the experimental plan, there is a large-scale experiment tomorrow, so the HVAC load should be increased in advance"); at the same time, energy consumption and environmental protection data are pushed to the management platform to realize the whole chain management of "experimental process-equipment-energy consumption-environmental protection"; If the laboratory is a department under the enterprise, the system can upload energy consumption data to the enterprise-level energy management system through industrial bus or cloud platform interface, and incorporate it into the overall energy management system of the enterprise to achieve energy consumption collaborative optimization between the enterprise and the laboratory (such as "when the enterprise has peak electricity consumption, the laboratory reduces the energy consumption of non-essential equipment"). Data interfaces can be opened as needed to upload laboratory emission data to the local environmental protection supervision system in real time, meeting the compliance requirements of "online monitoring and real-time supervision" and avoiding the cumbersome and delayed manual reporting.

[0084] Thirdly, this application provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to realize the integrated monitoring method for energy consumption and environmental protection data as described above.

[0085] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for integrated monitoring of energy consumption and environmental protection data in laboratories, characterized in that, include: Collect and clean equipment energy consumption data and waste emission data from various functional areas of the laboratory, and construct a global energy consumption and emission data chain. Based on multi-dimensional statistical analysis of historical energy consumption and pollution discharge data, the global energy consumption and emission data chain is analyzed, and combined with an intelligent early warning mechanism, abnormal data chains are identified and traced to determine abnormal data sources. Based on the experimental plan, optimize and adjust the abnormal data source to generate energy-saving management and control tasks and environmental protection control tasks.

2. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 1, characterized in that, The collection and cleaning of equipment energy consumption data and waste emission data from various functional areas of the laboratory will construct a global energy consumption and emission data chain, including: Energy consumption of experimental equipment in each functional area of ​​the laboratory was measured to obtain raw energy consumption data. Wastewater treatment equipment in the laboratory was tested for emissions to obtain raw emission data; Based on the data acquisition timestamp, the original energy consumption data and the original emission data are filtered, denoised, and time-aligned to obtain synchronized energy consumption data and synchronized emission data. The synchronous energy consumption data and synchronous emission data are grouped according to the functional areas to generate equipment energy consumption data and waste emission data. Based on the equipment code, the energy consumption data and waste emission data of the equipment are correlated to obtain the functional area-level data chain; Based on the laboratory layout, the functional area-level data chains are aggregated to generate a global energy consumption and emission data chain.

3. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 1, characterized in that, The method involves multi-dimensional statistical analysis of historical energy consumption and pollution discharge data to identify and trace abnormal data chains, and determine abnormal data sources, including: Based on the functional zones, the global energy consumption and emission data chain is spatially aggregated to calculate the regional total energy consumption ratio, energy intensity per unit area, emission outlet compliance rate, and pollutant concentration ranking table. The energy consumption time series curve is constructed by integrating the proportion of total regional energy consumption and the energy intensity per unit area. The emission outlet compliance rate and pollutant concentration ranking table are merged to generate a time series curve of emission concentration. Statistical calculations are performed on historical energy consumption and pollution discharge data based on data dimensions to generate historical data baselines; Based on the monitoring cycle, the historical data baseline is separated by time zone to generate time-separated benchmark energy consumption curves and benchmark emission concentration curves. Based on the intelligent early warning mechanism, the time-sharing benchmark energy consumption curve and benchmark emission concentration curve are separated by time period to generate corresponding differentiated dynamic threshold ranges; The energy consumption time-series curves and emission concentration time-series curves were compared item by item with the corresponding differentiated dynamic threshold intervals. If any item is not within the corresponding differentiated dynamic threshold range, then based on the data type, it is determined to be either an energy consumption anomaly or an emission anomaly, and an anomaly event identifier is generated. If none of the combined items are within the corresponding differentiated dynamic threshold range, it is determined to be an association anomaly, and an anomaly event identifier is generated. The abnormal event identifier is parsed to extract the abnormal trigger timestamp and device code; Based on the anomaly trigger timestamp, the global energy consumption and emission data chain is traced upstream and downstream to extract the abnormal data chain; Based on the device code, perform causal checks and cross-referencing on the abnormal data chain to locate the abnormal data source.

4. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 3, characterized in that, The step of performing causality checks and anomaly cross-comparisons on the abnormal data chain based on the device code to locate the abnormal data source includes: The abnormal data chains are classified according to the equipment codes, and the abnormal change rate of equipment energy consumption and the change rate of sewage concentration are calculated. If the abnormal rate of change in equipment energy consumption exceeds the preset energy consumption limit threshold, but the rate of change in wastewater concentration is less than the preset concentration change threshold, then the abnormal data source is determined to be equipment mechanical failure or pipeline leakage. If the abnormal change rate of equipment energy consumption is greater than the preset energy consumption limit threshold, and the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be excessive process load or insufficient treatment equipment efficiency. If the abnormal change rate of equipment energy consumption is less than or equal to the preset energy consumption limit threshold, but the change rate of sewage concentration is greater than or equal to the preset concentration change threshold, then the abnormal data source is determined to be a violation of front-end experimental operation or abnormal fluctuation of raw water concentration. If at least one device in the same functional area experiences the above-mentioned anomaly, the timestamps of all the abnormal devices are aligned to determine the first abnormal data source to be triggered.

5. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 1, characterized in that, The step of optimizing and adjusting the abnormal data source according to the experimental plan to generate energy-saving management tasks and environmental protection control tasks includes: The experimental plan is parsed to extract the start and end times of the experiment, the experimental functional areas, and the equipment codes. The start and end times of the experiment and the experimental functional areas are divided into time segments to determine the peak period, the low period, the non-working period and the standby idle period, and corresponding spatiotemporal labels are generated. The correlation between experimental equipment is determined by overlapping and matching the occurrence time window of the abnormal data source with the spatiotemporal label. If the time window coincides with the peak of the experiment, the correlation of the experimental equipment is determined to be a load-related anomaly. If the time window occurs during non-working hours or standby idle periods, the correlation of the experimental equipment is determined to be a non-load-related anomaly. Based on the relevance of the experimental equipment and the equipment code, generate corresponding equipment control notices and emission anomaly handling notices. Based on equipment management notices and emission anomaly handling notices, the abnormal data chain is simulated and corrected to generate energy-saving management tasks and environmental protection control tasks.

6. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 5, characterized in that, The process of generating corresponding equipment control notifications and emission anomaly handling notifications based on the relevance of experimental equipment and equipment codes includes: If the anomaly is load-related, a notification for floating adjustment of the temperature setpoint during the off-peak period will be generated for the HVAC load abnormality area during the off-peak period, and a notification for staggered pre-start or load balancing distribution will be generated for the equipment with abnormal energy efficiency ratio during the peak period. If the abnormality is not related to the load, then for abnormal energy consumption equipment during non-working periods or standby idle periods, generate an automatic shutdown notification for non-working periods or an idle timed hibernation notification. If the filter cartridge or adsorbent material of the laboratory's processing equipment becomes saturated, a consumable replacement reminder will be generated. If a violation of experimental procedures leads to an abnormal concentration of the stock solution, an experimental procedure review notice will be generated, and the corresponding laboratory personnel will be identified by their personnel codes.

7. The integrated monitoring method for energy consumption and environmental protection data in laboratories according to claim 1, characterized in that, The integrated monitoring method for energy consumption and environmental protection data also includes: The system analyzes energy conservation and environmental protection control tasks, transforms them into corresponding equipment control instructions, distributes them to the corresponding experimental equipment and sewage treatment equipment, and interfaces with external systems.

8. An integrated energy consumption and environmental protection data monitoring system for laboratories, used to implement the integrated energy consumption and environmental protection data monitoring method as described in any one of claims 1-7, characterized in that, include: The real-time data acquisition module is used to collect and clean the energy consumption data and waste emission data of equipment in various functional areas of the laboratory, and to build a global energy consumption and emission data chain. The integrated monitoring platform module is used to perform multi-dimensional statistical analysis of the global energy consumption and emission data chain based on historical energy consumption and pollution discharge data, and combined with an intelligent early warning mechanism, identify and trace abnormal data chains to determine abnormal data sources. The intelligent control module is used to optimize and adjust the abnormal data source according to the experimental plan, and generate energy-saving control tasks and environmental protection control tasks. The external collaboration module is used to parse energy-saving management and control tasks and environmental protection control tasks, convert them into corresponding equipment control instructions, distribute them to the corresponding experimental equipment and sewage treatment equipment, and interface with external systems.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the integrated monitoring method for energy consumption and environmental protection data as described in any one of claims 1 to 7.

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