Environmental energy management optimization method and system for tag chips

Abstract

The present invention relates to an environmental energy management optimization method and system for a tag chip, the method comprising: obtaining temperature data and environmental electromagnetic data of a chip environment, and performing spatial distribution analysis to obtain corresponding environmental parameter characteristics; obtaining real-time power consumption data of the tag chip, and performing correlation analysis on the real-time power consumption data and the environmental parameter characteristics to obtain corresponding energy consumption information; obtaining chip circuit acquisition data of the tag chip, and performing resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm; performing fault diagnosis on the chip circuit acquisition data to obtain corresponding system status information and a corresponding system operation prediction scheme; performing strategy optimization on the initial energy scheduling scheme according to the system operation prediction scheme to obtain a corresponding global optimization strategy. The present invention enables the system to flexibly respond to changes in energy demand in different application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of tag chips, and in particular to an environmental energy management optimization method and system for tag chips. Background Art

[0002] As a key component of the Internet of Things, tag chip technology plays an important role in smart manufacturing, supply chain management and other fields. With the continuous expansion and complexity of IoT application scenarios, how to achieve efficient energy management of tag chips and ensure their reliable operation in a changing environment has become one of the key issues in current research. Existing tag chip energy management methods usually only focus on energy collection or consumption characteristics in a single dimension, such as ambient temperature changes or electromagnetic field strength, while ignoring the coupling effects of multiple environmental factors and the dynamic balance of energy supply and demand. This one-sided management method often leads to unreasonable energy scheduling of chips in actual applications, affecting the overall performance and reliability of the system. Summary of the invention

[0003] The main purpose of the present invention is to provide an environmental energy management optimization method and system for a tag chip, which can enable the system to flexibly respond to changes in energy requirements in different application scenarios.

[0004] To achieve the above-mentioned purpose, the present invention provides an environmental energy management optimization method for a tag chip, comprising:

[0005] Obtain the temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain the corresponding environmental parameter characteristics;

[0006] Acquire the real-time power consumption data of the tag chip, and associate and analyze the real-time power consumption data with the environmental parameter characteristics to obtain corresponding energy consumption information;

[0007] Acquire chip circuit data collected by the tag chip, perform resonance frequency analysis on the chip circuit data collected according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters, and perform trend prediction on the environmental parameter characteristics to obtain corresponding energy supply trends;

[0008] Performing energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling plan;

[0009] Performing fault diagnosis on the chip circuit collected data to obtain corresponding system status information, performing reliability prediction on the system status information based on the energy supply trend and the resonance characteristic parameters to obtain a corresponding system operation prediction plan;

[0010] The initial energy scheduling plan is strategically optimized according to the system operation prediction plan to obtain a corresponding global optimization strategy.

[0011] Furthermore, the acquisition of temperature data and environmental electromagnetic data of the chip environment and the spatial distribution analysis to obtain corresponding environmental parameter characteristics include:

[0012] Performing fractal dimension analysis on the temperature data to obtain a fractal characteristic sequence of the temperature field;

[0013] Performing topological reconstruction according to the temperature field fractal feature sequence to obtain temperature field topological structure data;

[0014] Extracting environmental features from the environmental electromagnetic data to obtain corresponding environmental electromagnetic features;

[0015] Performing hyperbolic space mapping on the temperature field topological structure data and the environmental electromagnetic characteristics to obtain corresponding hyperbolic mapping information;

[0016] Calculate the topological entropy of environmental parameters according to the hyperbolic mapping information to obtain environmental topological invariants;

[0017] Performing Lie group transformation on the environment topological invariant to obtain the environment symmetry characteristics;

[0018] Parameter bifurcation evolution is performed according to the environmental symmetry characteristics to obtain the environmental parameter characteristics.

[0019] Furthermore, the acquiring of the real-time power consumption data of the tag chip, and the correlation analysis of the real-time power consumption data with the environmental parameter characteristics to obtain the corresponding energy consumption information includes:

[0020] The dynamic current data and static current data of the tag chip are collected, and the power consumption is calculated according to the preset voltage and current characteristic curve analysis to obtain the real-time power consumption data;

[0021] Performing working state analysis on the real-time power consumption data to obtain corresponding chip working mode parameters;

[0022] Performing data classification on the chip operating mode parameters to obtain corresponding power consumption feature vectors;

[0023] Performing a time series correlation analysis on the environmental parameter characteristics according to the power consumption characteristic vector to obtain corresponding energy correlation data;

[0024] Performing multi-dimensional data fusion processing on the energy correlation data to obtain corresponding energy coupling parameters;

[0025] Numerically calculating the power consumption characteristic vector according to the energy coupling parameter to obtain a corresponding energy distribution matrix;

[0026] The energy distribution matrix is ​​calibrated and the corresponding energy consumption information is obtained through data fitting.

[0027] Furthermore, the acquiring of chip circuit acquisition data of the tag chip, and performing resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters include:

[0028] Performing chip circuit analysis on the tag chip to obtain chip circuit acquisition data;

[0029] Performing nonlinear decoupling decomposition on the chip circuit collected data to obtain a corresponding multi-dimensional energy tensor and energy resonance mapping relationship;

[0030] Based on the multi-dimensional energy tensor and the energy resonance mapping relationship, a circuit resonance optimization network is constructed to obtain a corresponding circuit dynamic topology structure;

[0031] Performing a multi-point energy flow field analysis on the circuit dynamic topology structure according to the resonance optimization algorithm to form a circuit energy distribution mapping matrix;

[0032] Performing network dynamic reconstruction on the circuit energy distribution mapping matrix to obtain a corresponding optimized energy transmission path;

[0033] Performing multi-order coupling analysis on the optimized energy transmission path to obtain a corresponding characteristic coupling vector;

[0034] Performing harmonic mode decomposition on the coupling vector to obtain a mode optimization sequence;

[0035] Based on the modal optimization sequence, the non-equilibrium energy distribution is calculated to obtain the energy state characteristic parameters;

[0036] Performing multi-frequency domain analysis on the energy state characteristic parameters to obtain frequency domain optimization results;

[0037] Performing multi-scale entropy value calculation on the frequency domain optimization result to obtain the resonance characteristic parameter;

[0038] The calculation formula of the resonance optimization algorithm includes:

[0039] ;

[0040] : M represents the energy distribution mapping matrix, i represents the row number of the matrix, j represents the column number of the matrix, and each matrix element records the energy value of the corresponding position;

[0041] : Ex represents the component of the energy field in the x direction at the position (x, y) at time t;

[0042] : Ey represents the component of the energy field in the y direction at the position (x, y) at time t;

[0043] : w represents the weight function, is the i-th x-coordinate point, is the jth y-coordinate point, which determines the contribution of each point to the total energy;

[0044] S: A tiny unit representing an area, used for regional integral calculations, and represents the area size of each calculation point.

[0045] Furthermore, the trend prediction of the environmental parameter characteristics is performed to obtain the corresponding energy supply trend, including:

[0046] Performing frequency separation on the environmental parameter characteristics to obtain corresponding high-frequency components and low-frequency components;

[0047] Performing amplitude statistical analysis on the high-frequency component to obtain corresponding fluctuation range parameters;

[0048] Performing trend fitting processing on the low-frequency component to obtain a corresponding reference change curve;

[0049] Perform data fusion according to the fluctuation range parameter and the reference change curve to obtain a corresponding environmental change feature sequence;

[0050] Performing periodic analysis on the environmental change characteristic sequence to obtain corresponding periodic change rules;

[0051] Perform environmental distribution calculation according to the periodic variation law to obtain corresponding energy density distribution data;

[0052] Perform trend projection prediction on the energy density distribution data to obtain a corresponding energy supply trend.

