Energy management method and system for industrial mobile equipment

Abstract

The invention relates to the technical field of energy management, in particular to an energy management method and system for industrial mobile equipment. The method comprises the following steps: performing main chip communication on the stacked mainboard through a preset built-in bus, and recording communication transmission information; collecting battery electric quantity data of the communication transmission information, if it is detected that the battery electric quantity data is lower than a preset electric quantity threshold value, triggering a battery charging instruction, and continuously adjusting charging parameters; designing a heat dissipation structure according to the charging parameters, and simulating a high-energy-consumption task; determining the load energy consumption of the high-energy-consumption task, and dynamically adjusting the equipment power consumption based on the load energy consumption; adjusting the charging power of the charging parameters based on the equipment power consumption; and performing equipment energy management by using the charging power, and recording the energy management efficiency. According to the invention, energy management of the stacked mainboard is realized based on an energy management technology, and the energy utilization rate and the charging efficiency of industrial mobile equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to an energy management method and system for industrial mobile equipment. Background Technology

[0002] Currently, industrial mobile devices are widely used in manufacturing, warehousing and logistics, and intelligent inspection. However, their energy management methods still have many limitations in dealing with multi-tasking operations, high-power applications, and complex charging environments. Most devices rely on fixed charging modes or simple power monitoring mechanisms for energy consumption control, lacking the ability to jointly analyze real-time task load, heat dissipation status, and charging power, resulting in insufficient adaptability and refinement of energy management strategies. Relying solely on preset power thresholds or single charge-discharge curves for battery management ignores multi-dimensional factors such as instantaneous energy consumption fluctuations under high-load tasks, differences between different charging points, and equipment heat dissipation conditions, leading to significant deviations between actual operating energy consumption and expected control targets. Existing energy management optimization mechanisms mostly employ fixed power consumption limits or static scheduling methods, which are difficult to respond to in real-time complex situations such as sudden increases in task energy consumption, abnormal interface status, and multi-point charging switching, lacking intelligent and adaptive energy response capabilities. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide an energy management method and system for industrial mobile equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an energy management method for industrial mobile equipment includes the following steps: Step S1: Communicate with the stacked motherboard via the preset built-in bus to the main chip and record the communication transmission information; Step S2: Collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, trigger a battery charging command and continuously adjust the charging parameters. Step S3: Design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks and dynamically adjust the device power consumption based on the load energy consumption. Step S4: Adjust the charging power based on the device power consumption; use the charging power to manage device energy and record the energy management efficiency.

[0005] Preferably, this specification also provides an energy management system for industrial mobile equipment, for executing the energy management method for industrial mobile equipment as described above, the energy management system for industrial mobile equipment comprising: The communication transmission module is used to communicate with the stacked motherboard via a preset built-in bus and record the communication transmission information. The charging management module is used to collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, a battery charging command is triggered to continuously adjust the charging parameters. The power consumption adjustment module is used to design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks, and dynamically adjust the device power consumption based on the load energy consumption. The energy management module is used to adjust the charging power based on the device's power consumption; it also uses the charging power to manage the device's energy and records the energy management efficiency.

[0006] The beneficial effects of this invention are as follows: (1) By combining the main chip communication and impedance matching design of the stacked motherboard built-in bus, high-precision acquisition of communication transmission information between the core board and the expansion board is achieved, ensuring the stability of battery power data and operating parameters acquisition, and improving the reliability of subsequent energy management process.

[0007] (2) By adopting a joint judgment mechanism of battery power threshold detection and charging point identification, wired fast charging instructions, wireless charging instructions and POGOPin charging dock instructions can be automatically generated, realizing intelligent switching of different charging scenarios and improving the accuracy and flexibility of charging instructions.

[0008] (3) In the heat dissipation structure design, the instantaneous heat load is calculated by combining the charging parameters and the heat dissipation plate arrangement area is matched with the thermal grease to achieve uniform diffusion and distribution of heat in high power modules, which effectively improves the heat dissipation regulation capability of the equipment during charging and high load operation.

[0009] (4) By simulating high-energy-consuming tasks and collecting load energy consumption data, a dynamic correspondence between device power consumption and task load is established. Combined with the real-time adjustment of screen backlight and processor frequency, dynamic optimization configuration of device power consumption is realized, breaking through the limitations of traditional fixed power consumption control. Attached Figure Description

[0010] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the steps of an energy management method for industrial mobile equipment according to the present invention; Figure 2 This is a schematic diagram of a module of an energy management system for industrial mobile equipment according to the present invention; Figure 3 This is a schematic diagram of the stacked motherboard in this invention; Figure 4 This is a schematic diagram illustrating the results of simulating a high-energy-consuming task in this invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0012] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0013] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0014] To achieve the above objectives, please refer to Figures 1 to 4 This invention provides an energy management method for industrial mobile equipment, applied to a stacked motherboard, which includes a core board and an expansion board. The method includes the following steps: Step S1: Communicate with the stacked motherboard via the preset built-in bus to the main chip and record the communication transmission information; In one embodiment, the system interacts with the main chip of the stacked motherboard via a pre-defined built-in bus. This built-in bus is an on-chip high-speed serial bus that supports multi-channel parallel transmission and can simultaneously carry control signals and battery status data. During communication, the main control chip uses a hardware timer to calibrate the communication cycle, ensuring the time synchronization of data acquisition. Simultaneously, impedance matching resistors and differential signal lines are configured at both ends of the communication path to reduce reflections and interference during transmission. After establishing the communication link, the system records and stores the communication transmission information in a buffer for subsequent analysis and energy consumption modeling.

