Wireless charging power intelligent adjustment method

Abstract

The present application relates to the technical field of wireless charging, in particular to a kind of wireless charging power intelligent regulation method, it includes the following steps: using sensor network, battery voltage, current, temperature, coil mutual inductance coefficient, magnetic field intensity and environmental temperature data are collected, the correlation model of battery charging stage, coil transmission efficiency and safety threshold is established by learning algorithm, the frequency of transmitting end and receiving end is synchronized to resonance point by phase-locked loop technology, adjacent coil is activated using phase difference control, power superposition and area coverage are realized, over-temperature, over-voltage protection is realized by monitoring coil resistance, inductance change and temperature threshold, using software power reduction, aging trend is predicted using AI analysis historical data, and system safety operation guarantee mechanism is obtained.The present application solves the problems that parameter collection is not comprehensive and accurate in existing wireless charging, power prediction is difficult to adapt, power regulation instruction is single, and safety guarantee lacks intelligent prediction and flexible regulation.

Description

Technical Field

[0001] This invention belongs to the field of wireless charging technology, specifically relating to a method for intelligent adjustment of wireless charging power. Background Technology

[0002] With the widespread application of wireless charging technology, achieving intelligent, efficient, and safe power regulation has become a key issue. In traditional wireless charging systems, parameter acquisition is often limited, focusing only on basic parameters such as battery voltage and current. Insufficient data collection is needed for crucial parameters like coil mutual inductance, magnetic field strength, and ambient temperature. Furthermore, the lack of effective data fusion processing methods makes it difficult to obtain a comprehensive and accurate set of real-time state parameters, failing to provide a reliable basis for subsequent power regulation. Regarding power demand prediction, existing methods typically use simplistic models that fail to accurately reflect the complex relationships between battery charging stages, coil transmission efficiency, and safety thresholds. The lack of dynamic update mechanisms prevents timely parameter adjustments based on real-time data, leading to inaccurate power prediction and hindering adaptive power regulation. In resonant compensation topologies, while some technologies can synchronize the transmitter and receiver frequencies using phase-locked loops (PLLs), they lack flexibility in adjusting compensation network parameters and their application of phase difference control to optimize power transmission is insufficient, resulting in lower power transmission efficiency. In terms of spatial power allocation, traditional methods are not precise enough in the layout and control of the transmitting coil, making it difficult to accurately locate the receiving end and effectively activate adjacent coils to achieve power superposition and area coverage, thus limiting the range and flexibility of power transmission.

[0003] Existing technologies suffer from incomplete and insufficient data collection and integration, difficulty in dynamically updating power prediction models, inflexible resonance compensation adjustment, poor spatial power allocation positioning, limited power adjustment commands, lack of safety assurance, and deficiencies in flexible software control and accurate prediction of aging trends. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides an intelligent power adjustment method for wireless charging, which solves the problems of incomplete and inaccurate parameter acquisition, difficulty in adaptive power prediction, inflexible resonance compensation adjustment, limited spatial power allocation, single power adjustment command, and lack of intelligent prediction and flexible control for safety assurance in existing wireless charging systems. To achieve the above objectives, this invention adopts the following technical solution:

[0005] The aforementioned intelligent power adjustment method for wireless charging includes the following steps: A sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Features are extracted using a data fusion algorithm to obtain a real-time state parameter set. Based on the collected parameters, a correlation model is established between the battery charging stage, coil transmission efficiency, and safety threshold using a learning algorithm. Parameters are dynamically updated using real-time data to obtain an adaptive power demand prediction model. Phase-locked loop (PLL) technology is used to synchronize the transmitter and receiver frequencies to the resonant point. A variable capacitor array is used to adjust and compensate network parameters, and phase difference control is used to optimize power transmission, resulting in a resonant compensation topology. The transmitter coil is divided into a matrix array. A current sensor is used to locate the receiver position, and phase difference control is used to activate adjacent coils, achieving power superposition and area coverage, resulting in a spatial power allocation scheme. Based on a fuzzy logic algorithm, with the target power as the set value, the demand is tracked by phase shift angle, and the battery status is synchronized using bidirectional communication to obtain a real-time power adjustment command. By monitoring changes in coil resistance and inductance and temperature thresholds, software power reduction is used to achieve over-temperature and over-voltage protection. AI analysis of historical data is used to predict aging trends, resulting in a system safety operation guarantee mechanism.

