Mobile power supply intelligent charging management method based on wireless data transmission

Abstract

The invention relates to the technical field of wireless charging, in particular to an intelligent charging management method for a mobile power supply through wireless data transmission, which comprises the following steps: acquiring environmental interference intensity by using a multi-band electromagnetic sensor, and generating a multi-band electromagnetic interference intensity comprehensive data set; based on the interference mode clustering and charging strategy decision model, generating a charging parameter strategy and packaging the charging parameter strategy into an electromagnetic feature code; sending a feature code to the to-be-charged device through a BLE 5.0 protocol and receiving a verification signal, and performing similarity matching in combination with the battery feature hash value; after verification is passed, a charging parameter strategy is analyzed and executed, and dynamic power distribution, frequency hopping anti-interference and overheating protection control are achieved. According to the invention, the environmental adaptability and safety of the charging strategy are improved, and the method is suitable for intelligent charging application in a complex electromagnetic environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless charging technology, and in particular to a mobile power supply intelligent charging management method through wireless data transmission. BACKGROUND

[0002] With the large-scale popularity of mobile devices and the development of wireless charging technology, the importance of mobile power supplies (i.e. portable charging devices) in users' daily use is increasingly prominent. Especially in multi-scene applications (such as high-speed rail stations, airports, industrial sites or smart home environments), how to achieve efficient, safe and intelligent mobile charging has become a key direction of technological evolution. In recent years, the continuous development of Bluetooth Low Energy (BLE), multi-band electromagnetic sensing and intelligent charging control algorithms has provided a foundation for mobile power supplies to realize environmental perception, adaptive strategy generation and device state perception, and has promoted the upgrade of intelligent charging systems based on wireless data transmission from traditional "one-way power supply" to "dynamic collaborative decision-making".

[0003] However, there are still many bottlenecks in the existing technology in the process of mobile power supply intelligent charging management: first, most current systems only support single-band interference detection or static electromagnetic tolerance threshold setting, which is difficult to cope with the complex electromagnetic environment with dynamic changes and spectrum crossover in actual application scenarios; second, charging strategies are mostly based on pre-set parameter configurations, lacking dynamic adaptation capability to target device battery states, which can easily cause power mismatch, low charging efficiency and even battery overheating; in addition, existing systems generally lack a two-way authentication mechanism based on encrypted feature codes and device feedback, and do not form a thermal safety closed-loop control of the charging strategy execution process, making it difficult to meet the basic requirements of high-reliability intelligent charging systems for "environment-device-strategy" three-dimensional collaboration. SUMMARY

[0004] The present application provides a mobile power supply intelligent charging management method through wireless data transmission.

[0005] The mobile power supply intelligent charging management method through wireless data transmission comprises the following steps: S1: Real-time acquisition of electromagnetic interference intensity of the charging environment through a multi-band electromagnetic sensor to generate a multi-band electromagnetic interference intensity comprehensive data set; S2: Generating an electromagnetic feature code containing a charging parameter strategy according to the multi-band electromagnetic interference intensity comprehensive data set; S3: Sending the electromagnetic feature code to the device to be charged and receiving the verification signal feedback from the device; S4: When the verification signal matches the pre-stored feature threshold, executing the charging parameter strategy.

[0006] Optionally, the S1 comprises: S11: Collecting original electromagnetic signals of at least three independent frequency bands in parallel in a charging environment through a multi-band electromagnetic sensor, and the sampling frequency of each frequency band is not less than 2 times of the center frequency of the frequency band; S12: Decomposing the original electromagnetic signals into frequency domain components through Fourier transform, and extracting signal amplitude peaks of each frequency band within a preset time window as a basic electromagnetic interference intensity; S13: Normalizing the basic electromagnetic interference intensity to map frequency band intensity values of different dimensions to a standardized interval of 0-1, and generating a frequency band electromagnetic interference intensity feature vector; S14: Comparing the frequency band electromagnetic interference intensity feature vector with a pre-stored environment reference threshold matrix element by element, and outputting a real-time electromagnetic interference intensity analysis report containing an out-of-standard frequency band identification code and an intensity offset; S15: Based on the intensity offset in the real-time electromagnetic interference intensity analysis report, calculating dynamic weight coefficients of the electromagnetic interference intensity of each frequency band, and generating a multi-band electromagnetic interference intensity comprehensive data set with a time stamp.

