Intelligent matching method for energy consumption and task load of storage equipment

Abstract

The invention belongs to the technical field of warehousing energy consumption, and relates to an intelligent matching method for warehousing equipment energy consumption and task load, which comprises the following steps of: extracting the total number of current to-be-executed tasks, task emergency degree marks and standard operation duration of equipment; reading the current power value of the equipment, the working mode mark of the equipment and the residual electric quantity percentage of the equipment; acquiring a real-time power grid load rate in real time; executing a third-level matching decision; generating an equipment control instruction according to the matching decision result; triggering the controlled equipment to switch a power mode according to the equipment control instruction and execute a task; and verifying the task energy consumption matching degree, comparing the actual total energy consumption with the estimated energy consumption required by the task, and correcting the equipment energy consumption model parameters when the energy consumption deviation proportion exceeds a preset fault-tolerant threshold value. The problem that the energy scheduling strategy continuously deviates from the actual working condition is solved.

Description

Technical Field

[0001] This invention belongs to the technical field of warehouse energy consumption and relates to an intelligent matching method for energy consumption of warehouse equipment and task load. Background Technology

[0002] In current warehousing and logistics systems, equipment scheduling and energy management typically employ a separate control architecture. The task allocation module generates scheduling instructions based solely on task priority and equipment idle status, while the energy monitoring module independently collects grid load data. This fragmented management leads to the risk of power line overload when multiple high-power devices start simultaneously during the concentrated execution of high-priority tasks. Frequent circuit breaker tripping causes critical operations to be interrupted and equipment hardware damage.

[0003] Traditional solutions primarily rely on manual intervention based on experience or static threshold warnings. Maintenance personnel set fixed power limits based on historical data, and when the grid load exceeds the preset threshold, they trigger audible and visual alarms to manually delay tasks. Some systems employ a phased device startup strategy, but the batching rules lack dynamic adjustment capabilities. While these methods can temporarily alleviate overload problems, they cannot adapt to real-time changes in grid conditions and task combinations.

[0004] Based on the above problems, existing technologies have dynamic factors such as changes in energy consumption characteristics due to equipment aging and the impact of ambient temperature on battery power, which are not included in the decision-making model, causing the energy dispatch strategy to continuously deviate from the actual operating conditions. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides an intelligent matching method for energy consumption and task load of warehousing equipment.

[0006] A method for intelligently matching energy consumption and task load of warehousing equipment includes the following steps:

[0007] S1. Obtain information about the task queue to be executed, and extract the total number of tasks to be executed, the task urgency level flag, and the standard runtime of the device required for each task;

[0008] S2. Collect the operating status of physical devices, and read the current power value, device working mode flag, and remaining power percentage of the device;

[0009] S3. Read the power supply line load data to obtain the real-time power grid load rate;

[0010] S4. Perform a three-level matching decision:

[0011] The task urgency level flag is matched with the device operating mode flag to filter idle devices that can perform urgent tasks; the task standard runtime is matched with the remaining power percentage of the device to exclude devices with insufficient power to complete the task; the total number of tasks is matched with the real-time grid load rate to generate the matching decision result.

[0012] S5. Generate device control commands based on the matching decision results, including power adjustment parameters and task start time offset;

[0013] S6. Trigger the controlled device to switch power modes and execute tasks according to the device control command;

[0014] S7. Verify the energy consumption matching degree of the task, compare the actual total energy consumption with the estimated energy consumption required by the task, and correct the equipment energy consumption model parameters when the energy consumption deviation ratio exceeds the preset fault tolerance threshold.

[0015] A further aspect of the present invention involves obtaining information about the queue of tasks to be executed, including the following steps:

[0016] Connect to the task allocation port of the warehouse management system via the network interface and receive response data packets containing the queue of tasks to be executed.

[0017] Get the total number of tasks to be executed from the head of the current task queue or the global identifier;

[0018] Iterate through each task entry and extract its associated task urgency level markers;

[0019] Read the standard runtime of the device required for each task from the task metadata.

[0020] A further aspect of the present invention involves extracting the associated task urgency markers, including the following steps:

[0021] The priority identifiers attached to each task record, such as high, medium, and low, are based on a preset rule base. The preset rule base is pre-configured according to the warehousing business strategy and stores a set of rules that determine the urgency of a task based on the task type and business attributes.

[0022] When the task allocation port obtains the queue of tasks to be executed, it automatically calls the preset rule base to parse each task entry and dynamically assigns a three-level urgency label: high, medium, and low. High indicates immediate processing, medium indicates priority processing, and low indicates processing can be postponed.

[0023] A further aspect of the present invention involves collecting the operating status of physical devices, including the following steps:

[0024] The current power value of the equipment is read by a current sensing device pre-installed on the power control line of the storage equipment.