[0015] In another embodiment, the built-in bus is configured with 8 high-speed channels, each with a transmission rate of 6Gbps, for a total bandwidth of 48Gbps. During a complete communication acquisition cycle, the system records 10MB of transmission data packets, including one battery power curve (sampling frequency 1Hz), 1000 instantaneous current fluctuation values ​​(sampling frequency 100Hz), and 500 voltage stability parameters (sampling frequency 50Hz). The packet loss rate of this batch of communication data, after CRC verification, is 0.01%, meeting the accuracy requirements for subsequent energy consumption calculations.

[0016] Step S2: Collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, trigger a battery charging command and continuously adjust the charging parameters. In one embodiment, the system parses the communication transmission information obtained in step S1, extracts the battery power field, and compares it with a preset threshold. When the battery power is lower than the threshold, the system triggers the charging process. Simultaneously with triggering the charging command, the system automatically matches the corresponding charging mode based on the device's location and environment. For example, when the device detects a wired interface insertion, it selects DC fast charging mode; when it detects installation on a vehicle mount, it selects wireless inductive charging mode; and when it detects placement on a desktop base, it selects POGOPin interface charging mode. Subsequently, the system dynamically adjusts the charging current and voltage in real time based on the ambient temperature, charging interface type, and the current battery health status to ensure the safety and efficiency of the charging process.

[0017] In another embodiment, assuming the battery charge threshold is preset to 20%, when the current battery charge is detected to be 15%, the system automatically determines it to be in a low-charge state. At this time, the device identifies its location on the vehicle mount via a position sensor, thereby triggering the wireless charging mode and setting the initial charging current to 1.5A and the voltage to 9V. During charging, if the battery temperature is detected to rise from 30°C to 38°C, the system will automatically reduce the charging current to 1.2A to prevent overheating and improve charging safety.

[0018] Step S3: Design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks and dynamically adjust the device power consumption based on the load energy consumption. In one embodiment, after entering the charging process, the system first calculates the instantaneous heat load of each functional module based on charging parameters and locates the component areas with the most concentrated heat generation (such as the CPU, power amplifier, and charging chip). Based on this, the system designs a heat dissipation structure: a heat spreader is placed in the high-heat area, filled with thermal grease, the heat dissipation path is optimized, and thermal contact points are reserved at the motherboard metal frame to improve heat conduction efficiency. Subsequently, the system loads preset high-energy-consuming tasks (such as full-load processor operation, graphics rendering, and multi-threaded parallel computing), and obtains the load energy consumption under high-energy-consuming tasks by monitoring the device's power curve in real time. After obtaining the load energy consumption, the system dynamically reduces the processor frequency and display module brightness, thereby achieving reasonable adjustment of overall power consumption.

[0019] In another embodiment, assuming a charging parameter of 9V / 1.5A, the system calculates an instantaneous heat load of approximately 18W, concentrated in the CPU and RF power amplifier area. The system places two heat spreaders in this area and fills them with approximately 0.2mm thick thermal grease to improve heat dissipation. Subsequently, a high-energy-consuming task is applied, increasing the CPU load to 70% and the GPU load to 60%. The average power consumption of the device over a 60-second task cycle is measured to be 22W. Integral calculations show that the load energy consumption is approximately 79.2kJ. Based on this, the system reduces the display module brightness by 15% and decreases the CPU frequency from 2.2GHz to 1.8GHz to ensure device stability and energy balance.

[0020] Of particular importance is that, in step S3, determining the load energy consumption of high-energy-consuming tasks includes: The task allocation should be determined based on high-energy-consuming tasks; In one embodiment, tasks are allocated to heterogeneous computing modules, including CPUs, GPUs, and NPUs, based on the type and priority of the high-energy-consuming tasks to be executed. During task allocation, tasks are first categorized, and their computational intensity, memory usage, and data transfer requirements are marked. Then, based on the available computing power and historical load status of each computing module, tasks are allocated in batches to ensure that the modules can execute in parallel while maintaining a balanced load. Task allocation information is written to the module controller via a bus, recording the task type, estimated execution time, and resource requirements for each module, providing basic data for subsequent power consumption calculations.

[0021] In another embodiment, assume there are five categories of high-energy-consuming tasks to be executed by the system, corresponding to deep learning inference, image processing, simulation computation, data encryption, and signal analysis tasks. The task allocation is as follows: the CPU undertakes image processing and data encryption tasks, expected to execute approximately 1200 instructions per second; the GPU undertakes deep learning inference tasks, expected to execute approximately [missing information - likely related to instructions]. The NPU performs approximately 1000 floating-point operations per second; it handles simulation calculations and signal analysis tasks. The estimated execution times for each task type are 0.8 seconds, 1.2 seconds, 0.6 seconds, 0.9 seconds, and 1.0 seconds, respectively. After load balancing of the module tasks, a quantifiable basis can be provided for subsequent calculation of resource consumption.

[0022] Determine the computational resource consumption based on task allocation; In one embodiment, based on task allocation, information such as CPU utilization, GPU utilization, memory bandwidth, and data transfer frequency of each module during task execution is collected to form a module-level computing resource utilization curve. The module utilization is matched with its rated computing capacity to determine the actual computing resource consumption of each module under the current task, including the number of computation cycles, memory usage, and total data throughput. The computing resource consumption is updated in real time after each task is completed and recorded in the core board memory for subsequent load energy consumption conversion.

[0023] In another embodiment, assuming that during the execution of the five tasks, the average CPU utilization is 42%, the average GPU utilization is 65%, and the average NPU utilization is 55%; the memory bandwidth usage is 8GB / s, 12GB / s, and 7GB / s, respectively; and the data transfer volume is 1.2GB, 2.5GB, and 1.0GB, respectively. Based on these data, the computational resource consumption of the CPU, GPU, and NPU can be obtained as follows: The calculation cycle records a total memory usage of approximately 27GB and a total data transfer volume of approximately 4.7GB, providing a basic value for load energy consumption conversion.

