Autonomous power supply intelligent file cabinet based on radio frequency energy collection energy chip

Abstract

According to the autonomous power supply intelligent file cabinet based on the radio frequency energy collection energy chip, radio frequency energy in the environment is efficiently collected through a multi-band antenna and the maximum power point tracking control technology, and the problem of continuous power supply of intelligent equipment is effectively solved by combining a hybrid energy storage system formed by a super capacitor and a lithium battery. The system adopts an energy consumption prediction model based on multi-feature linear regression, can accurately predict future energy consumption, intelligently switches power supply paths on the basis of the future energy consumption, remarkably reduces the dependence on external commercial power, and improves the energy utilization efficiency. Meanwhile, the model has a self-learning capability, and prediction precision can be continuously optimized over time. According to the energy-saving file cabinet, intelligence and automation of energy management are achieved, the remarkable energy-saving effect is achieved while stable operation of all functions of the file cabinet is guaranteed, and the energy-saving file cabinet has high economical efficiency and environment friendliness.

Description

Technical Field

[0001] This invention relates to the field of energy chips, and in particular to an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip. Background Technology

[0002] With the rapid development of IoT technology and smart office equipment, intelligent document management systems have become an important direction for modern document preservation. These systems typically integrate functions such as automatic positioning, environmental monitoring, and electronic access, significantly improving the efficiency and security of document management. However, the increased functionality also leads to higher energy consumption. Traditional intelligent filing cabinets rely entirely on mains power, which not only increases long-term operating costs but also contradicts the concept of green and low-carbon development. Especially in emergency power outage scenarios, system malfunctions may result in the loss of document access records or failure of security monitoring, posing potential risks.

[0003] Existing intelligent filing cabinet systems suffer from significant shortcomings in energy supply. On one hand, systems employing auxiliary power sources such as solar power or wireless charging are heavily constrained by environmental conditions or spatial layout, making it difficult to guarantee a continuous and stable energy supply. On the other hand, existing energy management strategies are largely based on simple voltage comparisons or timed switching, lacking the ability to accurately predict and intelligently schedule system energy consumption behavior, thus failing to achieve efficient energy utilization. For example, some systems only switch back to mains power when the battery is depleted; frequent switching not only degrades the user experience but may also affect equipment lifespan. Furthermore, factors such as file access operations, changes in ambient light, and usage frequency at different times all cause dynamic fluctuations in energy consumption. Existing systems have failed to establish a correlation model between energy consumption and multi-dimensional characteristics, making it difficult to achieve advanced and accurate energy scheduling decisions.

[0004] Therefore, in the field of intelligent archive management, there is an urgent need for a system solution that can achieve energy self-sufficiency, has predictive energy management capabilities, and can intelligently and seamlessly switch between multiple power supply modes to solve key issues such as continuous power supply, energy efficiency optimization, and system stability. Summary of the Invention

[0005] The purpose of this invention is to provide an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, so as to solve the problems existing in the prior art.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] This invention provides an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, comprising a filing cabinet body, wherein the filing cabinet body integrates:

[0008] A radio frequency energy harvesting and power supply unit is used to capture radio frequency signals in the environment and convert them into DC power. The radio frequency energy harvesting and power supply unit includes a multi-band radio frequency antenna array, a rectification and impedance matching circuit, a maximum power point tracking controller, and a hybrid energy storage module.

[0009] The intelligent archive management unit is used to realize the storage, location, environmental monitoring and interactive control of archives. The intelligent archive management unit includes a main controller, an environmental sensor array, an ultra-wideband positioning and radio frequency identification module, an intelligent electronic lock, a human-computer interaction interface and a communication module.

[0010] An intelligent energy management unit is used to coordinate the power supply switching between the radio frequency energy harvesting power supply unit and the mains power. The intelligent energy management unit includes a power management integrated circuit and an energy consumption monitoring circuit.

[0011] The main controller runs an energy consumption prediction model, predicts the system energy consumption for the next time period based on historical energy consumption data, real-time operating frequency and environmental parameters, and controls the power management integrated circuit to switch the power supply path according to the prediction results and the remaining power of the hybrid energy storage module.