[0007] Optionally, the pre-stored environment reference threshold matrix includes a basic safety threshold vector, a dynamic correction coefficient matrix, and a device electromagnetic compatibility archive, wherein; the basic safety threshold vector is used to determine whether the electromagnetic interference intensity of each frequency band is out of standard; the dynamic correction coefficient matrix is used to correct the basic safety threshold vector in real time; the device electromagnetic compatibility archive is used to generate customized safety thresholds for specific devices.

[0008] Optionally, the S2 includes: S21: Analyzing the generated multi-band electromagnetic interference intensity comprehensive data set, extracting the dynamic weight coefficients of the electromagnetic interference intensity of each frequency band and the time stamp, and generating an electromagnetic interference intensity distribution feature map based on an interference mode clustering algorithm; S22: Inputting the electromagnetic interference intensity distribution feature map into a pre-trained charging strategy decision model, matching an optimal charging parameter strategy, and the charging parameter strategy includes a dynamic power distribution ratio, an anti-interference frequency hopping sequence, and an overheating protection trigger threshold; S23: Structurally encoding the charging parameter strategy, adding a timestamp-based anti-replay attack check code, and synthesizing an electromagnetic feature code carrying the charging parameter strategy, and the data structure satisfies: electromagnetic feature code = [strategy header identification] + [encrypted charging parameter strategy] + [check code] + [time stamp].

[0009] Optionally, the pre-trained charging strategy decision model includes an electromagnetic feature extraction layer and a multi-objective optimization decision layer, wherein; The electromagnetic feature extraction layer adopts a convolutional neural network to process an electromagnetic interference intensity distribution feature map, and outputs a 128-dimensional feature vector containing a space-frequency domain coupling relationship. The multi-target optimization decision layer solves the charging parameter strategy based on a Pareto optimality principle.

[0010] Optionally, the S3 comprises: S31: encapsulating the generated electromagnetic feature code into a low-power broadcast data packet through a BLE 5.0 protocol, and sending the low-power broadcast data packet to the device to be charged in a directional manner; S32: after the device to be charged receives the electromagnetic feature code, extracting the charging parameter strategy, combining the current battery state of the device to generate a verification signal comprising a strategy feasibility evaluation result, and the data structure of the verification signal is: Verification signal = [device ID] + [strategy status code] + [battery feature hash value] + [time synchronization timestamp]; S33: receiving the verification signal returned by the device to be charged through the same BLE link, analyzing the strategy status code and the battery feature hash value, and performing similarity matching calculation on the battery feature hash value and the pre-stored feature threshold, and outputting a matching degree percentage as a verification result.

[0011] Optionally, the low-power broadcast data packet structure comprises a device addressing field, a feature code load field and a cyclic redundancy check field.

[0012] Optionally, the S4 comprises: S41: when the matching degree percentage of the verification signal output by S33 is greater than or equal to a preset matching threshold, analyzing the strategy status code in the verification signal, confirming the executable state of the charging parameter strategy and generating a strategy execution instruction; S42: extracting the encrypted charging parameter strategy from the electromagnetic feature code generated in S23 according to the strategy execution instruction, and obtaining the complete charging parameter strategy after decryption, including a dynamic power distribution ratio, an anti-interference frequency hopping sequence and an overheating protection trigger threshold; S43: executing the charging parameter strategy, controlling the multi-coil wireless charging array to output energy according to the dynamic power distribution ratio, and driving the frequency synthesizer to execute the anti-interference frequency hopping sequence.

[0013] Optionally, during the execution of the charging parameter strategy, the battery temperature change rate is monitored in real time, and when the temperature change rate exceeds the overheating protection trigger threshold, the output is immediately cut off and a safety alarm signal is generated.

[0014] The beneficial effects of the present application are: The application introduces a multi-band electromagnetic sensor and a frequency band electromagnetic interference intensity feature vector construction mechanism, combines an environmental benchmark threshold matrix, a dynamic correction coefficient matrix and a device electromagnetic compatibility archive, realizes dynamic identification and quantitative evaluation of multi-source electromagnetic interference in an actual charging environment, generates an electromagnetic interference intensity distribution feature map through an interference mode clustering algorithm, and uses a pre-trained charging strategy decision model for multi-objective optimization matching, effectively avoiding problems such as charging interruption, unstable power or abnormal temperature in a high interference scene under a traditional fixed parameter charging strategy, and improving charging efficiency and strategy robustness in a complex environment.