[0024] Convert computing resource consumption into load energy consumption.

[0025] In one embodiment, computational resource consumption is converted into energy consumption. Using a module power consumption characteristic table, the power values ​​corresponding to the computational load of the CPU, GPU, and NPU are integrated to obtain the actual load energy consumption of each module, and these are summed to form the total energy consumption of the entire task. The energy consumption information also records changes in module temperature, current, and voltage, providing data for thermal management and energy efficiency optimization.

[0026] In another embodiment, assuming the CPU, GPU, and NPU power consumption under the current task load are 18W, 42W, and 25W respectively, and the task execution times are 1 second, 1.2 seconds, and 0.9 seconds respectively, then by integration, the CPU energy consumption is approximately 18J, the GPU energy consumption is approximately 50.4J, the NPU energy consumption is approximately 22.5J, and the total energy consumption is approximately 91J. Simultaneously, the voltages of each module are recorded as 12V, 11.5V, and 12.2V, and the currents are recorded as 1.5A, 3.65A, and 1.84A respectively. This data can be used for subsequent power consumption optimization and thermal analysis, as well as overall device energy consumption assessment.

[0027] Of particular importance, in step S3, dynamically adjusting device power consumption based on load energy consumption includes: Calculate device power consumption based on load energy consumption; reduce screen backlight power based on device power consumption. In one embodiment, firstly, based on the device load energy consumption obtained in the preceding steps, the load energy consumption is converted into module-level power values ​​using a power consumption mapping table, including modules such as CPU, GPU, NPU, and memory. Then, based on the current device load power, the screen backlight power is dynamically adjusted: when the load power consumption is high, the backlight brightness is reduced by adjusting the PWM signal to decrease display power consumption and balance overall energy consumption. During the adjustment process, the system monitoring module collects backlight current, voltage, and power values ​​in real time and writes them to a power consumption log for power tracking and analysis.

[0028] In another embodiment, assuming the device's current load power consumption is 45W, with the CPU consuming 18W, the GPU consuming 20W, and the NPU consuming 7W, the screen backlight power is reduced from the initial 12W to 9W according to the mapping table, while the backlight current is sampled as 0.75A and the voltage as 12V, representing a power reduction of 25%. This adjustment can keep the overall power consumption below 50W during high-load task execution, providing a quantifiable data basis for subsequent power consumption optimization.

[0029] Reduce the processor operating frequency based on device load power consumption; determine device power consumption based on screen backlight power and processor operating frequency.

[0030] In one embodiment, the processor's operating frequency, including the CPU and GPU's clock speeds and core frequencies, is dynamically reduced based on the device's load power consumption to reduce peak power consumption. Frequency adjustment is performed through the operating system's power management module or an embedded controller, while simultaneously recording the processor's actual frequency, power consumption, and temperature changes. Subsequently, the adjusted screen backlight power and the power value corresponding to the processor's actual frequency are summed to obtain the device's total power consumption, which is then output for system energy efficiency analysis and thermal management control.

[0031] In another embodiment, assume the screen backlight power is reduced to 9W, the CPU frequency is reduced from 2.8GHz to 2.1GHz, and the GPU frequency is reduced from 1.5GHz to 1.1GHz. At this point, the CPU power consumption drops to 13W, the GPU power consumption drops to 15W, the NPU remains at 7W, and the total module power consumption is approximately 35W. Adding the backlight power consumption of 9W, the total device power consumption is approximately 44W. This is achieved by recording a sequence of power consumption values ​​per second: W can provide a precise reference for system power consumption optimization and thermal control strategies.

[0032] Step S4: Adjust the charging power based on the device power consumption; use the charging power to manage device energy and record the energy management efficiency.

[0033] In one embodiment, the system provides feedback correction to charging parameters based on device power consumption, adjusting the charging power in real time. The final charging power takes into account both device operating power consumption and battery charging safety characteristics. Using this charging power as input, the system executes energy management strategies, including energy allocation optimization, charging rate control, and heat dissipation protection strategies. Simultaneously, the system records energy management efficiency indicators in real time in the charging control module and writes these indicators to the motherboard storage unit for subsequent energy efficiency evaluation and historical performance comparison.

[0034] In another embodiment, assuming that after dynamic adjustment, the overall power consumption of the device decreases from 25W to 20W, the system adjusts the original charging power from 30W to 24W. After a 10-minute charging cycle, the energy management efficiency (defined as the ratio of effective input energy to total supplied energy) is 85%. This efficiency value, along with the corresponding operating environment data (such as temperature 35°C and humidity 45%), is stored in the energy management log for subsequent big data modeling and performance optimization.

[0035] Preferably, in step S1, the stacked motherboard communicates with the main chip via a preset built-in bus, and the communication transmission information is recorded, including: The stacked motherboards communicate with the main chip via a pre-set built-in bus, and the stacked motherboards are connected via a high-speed bus. Before the main chip communicates, a pairing wire with an inverse signal is arranged for each high-speed bus. The pairing wires maintain a constant spacing to control the characteristic impedance, set the matching resistor, and determine the impedance matching of the signal transmission path. In one embodiment, the system communicates with the main chip of the stacked motherboard via a pre-defined built-in high-speed bus. The high-speed bus is a differential signal structure, with each high-speed signal line equipped with a pair of inverted signals, maintaining a constant spacing to control characteristic impedance. Matching resistors (e.g., 50Ω) are provided at the ends of each bus to ensure impedance matching, thereby reducing signal reflection and interference. Before communication, the system initializes the bus using a clock synchronization module and a hardware timer to ensure consistent signal propagation delay along the line, and activates a handshake protocol at the main chip port to verify the integrity of the communication link. During communication, the main chip sends control commands to the stacked motherboard and simultaneously records the transmitted data frames, timestamps, verification information, and voltage fluctuations, forming a communication transmission information buffer for subsequent energy management and data analysis.