[0012] Preferably, the hybrid energy storage module includes:

[0013] Supercapacitor, the supercapacitor being used to respond to instantaneous high-power requests;

[0014] A miniature lithium battery, connected in parallel with the supercapacitor, is used for continuous and stable power supply. Preferably, the energy consumption prediction model adopts a multi-feature linear regression model, the expression of which is:

[0015] P predicted (t+1)=α+β1*S usage (t)+β2*L(t)+β3*H(t)+β4*D(t)+ε;

[0016] Among them, P predicted (t+1) represents the predicted energy consumption for the next time slice, α represents the system's base power consumption parameter, β1 represents the access operation weighting coefficient, and S usage (t) represents the frequency of file access operations within the current time slice, β2 is the illumination weight coefficient, L(t) is the current illumination intensity, β3 is the time period weight coefficient, H(t) is the time factor, β4 is the date type weight coefficient, D(t) is the date factor, and ε is the random error term.

[0017] Preferably, the parameters of the multi-feature linear regression model are trained using historical data through the least squares method or gradient descent method, and are updated periodically.

[0018] Preferably, the main controller executes the following control logic at fixed time intervals:

[0019] If the sum of the remaining power of the hybrid energy storage module and the predicted radio frequency energy is greater than the product of the predicted energy consumption and the safety factor in the next time slice, then switch to radio frequency energy power supply.

[0020] If the remaining power is below the minimum power threshold, switch to mains power and charge the energy storage module;

[0021] Otherwise, maintain the current power supply mode.

[0022] Preferably, it further includes:

[0023] The communication interface is connected to the upper-level server and is used to upload energy consumption data, file access logs, and receive updated model parameters.

[0024] Preferably, the environmental sensor array includes:

[0025] Temperature sensor;

[0026] Humidity sensor;

[0027] Light sensor;

[0028] The output data from the temperature sensor, the humidity sensor, and the light sensor serve as input features for the energy consumption prediction model.

[0029] Preferably, the ultra-wideband positioning and radio frequency identification module is used to track the location of the file in real time and to highlight the target file cell with a bright color in the human-computer interaction interface.

[0030] The present invention also provides an energy management method for the above-mentioned autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, comprising the following steps:

[0031] S1. Collect historical energy consumption data, operation frequency, and environmental data of the system to construct a training dataset;

[0032] S2. Use the training data to fit a multi-feature linear regression model to obtain an energy consumption prediction model;

[0033] S3. Monitor the system status in real time and input the data into the model to calculate the predicted energy consumption for the next period;

[0034] S4. Based on predicted energy consumption and energy storage capacity, power supply switching is controlled through predefined decision rules.

[0035] Preferably, in step S4, a safety factor and a minimum power threshold are introduced into the decision rule to ensure power supply stability and prevent over-discharge.

[0036] The present invention achieves the following beneficial technical effects compared to the prior art:

[0037] This invention provides an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip. It efficiently harvests radio frequency energy from the environment through multi-band antennas and maximum power point tracking control technology, combined with a hybrid energy storage system consisting of supercapacitors and lithium batteries, effectively solving the problem of continuous power supply for intelligent devices. The system employs an energy consumption prediction model based on multi-feature linear regression, which can accurately predict future energy consumption and intelligently switch power supply paths accordingly, significantly reducing dependence on external mains power and improving energy utilization efficiency. Simultaneously, the model has self-learning capabilities, continuously optimizing prediction accuracy over time. This invention achieves intelligent and automated energy management, ensuring stable operation of all functions of the filing cabinet while achieving significant energy savings, demonstrating high economic efficiency and environmental friendliness. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A schematic diagram illustrating the control relationship of an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, provided for this invention.

[0040] Figure 2 The flowchart illustrates the energy management method for an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, as provided by this invention. Detailed Implementation

[0041] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The purpose of this invention is to provide an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip, in order to solve the problems existing in the prior art.

