Warehouse energy consumption monitoring and scheduling method, device, system and computer equipment

Abstract

This application relates to a method, apparatus, system, and computer equipment for monitoring and scheduling energy consumption in warehousing. The method includes: acquiring current data and dynamic events from different target monitoring points to obtain multiple monitoring data; identifying feature information in the current data and dynamic events to extract corresponding monitoring feature data from each monitoring data; calculating the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located based on the monitoring feature data, obtaining multiple first carbon emission monitoring values ​​corresponding to each target monitoring point; and solving the execution order of the pending operation sequence issued by the warehousing management system based on the first carbon emission monitoring values ​​to obtain the target scheduling sequence. This achieves refined monitoring of hidden energy consumption and real-time scheduling optimization based on carbon emissions, improving the accuracy and real-time performance of carbon emission monitoring.

Description

Technical Field

[0001] This application relates to the field of green logistics and intelligent warehousing technology, and in particular to a method, device, system and computer equipment for monitoring and scheduling warehousing energy consumption. Background Technology

[0002] With the development of green energy technologies and the growing awareness of energy conservation and emission reduction, logistics warehousing, as a major energy consumer, urgently needs to establish refined carbon management capabilities to achieve energy conservation and emission reduction in the field of smart warehousing.

[0003] In traditional technologies, energy consumption monitoring in the field of smart warehousing mainly relies on monitoring devices such as electricity meters to periodically sample data from the overall logistics and warehousing system, and then calculates the energy consumption of the overall system by analyzing the collected current data.

[0004] However, relying on monitoring devices such as electricity meters for periodic data sampling results in insufficient data sampling rates, making it impossible to capture millisecond-level surge currents. Furthermore, the method of monitoring the energy consumption of the entire logistics and warehousing system solely through current data ignores the hidden carbon costs during the operation of the logistics and warehousing system, only counting the explicit energy consumption directly measured by the monitoring devices, resulting in low accuracy in carbon monitoring of logistics and warehousing. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, device, system, and computer equipment for monitoring and scheduling warehouse energy consumption that takes into account the hidden carbon costs, in order to address the above-mentioned technical problems and improve the accuracy of carbon monitoring in logistics warehousing.

[0006] Firstly, this application provides a method for monitoring and scheduling energy consumption in warehousing. This method is applied to a warehouse management system, which deploys multiple target monitoring points. The warehouse management system includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensor terminal corresponding to each target monitoring point. The method includes:

[0007] Multiple monitoring data are obtained by acquiring current data and dynamic events from different target monitoring points through energy consumption sensing terminals corresponding to multiple target monitoring points;

[0008] The carbon model calculation terminal identifies feature information in current data and dynamic events to extract the corresponding monitoring feature data from each monitoring data.

[0009] Based on the energy consumption calculation model deployed in the carbon model calculation terminal, the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located is calculated according to the monitoring characteristic data, and multiple first carbon emission monitoring values ​​corresponding to each target monitoring point are obtained.

[0010] The task scheduling terminal calculates the execution order of the pending operation sequence issued by the warehouse management system based on the first carbon emission monitoring value to obtain the target scheduling sequence. The target scheduling sequence is used to schedule the execution equipment corresponding to the pending operations in the warehouse management system.

[0011] In one embodiment, the step of acquiring current data and dynamic events from different target monitoring points through energy consumption sensing terminals corresponding to multiple target monitoring points to obtain multiple monitoring data includes:

[0012] By using a Hall current sensor installed at the target monitoring point, the instantaneous change characteristics of the operating current of the execution device corresponding to the target monitoring point are collected at a preset period, and the first event tag corresponding to the operating current of the execution device during operation is recorded.

[0013] The start-stop characteristics of the target monitoring point are detected by a magnetic induction switch installed at the target monitoring point, and the second event tag corresponding to the start-stop characteristics of the executing device is recorded.

[0014] The instantaneous change characteristics, start-stop characteristics, first event label and second event label of the execution device are encapsulated into a standard data frame with timestamp and device identifier according to a preset protocol to obtain the monitoring data corresponding to the execution device of the target monitoring point.

[0015] In one embodiment, the monitoring feature data includes the number of start-stop times, load change rate, and temperature gradient of the execution device corresponding to the target monitoring point, and the energy consumption calculation model includes a calculation model corresponding to the number of start-stop times, load change rate, and temperature gradient.

[0016] Based on the energy consumption calculation model deployed in the carbon model calculation terminal, and according to the monitoring characteristic data, the steps to calculate the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located, and to obtain multiple first carbon emission monitoring values ​​corresponding to each target monitoring point, include:

[0017] Input the start-stop frequency, load change rate, and temperature gradient into the corresponding energy consumption calculation model, and calculate the estimated carbon emission values ​​for the target monitoring points.

[0018] The system obtains the real-time carbon emission rate of the power grid and calculates the first carbon emission monitoring value of the target monitoring point based on the estimated carbon emission value and the carbon emission rate.

[0019] In one embodiment, the step of obtaining the target scheduling sequence by solving the execution order of the pending job sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal includes:

[0020] Based on the task scheduler, determine the execution devices corresponding to multiple jobs in the job sequence to be executed;

[0021] An execution matrix is ​​constructed based on the first carbon emission monitoring value corresponding to the target monitoring point of the execution equipment; wherein, the execution matrix is ​​used to represent the initial execution order of multiple tasks to be executed under the initial decision;

[0022] The execution of multiple jobs in the execution matrix is ​​simulated, the operating parameters of each job are calculated, and the target scheduling sequence for each job to reach the target state is determined. The operating parameters include time, cost and carbon emission parameters, and the target scheduling sequence is used to represent the execution order of multiple jobs to reach the target equilibrium state.

[0023] In one embodiment, the task scheduling terminal connects to the grid green electricity ratio data interface in real time. After the task scheduling terminal calculates the execution order of the pending job sequence issued by the warehouse management system based on the first carbon emission monitoring value to obtain the target scheduling sequence, the method further includes:

[0024] The current green electricity ratio of the power grid is detected through the power grid green electricity ratio data interface;

[0025] If the green electricity ratio is greater than or equal to the first green electricity threshold, non-urgent tasks in the target scheduling sequence are identified, and the execution time of the non-urgent tasks is adjusted to be within the first target time window to obtain the adjusted target scheduling sequence; wherein, the first target time window is the time window in which the green electricity ratio is greater than or equal to the first green electricity threshold.

[0026] If the green electricity ratio is less than the second green electricity threshold, non-urgent tasks in the target scheduling sequence are identified, and the execution time of the non-urgent tasks is adjusted to be within the second target time window to obtain the adjusted target scheduling sequence; wherein, the second green electricity threshold is less than the first green electricity threshold, and the second target time window is the time window after the green electricity ratio is less than the second green electricity threshold;

[0027] Based on the adjusted target scheduling sequence, generate scheduling instructions for each execution device;

[0028] The scheduling instructions corresponding to the execution device are sent to the corresponding execution device so that the corresponding job can be executed at the time required by each execution device.

[0029] In one embodiment, after the step of calculating the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located based on the monitoring feature data, and obtaining multiple first carbon emission monitoring values ​​corresponding to each target monitoring point, the method further includes:

[0030] Obtain a second carbon emission monitoring value; wherein, the second carbon emission monitoring value is the carbon emission monitoring value collected by the monitoring device deployed in the warehouse management system to monitor the actual carbon emissions;

[0031] The first carbon emission monitoring value is compared with the second carbon emission monitoring value to determine the carbon emission deviation value;

[0032] If the carbon emission deviation value is greater than the preset deviation threshold, the second carbon emission monitoring value is corrected based on the first carbon emission monitoring value to determine the target carbon emission monitoring value, and the carbon emission deviation value is fed back to the energy consumption calculation model to update the energy consumption calculation model.

[0033] Secondly, this application also provides a warehouse energy consumption monitoring and scheduling system. The device is applied to a warehouse management system, which includes multiple target monitoring points. The warehouse management system includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensing terminal corresponding to each target monitoring point. The warehouse energy consumption monitoring and scheduling system includes:

[0034] The data acquisition module is used to acquire current data and dynamic events from different target monitoring points through the energy consumption sensing terminals corresponding to multiple target monitoring points, and obtain multiple monitoring data.

[0035] The feature extraction module is used to identify feature information in current data and dynamic events through the carbon model calculation terminal, so as to extract the corresponding monitoring feature data in each monitoring data.

[0036] The first calculation module is used to calculate the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located based on the energy consumption calculation model deployed in the carbon model calculation terminal, according to the monitoring characteristic data, and obtain multiple first carbon emission monitoring values ​​corresponding to each target monitoring point.

[0037] The second calculation module is used to solve the execution order of the pending operation sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal, so as to obtain the target scheduling sequence; wherein, the target scheduling sequence is used to schedule the execution equipment corresponding to the pending operation in the warehouse management system.

[0038] In one embodiment, the target monitoring point is equipped with the energy consumption sensing terminal, which is designed as a multi-source sensing terminal, including: a Hall current sensor, a reed switch, a temperature sensor, and a monitoring meter.

[0039] The carbon model computing end is designed as an edge AI carbon model computing box, which includes a chassis, processor, memory, custom chip and dual-channel data acquisition device;

[0040] The task scheduler is designed as a carbon-sensing scheduling host, which is equipped with a CPU, GPU, first communication module and solid-state storage.