[0036] In another embodiment, the system is assumed to use four high-speed differential buses, each 0.5m long, with a line spacing of 0.2mm. The end-matching resistor for each bus is set to 49.9Ω, resulting in a reflection coefficient of approximately 0.01. During a complete communication cycle, the main chip sends 5000 data frames to the stacked motherboard, each 2KB, for a total data transmission of approximately 10MB. Voltage fluctuations recorded during communication are within ±5mV, with a maximum timing deviation of 12ns and a data frame loss rate of 0.02%. This batch of communication data provides a precise basis for subsequent battery power consumption analysis and charging strategy development.

[0037] During the communication process of the main chip, the control signal after impedance matching is transmitted to the core board memory, and the communication transmission information is recorded.

[0038] In one embodiment, after the main chip completes communication, the impedance-matched control signal and the acquired communication transmission information are written to the core board memory. The core board memory is a high-speed cache (such as DDR4 3200MHz), supporting continuous writing and random access. During the writing process, the system performs CRC verification on each data stream to ensure data integrity, and simultaneously generates an index table in memory, marking the bus channel, timestamp, and data type corresponding to each data entry. Subsequently, the core board control module performs real-time monitoring and preliminary analysis of the communication data, providing basic data for the next step of battery power acquisition and power consumption analysis.

[0039] In another embodiment, it is assumed that the total amount of communication information recorded in each communication cycle is 10MB, including 2 battery power curves (sampling frequency 1Hz), 2000 instantaneous power consumption data points (sampling frequency 100Hz), and 500 signal integrity indicators (sampling frequency 50Hz). The core board memory address space is 256MB, the write rate is 15GB / s, and the average write latency is approximately 45ns. After the data is written, the index table generates 3500 entries, each including the bus number, frame number, timestamp, and data category. This record ensures that the data source can be accurately located and analyzed with high precision in subsequent charging command triggering, heat dissipation design, and power adjustment.

[0040] Preferably, in step S2, if the battery power data of the communication transmission information is collected and detected to be lower than a preset power threshold, a battery charging command is triggered, including: Collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, detect the battery charging location to record the fixed charging point, vehicle bracket point, and base point. In one embodiment, the system obtains battery power data from the main chip via the built-in bus of the stacked motherboard and records the battery power, remaining capacity, and voltage status in real time. When the battery power is lower than a preset power threshold (e.g., 20%), the energy management module initiates a charging location detection program. This program detects the current location of the device through an onboard positioning module, a near-field communication (NFC) module, or an infrared sensor, identifies the three-dimensional coordinates of fixed charging points, onboard bracket points, and base points, and binds the detected point coordinates with the device ID into memory, forming a complete charging point dataset, which provides the basis for subsequent charging command generation.

[0041] In another embodiment, assuming a preset battery power threshold of 25%, 1000 battery power data points are collected in one detection cycle. Within 5 consecutive seconds, the system detects 120 data points indicating a battery power below the threshold, corresponding to 3 charging points: fixed charging point coordinates. m, coordinates of the vehicle-mounted bracket m, coordinates of the base point m. A unique identifier ID is generated for each location, namely CP001, CS002, and PD003, for use in subsequent charging strategies.

[0042] At fixed charging points, generate wired fast charging commands; at vehicle mount points, generate wireless charging commands; at base points, generate POGOPin charging commands. In one embodiment, the system generates a corresponding charging command based on the type of charging point: At fixed charging points, wired fast charging commands are generated, including setting parameters such as maximum charging current, voltage limits, and charging time windows, and transmitted to the charging control module via the main chip. At vehicle mount points, wireless charging commands are generated, including wireless power adjustment, alignment correction, and power overflow protection parameters, while recording the charging start time and estimated end time. At dock points, POGOPin dock charging commands are generated, including contact detection, polarity verification, and safety triggering mechanisms to ensure proper contact and stable power supply to the dock charging interface.

[0043] In another embodiment, it is assumed that the system processes three charging points at a time: the fixed charging point instruction parameters are a maximum current of 15A, a maximum voltage of 48V, and a charging time of 120 minutes; the vehicle mount wireless charging instruction parameters are a power of 5kW, a transmission frequency of 85kHz, and an alignment error of ≤5mm; and the POGOPin base charging instruction parameters are a contact resistance of ≤50mΩ, a maximum current of 10A, and a detection interval of 2s. Each instruction is recorded in memory, including a timestamp, device ID, and execution status, for subsequent monitoring and optimized scheduling.

[0044] Wired fast charging commands, wireless charging commands, and POGOPin desktop charging commands will be used as battery charging commands.

[0045] In one embodiment, the system integrates wired fast charging commands, wireless charging commands, and POGOPin charging dock commands into a unified battery charging command sequence. This command sequence includes the priority, time window, and power allocation information for each charging point, and is sent to each charging control unit via a bus to achieve global coordinated control and ensure a safe and efficient charging process.

[0046] In another embodiment, it is assumed that the integrated battery charging command sequence contains three independent commands: command 1 is a fixed charging point fast charging command (IDCP001); command 2 is a vehicle mount wireless charging command (IDCS002); and command 3 is a dock charging command (IDPD003). The system generates an execution log for each command, totaling three records. Each record includes the command type, target point coordinates, voltage / current parameters, estimated charging time, and timestamp. Log analysis can track the charging status and provide data support for subsequent multi-device scheduling optimization.