[0043] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0044] Example 1:

[0045] Please see Figure 1This invention provides an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip. Its core lies in constructing a complete system integrating autonomous energy harvesting, storage, intelligent prediction, and scheduling. This system consists of three organically coordinated units: a radio frequency energy harvesting power supply unit, an intelligent filing management unit, and an intelligent energy management unit. These units are not simply a stack of functions, but rather achieve a high degree of integration of energy supply and intelligent filing management functions through deep integration of hardware interconnection and software algorithms.

[0046] Specifically, the radio frequency (RF) energy harvesting and power supply unit is the foundation for the system's energy autonomy. Its multi-band RF antenna array is typically positioned on the top or side of the filing cabinet to maximize the reception of widely present Wi-Fi, 4G / 5G cellular signals, and other RF radiation (frequency ranges such as 2.4 GHz and 5.8 GHz). The captured RF AC signal is then transmitted to a rectification and impedance matching circuit, typically composed of Schottky diodes and a passive inductor-capacitor network. Its core function is to efficiently convert the weak RF AC power into DC power and maximize energy transfer efficiency through dynamic impedance matching. The rectified DC power is input to a maximum power point tracking (MPPT) controller. This controller incorporates algorithms such as perturbation observation or incremental conductance methods to continuously monitor the output of the rectifier circuit and adjusts its equivalent load to ensure the energy harvesting circuit always operates at its maximum power output point, addressing the challenges of dynamically changing environmental RF signal strength. The MPPT-optimized power is finally delivered to a hybrid energy storage module for storage. This module employs a heterogeneous architecture of supercapacitors and micro-lithium batteries connected in parallel. Supercapacitors, with their fast charging and discharging speeds and high power density, are used to handle the pulsed high-power demands of motor starting and instantaneous transmissions by wireless communication modules in intelligent filing cabinets, acting as energy buffers and stabilizing voltage. Meanwhile, micro-lithium batteries, with their high energy density and suitability for long-term stable discharge, are used to store excess energy, providing a sustained power supply to low-power devices such as the main controller and sensors. This hybrid design balances the needs for instantaneous high-power response with long-term battery life, making it crucial for stable system operation.

[0047] Furthermore, the intelligent archive management unit is responsible for all intelligent functions related to archives. Its core is a main controller (such as an ARM Cortex-M series microcontroller unit, MCU), which acts as the system's brain, coordinating the operation of various sub-modules, processing sensor data, executing archive management logic, and running the core energy consumption prediction algorithm. The environmental sensor array includes temperature, humidity, and light sensors, which monitor the physical parameters of the archive storage environment in real time. This data not only ensures that the archive storage environment meets requirements but also serves as important input features for the energy consumption prediction model. An ultra-wideband (UWB) / radio frequency identification (RFID) positioning and identification module is used to bind electronic tags to each archive bag or box, thereby achieving precise indoor positioning of archives, rapid inventory, and automatic recording of access logs. When a user queries a specific archive, its location information can be marked with a bright color (such as red or blue) on the human-machine interface (such as an OLED display with integrated touch functionality), greatly improving search efficiency. The intelligent electronic lock is controlled by the main controller, responsible for the secure locking and unlocking of the cabinet and recording all access operations. The communication module (such as a Wi-Fi or 4G / 5G module) is responsible for uploading system status and log information to the cloud server, and can receive model parameter updates or remote commands from the server.

[0048] Furthermore, the intelligent energy management unit serves as a smart hub connecting energy supply and consumption units. Its core consists of a power management integrated circuit (PMIC) and a high-precision energy consumption monitoring circuit. The PMIC has multiple power input and output capabilities, receiving inputs from both the hybrid energy storage module and a traditional AC adapter. Based on commands from the main controller, the PMIC can seamlessly and accurately switch power sources. The energy consumption monitoring circuit typically comprises a high-precision analog-to-digital converter (ADC) and a current sensing chip, used to sample the voltage and current of the hybrid energy storage module in real time and accurately calculate its remaining state of charge (SoC). This data is crucial for energy dispatch decisions.