[0041] In one embodiment, the warehouse energy consumption monitoring and scheduling system further includes: an equipment coordination controller and a carbon footprint verification unit;

[0042] The equipment coordination controller connects to each target monitoring point via a CAN bus. The target monitoring points include the AGV scheduling system, the cold storage temperature controller, and the sorting control system.

[0043] The carbon footprint verification unit is communicatively connected to the multi-source sensing terminal and the carbon model calculation terminal. The carbon footprint verification unit includes a second communication module, a comparison circuit, and a calibration interface.

[0044] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the warehouse energy consumption monitoring and scheduling method provided in the first aspect.

[0045] Fourthly, this application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the warehouse energy consumption monitoring and scheduling method provided in the first aspect above.

[0046] Fifthly, this application also provides a program product, including a computer program that, when executed by a processor, implements the steps of the warehouse energy consumption monitoring and scheduling method provided in the first aspect.

[0047] The aforementioned warehouse energy consumption monitoring and scheduling method, apparatus, system, and computer equipment, wherein the warehouse energy consumption monitoring and scheduling method is applied to a warehouse management system. The warehouse management system deploys multiple target monitoring points and includes a carbon model calculation terminal, a task scheduling terminal, and energy consumption sensing terminals corresponding to each target monitoring point. Current data and dynamic events from different target monitoring points are acquired through the energy consumption sensing terminals corresponding to the multiple target monitoring points, resulting in multiple monitoring data. The carbon model calculation terminal identifies feature information in the current data and dynamic events to extract corresponding monitoring feature data from each monitoring data. Based on the energy consumption calculation model deployed in the carbon model calculation terminal, the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located is calculated according to the monitoring feature data, resulting in multiple first carbon emission monitoring values ​​corresponding to each target monitoring point. The task scheduling terminal solves for the execution order of the pending operation sequence issued by the warehouse management system based on the first carbon emission monitoring values, obtaining a target scheduling sequence. The target scheduling sequence is used to schedule the execution equipment corresponding to the pending operations in the warehouse management system. In this way, by monitoring the current data and dynamic events of the target monitoring points, feature data is extracted and the estimated carbon emission value is calculated. The scheduling of the operation sequence is optimized, which realizes the refined monitoring of hidden energy consumption and the real-time scheduling optimization based on carbon emissions, thereby improving the accuracy and real-time performance of carbon emission monitoring. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating a warehouse energy consumption monitoring and scheduling method in one embodiment;

[0050] Figure 2 This is a flowchart illustrating the warehouse energy consumption monitoring and scheduling method in another embodiment;

[0051] Figure 3 A schematic diagram of a scenario provided for a warehouse energy consumption monitoring and scheduling method in another embodiment;

[0052] Figure 4 This is a flowchart illustrating the warehouse energy consumption monitoring and scheduling method in yet another embodiment;

[0053] Figure 5 This is a schematic diagram of the structure of a warehouse energy consumption monitoring and scheduling system in one embodiment;

[0054] Figure 6 This is a schematic diagram of the internal structure of a computer device in one embodiment. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0057] In the process of carbon emission monitoring and scheduling in warehouse management systems, existing technologies use static carbon factors for calculation, without dynamically adjusting in conjunction with real-time changes in power grid load, leading to biases in carbon emission assessment. Furthermore, they fail to effectively identify and quantify hidden carbon emission sources such as instantaneous power fluctuations during equipment start-up and shutdown, as well as compensating energy consumption caused by environmental disturbances. Moreover, carbon data is only used for post-event statistical analysis and does not form a closed-loop feedback mechanism with task scheduling decisions, resulting in the inability to proactively control carbon emissions. In addition, the lack of fine-grained traceability makes it difficult for the system to decompose carbon responsibility, affecting the accuracy of carbon management.

[0058] In the daily operation of cold chain logistics warehouses, automated guided vehicles (AGVs) frequently start and stop when performing cargo handling tasks. The instantaneous power characteristics during their acceleration phase are not captured by existing monitoring systems, and only average power consumption data is recorded. At the same time, the opening of cold storage doors causes the internal temperature to rise, requiring the compressor to run additionally to restore the set temperature. The energy consumption of this compensation process is not included in the carbon emission model. Furthermore, the carbon data generated is only used to generate monthly reports, and the real-time fluctuations in the proportion of green electricity in the power grid are not considered in task scheduling decisions, resulting in the loss of opportunities to optimize carbon emissions.

[0059] Therefore, existing carbon emission monitoring data cannot accurately reflect the actual emission situation. The lack of hidden carbon emission sources will lead to a lack of data support for the formulation of emission reduction strategies, making it impossible to achieve the transformation from carbon monitoring to carbon regulation. The disconnect between carbon data and scheduling makes the system unable to dynamically respond to changes in grid conditions, resulting in low energy utilization efficiency. The continuous accumulation of model bias during long-term operation will further reduce the accuracy of carbon management.

[0060] For ease of understanding, the following explains some key terms in this embodiment:

[0061] A warehouse management system (WMS) is a comprehensive system used to manage various operations within a warehouse. Its functions include inbound and outbound operations, inventory management, and job scheduling, aiming to improve warehousing efficiency and accuracy. This WMS is typically deployed in a physical warehouse environment and integrated with various automated equipment and information systems.

[0062] Target monitoring points can refer to specific locations or equipment in a warehouse management system that require monitoring of hidden energy consumption and carbon emissions. Target monitoring points can have corresponding hidden energy consumption areas, as well as corresponding equipment or key operation areas in those hidden energy consumption areas. For example, the operation areas where stacker cranes, conveyor lines, forklift charging stations, and refrigeration units in automated warehouses are located.

[0063] To elaborate further, the hidden energy consumption area where the target monitoring point is located can refer to the additional energy consumption caused by the non-steady-state, intermittent, or state-switching operating characteristics of equipment in warehousing operations, which cannot be directly and accurately measured by traditional electricity meters or energy consumption monitoring systems.

[0064] Hidden carbon emissions refer to carbon emissions that are not directly recorded by traditional electricity meters but are caused by additional energy consumption resulting from the non-steady-state operation of equipment (e.g., start-stop shocks, temperature fluctuation recovery).

[0065] Carbon-aware scheduling can treat carbon emissions as an optimization objective alongside cost and timeliness, dynamically adjusting the task execution order and resource allocation.

[0066] Energy consumption sensing terminals can refer to hardware modules deployed at each target monitoring point for real-time collection of equipment operating status and energy consumption data. Energy consumption sensing terminals can convert electrical signals and events in the physical world into digital information that can be processed by the system.

[0067] The carbon model calculation terminal can be an edge AI carbon model calculation box, which is a core functional module in a warehouse management system used to receive and process monitoring data from energy consumption sensors. This carbon model calculation terminal internally deploys a complex energy consumption calculation model to convert raw energy consumption data into carbon emission estimates. Specifically, it can be an industrial-grade embedded device specifically designed to run a carbon emission LSTM model, possessing local inference and parameter self-calibration capabilities.

[0068] Among them, the dynamic carbon factor can be the grid carbon emission intensity value (unit: kgCO2e / kWh) that changes over time, reflecting the difference in the proportion of green electricity in different periods.

[0069] An energy consumption calculation model refers to the algorithm and mathematical model deployed in the carbon model calculation terminal. Its function is to accurately estimate the energy consumption and corresponding carbon emissions of equipment under specific operating conditions based on the equipment's operating characteristics and energy consumption data. The energy consumption calculation model can consider a variety of influencing factors to improve the accuracy of carbon emission estimation.

[0070] The task scheduling module is the core functional module in the warehouse management system used to optimize, sort, and schedule pending tasks within the warehouse system based on the first carbon emission monitoring value. The goal of the task scheduling module is to minimize overall carbon emissions while meeting operational needs.

[0071] Closed-loop carbon management can be a complete carbon footprint tracking and optimization chain, from task planning to execution and then to actual measurement and verification.

[0072] Green electricity can refer to electricity certified by the national green certificate system, originating from renewable energy sources such as wind, solar, and hydropower, and verified in real time through the dual-channel system. The dual-channel system refers to: ① obtaining real-time carbon factor (≤0.40 kgCO2e / kWh) by calling the API of the State Grid Green Electricity Monitoring Platform; ② reading the output power of the local photovoltaic inverter (≥50kW). If both conditions are met simultaneously, it is determined to be a period of surplus green electricity.

[0073] Execution equipment refers to various automated or semi-automated equipment that actually performs operations in a warehouse management system, such as Automated Guided Vehicles (AGVs), forklifts, conveyors, stacker cranes, sorting robots, and refrigeration compressors in cold storage facilities.

[0074] In one exemplary embodiment, such as Figure 1 As shown, a warehouse energy consumption monitoring and scheduling method is provided, applied to a warehouse management system. This system deploys multiple target monitoring points and includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensor terminal corresponding to each target monitoring point. The monitoring and scheduling method can include steps 11 to 14, wherein:

[0075] Step 11: Obtain current data and dynamic events from different target monitoring points through the energy consumption sensing terminals corresponding to multiple target monitoring points to obtain multiple monitoring data.

[0076] Among them, current data refers to the current information collected by the energy consumption sensor when the device is running. Current data can be used to reflect the real-time power consumption of the execution device corresponding to the hidden energy consumption area where the target monitoring point is located.