[0053] Furthermore, performing energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling scheme includes:

[0054] Performing periodic calculation on the energy consumption information to obtain a corresponding energy consumption period;

[0055] Performing time domain mapping on the energy supply trend according to the energy consumption cycle to obtain a corresponding energy balance feature vector;

[0056] Performing collaborative optimization calculation on the energy balance eigenvector and the resonance eigenparameter to obtain a corresponding energy redundancy matrix;

[0057] Performing piecewise linear fitting on the energy supply trend according to the energy redundancy matrix to obtain a corresponding energy supply curve;

[0058] Performing piecewise integration operation on the energy supply curve to obtain a corresponding energy accumulation function;

[0059] Performing dynamic programming calculation on the energy consumption cycle according to the energy accumulation function to obtain a corresponding energy scheduling vector;

[0060] Performing multi-dimensional constraint analysis on the energy scheduling vector to obtain corresponding scheduling constraint conditions;

[0061] The energy scheduling vector is iteratively optimized according to the scheduling constraint condition to obtain a corresponding initial energy scheduling solution.

[0062] Further, the chip circuit acquisition data is subjected to fault diagnosis to obtain corresponding system status information, and reliability prediction is performed on the system status information based on the energy supply trend and the resonance characteristic parameter to obtain a corresponding system operation prediction scheme, including:

[0063] Performing spectrum analysis on the chip circuit collected data to obtain corresponding frequency distribution rules;

[0064] Clustering the chip circuit collected data into fault types according to the frequency distribution law to obtain corresponding fault modes;

[0065] Performing type matching on the fault mode to obtain corresponding fault type information;

[0066] Performing state analysis on the system state information according to the fault type information to obtain corresponding state evolution parameters;

[0067] Optimizing and calculating the state evolution parameters according to the energy supply trend to obtain corresponding energy impact values;

[0068] Dynamically correct the energy impact value according to the resonance characteristic parameter to obtain a corresponding correction coefficient;

[0069] Performing reliability evaluation on the state evolution parameter according to the correction coefficient to obtain a corresponding failure probability distribution;

[0070] The failure probability distribution is trend extrapolated to obtain the system operation prediction plan.

[0071] Furthermore, the initial energy scheduling scheme is strategically optimized according to the system operation prediction scheme to obtain a corresponding global optimization strategy, including:

[0072] Performing data fusion on the system operation prediction scheme and the initial energy scheduling scheme to obtain a corresponding initial multi-dimensional operation state;

[0073] Performing an operation mode energy constraint analysis on the initial multi-dimensional operation state to obtain corresponding energy boundary conditions;

[0074] Performing parameter correction on the initial energy scheduling scheme according to the energy boundary condition to obtain a corresponding revised scheduling scheme;

[0075] Performing dynamic coefficient planning on the modified scheduling scheme to obtain a corresponding energy optimization coefficient;

[0076] Iteratively optimize the system operation prediction scheme according to the energy optimization coefficient to obtain a corresponding iterative optimization scheme;

[0077] The iterative optimization scheme and the revised scheduling scheme are integrated to obtain a corresponding initial optimization strategy;

[0078] Performing a global convergence analysis on the initial optimization strategy according to the energy optimization coefficient to obtain a corresponding convergence threshold;

[0079] The initial optimization strategy is globally optimized according to the convergence threshold to obtain the global optimization strategy.

[0080] The present invention further provides an environmental energy management optimization system for a tag chip, which is applied to any one of the above-mentioned environmental energy management optimization methods for a tag chip, comprising:

[0081] An acquisition module, which is used to acquire temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain corresponding environmental parameter characteristics;

[0082] An analysis module, the analysis module is used to obtain real-time power consumption data of the tag chip, associate and analyze the real-time power consumption data with the environmental parameter characteristics, and obtain corresponding energy consumption information;

[0083] An association module, the association module is used to obtain chip circuit acquisition data of the tag chip, perform resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters, and perform trend prediction on the environmental parameter characteristics to obtain corresponding energy supply trends;

[0084] A processing module, the processing module is used to perform energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling plan;

[0085] A control module, the control module is used to perform fault diagnosis on the chip circuit acquisition data to obtain corresponding system status information, perform reliability prediction on the system status information based on the energy supply trend and the resonance characteristic parameters, and obtain a corresponding system operation prediction plan;

[0086] An execution module is used to optimize the strategy of the initial energy scheduling plan according to the system operation prediction plan to obtain a corresponding global optimization strategy.

[0087] The present invention provides a method and system for optimizing environmental energy management of a tag chip, which has the following beneficial effects:

[0088] By analyzing the spatial distribution of the temperature data and electromagnetic data of the chip environment and combining it with the correlation analysis of the real-time power consumption data, the energy demand characteristics of the tag chip in different environments can be more comprehensively evaluated, thereby improving the accuracy of energy management and providing a reliable basis for system optimization. By analyzing the resonance frequency of the energy acquisition data and predicting the characteristic trend of environmental parameters, a dynamic evaluation of energy supply is achieved, which helps to accurately grasp the balance of energy supply and demand and avoid energy accumulation or depletion. Energy balance analysis based on resonance characteristic parameters ensures that the tag chip can maintain stable operation in a changing environment, reduces the performance loss caused by improper energy scheduling, and improves the overall reliability of the system. By performing fault diagnosis and reliability prediction on the system status, a more adaptive energy scheduling plan is formulated, and the efficient operation of the system is achieved through the implementation of a global optimization strategy, which effectively solves the energy management problem under the coupling of multiple environmental factors. At the same time, by dynamically adjusting the energy management strategy, the system can flexibly respond to changes in energy demand in different application scenarios, improving the adaptability of the tag chip in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 This is a flow chart of an environmental energy management optimization method for a tag chip provided by the present invention;

[0090] Figure 2 This is a structural diagram of an environmental energy management optimization system of a tag chip provided by the present invention.

[0091] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0092] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0093] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0094] Reference Figure 1 As shown, the present invention provides 1. A method for optimizing environmental energy management of a tag chip, characterized by comprising:

[0095] Step S1: Acquire temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain corresponding environmental parameter characteristics;

[0096] Step S2: Acquire the real-time power consumption data of the tag chip, associate and analyze the real-time power consumption data with the environmental parameter characteristics, and obtain corresponding energy consumption information;

[0097] Step S3: acquiring chip circuit data collected by the tag chip, performing resonance frequency analysis on the chip circuit data collected according to a preset resonance optimization algorithm, obtaining corresponding resonance characteristic parameters, and performing trend prediction on environmental parameter characteristics to obtain corresponding energy supply trends;

[0098] Step S4: performing energy balance analysis on energy consumption information and energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling plan;

[0099] Step S5: performing fault diagnosis on the chip circuit collected data to obtain corresponding system status information, performing reliability prediction on the system status information based on the energy supply trend and resonance characteristic parameters, and obtaining a corresponding system operation prediction plan;

[0100] Step S6: Optimize the strategy of the initial energy scheduling plan according to the system operation prediction plan to obtain the corresponding global optimization strategy.