[0049] The core of this invention lies in the energy consumption prediction model run by the main controller and the intelligent control strategy based on this model. This model is a multi-feature linear regression model, and its expression is:

[0050] P predicted (t+1)=α+β1*S usage (t)+β2*L(t)+β3*H(t)+β4*D(t)+ε;

[0051] Among them, P predicted(t+1) represents the predicted total system energy consumption for the next time slice (e.g., the next 15 minutes). Model parameters include: system base power consumption parameter α, representing the minimum power consumption when all units are idle; access operation weighting coefficient β1, representing the additional average power consumption caused by a single file access operation (involving motors, UWB scanning, etc.); S usage (t) represents the actual access frequency monitored within the current time slice; the illumination weighting coefficient β2 reflects the sensitivity of screen backlight and auxiliary lighting power consumption to illuminance; L(t) is the average ambient light intensity (unit: Lux) within the current time slice; the time period weighting coefficient β3 and the date type weighting coefficient β4 are used to capture the differences in system usage patterns on different workdays and time periods (such as working hours, lunch breaks, and nighttime); H(t) and D(t) are the encoded time factor and date factor; ε is the random error term.

[0052] The model's construction and operation is a dynamic learning process. After initial deployment, the system enters a learning period lasting several weeks. During this period, the system is forced to use mains power and continuously records time-series data, including historical energy consumption P. total (t), Operation frequency S usage (t), collected energy E rf The system constructs a training dataset using data such as L(t) and environmental data L(t). The main controller or cloud server then uses algorithms such as least squares (OLS) or gradient descent to fit and train the model onto this dataset, obtaining a set of optimal model parameters (α, β1, β2, β3, β4). Subsequently, the system can periodically (e.g., monthly) update and optimize the model parameters with new data, thereby increasing the accuracy of predictions.

[0053] Example 2:

[0054] Please see Figure 2 Based on the above model, the main controller executes a predictive energy scheduling method, with the following specific steps:

[0055] The system exits learning mode and enters intelligent operation mode. The main controller triggers a decision loop at fixed time intervals (e.g., every 15 minutes).

[0056] At each decision point, the controller first performs energy consumption prediction: it reads the feature value (S) of the current time slice. usage The energy consumption prediction model is calculated by substituting the given values ​​(t)L(t),H(t),D(t)) into the trained energy consumption prediction model to calculate the predicted energy consumption P for the next time slice. predicted .

[0057] Subsequently, the system performs an energy status assessment: it reads the current remaining power SoC of the hybrid energy storage module through the energy consumption monitoring circuit. currentIt also queries the historical database to obtain the predicted on-chip radio frequency energy harvesting value E for the next time period. rf (Usually, the average value of the same period in history is used.)

[0058] Finally, the main controller executes rule-based decisions and issues instructions to the PMIC:

[0059] Rule 1: If (SoC) current +E rf )>(P predicted If the power supply is *K (where K is a safety factor greater than 1, such as 1.2), it is determined that the self-owned energy is sufficient, and the PMIC is instructed to cut off the mains power and be powered entirely by the radio frequency energy harvesting power supply unit.

[0060] Rule 2: If SoC current <SoC min (SoC min If the set minimum power threshold (e.g., 20%) is reached, it is determined that the energy storage is about to be depleted. The PMIC is then instructed to immediately switch to AC power and charge the hybrid energy storage module to prevent over-discharge of the battery and ensure system safety.

[0061] Rule 3: If neither of the above two conditions is met, the system will maintain the current power supply mode until the next decision cycle.

[0062] This method represents a leap from "passive response" to "proactive prediction." Instead of waiting for the power to run out before switching, the system makes optimal power supply decisions in advance based on predicted future energy consumption and energy conditions. This maximizes the use of green radio frequency energy while ensuring 100% power supply stability, reducing unnecessary mains power consumption and mode switching frequency, and improving the overall economy and reliability of the system.

[0063] In summary, this invention, through hardware and software co-design, combines advanced radio frequency energy harvesting technology, heterogeneous energy storage architecture, and intelligent prediction algorithms to provide an innovative and practical solution for addressing the issues of continuous power supply and efficient energy management for smart IoT devices.

[0064] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] It should be noted that the components mentioned in the above embodiments are all general standard parts or components known to those skilled in the art. Their structures and principles can be learned by those skilled in the art through technical manuals or conventional experimental methods.