[0077] Here, dynamic events can refer to discontinuous state changes that occur during the operation of the execution equipment, such as the start-up and stop of the execution equipment, the sudden increase or decrease of the load, and the switching of the operating mode.

[0078] It should be noted that monitoring data can refer to data packets formed after the energy consumption sensor has preliminarily processed and encapsulated the collected current data and dynamic events. Monitoring data can include timestamps, device identifiers, and specific power consumption and event information.

[0079] One implementation method involves periodically reading the cumulative electricity consumption and basic operating status of the executing devices based on electricity meters deployed in the energy consumption area where the target monitoring point is located. This allows for the acquisition of basic energy consumption information within the hidden energy consumption area where the target monitoring point is located.

[0080] As another approach, manual recording can be used, where operators manually record dynamic events and their occurrence times when the equipment starts up, stops, or experiences load changes. The raw current data and dynamic event information are then collected to form preliminary monitoring data.

[0081] Step 12: Identify the feature information in the current data and dynamic events through the carbon model calculation terminal to extract the corresponding monitoring feature data in each monitoring data.

[0082] Among them, monitoring characteristic data can refer to key parameters extracted from raw monitoring data that are representative of energy consumption and carbon emissions. Key parameters in monitoring characteristic data can include the number of start-ups and shutdowns of the executing equipment, load change rate, and operating temperature.

[0083] In one implementation, the carbon model calculation terminal can perform a simple threshold judgment on the received current data. For example, if the current value is higher than a certain preset threshold, it can be identified as the device is in operation, and if it is lower than the threshold, it can be identified as standby or stopped.

[0084] In another implementation, for dynamic events, event information such as "start" and "stop" can be identified from manually recorded event logs based on preset keyword matching rules. After identifying the status and event information of the executing device, the tag information can be used as monitoring feature data.

[0085] Step 13: Based on the energy consumption calculation model deployed in the carbon model calculation terminal, calculate the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located according to the monitoring characteristic data, and obtain multiple first carbon emission monitoring values ​​corresponding to each target monitoring point.

[0086] The estimated carbon emissions figure can refer to the preliminary carbon emissions calculated by the energy consumption calculation model based on monitoring characteristic data, without considering external factors such as the real-time power grid carbon emission rate.

[0087] Furthermore, the first carbon emission monitoring value can be a more accurate estimate of carbon emissions, obtained by adjusting for external environmental factors such as real-time grid carbon emission rates, based on the predicted carbon emission values. This first carbon emission monitoring value can be used to make scheduling decisions for pending tasks issued by the warehouse management system.

[0088] Specifically, the energy consumption calculation model can correspond to the monitoring characteristic data, with different monitoring characteristic data having different energy consumption calculation models. For example, the number of start-stop cycles, load change rate, and operating temperature can each have corresponding hidden carbon models. Therefore, when determining the number of start-stop cycles, load change rate, and operating temperature, the hidden energy consumption carbon emissions caused by the number of start-stop cycles can be calculated based on the number of start-stop cycles and the corresponding first hidden carbon model; the hidden energy consumption carbon emissions caused by changes in the load change rate of the execution equipment can be calculated based on the load change rate and the corresponding second hidden carbon model; and the hidden energy consumption carbon emissions caused by changes in the operating temperature of the execution equipment can be calculated based on the second hidden carbon model corresponding to the operating temperature of the operating thermometer. Finally, the hidden energy consumption carbon emission values ​​calculated based on the number of start-stop cycles, load change rate, and operating temperature are added together to determine the estimated carbon emission value of the hidden energy consumption area or the corresponding execution equipment at the target monitoring point.

[0089] Furthermore, after determining the estimated carbon emission value, the first carbon emission monitoring value can be calculated by combining it with the real-time carbon emission coefficient.

[0090] Here, the carbon emission coefficient can be the real-time carbon factor obtained from the real-time grid carbon emission intensity application programming interface (API), which can be used to represent the real-time status of comprehensive carbon emissions.

[0091] Step 14: Using the task scheduling terminal, the execution order of the pending operation sequence issued by the warehouse management system is solved based on the first carbon emission monitoring value to obtain the target scheduling sequence.

[0092] The target scheduling sequence is used to schedule the execution equipment corresponding to the tasks to be performed in the warehouse management system.

[0093] Here, the target scheduling sequence can refer to the target order obtained after optimization calculation by the task scheduler, which is used to guide the warehouse equipment to perform the tasks to be performed. The target scheduling sequence can be used to achieve a balance between carbon emissions, efficiency and cost when the warehouse management system schedules different execution equipment to perform the tasks to be performed.

[0094] It should be noted that the pending operation sequence can refer to the operation sequence that needs to be completed by the warehouse management system based on business needs, such as the handling, picking, warehousing, and outbound of goods. The execution of the operation can be completed by the execution equipment in the warehouse management system.

[0095] In one implementation, the task scheduler can employ a priority-based scheduling strategy. For example, each job to be executed is assigned a priority based on its first carbon emission monitoring value, with jobs having lower carbon emission values ​​receiving higher priorities. The task scheduler then arranges the execution order of the jobs according to their priorities, thereby generating the target scheduling sequence.

[0096] The aforementioned warehouse energy consumption monitoring and scheduling method integrates energy consumption sensing, carbon emission modeling, and dynamic scheduling optimization to achieve refined management and proactive control of carbon emissions from warehousing operations. The energy consumption sensor provides real-time, fine-grained equipment operation data, the carbon model calculation unit converts this data into quantifiable carbon emission information, and the task scheduling unit uses this carbon emission information as a decision-making basis to optimize the work sequence, thereby achieving carbon emission reduction at the execution level. This forms a closed-loop carbon management and scheduling system, effectively solving the problems of coarse-grained carbon emission monitoring and disconnection from scheduling in traditional warehouse management. It enables refined monitoring, quantitative assessment, and proactive control of carbon emissions from warehousing operations, significantly improving the green operation capabilities of the warehouse management system.

[0097] In one exemplary embodiment, such as Figure 2 As shown, a warehouse energy consumption monitoring and scheduling method is also provided, which may include steps 201 to 12, and steps 12 to 208, wherein:

[0098] Step 201: Using a Hall current sensor installed at the target monitoring point, the instantaneous change characteristics of the operating current of the execution device corresponding to the target monitoring point are collected at a preset period, and the first event tag corresponding to the operating current of the execution device during operation is recorded.

[0099] Among them, the Hall current sensor is a current measurement device based on the Hall effect principle, which can realize non-contact current detection and has the advantages of fast response speed, high accuracy, and good isolation. In this embodiment, the Hall current sensor can be an open-loop Hall sensor, which is suitable for scenarios with general accuracy requirements and cost sensitivity; the Hall current sensor can also be a closed-loop Hall sensor, which provides higher measurement accuracy and linearity through the magnetic balance principle.

[0100] It should be noted that the preset period can refer to the time interval for the sensor to collect data. For example, it can be set to 100 milliseconds to capture rapidly changing current signals. Alternatively, the sampling period can be dynamically adjusted according to the operating characteristics of the device, for example, by increasing the sampling frequency when the device starts or stops.

[0101] As one implementation method, a Hall current sensor installed at the target monitoring point can be used to detect the rising edge of its output current (dI / dt > 5A / ms). Specifically, a Schmitt trigger circuit is connected to the output of the Hall current sensor to convert the analog current change into a standard TTL level transition. When the rising edge of the level transition is detected, the Schmitt trigger is triggered to generate a TTL interrupt signal. This interrupt signal drives the ARM processor to start a 10kHz high-speed ADC to continuously acquire the current waveform for 200ms, thereby obtaining the transient change characteristics.

[0102] It should also be noted that the first event label can be an identifier associated with the operating current characteristics, and the first time label can be used to classify or mark the current state. For example, the current state of the actuator can be defined as "no load", "light load", "heavy load" or "overload". Alternatively, the current state of the actuator can also be represented as "start-up current", "running current" or "stop current".

[0103] Step 202: Detect the start-stop characteristics of the target monitoring point using a magnetic induction switch installed at the target monitoring point, and record the second event tag corresponding to the start-stop characteristics of the executing device.

[0104] A magnetic induction switch is a sensor that uses changes in a magnetic field to control the on / off state of a circuit. It can be used to detect the approach, position, or movement of an object. In this embodiment, the magnetic induction switch can be a reed switch, which changes the contact state by the approach or departure of a magnet; it can also be a Hall effect switch, which outputs a digital signal by detecting changes in magnetic field strength.

[0105] Here, the second event label can be an identifier associated with the start and stop characteristics of the execution device, which can be used to explicitly mark the transition of the execution device's operating state. For example, it can be defined as "device start", "device stop", or "device pause".

[0106] Step 203: The instantaneous change characteristics, start-stop characteristics, first event tag and second event tag of the execution device are encapsulated into a standard data frame with timestamp and device identifier according to a preset protocol to obtain the monitoring data corresponding to the execution device of the target monitoring point.

[0107] The preset protocol can refer to the standard specifications for data transmission and encapsulation. For example, the MQTT protocol can be used, which is lightweight and its publish / subscribe model is suitable for the Internet of Things environment; or the Modbus TCP / IP protocol can be used to ensure the reliability and interoperability of data transmission.