[0025] The device's operating mode marker is determined by analyzing the level signal status of the device's control signal lines;

[0026] The remaining battery percentage reported by the battery chip is read through the standard communication interface of the battery management system.

[0027] A further aspect of the present invention involves reading power line load data to obtain the real-time grid load rate, including the following steps:

[0028] The smart meter interface is connected to the main power distribution box of the warehouse via a serial communication interface, and data reading commands are sent at a preset fixed sampling frequency.

[0029] The smart meter response includes real-time data frames of three-phase voltage, current, and power. The total active power is calculated by summing the actual energy consumption rates in the three-phase AC lines.

[0030] The ratio of the real-time total active power collected by the smart meter to the rated capacity marked on the nameplate of the electrical box generates the real-time grid load rate.

[0031] A further aspect of the present invention involves performing a three-level matching decision, including the following steps:

[0032] The task urgency level flag and the device working mode flag are used for the first-level matching and filtering to select idle devices or devices in an equivalent available state that can perform urgent tasks as the candidate device set;

[0033] The task's standard runtime is combined with the device's remaining battery percentage and the device's energy consumption model for a second-level matching, excluding devices with insufficient power to complete the task.

[0034] The total number of tasks in the second-level matching results is counted and compared with the read real-time power grid load rate. If the real-time power grid load rate exceeds the preset safety threshold, the task is not processed immediately.

[0035] A further aspect of this invention provides an equipment energy consumption model that satisfies the following formula:

[0036]

[0037] E avl =SOC×C rated

[0038] If E req >E avl The device was determined to have insufficient power to complete the task.

[0039] E req Estimate the energy consumption required for the mission; P avgThe historical average power value of the equipment is the historical average power value of a specific equipment model under a specific task type, based on statistical analysis of historical operating data; T std For the standard runtime obtained; E avl The actual usable power of the device; SOC is the remaining power percentage, data collected by the battery management system; C rated This refers to the rated capacity of the distribution box.

[0040] A further aspect of the present invention involves generating device control commands based on the matching decision results, comprising the following steps:

[0041] Calculate power adjustment parameters based on the device's remaining battery percentage and the standard runtime of the task;

[0042] The task start time offset is calculated based on the difference between the real-time power grid load rate and the preset safety threshold.

[0043] The compilation power adjustment parameters and task start time offset are standardized device control instructions.

[0044] A further aspect of the present invention involves triggering the controlled device to switch power modes and execute tasks according to device control commands, including the following steps:

[0045] Parse the power adjustment parameters and task start time offset of the device control commands;

[0046] The equipment controller switches the operating power of the controlled equipment to the power mode specified by the equipment control command;

[0047] An internal timer is started, and the task execution signal is triggered when the accumulated value of the timer reaches the set value of the task start time offset.

[0048] A further aspect of this invention verifies the energy consumption matching degree of a task, including the following steps:

[0049] The actual power value of the equipment is continuously collected by the deployed current sensing device to form a complete time and power curve;

[0050] The integral value of the time-power curve over the duration of the task is used as the actual total energy consumption.

[0051] Calculate the energy consumption deviation ratio between the actual total energy consumption and the estimated energy consumption required for the task;

[0052] When the energy consumption deviation ratio exceeds the preset fault tolerance threshold, update the historical average power value of the equipment;

[0053] The corrected historical average power value of the device is written into the device energy consumption model, and parameter correction log records are generated and updated for subsequent matching decision calculations.

[0054] In summary, the present invention has the following beneficial technical effects:

[0055] 1. By establishing a three-level dynamic matching mechanism based on task urgency, equipment operating status, and grid load, deep coordination between warehouse operation scheduling and energy supply is achieved. During the task allocation phase, the equipment's battery life and grid safety thresholds are considered simultaneously, automatically delaying non-urgent tasks to avoid load peaks, thus eliminating the risk of equipment overload power outages and grid tripping at the system level.

[0056] 2. By embedding power regulation parameters and task start time offsets into the equipment control commands, the controlled equipment is given the ability to dynamically respond to the energy environment. The equipment automatically adjusts its power mode and delays start-up based on the real-time grid load rate, ensuring the execution of high-priority tasks while achieving a balanced temporal and spatial distribution of power load;

[0057] 3. A closed-loop verification mechanism based on actual energy consumption and expected values ​​drives continuous self-optimization of the equipment energy consumption model. After each task execution, the energy consumption deviation ratio is automatically compared, and the average power parameters of the equipment are dynamically corrected when the deviation exceeds the fault tolerance threshold. This self-learning characteristic effectively overcomes the model inaccuracy caused by interference factors such as equipment aging and environmental temperature and humidity, ensuring that the energy dispatch strategy maintains optimal adaptability throughout its entire life cycle. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings are used to provide a further understanding of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart illustrating an embodiment of this application is disclosed.