[0015] The application proposes an electromagnetic feature code low-power directional broadcast mechanism based on a BLE 5.0 protocol, and constructs a verification signal through a strategy state code and a battery feature hash value, realizes local judgment and response of a receiving strategy by a device to be charged. Combined with strategy adaptability analysis of the current state of the battery, and through similarity matching of a pre-stored feature threshold, a "feature code-device feedback-strategy matching degree" closed loop verification path is formed, thereby effectively avoiding problems such as strategy misuse, compatibility error and strategy execution failure, and significantly improving the safety and individualization level of intelligent charging.

[0016] The application drives a multi-coil wireless charging array and a driving frequency synthesizer to operate cooperatively through a strategy execution instruction, not only accurately controls the energy output process through a dynamic power distribution ratio, but also combines a frequency hopping sequence execution mechanism to actively avoid interference frequency bands and realize enhancement of anti-interference capability. At the same time, based on a real-time battery temperature change rate calculation mechanism, the overheat risk is dynamically sensed and quickly responded, when the temperature rise rate exceeds a set threshold, the output is immediately cut off and a safety alarm signal is sent, effectively preventing the occurrence of extreme situations such as thermal runaway, and providing multi-level and dynamic intelligent charging safety protection for users. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only illustrate the application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0018] Fig. 1 The method flowchart of the embodiment of the application is shown in the figure. Fig. 2 The S2 flowchart of the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0021] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figs. 1-2 As shown, the intelligent charging management method for mobile power banks via wireless data transmission includes the following steps: S1: Real-time acquisition of electromagnetic interference intensity in the charging environment using multi-band electromagnetic sensors to generate a comprehensive dataset of multi-band electromagnetic interference intensity, specifically: S11, Parallel Acquisition of Multi-Band Raw Electromagnetic Signals: First, during the charging process of the power bank, the multi-band electromagnetic sensor set on the casing of the main control unit of the power bank synchronously starts the process of acquiring electromagnetic signals of multiple bands in the environment.

[0023] The multi-band electromagnetic sensor supports at least three independent frequency band signal channels, covering the low-frequency (100kHz–1MHz), mid-frequency (10MHz–100MHz), and high-frequency (2.4GHz–5GHz) bands respectively. Each channel's sampling frequency is set to be no less than twice the center frequency of the monitored band, according to the Nyquist theorem, to ensure signal integrity. For example, in the 2.4GHz band, the sampling frequency is set to 5GHz.

[0024] S12, Frequency Domain Component Extraction and Amplitude Peak Calculation: The acquired raw electromagnetic signal is buffered and then sent to the local digital signal processing unit for Fourier transform, converting the time domain signal into frequency domain components.

[0025] In each preset time window (e.g. 100 ms), the amplitude peak value in each frequency band spectrum is extracted as the basic electromagnetic interference intensity of the corresponding frequency band.

[0026] The basic electromagnetic interference intensities of all frequency bands form a set of initial interference intensity data sequences as the input basis for subsequent normalization and analysis.

[0027] S13, frequency band electromagnetic interference intensity feature vector generation: In order to unify the dimensional and scale differences between different frequency bands, normalization processing is performed on the basic electromagnetic interference intensity of each frequency band. The specific method is: The amplitude value is mapped to the closed interval of 0-1 using the min-max normalization method to form a normalized frequency band electromagnetic interference intensity feature vector, denoted as: ; Where represents the normalized electromagnetic interference intensity value of the th frequency band.

[0028] S14, real-time electromagnetic interference intensity analysis report generation: The above frequency band electromagnetic interference intensity feature vector is compared with the preset environmental reference threshold matrix element by element to identify interference exceeding conditions and generate a structured report.