[0006] Furthermore, the method of using a sensor network to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature, and extracting features through a data fusion algorithm to obtain a real-time state parameter set, includes the following steps: collecting data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature using a sensor network; aggregating the collected data to a data processing center through a data transmission mechanism; performing in-depth processing on the aggregated data using an advanced data fusion algorithm to extract representative feature information; and integrating and analyzing the extracted feature information to obtain comprehensive information reflecting the real-time operating status of the system, thus obtaining a real-time state parameter set.

[0007] Furthermore, the process of establishing a correlation model between battery charging stages, coil transmission efficiency, and safety thresholds based on the collected parameters using a learning algorithm, and dynamically updating parameters using real-time data to obtain an adaptive power demand prediction model, includes the following steps: Collecting basic parameters during battery charging using sensors, including battery status, voltage, current, temperature, and coil operating conditions; extracting key features from these parameters using data processing techniques, inputting these key features into a learning algorithm to obtain the correlation between battery charging stages, coil transmission efficiency, and safety thresholds, and establishing a correlation model; inputting the real-time collected data into the correlation model, dynamically updating parameters based on existing correlation rules, and continuously adjusting and optimizing to obtain a power demand prediction model that adapts to real-time conditions.

[0008] Furthermore, the process of synchronizing the transmitter and receiver frequencies to the resonant point using phase-locked loop (PLL) technology, adjusting the compensation network parameters using a variable capacitor array, and optimizing power transmission using phase difference control to obtain a resonant compensation topology includes the following steps: Using PLL technology, the frequencies of the transmitter and receiver are precisely tracked and synchronized; through continuous adjustment, the frequency is stabilized at the resonant point to ensure the foundation for efficient energy transmission; using a variable capacitor array, the compensation network parameters are flexibly adjusted according to the real-time operating status of the system; using phase difference control technology, the power transmission process is optimized, phase information is extracted, analyzed, and processed to obtain a high-performance resonant compensation topology capable of achieving efficient power transmission.

[0009] Furthermore, the process of dividing the transmitting coil into a matrix array, locating the receiving end position using a current sensor, and activating adjacent coils using phase difference control to achieve power superposition and area coverage, resulting in a spatial power allocation scheme, includes the following steps: The transmitting coil is meticulously divided into a regular matrix array using a matrix layout; the specific location information of the receiving end in space is monitored and located in real time using a current sensor, and the receiving end position data is extracted; phase difference control technology is used to activate specific adjacent coils in the transmitting end matrix array based on the position information; the power generated by the activated coils is cleverly superimposed to achieve full coverage of a specific area, resulting in a scientifically reasonable spatial power allocation scheme.

[0010] Furthermore, the method based on fuzzy logic algorithm, using the target power as a set value, tracks demand through phase shift angle, and combines bidirectional communication to synchronize battery status to obtain real-time power adjustment commands, includes the following steps: constructing a core framework for power adjustment using fuzzy logic algorithm, explicitly setting the target power as a reference value, extracting the deviation between the current power and the target power and the rate of change of the deviation by real-time monitoring of system operation; performing preliminary tracking and adjustment of output power through phase shift angle, acquiring real-time battery status data using bidirectional communication technology; comprehensively analyzing and processing the power tracking and adjustment status with battery status data, and obtaining real-time power adjustment commands that can accurately adapt to the current operating conditions through fuzzy logic algorithm calculation.

[0011] Furthermore, the system safety operation guarantee mechanism, which monitors changes in coil resistance and inductance as well as temperature thresholds, and uses software power reduction to achieve over-temperature and over-voltage protection, and utilizes AI to analyze historical data and predict aging trends, includes the following steps: Using monitoring equipment, the system continuously monitors changes in coil resistance and inductance in real time, monitors temperature values, and extracts temperature threshold information; through judgment logic, when the monitored temperature or voltage exceeds the safe range, the system output power is rapidly reduced using software power reduction to achieve over-temperature and over-voltage protection; AI technology is used to deeply analyze historical data accumulated during system operation, extract equipment aging characteristics, predict equipment aging trends, and combine protection measures with aging prediction results. Through comprehensive processing and optimization, a comprehensive and reliable system safety operation guarantee mechanism is obtained.