[0108] In addition, timestamps can be precise time information that records the moment data is collected or an event occurs. They can include year, month, day, hour, minute, second, or even millisecond, and are used for data synchronization and event sequence analysis.

[0109] In one implementation, an optocoupler is installed after the magnetic induction switch. After the magnetic induction switch collects the start-stop characteristics, it can output to the optocoupler and connect to the external interrupt pin of the same processor to ensure that the synchronization error between the event and the current sampling timestamp is < 1ms.

[0110] A device identifier can be a unique identifier assigned to each execution device, such as the device's MAC address, serial number, or custom asset number. This device identifier can be used to ensure that data can be accurately traced back to a specific device.

[0111] In this embodiment, a Hall current sensor and a magnetic induction switch are introduced to achieve refined perception of the operating status of the execution device. The Hall current sensor can capture the instantaneous changes in the device's operating current in real time and accurately, thereby reflecting the device's real-time power consumption and revealing the device's operating mode under different load conditions. Meanwhile, the magnetic induction switch provides a clear signal of the device's start-up and stop status, compensating for the inaccuracy of relying solely on current data to determine the physical start-up and stop of the device. By recording a first event tag corresponding to the operating current and a second event tag corresponding to the start-up and stop characteristics, continuous current data can be effectively correlated with discrete event information, forming structured and semantic monitoring data. These instantaneous change characteristics, start-up and stop characteristics, and corresponding event tags, along with precise timestamps and device identifiers, are encapsulated into standard data frames according to a preset protocol. This data encapsulation method ensures the integrity, consistency, and resolvability of the data, enabling the subsequent carbon model calculation end to more accurately identify the device's energy consumption characteristics, such as the device's actual operating time, start-up and stop frequency, and load variation amplitude, based on this rich and accurate monitoring data. The refined data collection mechanism described above has significantly improved the quality and effectiveness of raw monitoring data, and optimized the accuracy of carbon emission estimation and task scheduling decisions.

[0112] Step 12: Identify the feature information in the current data and dynamic events through the carbon model calculation terminal to extract the corresponding monitoring feature data in each monitoring data.

[0113] The monitoring feature data includes the number of start-stop cycles, load change rate, and temperature gradient of the execution equipment corresponding to the target monitoring point, and the energy consumption calculation model includes the calculation model corresponding to the number of start-stop cycles, load change rate, and temperature gradient.

[0114] The description of step 12 can be found in the previous embodiment and will not be repeated here.

[0115] Step 204: Input the number of start-stop cycles, load change rate, and temperature gradient into the corresponding energy consumption calculation model, and calculate the estimated carbon emission values ​​corresponding to the target monitoring point.

[0116] The number of start-stop cycles can be the number of times the execution device changes from a stopped state to a running state, or from a running state to a stopped state, within a specific time period.

[0117] It should be noted that the number of start-stop cycles can be used to reflect the intermittent operating mode of the equipment, because the start-up and shutdown process of the equipment is usually accompanied by instantaneous large current surges or energy losses, which have a significant impact on energy consumption and carbon emissions.

[0118] One implementation method is to count the number of start-stop cycles by monitoring changes in the power switch status of the actuator, sudden changes in motor current, or switching of internal control signals. Another method is to analyze the instantaneous power curve of the actuator to identify the number of power jumps from zero to non-zero or from non-zero to zero, thereby determining the number of start-stop cycles.

[0119] Furthermore, the load change rate can refer to the rate at which the load on the executing device changes over time during operation. The load change rate can be used to reflect the dynamic nature of the workload of the executing device during operation.

[0120] It should be noted that a high load change rate may mean that the equipment frequently accelerates, decelerates, or handles tasks of varying intensity, which often leads to fluctuations in energy efficiency and additional energy loss.

[0121] One implementation method is to continuously monitor the real-time current or power data of the actuator and calculate the ratio of the change in current or power over a unit of time to the current value to obtain the load change rate. Alternatively, load parameters can be acquired using sensors within the device (e.g., pressure sensors, torque sensors), and the rate of change of these load parameters can be calculated. This yields the load change rate.

[0122] Furthermore, a temperature gradient can be the spatial distribution difference or the rate of change of temperature within an instrument or its critical components. Temperature gradients can be used to indirectly reflect the operating status of the instrument, its heat dissipation efficiency, and potential energy losses. For example, under heavy load or prolonged operation, the temperature gradient increases, potentially leading to decreased efficiency or additional cooling energy consumption.

[0123] One approach is to install multiple temperature sensors at different locations on the device, measure the temperature values ​​at different points, and then calculate the differences between these temperature values ​​to obtain the spatial temperature gradient. Alternatively, a single temperature sensor can be used to continuously collect temperature data at a key point on the device, and the change in temperature over a unit of time can be calculated to obtain the temporal temperature gradient.

[0124] Energy consumption calculation models are a set of mathematical models used to map the operating characteristics of equipment to its energy consumption or carbon emissions. Specifically, start-stop frequency, load change rate, and temperature gradient can all have corresponding energy consumption calculation models. Energy consumption calculation models can be calculation models based on physical principles such as motor efficiency curves and thermodynamic models, or they can be data-driven calculation models based on regression models and neural network models.

[0125] For example, a linear regression model can be used, taking start-up and shutdown frequency, load change rate, and temperature gradient as input variables, and outputting estimated energy consumption or carbon emissions. Alternatively, a rule-based expert system can be used to determine energy consumption levels and estimate carbon emissions based on thresholds and combinations of different features.

[0126] Step 205: Obtain the real-time carbon emission rate of the power grid, and calculate the first carbon emission monitoring value of the target monitoring point based on the estimated carbon emission value and the carbon emission rate.

[0127] Among them, the real-time carbon emission rate of the power grid can be the amount of carbon emissions generated by the power grid for each unit of electricity produced at a specific point in time. The carbon emission rate is affected by the real-time combination of different power generation methods in the power grid (e.g., coal-fired, gas-fired, hydropower, wind power, photovoltaic, etc.), and is therefore dynamic.

[0128] Specifically, one can obtain power grid generation structure data by accessing the real-time data interface of the national or regional power grid, and then calculate the real-time carbon emission rate by combining the carbon emission factors of different power generation methods. Another approach is to subscribe to real-time power grid carbon emission intensity data through a third-party energy data service platform.

[0129] In this embodiment, by introducing more refined monitoring feature data and corresponding energy consumption calculation models, the accuracy of equipment carbon emission estimation in the warehouse management system is significantly improved. After acquiring current data and dynamic events from different target monitoring points, the carbon model calculation end no longer simply extracts generalized feature information, but further identifies and extracts specific monitoring feature data closely related to the actual operating status and energy consumption characteristics of the equipment, such as the number of start-stop cycles, load change rate, and temperature gradient. This feature data can more comprehensively and meticulously reflect the equipment's operating mode and energy consumption.

[0130] Specifically, the number of start-stop cycles directly correlates with the instantaneous energy consumption impact of the equipment, the load change rate reflects the dynamic fluctuations in the equipment's workload, and the temperature gradient indirectly reveals the equipment's operating efficiency and heat dissipation. The energy consumption calculation model deployed in the carbon model calculation terminal has been optimized for these specific monitoring characteristic data, including calculation models corresponding to the number of start-stop cycles, load change rate, and temperature gradient. When these refined monitoring characteristic data are input into the corresponding energy consumption calculation models, the estimated carbon emission values ​​corresponding to the target monitoring points can be calculated more accurately. Based on this, to further improve the real-time performance and accuracy of carbon emission monitoring, this solution also obtains the real-time carbon emission rate of the power grid. Since the carbon emission intensity of the power grid fluctuates in real time with changes in the power generation structure, combining the estimated carbon emission values ​​with the real-time power grid carbon emission rate yields a first carbon emission monitoring value that more closely reflects the actual situation. The first carbon emission monitoring value considers the energy consumption characteristics of the equipment itself and the carbon emission environment of the external power grid, making the carbon emission monitoring results more accurate and timely.

[0131] Therefore, when the task scheduler solves the sequence of tasks to be executed issued by the warehouse management system, it can make decisions based on a more accurate first carbon emission monitoring value, thereby optimizing the execution order of the task sequence and achieving more effective carbon emission management and scheduling.

[0132] Step 206: Based on the task scheduler, determine the execution devices corresponding to multiple jobs in the job sequence to be executed.

[0133] The task scheduler can determine the execution devices corresponding to multiple tasks in a sequence of tasks to be executed, thus clarifying the correspondence between the tasks to be executed and the actual execution devices. For example, material handling tasks can correspond to forklifts, AGVs, and other equipment; sorting tasks can correspond to sorting robots or conveyor belts.

[0134] In practical applications, the required execution equipment for the job can be determined by querying the job-equipment mapping database in the warehouse management system. Alternatively, it can be identified by the equipment ID contained in the job instruction.

[0135] Step 207: Construct an execution matrix based on the first carbon emission monitoring value corresponding to the target monitoring point of the execution device.

[0136] The execution matrix is ​​used to represent the initial execution order of multiple jobs to be executed under the initial decision.

[0137] Here, the execution matrix can be used to represent the execution order of the tasks to be executed under the initial decision. The execution matrix associates each task to be executed with the corresponding execution device and the first carbon emission monitoring value of the target monitoring point where the device is located.