[0060] Figure 2 Structural schematic diagrams of embodiments of this application are disclosed. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The following is in conjunction with the appendix Figures 1-2 A preferred description of the present invention is provided below.

[0063] See attached document Figure 1This invention proposes an intelligent matching method for energy consumption and task load of warehousing equipment, comprising the following steps:

[0064] S1. Obtain information about the task queue to be executed, and extract the total number of tasks to be executed, the task urgency level flag, and the standard runtime of the device required for each task;

[0065] S2. Collect the operating status of physical devices, and read the current power value, device working mode flag, and remaining power percentage of the device;

[0066] S3. Read the power supply line load data to obtain the real-time power grid load rate;

[0067] S4. Perform a three-level matching decision:

[0068] The task urgency level flag is matched with the device operating mode flag to filter idle devices that can perform urgent tasks; the task standard runtime is matched with the remaining power percentage of the device to exclude devices with insufficient power to complete the task; the total number of tasks is matched with the real-time grid load rate to generate the matching decision result.

[0069] S5. Generate device control commands based on the matching decision results, including power adjustment parameters and task start time offset;

[0070] S6. Trigger the controlled device to switch power modes and execute tasks according to the device control command;

[0071] S7. Verify the energy consumption matching degree of the task, compare the actual total energy consumption with the estimated energy consumption required by the task, and correct the equipment energy consumption model parameters when the energy consumption deviation ratio exceeds the preset fault tolerance threshold.

[0072] In one embodiment of the present invention, step S1 includes the following steps:

[0073] First, technicians connect to the task allocation port of the warehouse management system via a network interface. They send query requests to this port and receive a response data packet containing the queue of tasks currently pending execution. The task allocation port provides access to the task queue based on standard communication protocols (such as HTTP or TCP / IP).

[0074] Subsequently, the response data packet is parsed to extract the following information: the total number of tasks to be executed is obtained from the current task queue header or global identifier; each task entry is traversed to extract its associated task urgency flag; and the standard runtime required by the device for each task is read from the task metadata. The parsing process employs a data format parser, such as a JSON or XML parser, to ensure accurate information extraction and storage in a temporary cache. The entire process is executed periodically via a polling mechanism, which involves proactively sending requests to the target device or service at fixed time intervals to update the status of the task queue in real time.

[0075] The task allocation port refers to the network communication endpoint set up by the warehouse management system, used by external systems to query current task allocation information. Its address and protocol are pre-defined in the warehouse management system configuration file. The total number of tasks to be executed refers to the total number of task entries directly counted from the queue data, obtained by traversing and accumulating the task list based on the response data packets.

[0076] Standard runtime refers to the estimated time required for the equipment to execute each task, calculated based on historical operation data or standard operating schedules. Task urgency markers are priority identifiers attached to each task record, such as high, medium, and low levels. These are based on a preset rule base, pre-configured by the system administrator according to warehousing business strategies. The rule base stores a set of rules that determine task urgency based on task type (inbound, outbound, inventory, replenishment) and business attributes (order priority, deadline). When the task allocation port retrieves the queue of tasks to be executed, it automatically calls the preset rule base to parse each task entry and dynamically assigns a high, medium, or low urgency marker. "High" indicates immediate processing, "medium" indicates priority processing, and "low" indicates processing can be postponed. Examples include "Bonded warehouse outbound task → High," "Shelf replenishment task → Medium," and "Inventory cycle count → Low urgency."

[0077] For example, the task allocation port address is set to an internal network IP, and a query command is sent using an HTTP GET request. The response packet returns a queue containing two tasks: the first task entry is marked as high urgency with a standard runtime of 40 minutes; the second task entry is marked as medium urgency with a standard runtime of 55 minutes. The total number of tasks to be executed is obtained by iterating through the task list and summing the results, resulting in a confirmation value of 2.

[0078] In one embodiment of the present invention, step S2 includes the following steps:

[0079] After acquiring the queue of tasks to be executed, the operating parameters of the physical equipment are collected in real time using a current sensing device pre-installed on the power control circuit of the storage equipment. The current sensing device is coupled to the main circuit of the equipment via a non-invasive current clamp, and reads the waveform data of voltage and current at a preset fixed sampling frequency to calculate the current power value of the equipment. The current power value of the equipment refers to the actual energy consumption rate of the equipment, calculated by multiplying the read voltage and current values. The operating mode of the equipment is determined by analyzing the level signal status of the equipment control signal lines.

[0080] For mobile devices, the remaining battery percentage reported by the battery chip is additionally read through the standard communication interface of the battery management system, such as RS485 or CAN bus. All collected data is verified and written to the device's buffer, forming an association mapping with the task queue information obtained in step S1.