[0012] Furthermore, the use of phase-locked loop (PLL) technology for precise frequency tracking and synchronization between the transmitter and receiver, continuously adjusting to stabilize the frequency at the resonant point, ensures the foundation for efficient energy transmission. This includes the following steps: applying PLL technology with capture and tracking capabilities to the frequency control stages of the transmitter and receiver; extracting the phase difference information between the transmitter and receiver frequencies using a phase detector within the PLL and converting this information into a voltage signal; processing the voltage signal using a loop filter to remove interference noise and obtain a clean control signal; and gradually stabilizing the frequency at the receiver's oscillation frequency to establish a stable frequency synchronization bridge between the transmitter and receiver, ensuring the foundation for efficient energy transmission.

[0013] Furthermore, the matrix layout method, which meticulously divides the transmitting coils into a regular matrix array, includes the following steps: Adopting a matrix layout concept, based on the spatial range and power coverage requirements of the actual application scenario, the spacing and overall structure of the transmitting coils are determined through measurement and calculation; the transmitting coils are orderly divided into a regular matrix array according to row and column rules, ensuring that each coil has a clear and fixed position in the array; the installation accuracy and consistency of the coils are controlled to ensure the regularity of the array, resulting in a reasonably laid out and structurally stable transmitting coil matrix array.

[0014] In the technical solution provided by this invention, a sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Features are extracted through a data fusion algorithm to obtain a real-time state parameter set. Based on the collected parameters, a correlation model between battery charging stage, coil transmission efficiency, and safety threshold is established through a learning algorithm. Combined with real-time data, the parameters are dynamically updated to obtain an adaptive power demand prediction model. Phase-locked loop technology is used to synchronize the transmitter and receiver frequencies to the resonant point. A variable capacitor array is used to adjust and compensate network parameters, and phase difference control is used to optimize power transmission to obtain a resonant compensation topology. The transmitter coil is divided into a matrix array. The receiver position is located through a current sensor. Phase difference control is used to activate adjacent coils to achieve power superposition and area coverage, resulting in a spatial power allocation scheme. Based on a fuzzy logic algorithm, with the target power as the set value, the demand is tracked through phase shift angle, and the battery status is synchronized through bidirectional communication to obtain a real-time power adjustment command. By monitoring changes in coil resistance and inductance and temperature thresholds, software power reduction is used to achieve over-temperature and over-voltage protection. AI is used to analyze historical data to predict aging trends, resulting in a system safety operation guarantee mechanism. This invention solves the problems of incomplete and inaccurate parameter acquisition, difficulty in adaptive power prediction, inflexible resonance compensation adjustment, limited spatial power allocation, single power adjustment command, and lack of intelligent prediction and flexible control for safety assurance in existing wireless charging. Attached Figure Description

[0015] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0016] Figure 1 This is a schematic diagram of the first embodiment of a wireless charging power intelligent adjustment method according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of a second embodiment of a wireless charging power intelligent adjustment method according to an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of a third embodiment of a wireless charging power intelligent adjustment method according to an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram of the fourth embodiment of a wireless charging power intelligent adjustment method according to the present invention.

[0020] Figure 5 This is a schematic diagram of the fifth embodiment of a wireless charging power intelligent adjustment method according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] A method for intelligent adjustment of wireless charging power, such as Figure 1 As shown, the process includes the following steps: A sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Features are extracted using a data fusion algorithm to obtain a real-time state parameter set. Based on the collected parameters, a correlation model is established between the battery charging stage, coil transmission efficiency, and safety threshold using a learning algorithm. Parameters are dynamically updated using real-time data to obtain an adaptive power demand prediction model. Phase-locked loop (PLL) technology is used to synchronize the transmitter and receiver frequencies to the resonant point. A variable capacitor array is used to adjust and compensate network parameters, and phase difference control is used to optimize power transmission, resulting in a resonant compensation topology. The transmitter coil is divided into a matrix array. The receiver position is located using a current sensor, and phase difference control is used to activate adjacent coils, achieving power superposition and area coverage, resulting in a spatial power allocation scheme. Based on a fuzzy logic algorithm, with the target power as the set value, the demand is tracked by phase shift angle, and the battery status is synchronized using bidirectional communication to obtain a real-time power adjustment command. By monitoring changes in coil resistance and inductance and temperature thresholds, software power reduction is used to achieve over-temperature and over-voltage protection. AI analysis of historical data is used to predict aging trends, resulting in a system safety operation guarantee mechanism.