[0138] As one approach, tasks to be performed can be initially sorted according to task priority, equipment availability, or the FIFO principle, and the corresponding carbon emission monitoring values ​​can be marked as elements or attributes of a matrix.

[0139] As another implementation method, each job to be executed and its corresponding execution device and carbon emission value can be used as rows or columns to form a multidimensional matrix, which can contain the initial execution time window or resource allocation information.

[0140] For example, the execution matrix can be a two-dimensional data structure stored in the GPU memory of the carbon-aware scheduling host, with its row index being the ID of the task to be executed, its column index being the ID of the optional execution device, and its cell value being the estimated carbon emissions (in kgCO2e) when the task is executed by the device.

[0141] Step 208: Simulate the execution of multiple jobs to be executed in the execution matrix, calculate the running parameters of each job to be executed, and determine the target scheduling sequence for each job to achieve the target state.

[0142] The operating parameters include time, cost, and carbon emission parameters, and the target scheduling sequence is used to represent the execution order of multiple jobs to be executed when they reach the target equilibrium state.

[0143] Here, simulating the execution of multiple tasks in the execution matrix can be achieved by predicting the operation process of the tasks through a computational model without actually operating the equipment. Specifically, simulation technology can be used to calculate the possible operating parameters of each task during execution, including time, cost, and carbon emission parameters, based on the initial execution order defined in the execution matrix. For example, the completion time, energy cost, and carbon emission of the task can be predicted based on factors such as equipment operating speed, energy consumption model, and material handling distance.

[0144] As one approach, the execution order of tasks can be adjusted through iterative optimization algorithms or heuristic rules, so that these operational parameters (time, cost, carbon emissions) reach a preset target state or equilibrium state.

[0145] As another approach, rule-based expert systems can be used to evaluate and adjust the simulation results based on preset optimization objectives and constraints until a scheduling sequence that satisfies the target equilibrium state is found.

[0146] In this embodiment, the task scheduler identifies the specific execution device corresponding to each task in the sequence of tasks to be executed, thereby establishing a connection between the abstract task and the actual physical energy source. Subsequently, based on the first carbon emission monitoring values ​​obtained from the target monitoring points corresponding to these execution devices, an execution matrix is ​​constructed. This matrix not only includes the initial execution order of the tasks but also incorporates carbon emission considerations, forming a preliminary scheduling scheme. Based on this, the tasks to be executed in the execution matrix are simulated. During the simulation, the operating parameters involved in the execution of each task are comprehensively calculated, including the time required to complete the task, the cost incurred, and the estimated carbon emissions. Through comprehensive evaluation and iterative optimization of these multi-dimensional operating parameters, the task scheduler can dynamically adjust the execution order of the tasks until a target scheduling sequence is found, so that the operating parameters of all tasks to be executed reach a preset target equilibrium state. This method extends single carbon emission optimization to multi-objective balance optimization, ensuring that the scheduling result achieves the best balance between environmental benefits, economic benefits, and operational efficiency.

[0147] In one implementation, the task scheduling terminal connects to the green electricity ratio data interface of the power grid in real time. After obtaining the target scheduling sequence by solving the execution order of the pending work sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal, the method further includes:

[0148] (1) Detect the current green electricity ratio of the power grid through the power grid green electricity ratio data interface.

[0149] Among them, the power grid green electricity ratio data interface is the channel for data exchange between the task scheduling terminal and the external power grid information system. Its function is to obtain the real-time green electricity supply status in the power grid.

[0150] Specifically, the power grid green electricity ratio data interface can be an application programming interface (API) that communicates with the servers of the power grid operator or a third-party energy data service provider via network protocols to request and receive green electricity ratio data. Alternatively, the power grid green electricity ratio data interface can also be a direct data link with the local energy management system (EMS), which is responsible for receiving and processing green electricity-related information from the power grid side and providing the data to the task scheduling terminal through internal communication protocols.

[0151] It should be noted that detecting the current green electricity ratio of the power grid through the power grid green electricity ratio data interface can be achieved by the task scheduling terminal periodically or event-drivenly querying and obtaining the percentage of electricity generated by renewable energy in the power grid relative to the total electricity supply.

[0152] As one implementation method, data requests can be automatically initiated at preset time intervals to ensure the real-time nature of the acquired data. Furthermore, detection can be performed when specific events are triggered to respond to rapid changes in the power grid state.

[0153] (2) If the green electricity ratio is greater than or equal to the first green electricity threshold, identify the non-urgent execution jobs in the target scheduling sequence and adjust the execution time of the non-urgent execution jobs to the first target time window to obtain the adjusted target scheduling sequence.

[0154] The first target time window is the time window in which the proportion of green electricity is greater than or equal to the first green electricity threshold.

[0155] (3) If the green electricity ratio is less than the second green electricity threshold, identify the non-urgent execution jobs in the target scheduling sequence and adjust the execution time of the non-urgent execution jobs to the second target time window to obtain the adjusted target scheduling sequence.

[0156] The second green electricity threshold is less than the first green electricity threshold, and the second target time window is the time window after the green electricity ratio is less than the second green electricity threshold.

[0157] The first green electricity threshold is a preset value used to determine whether the current green electricity ratio of the power grid has reached the expected green electricity reference value that indicates a green electricity surplus, and the second green electricity threshold is a preset value used to determine whether the current green electricity ratio of the power grid has reached the expected green electricity reference value that indicates a green electricity shortage.

[0158] It should be noted that the first and second green electricity thresholds can be set based on the company's carbon emission targets, energy policy requirements, or historical data analysis results.

[0159] For example, in the carbon-sensing scheduling process, the State Grid Green Energy Monitoring Platform API can be called every 5 minutes via a 4G module to obtain the green energy ratio (G_ratio) for the current time period in real time. Based on this dynamic green energy ratio data, a tiered scheduling strategy is executed. Specifically, when the green energy ratio is detected to reach 70% or higher (the first green energy threshold), a green energy surplus status can be determined. The execution time windows of non-urgent tasks in the target scheduling sequence (e.g., SLA grace periods greater than 2 hours) can be shifted forward to be executed during this green energy surplus period. If the original time window is already in a green energy surplus period, it remains unchanged. When the proportion of green electricity falls below 30% (the second green electricity threshold), a state of green electricity scarcity can be identified, triggering a "load reduction mode." Energy-saving measures can be implemented for the top 20% of operations with the highest carbon emissions in the dispatch instructions. For example, AGVs can be switched to energy-saving cruise mode, reducing their speed to 0.8 meters per second and power by 35%. The temperature control bandwidth of cold storage can be widened by ±0.5 degrees Celsius to reduce the frequency of compressor start-up and shutdown. Unnecessary lighting and ventilation can also be suspended. In this way, the energy intensity of a single operation can be reduced to achieve emission reduction without delaying the execution of the task.

[0160] One specific implementation method is to pre-classify and label all jobs, for example, by setting an "urgency" attribute for each job in the job management system. Alternatively, the urgency of a job can be automatically determined by analyzing parameters such as job dependencies, deadlines, and resource requirements, using a rule engine or machine learning model.

[0161] When the green electricity ratio does not meet the requirements, the start or end times of these non-urgent tasks can be rescheduled to adjust the execution time of the tasks to be performed, thus delaying the tasks to periods when the green electricity ratio is expected to be higher, such as nighttime when wind power generation is higher or daytime when solar power generation is sufficient. Alternatively, based on the grid's green electricity ratio forecast data, the tasks can be performed earlier or during off-peak hours to avoid periods with a lower green electricity ratio.

[0162] (4) Generate scheduling instructions for each execution device based on the adjusted target scheduling sequence.

[0163] Among them, the scheduling instruction is a specific operation command issued by the task scheduler to the execution device, which is used to instruct the device when, where and how to execute a specific job. The scheduling instruction can include detailed information such as job ID, start time, end time, required resources, execution path and so on.

[0164] As one specific implementation, the adjusted target scheduling sequence can be parsed into specific tasks for individual devices and encapsulated into a device-recognizable format.

[0165] (5) Send the scheduling instructions corresponding to the execution device to the corresponding execution device so that the corresponding job can be executed at the required execution time of each execution device.

[0166] The communication method for distributing scheduling instructions to the corresponding execution devices can include wired or wireless networks, depending on the warehousing environment and equipment type. The transmission protocol can be a standard industrial protocol or a custom communication protocol, ensuring that instructions are accurately and reliably delivered to the devices so that the corresponding tasks can be performed at the required execution time for each execution device.

[0167] After receiving a scheduling instruction, the executing device can adjust its internal work plan and status according to the instruction, and start or stop the corresponding operation within the time window specified in the instruction.

[0168] For example, please refer to Figure 3 , Figure 3 This is a schematic diagram of a scenario in this embodiment. Specifically, this embodiment proposes a warehouse hidden energy consumption monitoring and scheduling system for carbon footprint traceability. It adopts a five-level hardware closed-loop architecture of perception-modeling-decision-execution-verification to realize full-link carbon management from equipment-level energy consumption collection to global low-carbon scheduling.