[0029] The environmental reference threshold matrix includes the following components: Basic safety threshold vector: defines the highest normalized electromagnetic interference intensity baseline allowed for each frequency band; Dynamic correction coefficient matrix: used to adjust the basic safety threshold vector in real time; Device electromagnetic compatibility archive: used for threshold customization adjustment for specific types of devices. The analysis logic is as follows: For each frequency band, determine whether the current frequency band electromagnetic interference intensity exceeds the basic safety threshold multiplied by the corresponding dynamic correction coefficient: If , it is marked as an exceeding frequency band and the corresponding exceeding frequency band identification code is generated.

[0030] Where is the basic safety threshold of the th frequency band, is the adjustment factor in the dynamic correction coefficient matrix.

[0031] The real-time calculation of the dynamic correction coefficient matrix is as follows: The current environmental temperature is obtained through the temperature sensor, and the "temperature-frequency band offset parameter" in the dynamic correction coefficient matrix is queried to obtain the temperature float amount. The number of active devices within a 5-meter radius of the charging environment is detected by a wireless probe, and the compensation coefficient is obtained by calling the device density-electromagnetic noise mapping table. For the low-frequency band (<1MHz), the detection data of the power grid harmonic analyzer is introduced, the electromagnetic influence factor is calculated based on the harmonic spectrum amplitude, and the weighted attenuation adjustment is performed on the basic threshold of the corresponding frequency band.

[0032] In addition, the device electromagnetic compatibility archive supports threshold fine-tuning for specific devices: Upon receiving the unique ID of the device to be charged, if it is marked as a "highly sensitive device" in the database, the original basic security threshold for the key frequency bands in its frequency band sensitivity priority list will be reduced by 20%. For implantable devices classified as "medical devices", an additional 15% safety margin is added to the 2.4GHz frequency band to avoid interference-induced false triggering.

[0033] The final output structure is a real-time electromagnetic interference intensity analysis report, which includes the current timestamp, the identification code of all non-compliant frequency bands, the intensity offset value of the corresponding frequency band (i.e., the difference between the current value and the threshold), the correction factor used, and the equipment compatibility adjustment information.

[0034] S15, Generation of Multi-Band Electromagnetic Interference Intensity Comprehensive Dataset: Finally, based on the intensity offset in the real-time electromagnetic interference intensity analysis report, the dynamic weighting coefficients for each frequency band are calculated using the following method: ; in, Indicates the first Each dynamic weight coefficient. This represents the maximum value among the normalized electromagnetic interference intensity values ​​for all frequency bands.

[0035] The weighting coefficients of all frequency bands are combined and timestamps are added to generate the final multi-band electromagnetic interference intensity comprehensive dataset, which is used for subsequent electromagnetic feature code generation and charging parameter strategy decision-making processes.

[0036] S2: Based on the comprehensive dataset of multi-band electromagnetic interference intensity, generate an electromagnetic feature code containing charging parameter strategies, specifically: S21, Generation of Electromagnetic Interference Intensity Distribution Feature Map: The output multi-band electromagnetic interference intensity comprehensive dataset is called, which contains dynamic weighting coefficients of electromagnetic interference intensity for each frequency band at multiple timestamps. The dynamic weighting coefficients of all frequency bands within each time period are extracted and formed into a two-dimensional matrix, which serves as the basic input data.

[0037] Next, the above matrix sequence is processed based on a preset interference mode clustering algorithm. The algorithm adopts a K-means++ and density peak detection (Density Peaks) hybrid clustering strategy, takes a time sequence as a horizontal axis and a frequency band dimension as a vertical axis, labels a cluster center and a disturbance boundary of each data point, and finally outputs a high-resolution electromagnetic interference intensity distribution feature map. The map represents the interference distribution law of each frequency band at each time point in a pixel level form, and a color gradient represents different interference weight levels.

[0038] Flow of the interference mode clustering algorithm: Let the electromagnetic interference intensity dynamic weight matrix sequence be: ; wherein, is the number of time windows, is the number of frequency bands; represents a dynamic weight coefficient of each frequency band in the i-th time window; Finally, the matrix is regarded as a point cloud data set in a two-dimensional space, and each point is .

[0039] The entire clustering algorithm is divided into two stages: The first stage: cluster center identification based on density peak detection, including: (1) Distance calculation: calculate the Euclidean distance between samples: ; wherein, represents the Euclidean distance between sample points and , and represents the similarity of electromagnetic interference features in two time windows.