[0081] Among them, the current sensing device refers to a non-contact current sensor in the power supply line of the series or parallel equipment, and its model and specifications must match the rated current range of the equipment. The remaining battery percentage refers to the ratio of the current usable capacity of the mobile device's battery to its rated total capacity, which is directly obtained through the power metering chip built into the battery management system. The equipment operating mode marker is a digital code representing the current operating state of the equipment, obtained through parsing the signal level of the equipment controller or bus commands; for example, "idle mode" corresponds to a low-level signal, and "operating mode" corresponds to a high-level signal.

[0082] For example, stacker cranes and electric forklifts were selected for verification. A current sensor was installed on the main power line of the stacker crane control cabinet. When the equipment performed a lifting action, the calculated current power value was approximately 8.5 kW. By analyzing the level signal status of the equipment's control signal line, a high-level signal corresponding to the "operating mode" was detected at its PLC output port. For the electric forklift, in addition to collecting a power value of 5.2 kW and the "handling mode" flag, the remaining battery percentage was obtained by parsing the battery message via the CAN bus, showing 72%. After verification, the data was written to the equipment's buffer, corresponding to the "standard runtime" of the task queue information obtained in step S1, thus verifying the collaborative acquisition capability of multi-source heterogeneous data.

[0083] In one embodiment of the present invention, step S3 includes the following steps:

[0084] After collecting the equipment's operating status, the data is connected to the smart meter interface of the warehouse's main power distribution box via a serial communication interface, typically an RS485 or Modbus protocol terminal. Data read commands are sent at a preset fixed sampling frequency. The smart meter response includes real-time data frames of three-phase voltage, current, and power, based on which the real-time grid load rate of the current main power distribution line is calculated.

[0085] Real-time grid load factor is defined as the ratio of the real-time total active power collected by the smart meter to the rated capacity marked on the nameplate of the electrical box. The result is stored in the device's cache as a percentage and forms a complete operating condition view together with the task queue information to be executed obtained in step S1 and the device operating status obtained in step S2.

[0086] The smart meter interface refers to the standard data output port of the compliant metering device installed in the main distribution box. Real-time grid load factor refers to the ratio of the real-time total active power collected by the smart meter to the rated capacity of the distribution box. The rated capacity of the distribution box refers to the maximum safe power value allowed by the power supply line design, measured in kilowatts. The rated capacity of the distribution box is a fixed constant, calculated based on the product of the rated current and nominal voltage of the circuit breaker in the distribution box. Real-time total active power refers to the sum of the actual energy consumption rates in the three-phase AC line, calculated by directly adding the instantaneous power values ​​of phases A, B, and C measured by the smart meter, also measured in kilowatts.

[0087] For example, after configuring communication parameters, a read command is sent to the smart meter to obtain data frames showing the actual power consumption rate of phase A as 12.3 kW, phase B as 10.8 kW, and phase C as 11.5 kW. The total actual power consumption rate of the three-phase AC line is calculated to be 34.6 kW. The rated capacity marked on the distribution box nameplate is 50 kW, and the real-time grid load rate is 34.6 divided by 50, which is 69.2%. The real-time grid load rate is logically correlated with the power data of the stacker crane (8.5 kW) and forklift (5.2 kW) recorded in the equipment's buffer area, verifying the collaborative monitoring capability between power supply data and equipment operating status.

[0088] In one embodiment of the present invention, step S4 includes the following steps:

[0089] After data acquisition is completed in steps S1-S3, the system initializes the matching decision engine;

[0090] Perform the first-level matching, iterate through the urgency marker of each task in the task queue to be executed obtained in step S1 and the device working mode marker collected in step S2, select only devices with the working mode marker "idle mode" or equivalent available state as the candidate device set, and associate them with tasks with the urgency marker "high" or equivalent high priority to generate a preliminary matching result list.

[0091] The second-level matching is performed by combining multiple task urgency markers and device operating mode markers from the first-level matching results. The standard runtime of the device in the first-level matching results is combined with the remaining battery percentage of the device from step S2, along with the device energy consumption model (calculating estimated power consumption and actual available power), to perform the second-level matching, excluding devices with insufficient power to complete the task. Based on the preset device energy consumption model, the following formula is satisfied:

[0092]

[0093] E avl =SOC×C rated

[0094] Judgment condition: If it is E req >E avl The device was determined to have insufficient power to complete the task.

[0095] Among them, E req The estimated energy consumption required for the mission, expressed in watt-hours (Wh); P avg T represents the historical average power value of the equipment, expressed in watts (W). It is the historical average power value of a specific equipment model under a specific task type, based on statistical analysis of historical operating data. std The standard runtime obtained in step S1 is in minutes (min).

[0096] E avl This indicates the actual available power of the device, in watt-hours (Wh); SOC indicates the remaining percentage of power, data collected by the battery management system in step S2; C rated The rated capacity of the distribution box is expressed in watt-hours (Wh).