[0024] like Figure 2 As shown, in this embodiment, a sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance coefficient, magnetic field strength, and ambient temperature. Through a data transmission mechanism, the collected data is aggregated to a data processing center, where advanced data fusion algorithms are used to perform in-depth processing on the aggregated data and extract representative feature information. The extracted feature information is then integrated and analyzed to obtain comprehensive information reflecting the real-time operating status of the system, resulting in a real-time status parameter set.

[0025] By employing a sensor network to comprehensively collect data from the battery, coils, and other components, the system can accurately acquire fundamental operational information. Data is aggregated to a processing center via a data transmission mechanism and deeply processed using advanced data fusion algorithms. This effectively eliminates redundant and erroneous data, extracting more representative feature information. The real-time status parameter set obtained after integrating and analyzing these features comprehensively and accurately reflects the system's real-time operating status. This provides a reliable basis for subsequent intelligent power adjustment, helps to identify potential problems in advance, ensures the stable, efficient, and safe operation of the wireless charging system, and enhances the user experience.

[0026] like Figure 3 As shown, in this embodiment, sensors are used to collect basic parameters during the battery charging process. These basic parameters include battery status, voltage, current, temperature, and coil operating status. Through data processing technology, key features from these parameters are extracted and input into a learning algorithm to obtain the correlation between battery charging stages, coil transmission efficiency, and safety thresholds, thus establishing a correlation model. The real-time collected data is input into the correlation model, and the parameters are dynamically updated by combining the existing correlation rules of the correlation model. Through continuous adjustment and optimization, a power demand prediction model that adapts to real-time conditions is obtained.

[0027] By employing sensors to collect fundamental battery charging parameters, comprehensive and accurate information about the charging process can be obtained. Data processing techniques are used to extract key features, providing a high-quality data foundation for subsequent analysis. A correlation model is established through learning algorithms, clearly presenting the complex relationships between battery charging stages, coil transmission efficiency, and safety thresholds. Real-time data is input into the model, and parameters are dynamically updated, enabling the power demand prediction model to adapt to real-time conditions, greatly improving the accuracy and timeliness of predictions.

[0028] like Figure 4 As shown, in this embodiment, phase-locked loop (PLL) technology is used to accurately track and synchronize the frequencies of the transmitter and receiver. Through continuous adjustment, the frequency is stabilized at the resonant point, ensuring the foundation for efficient energy transmission. A variable capacitor array is used to flexibly adjust the compensation network parameters according to the real-time operating status of the system. Phase difference control technology is used to optimize the power transmission process, extract phase information, analyze and process it to obtain a resonant compensation topology with excellent performance and efficient power transmission.

[0029] Phase-locked loop (PLL) technology is used to precisely track and synchronize the frequencies of the transmitter and receiver, stabilizing the frequency at the resonant point and laying a solid foundation for efficient energy transmission, significantly reducing energy loss. The variable capacitor array can flexibly adjust the compensation network parameters according to the real-time operating status of the system, enhancing the system's adaptability to different operating conditions. Phase difference control technology optimizes the power transmission process; by extracting and analyzing phase information, a high-performance resonant compensation topology is obtained, further improving power transmission efficiency and enabling the wireless charging system to operate stably and efficiently in various scenarios.

[0030] like Figure 5 As shown, in this embodiment, a matrix layout is adopted, in which the transmitting coil is meticulously divided into a regular matrix array; through a current sensor, the specific position information of the receiving end in space is monitored and located in real time, the position data of the receiving end is extracted, and phase difference control technology is used to activate specific adjacent coils in the transmitting end matrix array according to the position information; the power generated by the activated coils is cleverly superimposed to achieve full coverage of a specific area, resulting in a scientific and reasonable spatial power allocation scheme.