[0047] Preferably, the core board edge is provided with mechanical latches, and at the fixed charging point, the wired fast charging command generated includes: The core board controls the sensing circuit to output a probe current to the interface port of the industrial mobile device and collects the returned current value in real time. If the current value is within the preset current conduction range, it is determined that the interface port has been plugged in. The mechanical latch status signal is monitored to confirm the physical fixation of the interface port. In one embodiment, the core board controls the sensing circuit to output a probe current (e.g., 50mA ± 5mA) to the interface port of the industrial mobile device and collects the return current value in real time. The system sets a preset current conduction range of 45–55mA. When the return current value falls within this range, it is determined that the interface port has completed electrical engagement. Simultaneously, a mechanical latching status signal is collected by a microswitch or photoelectric sensor to determine the physical fixation status of the interface port. When the latching closure signal is high and lasts for more than 100ms, the physical fixation of the interface port is confirmed. The collected current value and latching status signal are recorded in the core board's memory for further analysis by the charging management module.

[0048] In another embodiment, assuming the interface port is probed 10 times consecutively, the probe current values ​​are as follows: The mA values ​​all fall within the preset conduction range of 45–55mA. The mechanical latch status signal remains high in all 10 samples, with a duration exceeding 120ms. Therefore, the system determines that the interface port has a 100% electrical engagement rate and a stable physical fixation state, providing a reliable basis for generating charging commands.

[0049] When the interface port is plugged in and physically fixed, the physical connection result is obtained and sent to the charging management module to generate a wired fast charging command.

[0050] In one embodiment, once the interface port determines that it has been plugged in and the mechanical latch is physically secured, the charging management module receives the physical connection result signal and initiates the wired fast charging command generation program. This program sets the maximum charging current, voltage limits, and charging time window based on the device type and battery status, and sends the command to the battery management unit (BMS) and charging control module via the bus. The entire command includes the interface ID, charging parameters, execution priority, and safety protection mechanisms to ensure monitoring of current, voltage, and temperature changes during charging, achieving safe and stable wired fast charging.

[0051] In another embodiment, assuming the interface port ID is IF-01, the system generates a wired fast charging command based on a remaining battery charge of 18%: maximum charging current 15A, maximum voltage 48V, charging time 120 minutes, and overcurrent protection threshold 16A. After the command is sent to the BMS, the system receives an acknowledgment signal within 5ms, indicating that the charging control module is ready. Simultaneously, a charging log entry is generated, including the command type, wired port ID, current / voltage parameters, charging start time, and estimated end time, recording one complete command entry for subsequent status monitoring and historical data analysis.

[0052] Preferably, the industrial mobile device is fixed on the bracket, and at the vehicle-mounted bracket location, the wireless charging command generated includes: A low-power detection signal is emitted by the transmitting coil of the bracket, and the receiving coil of the industrial mobile device collects the induction intensity of the low-power detection signal; the core board demodulates the induction intensity and calculates the resonant frequency of the coil. In one embodiment, the transmitting coil on the support emits a low-power detection signal at a preset modulation frequency (e.g., 85kHz ± 0.5kHz), with the signal power controlled within the range of 0.1–0.5W to avoid interference with surrounding circuits. The receiving coil at the industrial mobile device end collects the induction intensity of this low-power detection signal and sends the original analog signal to the core board for demodulation after amplification and filtering. Based on the received signal amplitude and phase information, the core board calculates the resonant frequency of the receiving coil using a Fast Fourier Transform (FFT). The resonant deviation is then compared with the reference frequency at the transmitting end. The amplitude, phase, and resonant frequency of the acquired inductive signals are recorded in the core board's memory for subsequent coupling determination and wireless charging control.

[0053] In another embodiment, assuming the modulation frequency of the bracket's transmitting coil is 85kHz and the detection power is 0.2W; the signal amplitude sequence acquired by the receiving coil at the industrial mobile device end is... V, corresponding to the calculated resonant frequency sequence is: kHz. The core board determines the signal is valid based on a deviation of 0.0kHz between the average resonant frequency of 85.0kHz and the transmitter reference frequency of 85kHz, and records the signal amplitude, phase, and resonant frequency sequence for coupling state analysis.

[0054] If the resonant frequency matches the preset resonant threshold range, it is determined that the industrial mobile device and the bracket are in an effective coupling state, and the coupling confirmation signal is returned to the charging management module to trigger the wireless charging command.

[0055] In one embodiment, when the calculated resonant frequency falls within a preset resonant threshold range (e.g., When the frequency reaches kHz, the core board determines that the industrial mobile device and the bracket are in a valid coupling state. The system sends a coupling confirmation signal to the charging management module via CAN bus or UART, triggering the wireless charging command generation program. The charging management module sets the wireless charging output power, target voltage, current limit, and protection mechanism according to the device type, battery status, and safety constraints, and then sends the complete command to the wireless charging driver module. The entire process ensures that coupling confirmation is completed before wireless charging to achieve safe and stable charging operation.

[0056] In another embodiment, assuming the preset resonance threshold range is 84.5–85.5 kHz, the resonance frequency sequence is calculated after 10 consecutive samplings. The kHz values ​​all fall within the threshold range. The system determines the effective coupling rate to be 100% and generates a wireless charging command: target power 500W, output voltage 48V, maximum charging current 10A, and overcurrent protection threshold 11A. After the command is sent to the wireless charging driver module, the system receives an acknowledgment response within 6ms and records a charging log, including coupling confirmation time, command parameters, device ID, and execution status, for use in subsequent charging monitoring and historical analysis.