[0097] Perform the third-level matching, count the total number of tasks in the second-level matching results, and compare it with the real-time grid load rate read in step S3. When the real-time grid load rate exceeds the preset safety threshold, automatically delay tasks in the task queue marked with "medium", "low" or equivalent low priority.

[0098] Periodically (e.g., initially every 5 minutes, and shortened to 3 minutes if the fluctuation amplitude is >10% based on historical load fluctuation data analysis; extended to 10 minutes if the fluctuation amplitude is <5%), the real-time grid load rate is checked. When the real-time grid load rate is lower than a certain percentage of the preset safety threshold (e.g., initially set to 5% of the preset safety threshold, the percentage increases to 7%-8% if the system oscillates frequently; the percentage decreases to 3%-4% if the task is delayed for a long time), the tasks in the delay queue are automatically added back to the task queue to be executed, triggering a new round of three-level matching decision-making. The decision result is output to the task allocation port for updating.

[0099] The device operating mode marker is a digital code representing the current operating state of the device. For example, "idle mode" corresponds to a low-level signal, and "running mode" corresponds to a high-level signal. This is obtained through parsing the device controller signal level or bus commands. The "idle mode" state corresponds to a specific digital code, indicating that the device is in an acceptable task state. The preset safety threshold refers to the upper limit of the pre-configured real-time grid load rate, stored in the system configuration file, for example, 75%, and is used for the third-level matching trigger delay mechanism.

[0100] For example, step S1 obtains a task queue containing two tasks: Task 1 is marked as "High" with a standard runtime of 30 minutes; Task 2 is marked as "Medium" with a standard runtime of 45 minutes. Step S2 collects device status: Device A's operating mode is marked as "Idle Mode" with a remaining battery percentage of 80%; Device B's operating mode is marked as "Running Mode" with a remaining battery percentage of 60%. Step S3 reads the real-time grid load rate as 65%, with a preset safety threshold of 70%.

[0101] The first level of matching selects device A (idle mode) as capable of executing Task 1. Task 2 is not matched because device B (running mode) is not in idle mode. The second level of matching checks device A's battery level. The estimated power consumption is calculated by multiplying 30 minutes (0.5 hours) by device A's average power of 300 watts, resulting in 150 watt-hours. The actual available power is calculated by multiplying 80% by the battery's rated capacity of 200 watt-hours, resulting in 160 watt-hours. Since 150 watt-hours is less than 160 watt-hours, the power is sufficient and not excluded. The third level of matching has a total of 1 task. The real-time grid load rate is 65%, which is lower than the preset safety threshold of 70%, so no task is delayed. If the real-time grid load rate is 80%, which exceeds the preset safety threshold of 70%, then Task 2 is automatically delayed.

[0102] In one embodiment of the present invention, step S5 includes the following steps:

[0103] After completing the three-level matching decision in step S4, a preliminary matching result list is extracted (including multiple combinations of task urgency markers and device operating mode markers), and a device control command is generated for each combination of task urgency markers and device operating mode markers. The device control command includes power adjustment parameters and task start time offset;

[0104] The power adjustment parameters are calculated based on the remaining power percentage of the device and the standard operating time. The remaining power percentage is converted into an adjustable power range through the device energy consumption model, and the device control commands are embedded to satisfy the following formula:

[0105]

[0106] Among them, P setE is the power regulation parameter. avl T represents the actual available power of the equipment. std P represents the standard runtime obtained in step S1; max This indicates the maximum allowable operating power of the equipment, obtained from the equipment's technical parameters, to avoid exceeding the equipment's physical limits.

[0107] The task start time offset is calculated by comparing the real-time grid load rate obtained in step S3 with a preset safety threshold. If the real-time grid load rate exceeds the preset safety threshold, the offset time value is automatically calculated, and the start of "medium" and "low" tasks is delayed to avoid peak load, satisfying the following formula:

[0108]

[0109] Where Δt represents the task start time offset; L real L represents the real-time grid load factor calculated in step S3; thres represents the preset safety threshold; k represents the base time constant. The base time constant is determined based on the real-time grid load rate and the sensitivity of the task delay. It is usually set to 5-15 seconds for every 1% increase in the real-time grid load rate over the preset safety threshold (e.g., the initial value is set to 10 seconds / %, calibrated by the duration of safe operation of the equipment in historical overload events).

[0110] Device control commands are transmitted in real time via standardized formats, such as JSON or Modbus protocol messages, through the device controller's network interface, such as Ethernet or CAN bus. The generation of device control commands is handled by a command compilation engine, which is responsible for translating task decision results (power adjustment parameters and task start time offset) into recognizable standardized control commands, such as JSON or Modbus protocol messages. This ensures that device control commands are accurately mapped to the corresponding device controllers and updates the status of the task allocation ports.