[0040] (2) Local density estimation: introduce a truncated kernel function to calculate the density of each point: ; wherein is a truncation distance threshold, which is set as the 1%-2% quantile of all distances, is a truncated kernel function, which is defined as: , used to judge whether falls within the density estimation range of , and represents the local density estimation value of point .

[0041] (3) Minimum distance to points with higher density: for each point , calculate the minimum distance to points with higher density:​​ Let ; (4) Density-distance product index calculation: Calculate the cluster center score index: ; Take the top largest points as the initial point set of the cluster center .

[0042] Second stage: K-means++ initialization and fine clustering, including: (1) Initialize the center point: use the selected in the first stage as the K-means++ initialization center, and perform a random perturbation to increase the initialization diversity.

[0043] (2) Iterative process: for all remaining points , execute the standard K-means update iteration, specifically: Assign cluster labels: ; Update cluster centers: ; Until the cluster center converges or the maximum number of iterations is reached.

[0044] Output results: electromagnetic interference intensity distribution feature map; Reorganize all samples according to the final cluster label, and draw the distribution of different clustering results in a two-dimensional heat map; The horizontal axis is time, the vertical axis is frequency band, the color value is the normalized perturbation weight of each point, and the boundary is highlighted with different clustering areas; Output as a structured electromagnetic interference intensity distribution feature map as the input of the S22 neural network.

[0045] S22, charging parameter strategy matching: input the above electromagnetic interference intensity distribution feature map into the pre-trained charging strategy decision model. The model consists of two core structures: Electromagnetic feature extraction layer: use convolutional neural network (CNN) as the feature extraction backbone structure, the network contains 3 convolutional layers, each layer with a convolution kernel size of 3x3, a step of 1, and a LeakyReLU activation function. The network is used to process the electromagnetic interference intensity distribution feature map, extract the spatial-frequency domain coupling relationship implied in the map, and finally output a set of 128-dimensional feature vectors as the input of the multi-objective strategy optimization.

[0046] ​Multi-objective optimization decision layer: A charging strategy solver is constructed using the Pareto optimization principle, and the optimization objectives include three dimensions: power output stability, electromagnetic interference avoidance, and overheating risk control. Through non-dominated sorting and fitness calculation, the model selects the optimal solution from the strategy candidate set and outputs a charging parameter strategy containing the following three parts: Dynamic power allocation ratio: defines the adjustment coefficient of the mobile power output power under different electromagnetic interference levels; Anti-interference frequency hopping sequence: provides a set of frequency hopping frequencies for the frequency hopping controller, which preferentially avoids frequency bands with high interference weights; Overheating protection trigger threshold: automatically adjusts the thermal protection limit of the power module according to the current environmental disturbance level.

[0047] S23, electromagnetic signature generation: structurally encode the above charging parameter strategy and embed anti-tampering and anti-replay mechanisms to generate the final electromagnetic signature. The encoding process includes the following three steps: Strategy header identification generation: assign a unique header identification code (Header ID) to the current charging parameter strategy to indicate the strategy type and version information; Encrypted charging parameter strategy generation: use AES-256 to encrypt and package the charging parameter strategy to ensure the confidentiality of the parameter data during wireless transmission; Checksum and timestamp generation: generate a replay attack prevention checksum based on the strategy generation timestamp. The checksum is calculated by the timestamp and the device's unique ID using the HMAC-SHA256 encryption algorithm, ensuring that the receiving device only responds to the current valid signature.

[0048] The final generated electromagnetic signature meets the following data structure format: Electromagnetic signature = [Strategy header identification] + [Encrypted charging parameter strategy] + [Checksum] + [Timestamp].

[0049] The signature will serve as the basic data carrier for communication with the device to be charged in the S3 step.

[0050] S3: Send the electromagnetic signature to the device to be charged, and receive the verification signal feedback from the device, specifically: S31: Broadcast data packet packaging and sending of the electromagnetic signature: use the generated electromagnetic signature as the core payload of wireless communication.

[0051] The BLE communication module built into the mobile power system packages the electromagnetic signature into a low-power broadcast data packet supported by the BLE 5.0 protocol.

