A design method and system for micro-energy collection electronic tags

Abstract

The present invention relates to a design method and system for a micro-energy harvesting electronic tag. The method includes: obtaining the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and performing matching analysis to obtain the corresponding tag energy reception scheme; obtaining the system architecture information of the micro-energy harvesting electronic tag, and performing energy management analysis with the tag energy reception scheme to obtain the corresponding energy management strategy group; obtaining the tag power consumption data of the micro-energy harvesting electronic tag, and performing power consumption prediction on the energy management strategy group to obtain the corresponding energy utilization trend; identifying module tasks based on the system architecture information for the tag power consumption data, and performing task planning with the energy utilization trend to obtain the corresponding task execution strategy; globally optimizing the initial power consumption control scheme and the task execution strategy to obtain the corresponding global energy optimization strategy. The present invention can achieve refined management of system energy allocation.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic tags, and particularly to a design method and system for a micro-energy harvesting electronic tag. Background Art

[0002] As a new type of energy self-sufficient electronic device, the micro-energy harvesting electronic tag plays an important role in the fields of the Internet of Things and intelligent sensing. With the rapid development of Internet of Things technology and the continuous expansion of application scenarios, how to improve the energy utilization efficiency of the micro-energy harvesting electronic tag and achieve the continuous and stable operation of the system has become one of the key research directions. The existing design methods for micro-energy harvesting electronic tags usually only focus on a single energy harvesting or power consumption control strategy, while ignoring the comprehensive impact of the overall system architecture, environmental conditions, and task requirements on the tag performance. This fragmented design idea often leads to low energy management efficiency, unable to fully exploit the potential of the micro-energy harvesting system, and restricting its popularization and development in practical applications. Summary of the Invention

[0003] The main object of the present invention is to provide a design method and system for a micro-energy harvesting electronic tag, which can achieve refined management of system energy distribution and help the tag achieve reasonable energy allocation among different functional modules.

[0004] To achieve the above object, the present invention provides a design method for a micro-energy harvesting electronic tag, including:

[0005] Obtaining the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and performing matching analysis to obtain the corresponding tag energy reception scheme;

[0006] Obtaining the system architecture information of the micro-energy harvesting electronic tag, and performing energy management analysis with the tag energy reception scheme to obtain the corresponding energy management strategy group;

[0007] Obtaining the tag power consumption data of the micro-energy harvesting electronic tag, and performing power consumption prediction on the energy management strategy group to obtain the corresponding energy utilization trend;

[0008] Performing power consumption planning on the energy management strategy group and the energy utilization trend according to the preset HHO algorithm to obtain the corresponding initial power consumption control scheme;

[0009] Performing module task recognition on the tag power consumption data according to the system architecture information, and performing task planning with the energy utilization trend to obtain the corresponding task execution strategy;

[0010] Performing global optimization on the initial power consumption control scheme and the task execution strategy to obtain the corresponding global energy optimization strategy.

[0011] Further, obtaining the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and performing matching analysis to obtain the corresponding tag energy reception scheme, includes:

[0012] Classify the energy reception parameters of the micro-energy harvesting electronic tag to obtain radio frequency energy reception parameters, light energy reception parameters, vibration energy reception parameters, and temperature difference energy reception parameters;

[0013] Extract features from the environmental condition data to obtain environmental temperature data, environmental light data, environmental vibration data, and environmental radio frequency data;

[0014] Perform spectrum analysis on the environmental radio frequency data according to the radio frequency energy reception parameters to obtain the radio frequency energy harvesting coefficient;

[0015] Perform light intensity distribution calculation on the environmental light data according to the light energy reception parameters to obtain the light energy conversion coefficient;

[0016] Perform amplitude feature analysis on the environmental vibration data according to the vibration energy reception parameters to obtain the vibration energy harvesting coefficient;

[0017] Perform temperature difference gradient calculation on the environmental temperature data according to the temperature difference energy reception parameters to obtain the temperature difference energy conversion coefficient;

[0018] Perform weighted fusion processing on the radio frequency energy harvesting coefficient, light energy conversion coefficient, vibration energy harvesting coefficient, and temperature difference energy conversion coefficient to obtain the comprehensive energy conversion evaluation value;

[0019] Perform energy reception parameter configuration calculation according to the comprehensive energy conversion evaluation value to obtain the tag energy reception scheme.