[0101] Based on the above steps, the detailed steps are as follows:

[0102] Step S1: A temperature sensor array and an electromagnetic field detector can be arranged around the tag chip to form a multi-point sampling network. The collection of temperature data should take into account the spatial distribution characteristics. Usually, a grid-based distribution method is adopted to evenly distribute the temperature measurement points on the chip surface and the surrounding environment. The sampling frequency is recommended to be set to above 1Hz to ensure the real-time data. For environmental electromagnetic data, the electric field strength and magnetic field strength are mainly collected, and the frequency distribution characteristics of electromagnetic waves are recorded at the same time. The collected raw data is processed by spatial interpolation to construct a complete temperature field distribution and electromagnetic field distribution model. The key features are extracted by principal component analysis, including temperature gradient, hot spot distribution, electromagnetic field intensity distribution, etc. These features will serve as important input parameters for subsequent energy management. In addition, a time series database of environmental parameters needs to be established to analyze the law of parameter changes over time, which is of great significance for subsequent trend prediction.

[0103] Step S2: Real-time power consumption data is mainly acquired by connecting a precision sampling resistor in series in the chip power supply circuit, using a high-precision ADC for current sampling, and monitoring the power supply voltage at the same time to calculate the instantaneous power. The sampling rate of power consumption data should be higher than the sampling rate of environmental parameters. It is recommended to set it to above 10kHz to capture transient power consumption characteristics. The collected power consumption data is time-aligned with the obtained environmental parameter characteristics, and a mapping relationship between power consumption and environmental parameters is established through correlation analysis methods. Specifically, a multivariate regression analysis or machine learning method can be used to establish a power consumption prediction model. This model needs to consider the impact of temperature on chip performance and the impact of electromagnetic environment on power supply efficiency. Through this correlation analysis, the degree of influence of environmental factors on chip energy consumption can be quantified, providing a basis for subsequent energy management.

[0104] Step S3: The voltage and current waveform data of each key node of the chip are collected through the on-chip sensor network. After the data is collected, the preset resonance optimization algorithm is applied for analysis. The algorithm usually implements spectrum analysis based on fast Fourier transform (FFT) to identify the natural oscillation frequency of the system. By analyzing the impedance characteristics of the circuit, the optimal energy transmission frequency can be determined to improve the energy collection efficiency. At the same time, the environmental parameter characteristics obtained in step S1 are combined with time series prediction methods (such as ARIMA model or neural network model) to predict the environmental energy supply in the future. This prediction needs to take into account the periodic change characteristics of the environment, such as the diurnal changes of solar energy and the time-varying characteristics of electromagnetic field intensity. The prediction results will be used for subsequent energy balance analysis to ensure the dynamic balance of energy supply and demand of the system. The output of this step includes resonance characteristic parameters such as resonance frequency and quality factor, as well as energy supply prediction curves for future time periods.

[0105] Step S4: Based on the obtained resonance characteristic parameters and combined with the energy consumption information, an energy balance model is established. The resonance characteristic parameters (such as resonance frequency and quality factor) are used as influencing factors of energy conversion efficiency to calculate the energy acquisition efficiency under different working conditions. The predicted environmental energy supply trend is converted into an available energy sequence, taking into account energy conversion loss and storage loss. By establishing the state equation of energy flow, the energy balance of the system on different time scales is analyzed. Based on this, an initial energy scheduling plan is formulated, including energy collection timing arrangement, load power distribution, energy storage unit charging and discharging strategy, etc. The plan needs to balance immediate supply and demand with long-term stability to ensure the continuous operation of the system under energy fluctuations.

[0106] Step S5: Perform comprehensive fault diagnosis on the chip circuit data collected, mainly including voltage anomaly detection, current waveform analysis, temperature anomaly identification, etc. Use the fault tree analysis method to establish a system failure mode library to identify potential failure risks. Combined with the energy supply trend obtained, analyze the impact of energy fluctuations on system reliability. In specific implementation, the Markov chain model can be used to describe the system state transition process and establish a state transition matrix. The Monte Carlo simulation method is used to predict the system reliability indicators under different energy supply conditions. The resonance characteristic parameters are used as an important reference for reliability evaluation to analyze the impact of energy transmission efficiency on system stability. Finally, a system operation prediction plan is generated, including reliability prediction values ​​of key nodes, system life prediction, maintenance recommendations and other information.

[0107] Step S6: This step integrates the initial energy scheduling plan with the system operation prediction plan, and uses a multi-objective optimization method to achieve global strategy optimization. In specific implementation, the optimization objective function is defined, which usually includes multiple dimensions such as energy utilization efficiency, system reliability, and operation stability. A set of constraints is established, including energy balance constraints, reliability constraints, temperature constraints, etc. A heuristic algorithm (such as genetic algorithm, particle swarm algorithm) is used to solve the optimization problem and obtain the optimal combination of operating parameters. The optimization results include dynamic power allocation strategy, resonance parameter adjustment scheme, energy storage management strategy, etc. The strategy also needs to have adaptive capabilities and be able to make real-time adjustments according to environmental changes and system status to ensure the practicality and robustness of the strategy.

[0108] The environmental energy management optimization method of a tag chip provided by the present invention has the following beneficial effects:

[0109] By analyzing the spatial distribution of the temperature data and electromagnetic data of the chip environment and combining it with the correlation analysis of the real-time power consumption data, the energy demand characteristics of the tag chip in different environments can be more comprehensively evaluated, thereby improving the accuracy of energy management and providing a reliable basis for system optimization. By analyzing the resonance frequency of the energy acquisition data and predicting the characteristic trend of environmental parameters, a dynamic evaluation of energy supply is achieved, which helps to accurately grasp the balance of energy supply and demand and avoid energy accumulation or depletion. Energy balance analysis based on resonance characteristic parameters ensures that the tag chip can maintain stable operation in a changing environment, reduces the performance loss caused by improper energy scheduling, and improves the overall reliability of the system. By performing fault diagnosis and reliability prediction on the system status, a more adaptive energy scheduling plan is formulated, and the efficient operation of the system is achieved through the implementation of a global optimization strategy, which effectively solves the energy management problem under the coupling of multiple environmental factors. At the same time, by dynamically adjusting the energy management strategy, the system can flexibly respond to changes in energy demand in different application scenarios, improving the adaptability of the tag chip in practical applications.

[0110] In one embodiment, the temperature data and environmental electromagnetic data of the chip environment are obtained, and spatial distribution analysis is performed to obtain corresponding environmental parameter characteristics, including:

[0111] The detection frequency range is 100kHz-1GHz, forming an electromagnetic field intensity data matrix.

[0112] When fractal dimension analysis is performed on the collected temperature data, the box counting method is used to calculate the fractal dimension of the temperature field. The specific calculation process is: the temperature field is divided into grids, the grid distribution is counted, and the fractal dimension is determined by the linear fitting slope. The fractal feature sequence of the temperature field is obtained by the sliding time window method.

[0113] In the temperature field topology reconstruction stage, the phase space is constructed based on the obtained fractal feature sequence, and the topological structure of the temperature field is reconstructed using the time delay method. The embedding dimension is determined by the false nearest neighbor method, and the time delay is determined by the mutual information method to form a trajectory matrix in the phase space.