[0111] The power regulation parameter refers to the upper limit of power that the equipment is allowed to adjust when performing a task. The equipment controller is deployed in the hardware of the power terminal equipment and sends equipment control commands in real time via a network interface, such as Ethernet or a CAN bus. The task start time offset is the delay between the actual task start time and the planned time; the task start time offset takes effect when the matching result is a non-urgent task. The equipment control command is a complete sequence of control commands containing the power regulation parameter and the task start time offset, and its format follows the communication protocol specified by the equipment controller.

[0112] For example, based on the verification example in step S4, task 1 is assigned to device A, the task urgency level is marked as "high", the standard task runtime is 30 minutes; device A has 80% remaining battery power, the real-time grid load rate is 65%, and the preset safety threshold is 70%. The estimated total energy consumption is calculated by multiplying 30 minutes (0.5 hours) by the average power of device A (300 watts) to obtain 150 watt-hours, and the available energy is calculated by multiplying 80% by the battery's rated capacity (200 watt-hours) to obtain 160 watt-hours. Therefore, the power adjustment parameter is set to a maximum of 4.8 kilowatts.

[0113] The real-time grid load rate is 65%, which is lower than the preset safety threshold of 70%. Substituting this into the formula, we know that the task start time offset is zero. The device control command is compiled into a JSON format message, containing the power adjustment parameter of 4.8 kW and the task start time offset of 0 seconds, and sent to the device A controller via the Ethernet interface.

[0114] If the real-time grid load rate rises to 80% and the task urgency is marked as "medium", the task start time offset is calculated by multiplying the excess ratio (80%-70%=10%) by 10 seconds / % to obtain 100 seconds. Based on this, the process of generating and sending equipment control commands can be reproduced.

[0115] In one embodiment of the present invention, step S6 includes the following steps:

[0116] After receiving the equipment control command sent in step S5, the equipment controller parses the power adjustment parameters and task start time offset of the equipment control command. The equipment controller switches the operating power of the controlled equipment to the power mode specified by the equipment control command. This process is achieved by adjusting the output frequency of the frequency converter or modifying the current limit of the motor driver. An internal timer is started. When the accumulated value of the timer reaches the set value of the task start time offset, the task execution signal is triggered. The equipment begins to execute the operation according to the original task parameters of the warehouse management system. The actual power value is fed back to the system status monitoring terminal in real time through the current sensing device, forming an execution closed loop.

[0117] In this context, the controlled equipment refers to the storage equipment entity that successfully receives and parses the equipment control command sent in step S5, and its equipment controller must have protocol parsing and power regulation functions. Switching to the power mode specified by the equipment control command refers to the behavior of the equipment controller modifying its own operating power limit according to the power regulation parameters of the equipment control command. This is achieved by changing the duty cycle of the power electronic devices in the drive circuit or adjusting the output frequency of the frequency converter, so that the actual power value of the equipment during task execution does not exceed the set power limit value.

[0118] The time offset setting value refers to the task start delay duration defined by the equipment control command in step S5. This delay is counted down by the internal clock module of the equipment controller, and the task execution signal is triggered when the countdown reaches zero. The task execution signal refers to the equipment performing material handling, stacking, and other operations according to the preset task flow of the warehouse management system. This flow is controlled by the equipment's local program, and task parameters are obtained through real-time communication between the equipment controller and the warehouse management system.

[0119] For example, the equipment controller receives a JSON-formatted equipment control command with a power adjustment parameter of 4.8 kW and a task start time offset of 100 seconds. The equipment controller immediately sets the stacker crane's lifting motor power limit to 4.8 kW, and the current sensor detects that the actual power increases from 0.2 kW in standby mode to 4.5 kW (below the limit). Simultaneously, a 100-second countdown begins, after which the stacker crane automatically executes the rack lifting command from the task queue.

[0120] In one embodiment of the present invention, step S7 includes the following steps:

[0121] During task execution, the current sensing device deployed in step S2 continuously collects the actual power value of the equipment, forming a complete time-power curve. After the task ends, the integral value of this curve over the task duration is calculated as the actual total energy consumption, and compared with the expected energy consumption calculated in the matching decision stage of step S4 based on the task's standard runtime and the equipment's average power value. When the absolute difference between the actual total energy consumption and the expected energy consumption exceeds a preset fault tolerance threshold, the system automatically initiates a parameter correction process, satisfying the following formula:

[0122]

[0123] Among them, E actual P represents the actual total energy consumption; i This represents the current power value of the i-th sampling point collected in step S2; T represents the fixed sampling frequency, which is a fixed configuration of the system; E req The estimated energy consumption required for the task calculated in step S4; δ represents the energy consumption deviation ratio.