[0052] The structure of the broadcast data packet includes the following three fields: Device Addressing Field: Contains the MAC address of the device to be charged, used for targeted identification of the target device, ensuring that the broadcasted instructions are only received by the target device. Feature Code Payload Field: Carries encrypted electromagnetic feature code data, with a field length dynamically adjusted according to the BLE protocol MTU limit, not exceeding 255 bytes; Cyclic Redundancy Check Field (CRC): Generated using the CRC-16 algorithm, used to detect data integrity during broadcasting, and the receiving end uses the same check method for data verification.

[0053] The data packet format is as follows: Broadcast Data Packet = [Device Addressing Field] + [Feature Code Payload Field] + [Cyclic Redundancy Check Field].

[0054] During transmission, a directed broadcast mode (Directed Advertising) is used to send broadcast requests to the target device to be charged in the pre-connected state, improving power consumption control capability and communication stability.

[0055] S32, the device to be charged receives and verifies the signal: After the device to be charged receives the broadcast data packet, it first parses the feature code payload field through its BLE receiving module, and uses the local key library to decrypt the encrypted content to extract the charging parameter strategy contained therein.

[0056] Next, the device to be charged reads the current battery state information through the battery management unit (Battery Management Unit, BMU), including: current battery percentage, battery temperature, voltage, current, and other key features, and performs adaptability analysis based on this information and the charging parameter strategy.

[0057] If the strategy is suitable for the current battery working condition, a strategy status code "01" is generated; if there is potential risk or inadaptation, a status code "00" is generated.

[0058] And based on the extracted battery state data, a battery feature hash value is generated, using the SHA-256 algorithm to hash compress the current battery features, ensuring privacy security.

[0059] The device to be charged finally constructs a structured verification response data packet, called a verification signal, with the following data structure: Verification Signal = [Device ID] + [Strategy Status Code] + [Battery Feature Hash Value] + [Time Synchronization Timestamp].

[0060] Among them: Device ID: The unique identifier of the device to be charged, used for source identification; Strategy Status Code: "01" represents that the strategy is passed, and "00" represents that the strategy is not adapted; Battery Feature Hash: an encrypted digest of the current battery state; Time Sync Stamp: used to synchronize the verification process time window, avoiding delay or replay attacks.

[0061] S33, Verification Signal Reception and Similarity Matching Calculation: The power bank listens and receives the verification signal returned from the device to be charged through the same BLE link.

[0062] First, the policy state code and battery feature hash are parsed, and it is determined whether to continue the charging process according to the policy state code.

[0063] If the policy state code is "01", proceed to the next similarity matching process: Retrieve the pre-stored feature threshold bound to the device ID from the internal storage, i.e., the "typical battery state hash value" recorded in the historical charging data.

[0064] Use a hash similarity matching algorithm, such as cosine similarity, to calculate the similarity matching degree between the battery feature hash value and the pre-stored feature threshold.

[0065] For example, if the hash value is considered as a binary vector, the cosine similarity can be calculated as follows: ; Where, represents the received battery feature hash value vector, represents the pre-stored feature threshold vector.

[0066] If the matching degree percentage is greater than the set receiving threshold, it is determined that the verification is successful, and the charging strategy is allowed to be executed; otherwise, the charging request is rejected, and a prompt is issued on the device interface.

[0067] S4: When the verification signal matches the pre-stored feature threshold, execute the charging parameter strategy, specifically: S41, Strategy Execution Instruction Generation: After the matching degree percentage calculation of the verification signal output in S33 is completed, compare the matching degree percentage with the pre-set matching threshold set locally. In this invention, the pre-set matching threshold is set to 85%, i.e., when the matching degree ≥ 85%, it is considered that the verification is passed.

[0068] If the matching condition is met, immediately parse the policy state code in the verification signal, and after confirming that the state code is "01" (indicating that the charging parameter strategy is executable), generate a strategy execution instruction to the charging management module. The instruction contains: Device ID to be executed; Current timestamp; Electromagnetic feature code index number; The policy execution identification bit marked as "Ready".

[0069] If the policy status code is "00" or the matching degree is insufficient, the subsequent process is aborted, and "policy verification failure" is prompted through the mobile power screen / APP interface.

[0070] S42, charging parameter policy extraction and decryption: after receiving the policy execution instruction, the encrypted charging parameter policy field in the structure is extracted from the electromagnetic feature code generated in S23.