[0114] In the process of extracting environmental electromagnetic characteristics, the electromagnetic field intensity data is decomposed by wavelet transform to extract the energy characteristics of different frequency bands. The wavelet basis function is selected, and the number of decomposition layers is 4 to obtain the energy distribution feature vector of each frequency band.

[0115] In the hyperbolic space mapping stage, the temperature field topological structure data and the environmental electromagnetic characteristic vector are mapped into the Poincare hyperbolic space. The mapping function uses the hyperbolic tangent function, and the mapping result is expressed as the point set distribution in the hyperbolic space.

[0116] The calculation of the topological entropy of environmental parameters is based on the distribution characteristics of the point set in the hyperbolic space. The Kolmogorov entropy calculation method is used to divide the hyperbolic space into grids, and the probability distribution of the points in each grid is counted to calculate the topological entropy. This topological entropy is used as a topological invariant to describe the complexity of the environment.

[0117] In the Lie group transformation processing stage, the topological invariant is transformed by a rotation group to obtain a characteristic matrix with rotational symmetry. During the transformation process, the rotation angle is taken within the range of a complete circle to form a symmetric characteristic vector.

[0118] The parameter bifurcation evolution analysis is based on the symmetry characteristic matrix. By constructing a set of bifurcation equations, the dynamic behavior of the system under different parameters is analyzed, and the bifurcation map describing the evolution characteristics of the environmental parameters is obtained.

[0119] This embodiment achieves accurate perception and feature extraction of the chip's surrounding environment by establishing a complete environmental energy data collection and analysis system. The fractal dimension analysis method is used to characterize the temperature field, capturing the complex dynamic characteristics of the ambient temperature distribution. Combined with time delay embedding technology for topological reconstruction, the spatiotemporal evolution law of the temperature field is effectively restored. The electromagnetic field data is decomposed at multiple scales through wavelet transform to achieve a detailed characterization of the environmental electromagnetic characteristics. The temperature field topological structure and electromagnetic characteristics are mapped to hyperbolic space, and a unified characterization framework for environmental parameters is established. The topological invariant calculation based on Kolmogorov entropy and Lie group transformation processing reveal the symmetry characteristics of environmental parameters. Finally, the evolution law of environmental parameters is analyzed through bifurcation theory, which provides a theoretical basis for chip energy management and effectively improves the working stability and energy utilization efficiency of the chip in a complex environment.

[0120] In one embodiment, the real-time power consumption data of the tag chip is obtained, and the real-time power consumption data is correlated and analyzed with the environmental parameter characteristics to obtain corresponding energy consumption information, including:

[0121] During the current data collection phase of the tag chip, a high-precision current sampling circuit is used to sample the dynamic current and static current of the chip in different working states in real time. The sampling circuit includes a current detection circuit composed of an operational amplifier, and the current value is obtained by detecting the voltage difference across the sampling resistor connected in series in the chip power supply circuit. The voltage-current characteristic curve establishes a lookup table based on laboratory calibration data, and the real-time power consumption data is obtained by multiplying the sampled current value with the corresponding working voltage.

[0122] The working state analysis stage performs pattern recognition based on the fluctuation characteristics of real-time power consumption data. By setting the power consumption threshold, the power consumption data within the sampling period is divided into three categories: high power consumption state, medium power consumption state, and low power consumption state. The characteristic parameters such as duration and occurrence frequency of each state are counted to generate a characteristic parameter set that describes the chip working mode. This parameter set contains quantitative indicators such as the time proportion of each state and the state transition frequency.

[0123] The data classification process uses a clustering algorithm to reduce the dimension of the working mode parameters. The parameter set is mapped to the feature space, the similarity between sample points is calculated based on the Euclidean distance, and similar working modes are clustered into one category through iterative optimization. The mean, variance and other statistics of the power consumption characteristics of each type of working mode are extracted to form a power consumption feature vector.

[0124] In the timing correlation analysis phase, the power consumption feature vector is time-aligned with the environmental parameter feature. The environmental parameter feature includes multiple dimensions such as temperature, humidity, and electromagnetic field strength. The correlation coefficient between the two sets of data is calculated by sliding the time window, and the corresponding relationship between power consumption and environmental factors is established to form energy correlation data.

[0125] Multidimensional data fusion uses an information fusion algorithm based on DS evidence theory. Different weights are assigned to the credibility of the impact of each environmental parameter on power consumption, and the comprehensive credibility is calculated through evidence accumulation to obtain quantitative parameters reflecting the degree of coupling between environmental factors and chip energy consumption.

[0126] The energy distribution calculation process weights the power consumption feature vector based on the energy coupling parameter. The environmental influencing factors are mapped to the power consumption feature space through matrix operations, and a mathematical model describing the energy consumption law of the chip under different environmental conditions is established. The model represents the energy distribution characteristics in the form of a matrix.

[0127] The parameter calibration phase uses the least squares method to fit the energy distribution matrix. The measured chip energy consumption data is used as a benchmark, and the mean square error between the model prediction value and the measured value is minimized through iterative optimization to obtain a mathematical description that accurately reflects the chip energy consumption law. The calibrated energy consumption information provides a basis for the chip's energy management optimization.

[0128] This embodiment realizes accurate measurement and evaluation of chip power consumption by real-time sampling and analysis of the dynamic current and static current of the tag chip, combined with a high-precision current detection circuit and a voltage-current characteristic curve. The working state classification method based on the power consumption threshold effectively identifies the energy consumption characteristics of the chip in different operating stages, providing a reliable basis for optimizing the energy management strategy. The clustering algorithm is used to reduce the dimension of the working mode parameters and extract the power consumption feature vector, which reduces the complexity of data processing and improves the analysis efficiency. Multi-dimensional data fusion is performed through the DS evidence theory to accurately quantify the degree of influence of environmental factors on chip energy consumption and enhance the environmental adaptability of energy management. The parameter calibration mechanism based on the least squares method ensures that the energy distribution model is highly consistent with the actual energy consumption characteristics, and improves the accuracy of energy consumption prediction. This method realizes the intelligent and refined energy management of the tag chip and significantly improves the energy utilization efficiency of the chip.

[0129] In one embodiment, the chip circuit acquisition data of the tag chip is obtained, and the resonance frequency analysis of the chip circuit acquisition data is performed according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters, including:

[0130] Analyze the circuit structure of the tag chip, obtain the parameters of the inductor, capacitor, resistor and other components in the circuit structure and their topological connection relationship, and form a complete chip circuit structure data set. The chip circuit acquisition data includes key circuit information such as resonant cavity structure parameters, resonant circuit characteristics, and impedance matching network.

[0131] After obtaining the chip circuit structure data, the Hilbert-Huang transform is used to perform nonlinear decoupling decomposition on the circuit structure data. This process decomposes the complex circuit structure into multiple independent resonance units, constructs a multidimensional energy tensor through these resonance units, and establishes an energy resonance mapping relationship. The multidimensional energy tensor reflects the energy transfer characteristics between the circuit resonance units, and the energy resonance mapping relationship describes the coupling state between the resonance units.

[0132] Based on the constructed multi-dimensional energy tensor and energy resonance mapping relationship, a circuit resonance optimization network is constructed using a deep neural network. The network dynamically optimizes the circuit resonance structure to generate a circuit dynamic topology that adapts to environmental changes. The circuit dynamic topology achieves adaptive adjustment of the resonance frequency to ensure that the circuit maintains the optimal resonance state under different environments.