[0066] This invention has illustrated its principles and implementation methods using specific examples. The descriptions of these embodiments are merely illustrative of the method and its core ideas; furthermore, those skilled in the art will recognize that modifications may be made to the specific implementation methods and application scope based on the principles of this invention. Therefore, the content of this specification should not be construed as limiting the invention.

Claims

1. A self-powered intelligent filing cabinet based on a radio frequency energy harvesting chip, comprising a filing cabinet body, characterized in that, The filing cabinet body integrates: A radio frequency energy harvesting and power supply unit is used to capture radio frequency signals in the environment and convert them into DC power. The radio frequency energy harvesting and power supply unit includes a multi-band radio frequency antenna array, a rectification and impedance matching circuit, a maximum power point tracking controller, and a hybrid energy storage module. The intelligent archive management unit is used to realize the storage, location, environmental monitoring and interactive control of archives. The intelligent archive management unit includes a main controller, an environmental sensor array, an ultra-wideband positioning and radio frequency identification module, an intelligent electronic lock, a human-computer interaction interface and a communication module. An intelligent energy management unit is used to coordinate the power supply switching between the radio frequency energy harvesting power supply unit and the mains power. The intelligent energy management unit includes a power management integrated circuit and an energy consumption monitoring circuit. The main controller runs an energy consumption prediction model, predicts the system energy consumption for the next time period based on historical energy consumption data, real-time operating frequency and environmental parameters, and controls the power management integrated circuit to switch the power supply path according to the prediction results and the remaining power of the hybrid energy storage module.

2. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, The hybrid energy storage module includes: Supercapacitor, the supercapacitor being used to respond to instantaneous high-power requests; A miniature lithium battery, which is connected in parallel with the supercapacitor, is used to provide continuous and stable power supply.

3. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, The energy consumption prediction model adopts a multi-feature linear regression model, the expression of which is: P predicted (t+1)=α+β1*S usage (t)+β2*L(t)+β3*H(t)+β4*D(t)+ε; Among them, P predicted (t+1) represents the predicted energy consumption for the next time slice, α represents the system's base power consumption parameter, β1 represents the access operation weighting coefficient, and S usage (t) represents the frequency of file access operations within the current time slice, β2 is the illumination weight coefficient, L(t) is the current illumination intensity, β3 is the time period weight coefficient, H(t) is the time factor, β4 is the date type weight coefficient, D(t) is the date factor, and ε is the random error term.

4. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 3, characterized in that, The parameters of the multi-feature linear regression model are trained using historical data through the least squares method or gradient descent method and are updated regularly.

5. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, The main controller executes the following control logic at fixed time intervals: If the sum of the remaining power of the hybrid energy storage module and the predicted radio frequency energy is greater than the product of the predicted energy consumption and the safety factor in the next time slice, then switch to radio frequency energy power supply. If the remaining power is below the minimum power threshold, switch to mains power and charge the energy storage module; Otherwise, maintain the current power supply mode.

6. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, Also includes: The communication interface is connected to the upper-level server and is used to upload energy consumption data, file access logs, and receive updated model parameters.

7. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, The environmental sensor array includes: Temperature sensor; Humidity sensor; Light sensor; The output data from the temperature sensor, the humidity sensor, and the light sensor serve as input features for the energy consumption prediction model.

8. The autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 1, characterized in that, The ultra-wideband positioning and radio frequency identification module is used to track the location of the file in real time and to highlight the target file cell with a bright color in the human-computer interaction interface.

9. An energy management method for an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip as described in any one of claims 1-8, characterized in that, Includes the following steps: S1. Collect historical energy consumption data, operation frequency, and environmental data of the system to construct a training dataset; S2. Use the training data to fit a multi-feature linear regression model to obtain an energy consumption prediction model; S3. Monitor the system status in real time and input the data into the model to calculate the predicted energy consumption for the next period; S4. Based on predicted energy consumption and energy storage capacity, power supply switching is controlled through predefined decision rules.

10. The energy management method for an autonomously powered intelligent filing cabinet based on a radio frequency energy harvesting chip according to claim 9, characterized in that, In step S4, a safety factor and a minimum power threshold are introduced into the decision rules to ensure power supply stability and prevent over-discharge.

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