[0031] A matrix layout divides the transmitting coils into a regular array, laying the foundation for precise power distribution. Real-time monitoring and data extraction using current sensors allow for rapid and accurate understanding of the receiver's dynamics. Phase difference control technology activates specific adjacent coils based on their location, achieving directional power transmission. The power of the activated coils is cleverly superimposed to achieve comprehensive coverage of a specific area, effectively solving the problems of uneven power distribution and limited coverage in traditional wireless charging, and significantly improving power utilization efficiency.

[0032] In this embodiment, a fuzzy logic algorithm is used to construct the core framework for power regulation. The target power is explicitly set as the reference value. By monitoring the system operation status in real time, the deviation between the current power and the target power, as well as the deviation change rate, are extracted. The output power is initially tracked and adjusted by shifting the phase angle. Two-way communication technology is used to obtain the real-time status data of the battery. The power tracking adjustment and battery status data are comprehensively analyzed and processed. After calculation by the fuzzy logic algorithm, a real-time power regulation command that can accurately adapt to the current operating conditions is obtained.

[0033] A core framework for power regulation is constructed using fuzzy logic algorithms. By setting a target power as a reference value, the system can accurately determine the direction of adjustment. Real-time monitoring extracts power deviation and rate of change information, providing a dynamic basis for adjustment. Initial adjustments are made using phase shift angles for rapid response to power changes. Two-way communication technology acquires real-time battery status data, which is comprehensively analyzed with the power adjustment situation. The resulting real-time power regulation command, processed by the fuzzy logic algorithm, can accurately adapt to different operating conditions, effectively improving the accuracy and flexibility of power regulation and ensuring the stable and efficient operation of the wireless charging system.

[0034] In this embodiment, monitoring equipment is used to continuously monitor changes in coil resistance and inductance in real time, monitor temperature values, and extract temperature threshold information. Through judgment logic, when the temperature or voltage exceeds the safe range, software power reduction is used to quickly reduce the system output power, achieving over-temperature and over-voltage protection. AI technology is used to perform in-depth analysis of historical data accumulated during system operation, extract the aging characteristics of the equipment, predict the aging trend of the equipment, and combine the protection measures with the aging prediction results. Through comprehensive processing and optimization, a comprehensive and reliable system safety operation guarantee mechanism is obtained.

[0035] Real-time monitoring of coil resistance, inductance changes, and temperature values, along with threshold information extraction, enables timely understanding of key system parameters. When temperature or voltage exceeds safe limits, the software rapidly reduces power, effectively providing over-temperature and over-voltage protection to prevent equipment damage. Utilizing AI technology for in-depth analysis of historical data, it accurately extracts patterns and predicts aging trends, combining protective measures with these predictions to proactively mitigate potential risks. This comprehensive and optimized system safety operation guarantee mechanism ensures the stable and reliable operation of the wireless charging system, significantly improving its safety and lifespan.

[0036] In this embodiment, a phase-locked loop (PLL) technology with capture and tracking capabilities is employed and applied to the frequency control stage between the transmitter and receiver. The phase detector within the PLL extracts the phase difference information between the transmitter and receiver frequencies and converts this information into a voltage signal. A loop filter processes the voltage signal to remove interference noise and obtain a clean control signal. By controlling the oscillation frequency at the receiver, the frequency gradually approaches and stabilizes at the resonant point, establishing a stable frequency synchronization bridge between the transmitter and receiver, ensuring a foundation for efficient energy transmission.

[0037] A phase-locked loop (PLL) technology with capture and tracking capabilities is used for frequency control at both the transmitter and receiver. This technology accurately acquires the frequency phase difference between the two and converts it into a voltage signal. A loop filter processes the voltage signal, effectively filtering out interference noise to obtain a clean control signal. This allows the receiver's oscillation frequency to approach and stabilize at the resonant point. This establishes a robust frequency synchronization bridge, significantly reducing energy loss due to frequency deviations, ensuring efficient energy transmission, providing a solid guarantee for the stable operation of the wireless charging system, and improving the overall system performance and charging efficiency.