[0133] According to the preset resonance optimization algorithm, the multi-point energy flow field analysis of the circuit dynamic topology is performed, the field strength distribution and phase relationship of each resonance unit are calculated, and the circuit energy distribution mapping matrix is ​​constructed. The matrix contains the energy distribution state information of the circuit resonance unit and reflects the energy transmission efficiency between the resonance units. The mapping matrix is ​​optimized through the network dynamic reconstruction algorithm to obtain the optimal resonance energy transmission path.

[0134] On the basis of determining the optimal resonant energy transmission path, the multi-order coupling analysis method is used to extract the characteristic coupling vector. The characteristic coupling vector contains information such as the coupling strength and coupling phase between the resonant units. The characteristic coupling vector is subjected to harmonic mode decomposition to obtain the modal optimization sequence, which reflects the inherent resonance characteristics of the circuit system.

[0135] The non-equilibrium statistical physics method is used to calculate the energy state distribution of the resonant system based on the modal optimization sequence to obtain the energy state characteristic parameters. The energy state characteristic parameters describe the resonant energy distribution law and resonance efficiency of the system. The energy state characteristic parameters are analyzed in multiple frequency domains through Fourier transform to obtain the frequency domain optimization results.

[0136] The multi-scale entropy algorithm is applied to the frequency domain optimization results to calculate the final resonance characteristic parameters. The resonance characteristic parameters include key indicators such as the system's resonance frequency, resonance impedance, and quality factor, which provide a basis for the environmental energy optimization management of the tag chip. This method improves the resonance energy transmission efficiency of the tag chip through systematic analysis and optimization.

[0137] Among them, the calculation formula of the resonance optimization algorithm includes:

[0138] ;

[0139] : M represents the energy distribution mapping matrix, i represents the row number of the matrix, j represents the column number of the matrix, and each matrix element records the energy value of the corresponding position;

[0140] : Ex represents the component of the energy field in the x direction at the position (x, y) at time t;

[0141] : Ey represents the component of the energy field in the y direction at the position (x, y) at time t;

[0142] : w represents the weight function, is the i-th x-coordinate point, is the jth y-coordinate point, which determines the contribution of each point to the total energy;

[0143] S: A tiny unit representing an area, used for regional integral calculations, and represents the area size of each calculation point.

[0144] This embodiment achieves efficient management of environmental energy by systematically analyzing and optimizing the circuit structure of the tag chip. The Hilbert-Huang transform is used for nonlinear decoupling decomposition to decompose the complex circuit structure into independent resonance units, effectively improving the energy transmission efficiency. The circuit resonance optimization network constructed based on the deep neural network enables the circuit dynamic topology structure to have adaptive adjustment capabilities and maintain the optimal resonance state under different environments. Through the multi-point field analysis method and the network dynamic reconstruction algorithm, the resonant energy transmission path is optimized and the energy utilization rate is significantly improved. The application of multi-order coupling analysis and harmonic modal decomposition achieves precise matching between resonance units and reduces energy loss. The multi-scale entropy algorithm is used to calculate the frequency domain optimization results to obtain accurate resonance characteristic parameters, which provides a reliable basis for environmental energy management, thereby achieving an overall improvement in the energy collection and transmission efficiency of the tag chip.

[0145] In one embodiment, trend prediction is performed on the environmental parameter characteristics to obtain the corresponding energy supply trend, including:

[0146] The wavelet decomposition method is used to separate the frequency of environmental parameter characteristics, and the original environmental parameter signal is decomposed into wavelet coefficients of multiple scales. The high-frequency component and the low-frequency component are divided according to the frequency threshold, where the high-frequency component represents the rapid change of the environmental parameter, and the low-frequency component represents the slow trend of the environmental parameter.

[0147] When performing amplitude statistical analysis on the obtained high-frequency components, the fluctuation range parameters are calculated according to the sliding time window. The maximum value, minimum value and standard deviation of the statistical signal in each time window are used to construct a set of fluctuation range parameters. The fluctuation range parameters reflect the intensity of changes in environmental parameters in a short period of time.

[0148] The trend fitting process of low-frequency components is based on polynomial functions. The polynomial order is selected according to the characteristics of low-frequency component data to obtain the benchmark change curve. The benchmark change curve reflects the long-term change trend of environmental parameters and excludes the influence of short-term fluctuations.

[0149] In the data fusion stage, the fluctuation range parameters and the benchmark change curve are fused in a weighted manner. The weight coefficient is determined according to the application scenario to generate an environmental change feature sequence. This sequence contains the long-term trend and short-term characteristics of the environmental parameters.

[0150] The periodic analysis of the environmental change characteristic sequence is based on spectrum calculation to identify the main periodic components of the sequence. The amplitude and phase statistics of the periodic components are calculated to form a periodic change law. This law describes the periodic change characteristics of environmental parameters.

[0151] In the environmental distribution calculation phase, the environment-energy correspondence is established, and the environmental parameter values ​​are converted into energy density values ​​to obtain energy density distribution data. This data represents the energy acquisition level under different environmental conditions.

[0152] The trend forecast of energy density distribution data adopts time series analysis. A prediction function is constructed to estimate the energy density of future periods and obtain the energy supply trend. The prediction results guide the tag chip to adjust the energy management strategy and optimize the energy utilization efficiency.

[0153] This embodiment realizes the frequency separation of environmental parameters through wavelet decomposition, accurately captures the fast and slow characteristics of environmental changes, and provides a reliable data basis for subsequent analysis. The sliding time window is used to perform statistics on the fluctuation range parameters, combined with the benchmark change curve of polynomial fitting, to achieve effective separation of short-term fluctuations and long-term trends of environmental parameters. The feature sequence construction method based on weighted fusion ensures the integrity of environmental change characteristics and avoids information loss. The periodic change law is identified through spectrum analysis, and the environment-energy correspondence is established to calculate the energy density distribution, so as to accurately grasp the changing characteristics of environmental energy. The use of time series analysis for trend prediction enables the tag chip to adjust the energy management strategy in advance, significantly improving the energy utilization efficiency. The overall method constructs a complete environmental energy feature analysis and prediction system, providing reliable technical support for the energy management optimization of tag chips.

[0154] In one embodiment, an energy balance analysis is performed on energy consumption information and energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling scheme, including:

[0155] The tag chip performs energy balance analysis on energy consumption information and energy supply trends based on resonance characteristic parameters. The tag chip performs periodic statistics on the collected energy consumption information, extracts the periodic characteristics of energy consumption through spectrum analysis methods such as Fourier transform, and obtains the energy consumption cycle. After obtaining the energy consumption cycle, the tag chip maps the energy supply trend to the time domain, establishes a corresponding relationship between the time domain and energy supply, and generates an energy balance feature vector. This feature vector contains the energy supply distribution information in the time domain.

[0156] The tag chip performs collaborative optimization calculations on the energy balance feature vector and the pre-set resonance feature parameters, and obtains the energy redundancy matrix through optimization algorithms such as the least squares method. This matrix reflects the degree of energy redundancy in the system. Based on the energy redundancy matrix, the tag chip uses a piecewise linear fitting method to fit the energy supply trend and obtain an energy supply curve. This curve can accurately describe the energy supply characteristics of the system.