[0020] Further, obtaining the system architecture information of the micro-energy harvesting electronic tag, and performing energy management analysis with the tag energy reception scheme to obtain the corresponding energy management strategy group, includes:

[0021] Perform hierarchical analysis on the system architecture information to obtain the corresponding system function module tree;

[0022] Perform energy requirement quantification calculation based on the system function module tree to obtain the corresponding module energy requirement matrix;

[0023] Perform energy curve analysis on the tag energy reception scheme to obtain the corresponding energy input characteristic curve;

[0024] Perform periodic analysis on the energy fluctuation law according to the energy input characteristic curve to obtain the corresponding energy fluctuation period set;

[0025] Perform spatio-temporal mapping on the module energy demand matrix and the energy fluctuation period set to obtain a corresponding energy allocation mapping table;

[0026] Rank the system energy allocation according to the energy allocation mapping table to obtain a corresponding energy allocation priority sequence;

[0027] Perform multi-objective constraint optimization on the energy allocation priority sequence to obtain a corresponding energy management strategy group.

[0028] Furthermore, obtaining the tag power consumption data of the micro-energy collection electronic tag and performing power consumption prediction on the energy management strategy group to obtain a corresponding energy utilization trend, including:

[0029] Split the tag power consumption data in the time dimension to obtain real-time power consumption data and historical power consumption data;

[0030] Perform multi-dimensional decomposition on the historical power consumption data to obtain a historical energy feature matrix;

[0031] Perform Hilbert transform processing on the real-time power consumption data to obtain an instantaneous energy distribution sequence;

[0032] Perform spectral correlation mapping on the historical energy feature matrix and the energy management strategy group to obtain a corresponding historical correlation map;

[0033] Perform spectral correlation mapping on the instantaneous energy distribution sequence and the energy management strategy group to obtain a corresponding real-time correlation map;

[0034] Perform spectral feature correlation on the historical correlation map and the real-time correlation map to obtain a corresponding strategy-energy correlation map;

[0035] Calculate the energy entropy value according to the strategy-energy correlation map to obtain an energy state transition matrix;

[0036] Perform Markov chain analysis on the energy state transition matrix to obtain the energy utilization trend.

[0037] Furthermore, performing power consumption planning on the energy management strategy group and the energy utilization trend according to the preset HHO algorithm to obtain a corresponding initial power consumption control scheme, including:

[0038] Perform initialization processing on the energy parameters in the energy management strategy group to obtain an initialization strategy parameter group;

[0039] Perform energy distribution analysis on the energy utilization trend according to the initialization strategy parameter group to obtain an energy distribution feature vector;

[0040] Perform exploration-phase search processing on the energy distribution eigenvector to obtain local energy optimization parameters;

[0041] Perform soft bounding processing on the energy management strategy group according to the local energy optimization parameters to obtain a soft-bounded energy control vector;

[0042] Perform hard-bounding phase processing on the soft-bounded energy control vector to obtain hard-bounded energy optimization parameters;

[0043] Perform Lévy flight jump processing on the energy utilization trend based on the hard-bounded energy optimization parameters to obtain a set of jump optimization strategies;

[0044] Perform iterative convergence processing on the set of jump optimization strategies to obtain iterative energy optimization parameters;

[0045] Perform global search processing on the energy utilization trend based on the iterative energy optimization parameters to obtain a global energy control vector;

[0046] Perform weight fusion processing on the global energy control vector and the energy management strategy group to obtain the initial power consumption control scheme.