[0169] In the multi-source energy consumption sensing layer, Hall current sensors can be deployed in the power circuit of automated guided vehicles, reed switch-type magnetic induction switches can be deployed in the cold storage door frame of the refrigeration unit, and encoder interface modules can be deployed in the stacker crane, forming a distributed sensing terminal cluster. When the Hall current sensor detects the rising edge of the current (dI / dt greater than 5A per millisecond), it immediately starts 10kHz high-speed ADC sampling and captures the start-up transient waveform for 200 milliseconds. When the magnetic induction switch outputs a level transition signal, the corresponding timestamp and event type are recorded, including door open or door close events. The sound acquisition data is encapsulated in a unified frame format, including device ID, timestamp, transient waveform array, and event tag, to obtain the monitoring data collected by the distributed sensing terminals.

[0170] After collecting the monitoring data, it can be input into the edge AI carbon model calculation terminal. Through calculation, monitoring feature data can be extracted from the monitoring data, including the number of start-stop cycles, load change rate, and temperature gradient. Specifically: the number of start-stop cycles is obtained by matching the current rising edge count with door opening and closing events; the load change rate is calculated by the ratio of the peak current of the transient waveform to the steady-state current; and the temperature gradient is calculated by the temperature change rate of the cold storage temperature sensor over 10 consecutive seconds.

[0171] After determining the aforementioned monitoring characteristic data, carbon emission estimation can be performed at the edge computing layer by deploying a customized AI computing box (ARM Cortex-A72 industrial motherboard, 128GB solid-state storage) with a pre-installed LSTM model chip, using a pre-built energy consumption calculation model. Specifically, for the extracted implicit characteristics such as start / stop frequency, load change rate, and temperature gradient, the corresponding implicit energy consumption formulas can be called to calculate energy consumption, where:

[0172] For AGV equipment, the formula for calculating hidden energy consumption is: E_hidden = α × ∑(I_peak² × R × t_accel).

[0173] Where α is the equipment aging coefficient, which is 1 for new equipment and increases with the service life of the equipment; I_peak is the impact current generated by the AGV from a standstill to acceleration, i.e., the starting peak current; R is the resistance of the motor winding and the line resistance, i.e., the equivalent circuit resistance; and t_accel is the time taken from starting to reaching a constant speed.

[0174] For cold storage door equipment, the formula for calculating hidden energy consumption is: E_hidden = β × N_door_open × C_p × m × ΔT_recovery.

[0175] Where β is the system efficiency factor, which can be used to represent the actual operating efficiency of the refrigeration system; N_door_open is the number of times the cold storage door is opened; C_p is the specific heat capacity of air; m is the mass of hot air entering the cold storage; and ΔT_recovery is the recovery temperature difference, which is used to represent the difference between the highest point of temperature rise inside the cold storage after the door is opened and the set temperature.

[0176] At the same time, the warehouse management system synchronously connects to a 0.5-level high-precision electricity meter to read real-time electricity consumption as explicit energy consumption. The total energy consumption is obtained by calculating the sum of explicit and implicit energy consumption. The estimated carbon emissions are calculated by multiplying the total energy consumption by the real-time carbon factor returned by the State Grid API for the current period.

[0177] Here, the real-time carbon factor API obtains dynamic grid carbon intensity and calculates a real-time carbon data stream that includes implicit carbon increments and the overall carbon emission rate (unit: kgCO2e / min).

[0178] After determining the total energy consumption and estimated carbon emissions, the estimated carbon emission values ​​of each target monitoring point can be bound to the sequence of tasks to be executed issued by the warehouse management system through the central scheduling layer, thus constructing a two-dimensional task-carbon cost matrix M[i][j]. In this matrix, row i represents the i-th task to be executed, column j represents the j-th optional execution resource, and matrix element M[i][j] represents the estimated carbon emission value when the task is executed by that resource.

[0179] Meanwhile, the warehouse management system connects to the power grid's green electricity ratio data interface in real time. When the green electricity ratio is detected to be higher than 70%, the low-carbon scheduling mode is automatically activated, and non-urgent tasks are dynamically adjusted to be executed during periods of surplus green electricity.

[0180] A target scheduling sequence is generated based on the first and second emission monitoring values ​​mentioned above.

[0181] After inputting the aforementioned target scheduling sequence into the carbon-sensing scheduling terminal, the improved NSGA-II multi-objective optimization algorithm can be run on the terminal to solve for the Pareto optimal solution set under multiple hard constraints. For example, constraints may include: order SLA fulfillment rate not less than 95%, continuous operation time of a single device not exceeding 4 hours, prohibition of charging pile occupancy conflicts, and AGV battery SOC not less than 20%. The final output is a structured scheduling instruction set, containing information such as execution time window, device ID, and task ID.

[0182] After outputting the carbon sensing scheduling terminal, it can notify the equipment execution layer to form an equipment collaborative controller with the Siemens S7-1200 PLC control module through a CAN communication gateway. This controller receives low-carbon operation instructions such as "delay replenishment" and "prioritize the use of AGVs in the photovoltaic power supply area" issued by the scheduling host, parses and converts them into executable start and stop control signals for the equipment in real time, accurately controls each warehouse equipment to operate according to the optimized timing, and records the execution log and actual start and end times to form a closed-loop execution feedback.

[0183] In the carbon footprint verification and feedback layer, a high-precision 0.5-level electricity meter is independently deployed as a real energy consumption benchmark, and a regional carbon database is configured. The verification module converts the measured total energy consumption of the electricity meter into the measured total carbon emissions every hour, comparing it with the model-estimated total carbon emissions reported by the edge computing box during the same period. When the relative deviation consistently exceeds 10% for three consecutive cycles, a self-calibration process for model parameters is automatically triggered. The LSTM network weights are fine-tuned using gradient descent, and the updated model parameters are pushed to the edge computing box, enabling online evolution of the carbon model and continuous improvement in accuracy.

[0184] Thus, through the hardware collaboration and data closed loop of the above five levels, this invention has for the first time realized a full-link carbon management hardware architecture in the warehousing scenario, which includes "task triggering, energy consumption perception, implicit modeling, carbon perception scheduling, instruction execution, and actual measurement calibration", significantly improving the accuracy of carbon accounting and the efficiency of low-carbon scheduling.

[0185] In this embodiment, based on monitored equipment energy consumption data and estimated carbon emissions, the carbon model calculation terminal and task scheduling terminal perform preliminary carbon emission optimization scheduling on the warehousing operation sequence to generate an initial target scheduling sequence. Furthermore, to further improve carbon emission reduction and respond to the real-time green electricity supply status of the power grid, the task scheduling terminal connects to the power grid green electricity ratio data interface in real time to continuously monitor the current green electricity ratio of the power grid. When the green electricity ratio of the power grid is detected to be lower than a preset green electricity threshold, it indicates that the proportion of non-green electricity components in the power grid's energy structure is relatively high during the current period, and performing operations at this time will generate relatively high carbon emissions.

[0186] To avoid high-energy-consuming operations during periods of low green electricity ratio, the task scheduler intelligently identifies non-urgent tasks with flexible execution times within the current target scheduling sequence. Subsequently, based on real-time changes or forecasts of the green electricity ratio, the task scheduler dynamically adjusts the execution times of these non-urgent tasks, for example, delaying them to periods with expected higher green electricity ratios, thus obtaining an adjusted target scheduling sequence. Finally, based on this adjusted scheduling sequence, the task scheduler generates specific scheduling instructions for each execution device and sends these instructions to the corresponding devices via the communication network. Upon receiving the instructions, the execution devices strictly adhere to the execution times specified in the instructions, ensuring that warehousing operations can be carried out during periods of high green electricity ratio and low carbon emission intensity, maximizing the use of clean energy and achieving deeper carbon reduction. This combination of static carbon emission optimization scheduling and dynamic green electricity utilization strategies makes carbon emission management in warehousing operations more refined and real-time, effectively addressing the volatility of the power grid's energy structure and significantly improving the overall level of green operation.

[0187] In one exemplary embodiment, such as Figure 4 As shown, a warehouse energy consumption monitoring and scheduling method is also provided, which may include steps 41 to 43, wherein:

[0188] Step 41: Obtain the second carbon emission monitoring value.

[0189] The second carbon emission monitoring value is the carbon emission monitoring value collected by the monitoring device deployed in the warehouse management system to monitor the actual carbon emissions.

[0190] Here, the second carbon emission monitoring value can be used to provide a practical reference value for carbon emissions in order to assess the accuracy of the energy consumption calculation model.

[0191] Specifically, the second carbon emission monitoring value can be collected by monitoring devices specifically deployed within the warehouse management system. For example, infrared gas analyzers or electrochemical sensors can be installed at key emission sources to directly measure the concentration and flow rate of greenhouse gases such as carbon dioxide and methane, and the actual carbon emissions can be calculated by combining this with the equipment's operating time. Alternatively, the actual electricity carbon emissions can be calculated as the second carbon emission monitoring value by integrating smart meter data from the warehouse management system with the real-time carbon emission factor of the power grid.

[0192] Step 42: Compare the first carbon emission monitoring value with the second carbon emission monitoring value to determine the carbon emission deviation value.

[0193] Among them, the carbon emission deviation value can be used to quantify the difference between the estimated value and the actual value of the energy consumption calculation model.

[0194] One approach is to directly calculate the absolute difference between the first and second carbon emission monitoring values, i.e., carbon emission deviation = |first carbon emission monitoring value - second carbon emission monitoring value|. Alternatively, a relative deviation can be calculated, i.e., carbon emission deviation = (first carbon emission monitoring value - second carbon emission monitoring value) / second carbon emission monitoring value.