[0124] Triggering conditions for parameter correction process:

[0125]

[0126] Where, δ tol The preset fault tolerance threshold refers to the maximum percentage by which actual energy consumption deviates from the expected value, which is preset based on engineering experience or differences in equipment type. P represents the corrected average power value. avg This refers to the historical average power value of the device in step S4.

[0127] Parameter correction process: Based on the direction and magnitude of the deviation, the average power value of the device in the device energy consumption model is adjusted proportionally. The correction result takes effect immediately in subsequent matching calculations, and a correction log is generated at the same time. The verification process is automatically triggered after each task is completed.

[0128] The time-power curve is a continuous function graph of power changing over time during the device's task execution, generated by connecting discrete power data points collected by a current sensing device at a fixed sampling frequency. Actual total energy consumption refers to the total amount of electrical energy consumed by the device in performing a specific task, obtained by integrating the time-power curve between the start and end times of the task. The corrected average power value is immediately updated to the device's energy consumption model parameters.

[0129] For example, after the stacker crane performs a lifting task, a time and power curve is collected for 30 minutes, and the actual total energy consumption is calculated to be 162 watt-hours. In step S4, the matching decision stage uses the historical average power value of the equipment, 5 kilowatts, to calculate the expected energy consumption as 150 watt-hours (5 kilowatts × 0.5 hours). The preset fault tolerance threshold is 15%, and the energy consumption deviation ratio is calculated as |162-150| / 150 = 8%, which is lower than the preset fault tolerance threshold of 15%, so no correction is triggered.

[0130] If the actual total energy consumption reaches 180 watt-hours, the energy consumption deviation ratio of 20% exceeds the preset fault tolerance threshold of 15%. The new average power value is calculated according to the correction formula as 5 kW × (180 ÷ 150) = 6 kW. The corrected average power value is updated to the energy consumption model parameters of the device. In the subsequent step S4, when matching new tasks, the power consumption of the device will be determined using the corrected value of 6 kW to calculate the expected energy consumption.

[0131] See appendix Figure 2 The present invention also proposes an intelligent matching system for energy consumption and task load of warehousing equipment, comprising the following modules:

[0132] The task queue acquisition module is used to extract the total number of tasks currently to be executed, the task urgency level marker, and the standard runtime of the device required for each task.

[0133] The equipment status monitoring module is used to collect the operating status of physical equipment, read the current power value of the equipment, the equipment working mode flag, and the remaining power percentage of the equipment;

[0134] The power grid load monitoring module is used to read power supply line load data and obtain the real-time power grid load rate.

[0135] The three-level matching decision module is used to perform three-level matching decisions: the first level of matching is to filter idle devices that can perform urgent tasks by matching task urgency level tags with device operating mode tags; the second level of matching is to match task standard runtime with device remaining power percentage to exclude devices with insufficient power to complete the task; the third level of matching is to match the total number of tasks with real-time grid load rate to generate matching decision results.

[0136] The instruction generation module generates device control instructions based on the matching decision results, including power adjustment parameters and task start time offset.

[0137] The equipment control execution module is used to trigger the controlled equipment to switch power modes and execute tasks according to equipment control instructions;

[0138] The energy consumption verification and correction module is used to verify the matching degree of task energy consumption, compare the actual total energy consumption with the estimated energy consumption required by the task, and correct the equipment energy consumption model parameters when the energy consumption deviation ratio exceeds the preset fault tolerance threshold.

[0139] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization methods (such as normalization, dimensionless parameter conversion, or unit system unification), can translate physical quantities with different properties into unitless standard values ​​or superimposed parameters of the same dimension. This eliminates the interference of different dimensions on the computational logic, allowing the formulas to retain the original data distribution characteristics while possessing mathematical rationality and adaptability to objective laws. The descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the invention.

[0140] Each of the modules can be implemented in whole or in part through software, hardware, or a combination thereof. It supports hardware embedded in or independent of the processor in the computer device, and also supports software stored in the memory of the computer device, so that the processor can call and execute the operations corresponding to each of the above modules.

[0141] It should be noted that the human information (including but not limited to human device information and personal information) and data (including but not limited to data used for analysis, data stored and data displayed) involved in this invention are all information and data authorized by the human body or fully authorized by all parties. The collection, use and processing of related data require relevant legal standards.

[0142] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for intelligently matching energy consumption and task load of warehousing equipment, characterized in that, Includes the following steps: S1. Obtain information about the task queue to be executed, and extract the total number of tasks to be executed, the task urgency level flag, and the standard runtime of the device required for each task; S2. Collect the operating status of physical devices, and read the current power value, working mode flag, and remaining power percentage of the devices; S3. Read the power supply line load data to obtain the real-time power grid load rate; S4. Perform a three-level matching decision: The task urgency level flag is matched with the device operating mode flag to filter idle devices that can perform urgent tasks; the task standard runtime is matched with the remaining power percentage of the device to exclude devices with insufficient power to complete the task; the total number of tasks is matched with the real-time grid load rate to generate the matching decision result. S5. Generate device control instructions based on the matching decision results, including power adjustment parameters and task start time offset; S6. Trigger the controlled device to switch power modes and execute tasks according to the device control command; S7. Verify the energy consumption matching degree of the task, compare the actual total energy consumption with the estimated energy consumption required by the task, and correct the equipment energy consumption model parameters when the energy consumption deviation ratio exceeds the preset fault tolerance threshold.

2. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 1, characterized in that, Obtaining information about the queue of tasks to be executed includes the following steps: Connect to the task allocation port of the warehouse management system via the network interface and receive response data packets containing the queue of tasks to be executed. Get the total number of tasks to be executed from the head of the current task queue or the global identifier; Iterate through each task entry and extract its associated task urgency level markers; Read the standard runtime of the device required for each task from the task metadata.

3. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 2, characterized in that, Extracting its associated task urgency level markers includes the following steps: The priority identifiers attached to each task record, such as high, medium, and low, are based on a preset rule base. The preset rule base is pre-configured according to the warehousing business strategy and stores a set of rules that determine the urgency of a task based on the task type and business attributes. When the task allocation port obtains the queue of tasks to be executed, it automatically calls the preset rule base to parse each task entry and dynamically assigns a three-level urgency label: high, medium, and low. High indicates immediate processing, medium indicates priority processing, and low indicates processing can be postponed.

4. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 1, characterized in that, Collecting the operating status of physical devices includes the following steps: The current power value of the equipment is read by a current sensing device pre-installed on the power control line of the storage equipment. The device's operating mode marker is determined by analyzing the level signal status of the device's control signal lines; The remaining battery percentage reported by the battery chip is read through the standard communication interface of the battery management system.

5. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 1, characterized in that, To read power line load data and obtain real-time grid load rate, the following steps are included: The smart meter interface is connected to the main power distribution box of the warehouse via a serial communication interface, and data reading commands are sent at a preset fixed sampling frequency. The smart meter response includes real-time data frames of three-phase voltage, current, and power. The total active power is calculated by summing the actual energy consumption rates in the three-phase AC lines. The ratio of the real-time total active power collected by the smart meter to the rated capacity marked on the nameplate of the electrical box generates the real-time grid load rate.

6. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 5, characterized in that, Performing a three-level matching decision includes the following steps: The task urgency level flag and the device working mode flag are used for the first-level matching and filtering to select idle devices or devices in an equivalent available state that can perform urgent tasks as the candidate device set; The task's standard runtime is combined with the device's remaining battery percentage and the device's energy consumption model for a second-level matching, excluding devices with insufficient power to complete the task. The total number of tasks in the second-level matching results is counted and compared with the read real-time power grid load rate. If the real-time power grid load rate exceeds the preset safety threshold, the task is not processed immediately.

7. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 6, characterized in that, The equipment energy consumption model satisfies the following formula: AND avl =SOC×C rated If E req >E avl The device was determined to have insufficient power to complete the task. E req Estimate the energy consumption required for the mission; P avg The historical average power value of the equipment is the historical average power value of a specific equipment model under a specific task type, based on statistical analysis of historical operating data; T std For the standard runtime obtained; E avl The actual usable power of the device; SOC is the remaining power percentage, data collected by the battery management system; C rated This refers to the rated capacity of the distribution box.

8. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 7, characterized in that, Based on the matching decision results, device control commands are generated, including the following steps: Calculate power adjustment parameters based on the device's remaining battery percentage and the standard runtime of the task; The task start time offset is calculated based on the difference between the real-time power grid load rate and the preset safety threshold. The compilation power adjustment parameters and task start time offset are standardized device control instructions.

9. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 8, characterized in that, Triggering the controlled device to switch power modes and execute tasks according to device control commands includes the following steps: Parse the power adjustment parameters and task start time offset of the device control commands; The equipment controller switches the operating power of the controlled equipment to the power mode specified by the equipment control command; An internal timer is started, and the task execution signal is triggered when the accumulated value of the timer reaches the set value of the task start time offset.

10. The intelligent matching method for energy consumption and task load of warehousing equipment according to claim 9, characterized in that, Verifying the energy consumption matching of the task includes the following steps: The actual power value of the equipment is continuously collected by the deployed current sensing device to form a complete time and power curve; The integral value of the time-power curve over the duration of the task is used as the actual total energy consumption. Calculate the energy consumption deviation ratio between the actual total energy consumption and the estimated energy consumption required for the task; When the energy consumption deviation ratio exceeds the preset fault tolerance threshold, update the historical average power value of the equipment; The corrected historical average power value of the device is written into the device energy consumption model, and parameter correction log records are generated and updated for subsequent matching decision calculations.

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