[0047] Further, the performing Lévy flight jump processing on the energy utilization trend based on the hard-bounded energy optimization parameters to obtain a set of jump optimization strategies includes:

[0048] Perform distribution characteristic analysis processing on the hard-bounded energy optimization parameters to obtain an energy distribution probability vector;

[0049] Perform step size calculation processing on the energy utilization trend based on the energy distribution probability vector to obtain a set of Lévy step size parameters;

[0050] Perform random number generation processing on the set of Lévy step size parameters to obtain a Lévy random distribution matrix;

[0051] Perform direction vector calculation on the energy utilization trend according to the Lévy random distribution matrix to obtain a set of jump search directions;

[0052] Perform position update processing on the set of jump search directions and the hard-bounded energy optimization parameters to obtain an initial jump position vector;

[0053] Perform boundary constraint processing on the energy utilization trend according to the initial jump position vector to obtain a set of effective jump ranges;

[0054] Perform energy evaluation processing on the set of effective jump ranges to obtain a jump energy state matrix;

[0055] Perform strategy optimization processing on the energy utilization trend according to the jump energy state matrix to obtain the jump optimization strategy set;

[0056] Among them, the calculation formula for step size calculation processing is:

[0057] ;

[0058] L is the Lévy step size parameter group, and are random numbers drawn from a normal distribution, is a parameter used to control the distribution shape, usually taking a value of 1.5, is the variance factor.

[0059] Furthermore, identifying module tasks for the tag power consumption data based on the system architecture information and performing task planning with the energy utilization trend to obtain corresponding task execution strategies includes:

[0060] Performing hierarchical decomposition processing on the system architecture information to obtain a corresponding system function module tree;

[0061] Segmenting the tag power consumption data in the time domain according to the system function module tree to obtain the energy consumption distribution data of each function module;

[0062] Extracting task fluctuation characteristics from the energy consumption distribution data to obtain corresponding task activity sequences;

[0063] Quantifying the module association degree based on the task activity sequence to obtain a module task dependency graph;

[0064] Performing initial scheduling analysis on the module task dependency graph and the energy utilization trend to obtain an initial task scheduling plan;

[0065] Performing module task energy allocation based on the initial task scheduling plan to obtain a module energy configuration plan;

[0066] Performing task optimization adjustment on the module energy configuration plan according to the energy utilization trend to obtain the task execution strategy.

[0067] Furthermore, globally optimizing the initial power consumption control plan and the task execution strategy to obtain a corresponding global energy optimization strategy includes:

[0068] Extracting parameters from the initial power consumption control plan and the task execution strategy to obtain corresponding parameter association degree data;

[0069] Performing hierarchical clustering on the parameter association degree data to obtain corresponding parameter optimization hierarchical data;

[0070] Perform hierarchical mapping on the parameter optimization level data to obtain a corresponding multi-level optimization parameter group;

[0071] Perform particle swarm optimization on the multi-level optimization parameter group to obtain a corresponding optimization parameter set;

[0072] Dynamically merge the initial power consumption control scheme and the task execution strategy according to the optimization parameter set to obtain an initial dynamic merge control scheme;

[0073] Perform energy balance calculation on the initial dynamic merge control scheme to obtain a corresponding energy balance index;

[0074] Iteratively optimize the initial dynamic merge control scheme according to the energy balance index to obtain the global energy optimization strategy.

[0075] The present invention also provides a design system for a micro-energy harvesting electronic tag, which is applied to the design method of the micro-energy harvesting electronic tag described in any one of the above, and includes:

[0076] A collection module, which is used to obtain the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and perform matching analysis to obtain a corresponding tag energy reception scheme;

[0077] An analysis module, which is used to obtain the system architecture information of the micro-energy harvesting electronic tag, and perform energy management analysis with the tag energy reception scheme to obtain a corresponding energy management strategy group;

[0078] An association module, which is used to obtain the tag power consumption data of the micro-energy harvesting electronic tag, and perform power consumption prediction on the energy management strategy group to obtain a corresponding energy utilization trend;

[0079] A processing module, which is used to perform power consumption planning on the energy management strategy group and the energy utilization trend according to a preset HHO algorithm to obtain a corresponding initial power consumption control scheme;

[0080] A control module, which is used to identify module tasks for the tag power consumption data according to the system architecture information, and perform task planning with the energy utilization trend to obtain a corresponding task execution strategy;

[0081] An execution module, which is used to globally optimize the initial power consumption control scheme and the task execution strategy to obtain a corresponding global energy optimization strategy.