[0057] Preferably, the base point includes a magnetic component, and at the base point, the POGOPin charging command is generated including: Detect the electrical connection status between the industrial mobile device and the POGOPin magnetic contacts on the base; confirm that the contact resistance of the magnetic contacts meets the charging requirements; and send a POGOPin charging command to the charging management module. In one embodiment, the electrical connection between the POGOPin magnetic contacts on the base and the corresponding contacts on the industrial mobile device is detected. The system applies a test current of 0.5mA to each pair of contacts using a small constant current source, while simultaneously measuring the voltage drop and calculating the contact resistance. If the resistor meets the preset charging requirements (e.g.) If the core board sends a POGOPin charging command to the charging management module via an internal communication interface (such as CAN or UART), the command includes the contact ID, device ID, target charging voltage and current limits, and contact status flags. Upon receiving a confirmation response, the system records the resistance value, contact number, and sending time for subsequent charging logs and status analysis.

[0058] In another embodiment, assuming the base has 10 sets of POGOPin contacts, the voltage drop sequence (in mV) measured after applying a test current of 0.5mA to the corresponding contacts on the industrial mobile device is as follows: .

[0059] Therefore, the contact resistance sequence is calculated ( )for mΩ. Among them, 9 groups of resistance are ≤50mΩ, and 1 group is slightly higher (50.6mΩ). The system still determines that the overall contact meets the charging requirements and issues the POGOPin charging command: target voltage 48V, target current 10A, contact ID [1…10]. At the same time, a charging log record is generated in the core board memory, including contact resistance, contact number, and command issuance time, for a total of 10 records.

[0060] The system collects acceleration signals from industrial mobile devices. If the acceleration signal exceeds a preset acceleration threshold, it is determined to be in a vibration state. The system then controls the magnetic components at the base points to enhance the attraction force and lock the position of the industrial mobile device.

[0061] In one embodiment, the industrial mobile device incorporates a triaxial accelerometer (±16g) that continuously acquires acceleration signals at a sampling frequency of 100Hz. The core board calculates the acceleration amplitude for each frame in real time. If the acceleration amplitude is greater than the preset acceleration threshold (e.g., 0.5g) The system determines this as a vibration state. Then, by controlling the magnetic components at the base points, the adsorption force is dynamically increased (e.g., the drive current is increased from the default 300mA to 500mA) to ensure the industrial mobile device is locked in place and to reduce the impact of shaking on the charging contacts. Simultaneously, the acceleration amplitude, vibration determination status, and magnetic current value are recorded for each frame for subsequent vibration analysis and optimization.

[0062] In another embodiment, it is assumed that the acceleration amplitude (in g) collected over 10 consecutive frames is: .

[0063] Set a preset acceleration threshold Frames 2, 4, 6, 8, and 10 are determined to be in vibration state, for a total of 5 frames. The system accordingly increases the magnetic attraction current of the base to 500mA, while the remaining frames maintain the default current of 300mA. All acceleration amplitudes, vibration states, and magnetic attraction currents are recorded in the core board memory to form a time-series data table for subsequent evaluation of magnetic attraction performance and charging stability.

[0064] Preferably, the heat dissipation structure design in step S3 based on the charging parameters includes: Calculate the instantaneous heat load based on the charging parameters to determine the location of the heat source; determine the heat spreader arrangement area based on the heat source location, and apply the appropriate amount of thermal grease to the contact surface of the heat spreader arrangement area. In one embodiment, the instantaneous heat load and main heat source locations during the charging process are determined based on the charging status and battery power data of the industrial mobile device. Thermal simulation analysis reveals that the heat concentration areas are typically located on the upper surface of the battery pack and the power conversion module. A heat spreader is placed to cover these heat sources, and thermal grease is applied to the contact surfaces of the heat spreader to ensure uniform heat conduction. Approximately 0.15 grams of thermal grease is uniformly applied to the bottom surface of the heat spreader corresponding to each heat source, covering an area of ​​approximately 50 square centimeters. Infrared thermal imaging verification shows that the maximum temperature difference is controlled within 3 degrees Celsius.

[0065] In another embodiment, assuming the industrial mobile device has a battery power of 500 watts, the heat is mainly concentrated in the left battery module and power conversion module. The heat spreader is divided into four areas, corresponding to the battery center, left side, right side, and power module, respectively. The amount of thermal grease applied to each area is 0.12 g, 0.14 g, 0.13 g, and 0.15 g, with coating areas of 45, 50, 48, and 52 square centimeters, respectively. After coating, the maximum temperature difference on the surface of the heat spreader was measured to be 3.2 degrees Celsius using infrared spectroscopy.

[0066] By reserving contact points between the metal frame and the heat spreader in the industrial mobile equipment, the heat conducted by the heat spreader is diffused through the metal frame to the body of the industrial mobile equipment.

[0067] In one embodiment, the heat spreader is tightly contacted with the metal frame of the industrial mobile device, secured with bolts, and thermal grease is used to enhance contact thermal conductivity. The heat absorbed by the heat spreader diffuses along the metal frame to the device's outer casing, achieving a uniform temperature distribution across the entire casing. During 10 minutes of continuous charging, the temperature at various measuring points on the heat spreader contact surface and the outer casing is monitored. When a local temperature exceeds a set threshold, the heat flow path is improved by increasing the contact pressure or adjusting the thermal grease coating thickness, ensuring stable thermal management.