[0157] The obtained energy supply curve is segmented and integrated, and the tag chip obtains the energy accumulation function. This function reflects the accumulated energy of the system at different times. The tag chip performs dynamic programming calculations on the energy consumption cycle based on the energy accumulation function and generates an energy scheduling vector. This vector contains specific energy scheduling timing information.

[0158] The tag chip performs multi-dimensional constraint analysis on the energy scheduling vector and sets scheduling constraints including maximum power constraint, minimum power constraint, time constraint, etc. Under the guidance of the constraints, the tag chip performs multiple rounds of optimization calculations on the energy scheduling vector through iterative optimization methods, and finally forms an initial energy scheduling plan. This plan meets all the requirements of the system for energy scheduling.

[0159] In practical applications, this method fully considers the energy consumption law of the tag chip and the environmental energy supply characteristics, and realizes efficient management of environmental energy through mathematical modeling and optimization calculation of the system. This method has strong adaptability and can dynamically adjust the energy scheduling strategy according to actual conditions to ensure the stable operation of the tag chip.

[0160] This embodiment uses the tag chip to perform periodic statistics and spectrum analysis on energy consumption information, which can accurately grasp the law of energy consumption and provide reliable data support for subsequent energy scheduling. The collaborative optimization operation of resonance characteristic parameters and energy balance characteristic vectors effectively improves the accuracy of energy management, enabling the system to use environmental energy more efficiently. The energy supply curve is obtained by the piecewise linear fitting method, which accurately reflects the supply characteristics of environmental energy and provides a scientific basis for the formulation of energy scheduling plans. The implementation of dynamic programming calculations based on the energy accumulation function makes energy scheduling more forward-looking and systematic, and effectively avoids the problem of imbalance between energy supply and demand. By setting multi-dimensional constraints and performing iterative optimization, the feasibility and reliability of the energy scheduling plan are ensured, and the working stability of the tag chip is greatly improved.

[0161] In one embodiment, fault diagnosis is performed on chip circuit acquisition data to obtain corresponding system status information, and reliability prediction is performed on the system status information based on energy supply trend and resonance characteristic parameters to obtain a corresponding system operation prediction scheme, including:

[0162] The data collected by the chip circuit is processed by fast Fourier transform to obtain the frequency domain characteristics of the signal, establish the frequency-amplitude relationship curve, and analyze the frequency distribution law. The spectrum analysis uses the Hanning window function for windowing processing, the window length is set to 1024 points, and the overlap rate is 50%. The peak value, bandwidth, harmonic component and other characteristic parameters of the spectrum are extracted to form a frequency feature vector.

[0163] Based on the extracted frequency feature vectors, the K-means clustering algorithm is used to classify the fault types. The number of cluster centers is set to 5, corresponding to different fault modes. The clustering process uses Euclidean distance as the similarity metric, the upper limit of the number of iterations is set to 100 times, and the iteration is terminated when the change rate of the inter-class distance is less than 0.1%. The clustering results form a fault mode feature library, which contains typical feature templates of various types of faults.

[0164] The fault type matching process uses fuzzy pattern recognition method to establish a fuzzy relationship matrix. The fuzzy membership function uses Gaussian function, and the fuzzy rule base contains 50 basic rules. The fault type is determined by calculating the similarity between the sample to be matched and the feature template. The matching threshold is set to 0.85, and when the similarity exceeds the threshold, it is determined to be this type of fault.

[0165] In the state analysis phase, Markov chain is used to construct the state transition probability matrix. The state space is divided into four levels: normal, minor fault, moderate fault, and major fault. The state transition probability is obtained based on historical data statistics. The time scale is set to the hour level, the observation period is 168 hours, and the state evolution parameters are calculated through the state transition matrix.

[0166] The calculation of energy impact value is based on the analysis of energy supply trend and the establishment of energy-state correlation. The least square method is used to fit the energy change curve and predict the energy supply in the next 24 hours. The energy threshold is set to ±20% of the nominal value. When the predicted value exceeds the threshold range, the state evolution parameter is adjusted accordingly.

[0167] The dynamic correction of resonance characteristic parameters uses an adaptive filtering algorithm to achieve parameter correction. The filter order is set to 8th order, the step factor is 0.01, and the recursive least squares method is used to update the filter coefficient. The calculation of the correction coefficient takes into account the matching relationship between the ambient vibration frequency and the system's natural frequency, and the vibration amplitude threshold is set to 0.5g.

[0168] The failure probability distribution is calculated using Weibull distribution, and the relevant parameters are determined by maximum likelihood estimation. The initial value of the shape parameter is set to 2.5, and the scale parameter is obtained from historical data statistics. During the reliability assessment process, the failure probability threshold is set to 0.1, and a warning signal is issued when the predicted failure probability exceeds the threshold.

[0169] The grey prediction method GM(1,1) is used to generate the system operation prediction scheme, and the prediction time domain is set to the next 72 hours. The background value is generated using the equal weight average method. The prediction results include the changing trends of key parameters such as system status, energy supply, and failure probability, providing a basis for system maintenance decisions.

[0170] This embodiment uses fast Fourier transform to perform spectrum analysis on chip circuit acquisition data, combined with the windowing processing of Hanning window function, to achieve accurate extraction of signal frequency domain features. Based on the K-means clustering algorithm, the fault type is classified, and the fuzzy pattern recognition method is combined for fault matching, which improves the accuracy of fault diagnosis. The state transition probability matrix is ​​constructed using Markov chain to achieve dynamic analysis and prediction of system state. Through energy supply trend analysis and dynamic correction of resonance characteristic parameters, an energy-state correlation model is established to improve the reliability of system operation. Based on Weibull distribution and gray prediction method, accurate prediction of system failure probability and optimized generation of operation plan are achieved, providing a reliable basis for system maintenance decision-making. While ensuring the stable operation of the system, this method effectively reduces energy consumption and improves the overall performance and service life of the tag chip.

[0171] In one embodiment, the initial energy scheduling scheme is optimized according to the system operation prediction scheme to obtain the corresponding global optimization strategy, including:

[0172] Based on data fusion technology, the operation prediction plan and the initial energy scheduling plan are integrated to generate an initial multi-dimensional operation state matrix containing information such as environmental parameters, system status, energy distribution, etc. This matrix reflects the relationship between the system's operating characteristics and energy demand under different working conditions.

[0173] Based on the initial multi-dimensional operating state obtained, the operating mode energy constraint analysis is performed. The analysis process involves quantitative evaluation of parameters such as energy consumption characteristics, environmental influencing factors, and system response time under different operating modes. The boundary conditions such as the upper and lower energy thresholds and power fluctuation range of the system operation are obtained by calculation. These boundary conditions provide a basis for subsequent parameter correction.

[0174] After determining the boundary conditions, the key parameters in the initial energy scheduling plan are corrected. The correction process includes optimizing and adjusting parameters such as the scheduling cycle, energy allocation ratio, and response timing. The correction results form a revised scheduling plan that meets the constraint requirements of the boundary conditions.

[0175] The modified scheduling scheme is processed by dynamic coefficient planning to establish an objective function that reflects the energy optimization goal. The objective function comprehensively considers factors such as energy utilization efficiency, chip performance requirements, and environmental adaptability, and obtains the corresponding energy optimization coefficient by solving the optimization problem. This coefficient is used to guide the iterative optimization of the system operation prediction scheme.