[0071] The decryption process is completed by the preset AES-256 decryption algorithm, and the key is derived from the dynamic session key generated when the device is started. After successful decryption, the charging parameter policy with complete plaintext structure is obtained, including the following three parts: Dynamic power distribution ratio: This ratio is used to control the dynamic distribution of wireless charging output power and time, for example, set to: [70%, 20%, 10%] corresponding to the power output ratio in the first, middle and last charging period, to avoid the impact of high power on the battery in the early stage.

[0072] Anti-interference frequency hopping sequence: define the frequency hopping sequence used in the charging process, for example: [6.78MHz → 13.56MHz → 19.2MHz], automatically complete frequency switching by the driving frequency synthesizer according to the preset frequency hopping interval. The sequence avoids the interference high frequency band identified in S2.

[0073] Overheating protection trigger threshold: set to the maximum allowed value of battery temperature change rate, for example 1.2℃ / min, when the actual temperature rise rate exceeds the threshold, the protection mechanism is triggered immediately.

[0074] Cache the above decrypted policy into the temporary storage area for subsequent execution.

[0075] S43, charging parameter policy execution and security response: after completing the charging parameter policy analysis, enter the charging control execution phase, which includes the following control logic: Multi-coil wireless charging array control: by controlling the PWM module and driving bridge arm, the driving current of each transmitting coil is gradually adjusted according to the dynamic power distribution ratio, realizing the phased control of output energy. The current power value is adjusted by real-time power detection feedback closed loop, and maintained within the target output range.

[0076] Driving frequency synthesizer frequency hopping control: input the anti-interference frequency hopping sequence to the driving frequency synthesizer control unit, and the synthesizer automatically jumps the driving frequency according to the set time interval (such as every 60 seconds), avoiding the electromagnetic interference peak frequency band detected during charging, effectively improving the charging stability.

[0077] Battery temperature change rate real-time monitoring: collect temperature value every second through battery temperature sensor, calculate temperature change rate based on sliding window difference algorithm: ; Wherein, represents the current temperature, represents the temperature at the last time, represents the time interval, which is 60 seconds by default.

[0078] When the temperature change rate exceeds the overheat protection trigger threshold, the following protection operations are immediately performed: Turn off wireless power output; Send a safety alarm signal to the upper computer or user terminal, including the current temperature value, change rate, and trigger time; Display "overheating protection, charging suspended" prompt on the device body LED or screen.

[0079] This protection mechanism ensures that battery safety risks will not be caused by electromagnetic anomalies or power accumulation during charging, and is suitable for various energy storage units such as lithium-ion batteries and polymer batteries.

[0080] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0081] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for intelligent charging management of mobile power banks via wireless data transmission, characterized in that: Includes the following steps: S1: Real-time acquisition of electromagnetic interference intensity in the charging environment through multi-band electromagnetic sensors to generate a comprehensive dataset of multi-band electromagnetic interference intensity. S2: Generate an electromagnetic feature code containing charging parameter strategies based on the comprehensive dataset of multi-band electromagnetic interference intensity. S3: Send the electromagnetic feature code to the device to be charged and receive the verification signal fed back by the device; S4: When the verification signal matches the pre-stored feature threshold, the charging parameter strategy is executed.

2. The mobile power bank intelligent charging management method via wireless data transmission according to claim 1, characterized in that, S1 includes: S11: At least three independent frequency bands of raw electromagnetic signals are collected in parallel in the charging environment using a multi-band electromagnetic sensor, and the sampling frequency of each frequency band is not less than twice the center frequency of that frequency band. S12: The original electromagnetic signal is decomposed into frequency domain components by Fourier transform, and the peak value of the signal amplitude of each frequency band within a preset time window is extracted as the basic electromagnetic interference intensity. S13: Normalize the basic electromagnetic interference intensity, map the frequency band intensity values ​​of different dimensions to the standardized range of 0-1, and generate a frequency band electromagnetic interference intensity feature vector. S14: Compare the frequency band electromagnetic interference intensity feature vector with the pre-stored environmental reference threshold matrix element by element, and output a real-time electromagnetic interference intensity analysis report containing the out-of-standard frequency band identification code and intensity offset. S15: Based on the intensity offset in the real-time electromagnetic interference intensity analysis report, calculate the dynamic weighting coefficient of electromagnetic interference intensity for each frequency band, and generate a multi-frequency electromagnetic interference intensity comprehensive dataset with timestamps.