[0068] In another embodiment, it is assumed that the contact area between the heat spreader and the metal frame is 120 square centimeters, the length of the metal frame is 150 millimeters, and the cross-sectional area of ​​the frame is 10 square centimeters. The average surface temperature of the heat spreader is 60 degrees Celsius. The heat diffuses through the metal frame to 10 measuring points on the fuselage, with measured temperatures of 58.1, 57.6, 58.4, 57.9, 58.0, 57.8, 58.2, 57.7, 58.3, and 57.9 degrees Celsius, respectively, with a maximum temperature difference of 0.8 degrees Celsius. When the local temperature of the heat spreader rises to 62 degrees Celsius, increasing the contact pressure by 10 Newtons reduces the rate of temperature rise on the fuselage by approximately 15%, ensuring safe and effective heat dissipation.

[0069] Preferably, applying thermal grease to the contact surface of the heat spreader area includes: Calculate the heat accumulation in the heat spreader area to identify high heat areas; calculate the contact area of ​​high heat areas; apply pressure to the high heat areas to force the thermal grease to fill the contact area and adjust the thermal grease thickness.

[0070] In one embodiment, real-time temperature data is collected in the vapor chamber area using industrial thermal imaging sensors and thermal simulation software. The heat accumulation in each local area is calculated to identify high-heat areas, such as the top of the battery module, the vicinity of the power conversion module, and the interface of the motor control unit. The contact area and available vapor chamber surface area are calculated for each high-heat area; typically, the contact area for each heat source is between 45 and 60 square centimeters. After determining the contact area, uniform pressure is applied to the vapor chamber surface using a dedicated pressure device to ensure that the thermal grease fully fills the contact surface of the high-heat areas. The pressure is controlled between 8 and 12 Newtons, while the thermal grease thickness is adjusted to maintain a thickness between 0.3 and 0.5 millimeters to ensure a tight heat conduction path without gaps. Subsequently, infrared thermal imaging is used to scan the vapor chamber surface to check if the temperature difference between each high-heat area is less than 3 degrees Celsius to verify the heat dissipation effect. The entire process ensures efficient heat conduction from the heat source through the vapor chamber and avoids the accumulation of local hot spots.

[0071] In another embodiment, it is assumed that six high-heat points in the vapor chamber area are determined through thermal simulation, with accumulated heat of 3.2, 2.8, 3.5, 3.0, 2.9, and 3.1 watts, respectively; corresponding contact areas of 48, 52, 50, 55, 50, and 53 square centimeters. Approximately 10 Newtons of pressure are applied to each high-heat point to fill the entire contact surface with thermal grease, while adjusting the grease thickness to 0.32, 0.35, 0.38, 0.34, 0.36, and 0.33 millimeters. Infrared thermography shows that the surface temperature of the vapor chamber at the six locations is 58.1, 57.6, 58.4, 57.9, 58.0, and 57.8 degrees Celsius, with a maximum temperature difference of 0.8 degrees Celsius, ensuring effective local heat dissipation and achieving uniform heat distribution.

[0072] Preferably, the simulation of high-energy-consuming tasks in step S3 includes: In the early stages of task execution, the module load is increased in stages. In the first stage, the CPU is reduced to 30% to 50% of its rated power consumption. In the second stage, the load is increased to the remaining 50% to 70%, gradually simulating the peak power consumption state of the module. During the load increase process, load power data is continuously collected through the built-in bus and the data is transmitted to the core board memory.

[0073] In one embodiment, at the initial stage of task execution, the core computing module in the industrial mobile device is first subjected to phased load control. In the first phase, the power consumption of the CPU and GPU modules is gradually increased to 30% to 50% of their rated power consumption and maintained stably for approximately 5 seconds to observe the module's thermal response and power consumption. In the second phase, the module load is further increased to the remaining 50% to 70% and maintained for 10 seconds, while monitoring module temperature, current, and voltage changes to ensure a smooth load increase without sudden jumps. Throughout the process, the instantaneous power of each module is collected via the built-in bus, including real-time data on CPU, GPU, and memory load. The collected data is then transmitted to the core board memory for caching and recording, providing foundational data for subsequent power consumption analysis and peak prediction.

[0074] In another embodiment, assume the industrial mobile device comprises four computing modules, each with a rated power consumption of 45 watts, 50 watts, 40 watts, and 60 watts, respectively. In the first phase, the module power consumption is increased proportionally: Module 1 from 16 to 22 watts, Module 2 from 15 to 25 watts, Module 3 from 12 to 20 watts, and Module 4 from 18 to 30 watts; in the second phase, it is further increased to: Module 1 from 22 to 31 watts, Module 2 from 25 to 35 watts, Module 3 from 20 to 28 watts, and Module 4 from 30 to 42 watts. During the load increase, the core board memory continuously records the instantaneous power of each module, five times per second, for a total of 30 seconds, generating a data sequence: Watts are used to analyze the power consumption trend and peak response of the module. Through this process, the power response of the module under phased load increases can be clearly observed, providing a quantitative basis for subsequent thermal management, heat dissipation structure optimization and energy consumption control.

[0075] Preferably, this specification also provides an energy management system for industrial mobile equipment, for executing the energy management method for industrial mobile equipment as described above, the energy management system for industrial mobile equipment comprising: The communication transmission module 101 is used to communicate with the stacked motherboard via a preset built-in bus and record the communication transmission information. The charging management module 102 is used to collect battery power data of communication transmission information. If the battery power data is detected to be lower than the preset power threshold, a battery charging command is triggered to continuously adjust the charging parameters. The power consumption adjustment module 103 is used to design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks, and dynamically adjust the power consumption of the device based on the load energy consumption. The energy management module 104 is used to adjust the charging power of charging parameters based on the device power consumption; to perform device energy management using the charging power; and to record the energy management efficiency.