[0176] The iterative optimization process uses the gradient descent method to perform multiple rounds of iterative calculations on the prediction scheme based on the energy optimization coefficient. Each round of iteration updates the system state prediction value until the prediction accuracy meets the requirements. The iterative results are fused with the revised scheduling plan to generate the initial optimization strategy.

[0177] In the global convergence analysis phase, the system constructs a Lyapunov function based on the energy optimization coefficient to evaluate the stability and convergence characteristics of the initial optimization strategy. The convergence parameters such as the threshold of the number of iterations and the error tolerance required for the strategy to converge are obtained through calculation. These parameters ensure the controllability and convergence effect of the optimization process.

[0178] The initial optimization strategy is globally optimized based on the convergence threshold. The optimization process uses a dynamic programming algorithm to achieve global optimization of chip energy management while meeting the convergence requirements. The optimization results form a global optimization strategy.

[0179] Perform data fusion on the system operation prediction plan and the initial energy scheduling plan to obtain the corresponding initial multi-dimensional operation state;

[0180] Perform energy constraint analysis on the initial multi-dimensional operating state to obtain the corresponding energy boundary conditions;

[0181] Correct the parameters of the initial energy scheduling plan according to the energy boundary conditions to obtain the corresponding revised scheduling plan;

[0182] Perform dynamic coefficient planning on the revised scheduling scheme to obtain the corresponding energy optimization coefficient;

[0183] Iteratively optimize the system operation prediction plan according to the energy optimization coefficient to obtain the corresponding iterative optimization plan;

[0184] The iterative optimization plan and the revised scheduling plan are integrated to obtain the corresponding initial optimization strategy;

[0185] Perform global convergence analysis on the initial optimization strategy based on the energy optimization coefficient and obtain the corresponding convergence threshold;

[0186] The initial optimization strategy is globally optimized according to the convergence threshold to obtain a global optimization strategy.

[0187] Reference Figure 2 As shown, the present invention also provides an environmental energy management optimization system for a tag chip, which is applied to any one of the above-mentioned environmental energy management optimization methods for a tag chip, comprising:

[0188] The acquisition module is used to obtain the temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain the corresponding environmental parameter characteristics;

[0189] The analysis module is used to obtain the real-time power consumption data of the tag chip, associate the real-time power consumption data with the environmental parameter characteristics, and obtain the corresponding energy consumption information;

[0190] The association module is used to obtain the chip circuit collection data of the tag chip, perform resonance frequency analysis on the chip circuit collection data according to a preset resonance optimization algorithm, obtain corresponding resonance characteristic parameters, and perform trend prediction on the environmental parameter characteristics to obtain corresponding energy supply trends;

[0191] A processing module, the processing module is used to perform energy balance analysis on energy consumption information and energy supply trend based on resonance characteristic parameters to obtain a corresponding initial energy scheduling plan;

[0192] A control module is used to perform fault diagnosis on the chip circuit acquisition data to obtain corresponding system status information, and to perform reliability prediction on the system status information based on the energy supply trend and resonance characteristic parameters to obtain a corresponding system operation prediction plan;

[0193] The execution module is used to optimize the strategy of the initial energy scheduling plan according to the system operation prediction plan to obtain the corresponding global optimization strategy.

[0194] The environmental energy management optimization system of a tag chip provided by the present invention can more comprehensively evaluate the energy demand characteristics of the tag chip in different environments by performing spatial distribution analysis on the temperature data and electromagnetic data of the chip environment and combining the correlation analysis of the real-time power consumption data, thereby improving the accuracy of energy management and providing a reliable basis for system optimization. By performing resonance frequency analysis on the energy acquisition data and predicting the characteristic trend of environmental parameters, a dynamic evaluation of energy supply is achieved, which helps to accurately grasp the balance of energy supply and demand and avoid energy accumulation or depletion. Energy balance analysis is performed based on resonance characteristic parameters to ensure that the tag chip can maintain stable operation in a changing environment, reduce the performance loss caused by improper energy scheduling, and improve the overall reliability of the system. By performing fault diagnosis and reliability prediction on the system status, a more adaptive energy scheduling plan is formulated, and the efficient operation of the system is achieved through the implementation of a global optimization strategy, thereby effectively solving the energy management problem under the coupling of multiple environmental factors. At the same time, by dynamically adjusting the energy management strategy, the system can flexibly respond to changes in energy demand in different application scenarios, thereby improving the adaptability of the tag chip in practical applications.

[0195] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0196] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for optimizing environmental energy management of a tag chip, characterized in that: include: Obtain the temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain the corresponding environmental parameter characteristics; Acquire the real-time power consumption data of the tag chip, and associate and analyze the real-time power consumption data with the environmental parameter characteristics to obtain corresponding energy consumption information; Acquire chip circuit data collected by the tag chip, perform resonance frequency analysis on the chip circuit data collected according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters, and perform trend prediction on the environmental parameter characteristics to obtain corresponding energy supply trends; Performing energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling plan; Performing fault diagnosis on the chip circuit collected data to obtain corresponding system status information, performing reliability prediction on the system status information based on the energy supply trend and the resonance characteristic parameters to obtain a corresponding system operation prediction plan; Optimizing the strategy of the initial energy scheduling plan according to the system operation prediction plan to obtain a corresponding global optimization strategy; The step of acquiring chip circuit acquisition data of the tag chip and performing resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters includes: Performing chip circuit analysis on the tag chip to obtain chip circuit acquisition data; Performing nonlinear decoupling decomposition on the chip circuit acquisition data to obtain a corresponding multi-dimensional energy tensor and energy resonance mapping relationship; Based on the multi-dimensional energy tensor and the energy resonance mapping relationship, a circuit resonance optimization network is constructed to obtain a corresponding circuit dynamic topology structure; Performing a multi-point energy flow field analysis on the circuit dynamic topology structure according to the resonance optimization algorithm to form a circuit energy distribution mapping matrix; Performing network dynamic reconstruction on the circuit energy distribution mapping matrix to obtain a corresponding optimized energy transmission path; Performing multi-order coupling analysis on the optimized energy transmission path to obtain a corresponding characteristic coupling vector; Performing harmonic mode decomposition on the coupling vector to obtain a mode optimization sequence; Based on the modal optimization sequence, non-equilibrium energy distribution is calculated to obtain energy state characteristic parameters; Performing multi-frequency domain analysis on the energy state characteristic parameters to obtain frequency domain optimization results; Performing multi-scale entropy value calculation on the frequency domain optimization result to obtain the resonance characteristic parameter; The calculation formula of the resonance optimization algorithm includes: ； : M represents the energy distribution mapping matrix, i represents the row number of the matrix, j represents the column number of the matrix, and each matrix element records the energy value of the corresponding position; : represents the component of the energy field in the x direction at the position (x, y) at time t; : represents the component of the energy field in the y direction at the position (x, y) at time t; : w represents the weight function, is the i-th x-coordinate point, is the jth y-coordinate point, which determines the contribution of each point to the total energy; S: A tiny unit representing an area, used for regional integral calculations, and represents the area size of each calculation point.