[0195] Step 43: If the carbon emission deviation value is greater than the preset deviation threshold, the second carbon emission monitoring value is corrected based on the first carbon emission monitoring value to determine the target carbon emission monitoring value, and the carbon emission deviation value is fed back to the energy consumption calculation model to update the energy consumption calculation model.

[0196] If the carbon emission deviation value is greater than the preset deviation threshold, the second carbon emission monitoring value is corrected based on the first carbon emission monitoring value to determine the target carbon emission monitoring value.

[0197] It should be noted that when the deviation between the estimated value and the actual value exceeds the acceptable range, the data needs to be processed to obtain a more reliable reference. For example, if the second carbon emission monitoring value may be affected by instantaneous measurement noise or abnormal fluctuations, the first carbon emission monitoring value can be used as a reference to smooth or weight the second carbon emission monitoring value to obtain a more stable and representative target carbon emission monitoring value.

[0198] Furthermore, carbon emission deviations can be fed back into the energy consumption calculation model to update it, preventing long-term deviation accumulation and achieving the key to model adaptation and continuous optimization. When the energy consumption calculation model receives carbon emission deviations, it can treat them as error signals and use online learning algorithms in machine learning, such as gradient descent, to adjust the model's internal parameters, weights, or coefficients to reduce future prediction errors. In addition, historical monitoring characteristic data and corrected target carbon emission monitoring values ​​can be used periodically or when deviations accumulate to a certain level as new training samples to retrain or incrementally train the energy consumption calculation model, enabling it to adapt to changes in the storage environment, equipment status, or operating modes.

[0199] In this embodiment, a closed-loop adaptive update system for the energy consumption calculation model is constructed by introducing a comparison and feedback mechanism between actual and estimated carbon emission monitoring values. In the warehouse management system, after calculating the first carbon emission monitoring value, the carbon model calculation terminal compares it with the second carbon emission monitoring value collected by the actual monitoring device. If a significant deviation is found, the system triggers a correction process to generate a more reliable target carbon emission monitoring value and feeds this deviation information back to the energy consumption calculation model. The energy consumption calculation model uses this deviation information to continuously optimize its internal algorithms and parameters through self-learning or parameter adjustment, thereby improving the accuracy of its future carbon emission predictions. This continuous correction and update process allows the energy consumption calculation model to adapt to dynamic factors such as equipment aging and changes in the operating environment, ensuring that its prediction results remain highly consistent with the actual situation. Therefore, based on these more accurate first carbon emission monitoring values, the task scheduling terminal can perform more precise job sequence scheduling, effectively avoiding scheduling decision errors caused by inaccurate model predictions and significantly improving the overall energy efficiency and carbon emission management level of the warehouse management system.

[0200] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0201] Based on the same inventive concept, this application also provides a warehouse energy consumption monitoring and scheduling system for implementing the warehouse energy consumption monitoring and scheduling method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more warehouse energy consumption monitoring and scheduling system embodiments provided below can be found in the limitations of the warehouse energy consumption monitoring and scheduling method described above, and will not be repeated here.

[0202] In one exemplary embodiment, such as Figure 5 As shown, a warehouse energy consumption monitoring and scheduling system is provided. This device is applied to a warehouse management system, which deploys multiple target monitoring points. The warehouse management system includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensing terminal corresponding to each target monitoring point. The warehouse energy consumption monitoring and scheduling system includes: a data acquisition module, a feature extraction module, a first calculation module, and a second calculation module, wherein:

[0203] The data acquisition module is used to acquire current data and dynamic events from different target monitoring points through the energy consumption sensing terminals corresponding to multiple target monitoring points, and obtain multiple monitoring data.

[0204] The feature extraction module is used to identify feature information in current data and dynamic events through the carbon model calculation terminal, so as to extract the corresponding monitoring feature data in each monitoring data.

[0205] The first calculation module is used to calculate the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located based on the energy consumption calculation model deployed in the carbon model calculation terminal, according to the monitoring characteristic data, and obtain multiple first carbon emission monitoring values ​​corresponding to each target monitoring point.

[0206] The second calculation module is used to solve the execution order of the pending operation sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal, so as to obtain the target scheduling sequence; wherein, the target scheduling sequence is used to schedule the execution equipment corresponding to the pending operation in the warehouse management system.

[0207] In one embodiment, the data acquisition module includes: a first detection unit, a second detection unit, and a signal encapsulation unit, wherein:

[0208] The first detection unit is used to collect the instantaneous change characteristics of the operating current of the execution device corresponding to the target monitoring point at a preset period by using a Hall current sensor installed at the target monitoring point, and to record the first event tag corresponding to the operating current of the execution device during operation.

[0209] The second detection unit is used to detect the start-stop characteristics of the target monitoring point by means of a magnetic induction switch installed at the target monitoring point, and to record the second event tag corresponding to the start-stop characteristics of the executing device.

[0210] The signal encapsulation unit is used to encapsulate the instantaneous change characteristics, start-stop characteristics, first event tag and second event tag of the execution device into a standard data frame with a timestamp and device identifier according to a preset protocol, so as to obtain the monitoring data corresponding to the execution device of the target monitoring point.

[0211] In one embodiment, the first computing module includes: a model computing unit and a dynamic computing unit, wherein:

[0212] The model calculation unit is used to input the number of start-stop cycles, load change rate and temperature gradient into the corresponding energy consumption calculation model, and calculate the estimated carbon emission values ​​corresponding to the target monitoring points.

[0213] The dynamic calculation unit is used to obtain the real-time carbon emission rate of the power grid and calculate the first carbon emission monitoring value of the target monitoring point based on the estimated carbon emission value and carbon emission rate.

[0214] In one embodiment, the second calculation module includes: a device determination unit, a matrix construction unit, and a simulation calculation unit, wherein:

[0215] The device determination unit is used to determine the execution devices corresponding to multiple jobs in the job sequence to be executed, based on the task scheduling terminal.

[0216] The matrix construction unit is used to construct an execution matrix based on the first carbon emission monitoring value corresponding to the target monitoring point of the execution device; wherein, the execution matrix is ​​used to represent the initial execution order of multiple tasks to be executed under the initial decision;

[0217] The simulation and calculation unit is used to simulate the execution of multiple jobs to be executed in the execution matrix, calculate the running parameters of each job to be executed, and determine the target scheduling sequence for each job to reach the target state. The running parameters include time, cost and carbon emission parameters, and the target scheduling sequence is used to represent the execution order of multiple jobs to be executed when they reach the target equilibrium state.

[0218] In one embodiment, the warehouse energy consumption monitoring and scheduling system further includes a scheduling adjustment module, which comprises: a green electricity detection unit, a first task identification unit, a second task identification unit, an instruction generation unit, and an instruction distribution unit, wherein:

[0219] The green electricity detection unit is used to detect the current green electricity ratio of the power grid through the power grid green electricity ratio data interface;

[0220] The first task identification unit is used to identify non-urgent tasks in the target scheduling sequence if the green electricity ratio is greater than or equal to the first green electricity threshold, and adjust the execution time of the non-urgent tasks to be within the first target time window to obtain the adjusted target scheduling sequence; wherein, the first target time window is the time window in which the green electricity ratio is greater than or equal to the first green electricity threshold.

[0221] The second task identification unit is used to identify non-urgent tasks in the target scheduling sequence if the green electricity ratio is less than the second green electricity threshold, and adjust the execution time of the non-urgent tasks to the second target time window to obtain the adjusted target scheduling sequence; wherein, the second green electricity threshold is less than the first green electricity threshold, and the second target time window is the time window after the green electricity ratio is less than the second green electricity threshold.

[0222] The instruction generation unit is used to generate scheduling instructions for each execution device based on the adjusted target scheduling sequence.

[0223] The instruction distribution unit is used to send the scheduling instructions corresponding to the execution device to the corresponding execution device so that the corresponding job can be executed at the required execution time of each execution device.

[0224] In one embodiment, the warehouse energy consumption monitoring and scheduling system further includes a verification feedback module, which comprises: an actual monitoring value acquisition unit, a comparison unit, and a feedback correction unit, wherein:

[0225] The actual monitoring value acquisition unit is used to acquire the second carbon emission monitoring value; wherein, the second carbon emission monitoring value is the carbon emission monitoring value collected by the monitoring device deployed in the warehouse management system to monitor the actual carbon emissions;

[0226] The comparison unit is used to compare the first carbon emission monitoring value with the second carbon emission monitoring value to determine the carbon emission deviation value.

[0227] The feedback correction unit is used to correct the second carbon emission monitoring value based on the first carbon emission monitoring value if the carbon emission deviation value is greater than the preset deviation threshold, so as to determine the target carbon emission monitoring value, and feed the carbon emission deviation value back to the energy consumption calculation model to update the energy consumption calculation model.

[0228] Each module in the aforementioned warehouse energy consumption monitoring and scheduling system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0229] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores monitoring data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a warehouse energy consumption monitoring and scheduling method.

[0230] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0231] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described warehouse energy consumption monitoring and scheduling method.

[0232] In one embodiment, a readable storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the steps of the above-described warehouse energy consumption monitoring and scheduling method.

[0233] In one embodiment, a program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described warehouse energy consumption monitoring and scheduling method.