[0076] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0077] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. An energy management method for industrial mobile equipment, characterized in that, Applied to a stacked motherboard, which includes a core board and an expansion board, the method includes the following steps: Step S1: Communicate with the stacked motherboard via the preset built-in bus to the main chip and record the communication transmission information; Step S2: Collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, trigger a battery charging command and continuously adjust the charging parameters. Step S3: Design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks and dynamically adjust the device power consumption based on the load energy consumption. Step S4: Adjust the charging power based on the device power consumption; use the charging power to manage device energy and record the energy management efficiency.

2. The energy management method for industrial mobile equipment according to claim 1, characterized in that, In step S1, the stacked motherboard communicates with the main chip via a preset built-in bus, and the communication transmission information is recorded, including: The stacked motherboards communicate with the main chip via a pre-set built-in bus, and the stacked motherboards are connected via a high-speed bus. Before the main chip communicates, a pairing wire with an inverse signal is arranged for each high-speed bus. The pairing wires maintain a constant spacing to control the characteristic impedance, set the matching resistor, and determine the impedance matching of the signal transmission path. During the communication process of the main chip, the control signal after impedance matching is transmitted to the core board memory, and the communication transmission information is recorded.

3. The energy management method for industrial mobile equipment according to claim 1, characterized in that, In step S2, battery power data for communication transmission is collected. If the battery power data is detected to be lower than a preset power threshold, a battery charging command is triggered, including: Collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, detect the battery charging location to record the fixed charging point, vehicle bracket point, and base point. At fixed charging points, generate wired fast charging commands; at vehicle mount points, generate wireless charging commands; at base points, generate POGOPin charging commands. Wired fast charging commands, wireless charging commands, and POGOPin desktop charging commands will be used as battery charging commands.

4. The energy management method for industrial mobile equipment according to claim 3, characterized in that, The core board has mechanical latches on its edge. At fixed charging points, it generates wired fast charging commands including: The core board controls the sensing circuit to output a probe current to the interface port of the industrial mobile device and collects the returned current value in real time. If the current value is within the preset current conduction range, it is determined that the interface port has been plugged in. The mechanical latch status signal is monitored to confirm the physical fixation of the interface port. When the interface port is plugged in and physically fixed, the physical connection result is obtained and sent to the charging management module to generate a wired fast charging command.

5. The energy management method for industrial mobile equipment according to claim 3, characterized in that, The industrial mobile device is fixed to a bracket. At the vehicle-mounted bracket location, wireless charging commands are generated, including: A low-power detection signal is emitted by the transmitting coil of the bracket, and the receiving coil of the industrial mobile device collects the induction intensity of the low-power detection signal; the core board demodulates the induction intensity and calculates the resonant frequency of the coil. If the resonant frequency matches the preset resonant threshold range, it is determined that the industrial mobile device and the bracket are in an effective coupling state, and the coupling confirmation signal is returned to the charging management module to trigger the wireless charging command.

6. The energy management method for industrial mobile equipment according to claim 3, characterized in that, The base point includes a magnetic component. At the base point, the generated POGOPin charging command includes: Detect the electrical connection status between the industrial mobile device and the POGOPin magnetic contacts on the base; confirm that the contact resistance of the magnetic contacts meets the charging requirements; and send a POGOPin charging command to the charging management module. The system collects acceleration signals from industrial mobile devices. If the acceleration signal exceeds a preset acceleration threshold, it is determined to be in a vibration state. The system then controls the magnetic components at the base points to enhance the attraction force and lock the position of the industrial mobile device.

7. The energy management method for industrial mobile equipment according to claim 1, characterized in that, Step S3, which involves designing a heat dissipation structure based on charging parameters, includes: Calculate the instantaneous heat load based on the charging parameters to determine the location of the heat source; determine the heat spreader arrangement area based on the heat source location, and apply the appropriate amount of thermal grease to the contact surface of the heat spreader arrangement area. By reserving contact points between the metal frame and the heat spreader in the industrial mobile equipment, the heat conducted by the heat spreader is diffused through the metal frame to the body of the industrial mobile equipment.

8. The energy management method for industrial mobile equipment according to claim 7, characterized in that, Applying thermal grease to the contact surface of the heat spreader area includes: Calculate the heat accumulation in the heat spreader area to identify high heat areas; calculate the contact area of ​​high heat areas; apply pressure to the high heat areas to force the thermal grease to fill the contact area and adjust the thermal grease thickness.

9. The energy management method for industrial mobile equipment according to claim 1, characterized in that, Step S3, which simulates high-energy-consuming tasks, includes: In the early stages of task execution, the module load is increased in stages. In the first stage, the CPU is reduced to 30% to 50% of its rated power consumption. In the second stage, the load is increased to the remaining 50% to 70%, gradually simulating the peak power consumption state of the module. During the load increase process, load power data is continuously collected through the built-in bus and the data is transmitted to the core board memory.

10. An energy management system for industrial mobile equipment, characterized in that, For performing the energy management method for industrial mobile devices as described in claim 1, the energy management system for industrial mobile devices includes: The communication transmission module is used to communicate with the stacked motherboard via a preset built-in bus and record the communication transmission information. The charging management module is used to collect battery power data for communication transmission. If the battery power data is detected to be lower than the preset power threshold, a battery charging command is triggered to continuously adjust the charging parameters. The power consumption adjustment module is used to design a heat dissipation structure based on charging parameters and simulate high-energy-consuming tasks; determine the load energy consumption of high-energy-consuming tasks, and dynamically adjust the device power consumption based on the load energy consumption. The energy management module is used to adjust the charging power based on the device's power consumption; it also uses the charging power to manage the device's energy and records the energy management efficiency.

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