2. The environmental energy management optimization method of the tag chip according to claim 1 is characterized in that: The temperature data and environmental electromagnetic data of the chip environment are obtained, and spatial distribution analysis is performed to obtain corresponding environmental parameter characteristics, including: Performing fractal dimension analysis on the temperature data to obtain a fractal characteristic sequence of the temperature field; Performing topological reconstruction according to the temperature field fractal feature sequence to obtain temperature field topological structure data; Extracting environmental features from the environmental electromagnetic data to obtain corresponding environmental electromagnetic features; Performing hyperbolic space mapping on the temperature field topological structure data and the environmental electromagnetic characteristics to obtain corresponding hyperbolic mapping information; Calculate the topological entropy of environmental parameters according to the hyperbolic mapping information to obtain environmental topological invariants; Performing Lie group transformation on the environment topological invariant to obtain the environment symmetry characteristics; Parameter bifurcation evolution is performed according to the environmental symmetry characteristics to obtain the environmental parameter characteristics.

3. The environmental energy management optimization method of the tag chip according to claim 1 is characterized in that: The acquiring of the real-time power consumption data of the tag chip, and correlating and analyzing the real-time power consumption data with the environmental parameter characteristics to obtain corresponding energy consumption information includes: The dynamic current data and static current data of the tag chip are collected, and the power consumption is calculated according to the preset voltage and current characteristic curve analysis to obtain the real-time power consumption data; Performing working state analysis on the real-time power consumption data to obtain corresponding chip working mode parameters; Performing data classification on the chip operating mode parameters to obtain corresponding power consumption feature vectors; Performing a time series correlation analysis on the environmental parameter characteristics according to the power consumption characteristic vector to obtain corresponding energy correlation data; Performing multi-dimensional data fusion processing on the energy correlation data to obtain corresponding energy coupling parameters; Numerical calculation is performed on the power consumption characteristic vector according to the energy coupling parameter to obtain a corresponding energy distribution matrix; The energy distribution matrix is ​​calibrated and the corresponding energy consumption information is obtained through data fitting.

4. The environmental energy management optimization method of the tag chip according to claim 1, characterized in that: The method further comprises: performing trend prediction on the environmental parameter characteristics to obtain a corresponding energy supply trend, including: Performing frequency separation on the environmental parameter characteristics to obtain corresponding high-frequency components and low-frequency components; Performing amplitude statistical analysis on the high-frequency component to obtain corresponding fluctuation range parameters; Performing trend fitting processing on the low-frequency component to obtain a corresponding reference change curve; Perform data fusion according to the fluctuation range parameter and the reference change curve to obtain a corresponding environmental change feature sequence; Performing periodic analysis on the environmental change characteristic sequence to obtain corresponding periodic change rules; Perform environmental distribution calculation according to the periodic variation law to obtain corresponding energy density distribution data; Perform trend projection prediction on the energy density distribution data to obtain a corresponding energy supply trend.

5. The environmental energy management optimization method of the tag chip according to claim 1, characterized in that: The performing energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameter to obtain a corresponding initial energy scheduling scheme includes: Performing periodic calculation on the energy consumption information to obtain a corresponding energy consumption period; Performing time domain mapping on the energy supply trend according to the energy consumption cycle to obtain a corresponding energy balance feature vector; Performing collaborative optimization calculation on the energy balance eigenvector and the resonance eigenparameter to obtain a corresponding energy redundancy matrix; Performing piecewise linear fitting on the energy supply trend according to the energy redundancy matrix to obtain a corresponding energy supply curve; Performing piecewise integration operation on the energy supply curve to obtain a corresponding energy accumulation function; Performing dynamic programming calculation on the energy consumption cycle according to the energy accumulation function to obtain a corresponding energy scheduling vector; Performing multi-dimensional constraint analysis on the energy scheduling vector to obtain corresponding scheduling constraint conditions; The energy scheduling vector is iteratively optimized according to the scheduling constraint condition to obtain a corresponding initial energy scheduling solution.

6. The environmental energy management optimization method of the tag chip according to claim 1, characterized in that: Performing fault diagnosis on the chip circuit collection data to obtain corresponding system status information, performing reliability prediction on the system status information based on the energy supply trend and the resonance characteristic parameters to obtain a corresponding system operation prediction scheme, including: Performing spectrum analysis on the chip circuit collected data to obtain corresponding frequency distribution rules; Clustering the chip circuit collected data into fault types according to the frequency distribution law to obtain corresponding fault modes; Performing type matching on the fault mode to obtain corresponding fault type information; Performing state analysis on the system state information according to the fault type information to obtain corresponding state evolution parameters; Optimizing and calculating the state evolution parameters according to the energy supply trend to obtain corresponding energy impact values; Dynamically correct the energy impact value according to the resonance characteristic parameter to obtain a corresponding correction coefficient; Performing reliability evaluation on the state evolution parameter according to the correction coefficient to obtain a corresponding failure probability distribution; The failure probability distribution is trend extrapolated to obtain the system operation prediction plan.

7. The environmental energy management optimization method of the tag chip according to claim 1, characterized in that: The initial energy scheduling scheme is strategically optimized according to the system operation prediction scheme to obtain a corresponding global optimization strategy, including: Performing data fusion on the system operation prediction scheme and the initial energy scheduling scheme to obtain a corresponding initial multi-dimensional operation state; Performing an operation mode energy constraint analysis on the initial multi-dimensional operation state to obtain corresponding energy boundary conditions; Performing parameter correction on the initial energy scheduling scheme according to the energy boundary condition to obtain a corresponding revised scheduling scheme; Performing dynamic coefficient planning on the modified scheduling scheme to obtain a corresponding energy optimization coefficient; Iteratively optimize the system operation prediction scheme according to the energy optimization coefficient to obtain a corresponding iterative optimization scheme; The iterative optimization scheme and the revised scheduling scheme are integrated to obtain a corresponding initial optimization strategy; Performing a global convergence analysis on the initial optimization strategy according to the energy optimization coefficient to obtain a corresponding convergence threshold; The initial optimization strategy is globally optimized according to the convergence threshold to obtain the global optimization strategy.

8. An environmental energy management optimization system for a tag chip, characterized in that: The environmental energy management optimization method for the tag chip according to any one of claims 1 to 7 comprises: An acquisition module, which is used to acquire temperature data and environmental electromagnetic data of the chip environment, and perform spatial distribution analysis to obtain corresponding environmental parameter characteristics; An analysis module, the analysis module is used to obtain real-time power consumption data of the tag chip, associate and analyze the real-time power consumption data with the environmental parameter characteristics, and obtain corresponding energy consumption information; An association module, the association module is used to obtain chip circuit acquisition data of the tag chip, perform resonance frequency analysis on the chip circuit acquisition data according to a preset resonance optimization algorithm to obtain corresponding resonance characteristic parameters, and perform trend prediction on the environmental parameter characteristics to obtain corresponding energy supply trends; A processing module, the processing module is used to perform energy balance analysis on the energy consumption information and the energy supply trend based on the resonance characteristic parameters to obtain a corresponding initial energy scheduling plan; A control module, the control module is used to perform fault diagnosis on the chip circuit acquisition data to obtain corresponding system status information, perform reliability prediction on the system status information based on the energy supply trend and the resonance characteristic parameters, and obtain a corresponding system operation prediction plan; An execution module is used to optimize the strategy of the initial energy scheduling plan according to the system operation prediction plan to obtain a corresponding global optimization strategy.

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