[0234] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and monitoring data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0235] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0236] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0237] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A warehouse energy consumption monitoring and scheduling method, characterized in that, The method is applied to a warehouse management system, which deploys multiple target monitoring points. The warehouse management system includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensor terminal corresponding to each target monitoring point. The method includes: Multiple monitoring data are obtained by acquiring current data and dynamic events from different target monitoring points through energy consumption sensing terminals corresponding to multiple target monitoring points; The carbon model calculation terminal identifies the feature information in the current data and the dynamic events to extract the corresponding monitoring feature data from each monitoring data. Based on the energy consumption calculation model deployed in the carbon model calculation terminal, the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located is calculated according to the monitoring feature data, and multiple first carbon emission monitoring values ​​corresponding to each target monitoring point are obtained. The task scheduling terminal calculates the execution order of the pending tasks issued by the warehouse management system based on the first carbon emission monitoring value to obtain the target scheduling sequence; wherein, the target scheduling sequence is used to schedule the execution equipment corresponding to the pending tasks in the warehouse management system. The monitoring feature data includes the number of start-stop cycles, load change rate, and temperature gradient of the execution equipment corresponding to the target monitoring point, and the energy consumption calculation model includes a calculation model corresponding to the number of start-stop cycles, the load change rate, and the temperature gradient. The step of the energy consumption calculation model deployed in the carbon model calculation terminal, which calculates the estimated carbon emission value corresponding to each target monitoring point where the monitoring data is located based on the monitoring characteristic data, and obtains multiple first carbon emission monitoring values ​​corresponding to each target monitoring point, includes: The number of start-stop cycles, the load change rate, and the temperature gradient are respectively input into the corresponding energy consumption calculation model, and the estimated carbon emission value corresponding to the target monitoring point is calculated. The real-time carbon emission rate of the power grid is obtained, and the first carbon emission monitoring value of the target monitoring point is calculated based on the estimated carbon emission value and the carbon emission rate. The step of solving the execution order of the pending job sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal to obtain the target scheduling sequence includes: Based on the task scheduling terminal, the execution devices corresponding to multiple tasks in the sequence of tasks to be executed are determined; An execution matrix is ​​constructed based on the first carbon emission monitoring value corresponding to the target monitoring point of the execution device; wherein, the execution matrix is ​​used to represent the initial execution order of multiple tasks to be executed under the initial decision; The execution of multiple jobs to be executed in the execution matrix is ​​simulated, the operating parameters of each job to be executed are calculated, and the target scheduling sequence for each job to be executed to reach the target state is determined; wherein, the operating parameters include time, cost and carbon emission parameters, and the target scheduling sequence is used to represent the execution order of multiple jobs to be executed to reach the target equilibrium state.

2. The method according to claim 1, characterized in that, The step of acquiring current data and dynamic events from different target monitoring points through energy consumption sensing terminals corresponding to multiple target monitoring points to obtain multiple monitoring data includes: By using a Hall current sensor installed at the target monitoring point, the instantaneous change characteristics of the operating current of the execution device corresponding to the target monitoring point are collected at a preset period, and the first event tag corresponding to the operating current of the execution device during operation is recorded. The start-stop characteristics of the target monitoring point are detected by a magnetic induction switch installed at the target monitoring point, and the second event tag corresponding to the start-stop characteristics of the execution device is recorded. The instantaneous change characteristics of the execution device, the start-stop characteristics, the first event tag, and the second event tag are encapsulated into a standard data frame with a timestamp and a device identifier according to a preset protocol to obtain the monitoring data corresponding to the execution device of the target monitoring point.

3. The method of claim 1, wherein, The task scheduling terminal is connected to the power grid green electricity ratio data interface in real time. After the step of solving the execution order of the pending operation sequence issued by the warehouse management system through the task scheduling terminal based on the first carbon emission monitoring value to obtain the target scheduling sequence, the method further includes: The current green electricity ratio of the power grid is detected through the power grid green electricity ratio data interface; If the green electricity ratio is greater than or equal to the first green electricity threshold, non-urgent tasks in the target scheduling sequence are identified, and the execution time of the non-urgent tasks is adjusted to be within the first target time window to obtain the adjusted target scheduling sequence; wherein, the first target time window is the time window in which the green electricity ratio is greater than or equal to the first green electricity threshold. If the green electricity ratio is less than the second green electricity threshold, non-urgent tasks in the target scheduling sequence are identified, and the execution time of the non-urgent tasks is adjusted to be within the second target time window to obtain the adjusted target scheduling sequence; wherein, the second green electricity threshold is less than the first green electricity threshold, and the second target time window is the time window after the green electricity ratio is less than the second green electricity threshold; Based on the adjusted target scheduling sequence, a scheduling instruction corresponding to each execution device is generated; The scheduling instruction corresponding to the execution device is sent to the corresponding execution device so that the corresponding job is executed at the execution time required by each execution device.

4. The method according to claim 1, characterized in that, After the step of calculating the estimated carbon emission value corresponding to the target monitoring point where each monitoring data is located based on the monitoring feature data, and obtaining multiple first carbon emission monitoring values ​​corresponding to each target monitoring point, the method further includes: Obtain a second carbon emission monitoring value; wherein the second carbon emission monitoring value is the carbon emission monitoring value collected by the monitoring device deployed in the warehouse management system to monitor the actual carbon emissions; The first carbon emission monitoring value is compared with the second carbon emission monitoring value to determine the carbon emission deviation value; If the carbon emission deviation value is greater than a preset deviation threshold, the second carbon emission monitoring value is corrected based on the first carbon emission monitoring value to determine the target carbon emission monitoring value, and the carbon emission deviation value is fed back to the energy consumption calculation model to update the energy consumption calculation model.

5. A warehouse energy consumption monitoring and dispatching system, characterized in that, The system is applied to a warehouse management system, which includes multiple target monitoring points. The warehouse management system includes a carbon model calculation terminal, a task scheduling terminal, and an energy consumption sensor corresponding to each target monitoring point. The system includes: The data acquisition module is used to acquire current data and dynamic events from different target monitoring points through energy consumption sensing terminals corresponding to multiple target monitoring points, and obtain multiple monitoring data. The feature extraction module is used to identify feature information in the current data and the dynamic events through the carbon model calculation terminal, so as to extract the corresponding monitoring feature data in each monitoring data. The first calculation module is used to calculate the estimated carbon emission value corresponding to each target monitoring point where the monitoring data is located, based on the energy consumption calculation model deployed in the carbon model calculation terminal and the monitoring feature data, thereby obtaining multiple first carbon emission monitoring values ​​corresponding to each target monitoring point; wherein, the monitoring feature data includes the number of start-stop cycles, load change rate, and temperature gradient of the execution equipment corresponding to the target monitoring point, and the energy consumption calculation model includes a calculation model corresponding to the number of start-stop cycles, the load change rate, and the temperature gradient; The second calculation module is used to solve the execution order of the pending operation sequence issued by the warehouse management system based on the first carbon emission monitoring value through the task scheduling terminal, so as to obtain the target scheduling sequence; wherein, the target scheduling sequence is used to schedule the execution equipment corresponding to the pending operation in the warehouse management system; The first calculation module is further configured to input the number of start-stop cycles, the load change rate, and the temperature gradient into the corresponding energy consumption calculation model, and calculate the estimated carbon emission value corresponding to the target monitoring point; obtain the real-time carbon emission rate of the power grid, and calculate the first carbon emission monitoring value of the target monitoring point based on the estimated carbon emission value and the carbon emission rate. The second calculation module is further configured to, based on the task scheduling terminal, determine the execution devices corresponding to multiple tasks in the sequence of tasks to be executed; construct an execution matrix according to the first carbon emission monitoring value corresponding to the target monitoring point of the execution device; wherein the execution matrix is ​​used to represent the initial execution order of multiple tasks to be executed under the initial decision; simulate the execution of multiple tasks to be executed in the execution matrix, calculate the operating parameters of each task to be executed, and determine the target scheduling sequence for each task to be executed to reach the target state; wherein the operating parameters include time, cost, and carbon emission parameters, and the target scheduling sequence is used to represent the execution order of multiple tasks to be executed to reach the target equilibrium state.

6. The warehouse energy consumption monitoring and dispatching system of claim 5, wherein, The target monitoring point is equipped with the energy consumption sensing terminal, which is designed as a multi-source sensing terminal. The multi-source sensing terminal includes: a Hall current sensor, a reed switch magnetic induction switch, a temperature sensor, and a monitoring meter. The carbon model computing terminal is designed as an edge AI carbon model computing box, which includes a chassis, processor, memory, custom chip and dual-channel data acquisition device; The task scheduling terminal is designed as a carbon sensing scheduling host, which is equipped with a CPU, GPU, first communication module and solid-state storage.

7. The warehouse energy consumption monitoring and dispatching system of claim 6, wherein, The warehouse energy consumption monitoring and scheduling system also includes: an equipment collaborative controller and a carbon footprint verification unit; The equipment coordination controller is connected to each of the target monitoring points via a CAN bus. The target monitoring points include an AGV scheduling system, a cold storage temperature controller, and a sorting control system. The carbon footprint verification unit is communicatively connected to the multi-source sensing terminal and the carbon model calculation terminal. The carbon footprint verification unit includes a second communication module, a comparison circuit, and a calibration interface.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

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