[0038] In this embodiment, a matrix layout concept is adopted. Based on the actual application scenario's requirements for spatial range and power coverage, the arrangement spacing and overall structure of the transmitting coils are determined through measurement and calculation. The transmitting coils are divided into a regular matrix array according to row and column rules to ensure that each coil has a clear and fixed position in the array. The installation accuracy and consistency of the coils are controlled to ensure the regularity of the array, resulting in a reasonably laid out and structurally stable transmitting coil matrix array.

[0039] Adopting a matrix layout concept, the spacing and structure of the transmitter coils are determined based on the space and power requirements of the actual scenario, enabling precise adaptation to different application scenarios and improving space utilization. The coils are orderly divided into a regular matrix array, clearly defining the position of each coil for easy management and control. Controlling installation precision and consistency ensures array regularity, effectively reducing energy loss and interference caused by unreasonable layout. The resulting rationally laid out and structurally stable transmitter coil matrix array lays a solid foundation for efficient, stable, and uniform power transmission in the wireless charging system, improving the overall system performance.

[0040] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent adjustment of wireless charging power, characterized in that, The intelligent adjustment method for wireless charging power includes the following steps: A sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Features are extracted through a data fusion algorithm to obtain a real-time state parameter set. Based on the collected parameters, a correlation model between battery charging stage, coil transmission efficiency and safety threshold is established through a learning algorithm. Combined with real-time data, the parameters are dynamically updated to obtain an adaptive power demand prediction model. By synchronizing the frequencies of the transmitter and receiver to the resonant point using phase-locked loop technology, adjusting the parameters of the compensation network using a variable capacitor array, and optimizing power transmission by using phase difference control, a resonant compensation topology is obtained. The transmitting coil is divided into a matrix array, the receiving end position is located by a current sensor, and adjacent coils are activated by phase difference control to achieve power superposition and area coverage, thus obtaining a spatial power allocation scheme. Based on fuzzy logic algorithm, with target power as the set value, demand is tracked by phase shift angle and battery status is synchronized by bidirectional communication to obtain real-time power adjustment command; By monitoring changes in coil resistance and inductance, as well as temperature thresholds, software-based power reduction is used to achieve over-temperature and over-voltage protection. AI is used to analyze historical data and predict aging trends, thus obtaining a system safety operation guarantee mechanism.

2. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The process employs a sensor network to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Features are extracted using a data fusion algorithm to obtain a real-time state parameter set, including the following steps: A sensor network is used to collect data on battery voltage, current, temperature, coil mutual inductance, magnetic field strength, and ambient temperature. Through the data transmission mechanism, the collected data is aggregated to the data processing center, where advanced data fusion algorithms are used to perform in-depth processing on the aggregated data and extract representative feature information. The extracted feature information is integrated and analyzed to obtain comprehensive information reflecting the real-time operating status of the system, resulting in a real-time status parameter set.

3. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The aforementioned adaptive power demand prediction model is obtained by establishing a correlation model between battery charging stage, coil transmission efficiency, and safety threshold based on the collected parameters through a learning algorithm, and dynamically updating the parameters by combining real-time data, including the following steps: Sensors are used to collect basic parameters during the battery charging process. These basic parameters include battery status, voltage, current, temperature, and coil operation. By using data processing technology, key features are extracted from these parameters, and these key features are input into the learning algorithm to obtain the correlation between battery charging stage, coil transmission efficiency and safety threshold, and to establish a correlation model. The real-time collected data is input into the correlation model, and the parameters are dynamically updated by combining the existing correlation rules of the correlation model. Through continuous adjustment and optimization, a power demand prediction model that adapts to changes in real-time conditions is obtained.

4. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The process of synchronizing the frequencies of the transmitter and receiver to the resonant point using phase-locked loop technology, adjusting the compensation network parameters using a variable capacitor array, and optimizing power transmission using phase difference control to obtain a resonant compensation topology includes the following steps: Employing phase-locked loop technology, the frequency of the transmitter and receiver is precisely tracked and synchronized. Through continuous adjustment, the frequency is stabilized at the resonant point, ensuring the foundation for efficient energy transmission. A variable capacitor array is used to flexibly adjust the compensation network parameters according to the real-time operating status of the system; By utilizing phase difference control technology, the power transmission process is optimized, phase information is extracted, and analyzed to obtain a resonant compensation topology with excellent performance and high-efficiency power transmission.

5. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The process of dividing the transmitting coil into a matrix array, locating the receiving end position using a current sensor, activating adjacent coils using phase difference control to achieve power superposition and area coverage, and obtaining a spatial power allocation scheme includes the following steps: A matrix layout is adopted, in which the transmitting coil is meticulously divided into a regular matrix array; By using a current sensor, the specific location information of the receiver in space is monitored and located in real time. The receiver position data is extracted, and phase difference control technology is used to activate specific adjacent coils in the transmitter matrix array based on the position information. By cleverly superimposing the power generated by the activation coil, a comprehensive coverage of a specific area can be achieved, resulting in a scientifically sound and reasonable spatial power distribution scheme.

6. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The method based on fuzzy logic algorithm, using the target power as a set value, tracks demand through phase shift angle, and combines bidirectional communication to synchronize battery status to obtain real-time power adjustment commands, includes the following steps: A fuzzy logic algorithm is used to construct the core framework for power regulation. The target power is explicitly set as the reference value. By monitoring the system operation status in real time, the deviation between the current power and the target power, as well as the rate of change of the deviation, are extracted. The output power is initially tracked and adjusted by shifting the phase angle, and real-time battery status data is obtained using two-way communication technology. By comprehensively analyzing and processing power tracking adjustment data and battery status data, and performing fuzzy logic algorithm calculations, a real-time power adjustment command that can accurately adapt to the current operating conditions is obtained.

7. The method for intelligent adjustment of wireless charging power according to claim 1, characterized in that, The system safety operation guarantee mechanism is obtained by monitoring changes in coil resistance and inductance, as well as temperature thresholds, and implementing over-temperature and over-voltage protection through software power reduction. It also utilizes AI to analyze historical data and predict aging trends, and includes the following steps: Monitoring equipment is used to monitor the changes in coil resistance and inductance in real time and continuously, monitor temperature values ​​and extract temperature threshold information; By using the judgment logic, when the temperature or voltage is detected to be outside the safe range, the system output power is quickly reduced by software power reduction to achieve over-temperature and over-voltage protection. By using AI technology to conduct in-depth analysis of historical data accumulated during system operation, the characteristics and patterns of equipment aging are extracted, and the aging trend of equipment is predicted. By combining protective measures with the aging prediction results and through comprehensive processing and optimization, a comprehensive and reliable system safety operation guarantee mechanism is obtained.

8. The method for intelligent adjustment of wireless charging power according to claim 4, characterized in that, The aforementioned phase-locked loop (PLL) technology is used to precisely track and synchronize the frequencies of the transmitter and receiver. Through continuous adjustments, the frequency is stabilized at the resonant point, ensuring the foundation for efficient energy transmission. This includes the following steps: Phase-locked loop (PLL) technology with capture and tracking capabilities is adopted and applied to the frequency control stage of the transmitting and receiving ends. The phase detector inside the phase-locked loop extracts the phase difference information between the transmitting and receiving frequencies and converts this information into a voltage signal. By using a loop filter to process the voltage signal, interference noise is filtered out to obtain a clean control signal. The frequency is gradually brought closer to and stabilized at the resonant point by the oscillation frequency of the receiving end, thus building a solid frequency synchronization bridge between the transmitting and receiving ends and ensuring the foundation for efficient energy transmission.

9. The method for intelligent adjustment of wireless charging power according to claim 5, characterized in that, The method of using a matrix layout, which meticulously divides the transmitting coil into a regular matrix array, includes the following steps: Adopting a matrix layout concept, and based on the actual application scenario's requirements for spatial range and power coverage, the arrangement spacing and overall architecture of the transmitting coils are determined through measurement and calculation. The transmitting coils are divided into a regular matrix array according to row and column rules, ensuring that each coil has a clear and fixed position in the array; By controlling the installation accuracy and consistency of the coils, the regularity of the array is ensured, resulting in a reasonably laid out and structurally stable transmitter coil matrix array.

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