3. The mobile power bank intelligent charging management method via wireless data transmission according to claim 2, characterized in that, The pre-stored environmental baseline threshold matrix includes a basic safety threshold vector, a dynamic correction coefficient matrix, and a device electromagnetic compatibility archive, wherein; The basic safety threshold vector is used to determine whether the electromagnetic interference intensity of each frequency band exceeds the standard. The dynamic correction coefficient matrix is ​​used to correct the basic security threshold vector in real time. The device electromagnetic compatibility archive is used to generate customized safety thresholds for specific devices.

4. The mobile power bank intelligent charging management method via wireless data transmission according to claim 3, characterized in that, S2 includes: S21: Analyze the generated multi-band electromagnetic interference intensity comprehensive dataset, extract the dynamic weight coefficients and timestamps of electromagnetic interference intensity in each frequency band, and generate an electromagnetic interference intensity distribution feature map based on the interference pattern clustering algorithm. S22: Input the electromagnetic interference intensity distribution feature map into the pre-trained charging strategy decision model to match the optimal charging parameter strategy. The charging parameter strategy includes dynamic power allocation ratio, anti-interference frequency hopping sequence and overheat protection trigger threshold. S23: The charging parameter strategy is structured and encoded, a timestamp-based anti-replay attack check code is added, and an electromagnetic feature code carrying the charging parameter strategy is synthesized, the data structure of which satisfies: Electromagnetic signature = [strategy header identifier] + [encrypted charging parameter strategy] + [verification code] + [time stamp].

5. The mobile power bank intelligent charging management method via wireless data transmission according to claim 4, characterized in that, The pre-trained charging strategy decision model includes an electromagnetic feature extraction layer and a multi-objective optimization decision layer, wherein; The electromagnetic feature extraction layer uses a convolutional neural network to process the electromagnetic interference intensity distribution feature map and outputs a 128-dimensional feature vector containing spatial-frequency domain coupling relationship. The multi-objective optimization decision layer solves the charging parameter strategy based on the Pareto optimality principle.

6. The mobile power bank intelligent charging management method via wireless data transmission according to claim 5, characterized in that, S3 includes: S31: The generated electromagnetic signature is encapsulated into a low-power broadcast data packet via the BLE 5.0 protocol and sent to the device to be charged. S32: After receiving the electromagnetic signature, the device to be charged extracts the charging parameter strategy and generates a verification signal including the feasibility assessment result of the strategy based on the current battery status of the device. The data structure of the verification signal is as follows: Verification signal = [Device ID] + [Policy Status Code] + [Battery Feature Hash Value] + [Time Synchronization Stamp]; S33: Receive the verification signal returned by the device to be charged through the same BLE link, parse out the policy status code and battery feature hash value, perform similarity matching calculation between the battery feature hash value and the pre-stored feature threshold, and output the matching percentage as the verification result.

7. The mobile power bank intelligent charging management method via wireless data transmission according to claim 6, characterized in that, The low-power broadcast data packet structure includes a device addressing field, a signature payload field, and a cyclic redundancy check field.

8. The mobile power bank intelligent charging management method via wireless data transmission according to claim 7, characterized in that, S4 includes: S41: When the matching percentage of the verification signal output by S33 is greater than or equal to the preset matching threshold, the strategy status code in the verification signal is parsed to confirm the executable status of the charging parameter strategy and generate a strategy execution instruction. S42: According to the strategy execution instruction, extract the encrypted charging parameter strategy from the electromagnetic feature code generated in S23, and obtain the complete charging parameter strategy after decryption, including dynamic power allocation ratio, anti-interference frequency hopping sequence and overheat protection trigger threshold. S43: Executes charging parameter strategy, controls the multi-coil wireless charging array to output energy according to dynamic power distribution ratio, and drives the frequency synthesizer to execute anti-interference frequency hopping sequence.

9. The mobile power bank intelligent charging management method via wireless data transmission according to claim 8, characterized in that, During the execution of the charging parameter strategy, the battery temperature change rate is monitored in real time. When the temperature change rate exceeds the overheat protection trigger threshold, the output is immediately cut off and a safety alarm signal is generated.

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