[0082] A design method and system for a micro-energy harvesting electronic tag provided by the present invention have the following beneficial effects:

[0083] By performing matching analysis on the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, the energy acquisition ability of the system can be more accurately evaluated, thereby providing a reliable energy basis for the efficient operation of the tag. By combining the energy management strategy with the system architecture information for analysis, fine-grained management of the system energy distribution is achieved, which helps the tag achieve reasonable energy allocation among different functional modules and avoid energy waste. Based on the tag power consumption data, power consumption prediction is performed on the energy management strategy group to ensure that the system can operate continuously and stably under different environments and working conditions, reducing the blindness of energy utilization. Through the HHO algorithm, power consumption planning is carried out for the energy management strategy and energy utilization trend, and a more reasonable energy control scheme is formulated, improving the overall operation efficiency of the system. By identifying the module tasks of the system architecture and combining with the energy utilization trend for task planning, the task execution strategy can be flexibly adjusted according to the characteristics and energy requirements of different tasks, making the system more adaptable to complex and changeable application scenarios. Finally, through global optimization, the comprehensive improvement of the system energy utilization efficiency is achieved, effectively solving the problem of low energy management efficiency in the existing technology. Brief Description of the Drawings

[0084] Figure 1 is a flowchart of a design method for a micro-energy harvesting electronic tag provided by the present invention;

[0085] Figure 2 is a structural diagram of a design system for a micro-energy harvesting electronic tag provided by the present invention.

[0086] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0087] In order to make the object, technical solution, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0088] Next, with reference to the accompanying drawings and specific embodiments, the present invention will be further described.

[0089] Referring to Figure 1 as shown, the present invention provides a design method for a micro-energy harvesting electronic tag, which is characterized by including:

[0090] Step S1: Obtain the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and perform matching analysis to obtain the corresponding tag energy reception scheme;

[0091] Step S2: Obtain the system architecture information of the micro-energy harvesting electronic tag, perform energy management analysis with the tag energy reception scheme, and obtain the corresponding energy management strategy group;

[0092] Step S3: Obtain the tag power consumption data of the micro-energy harvesting electronic tag, perform power consumption prediction on the energy management strategy group, and obtain the corresponding energy utilization trend;

[0093] Step S4: Perform power consumption planning on the energy management strategy group and the energy utilization trend according to the preset HHO algorithm, and obtain the corresponding initial power consumption control scheme;

[0094] Step S5: Identify the module tasks for the tag power consumption data according to the system architecture information, and perform task planning with the energy utilization trend to obtain the corresponding task execution strategy;

[0095] Step S6: Globally optimize the initial power consumption control scheme and the task execution strategy to obtain the corresponding global energy optimization strategy.

[0096] Based on the above steps, the detailed step process is as follows:

[0097] Step S1: The process of obtaining the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag and performing matching analysis first requires comprehensive collection of energy source information in the environment, including possible energy sources such as radio frequency energy, light energy, and vibration energy. For each energy source, its intensity, stability, and time-varying characteristics need to be measured. For example, for radio frequency energy, the received power density at different distances and angles needs to be measured; for light energy, the light intensity at different time periods needs to be measured; for vibration energy, the vibration frequency and amplitude need to be measured. At the same time, environmental parameters such as ambient temperature and humidity that may affect the energy harvesting efficiency need to be recorded. By establishing an energy reception parameter database, these data are classified and stored. According to the pre-established energy characteristic model, analyze the availability and reliability of various energy sources, and evaluate their energy supply capabilities under different environmental conditions. Through comparative analysis, select the most suitable energy reception scheme, which may be a single energy source or a combination of multiple energy sources.

[0098] Step S2: The process of obtaining the micro-energy harvesting electronic tag system architecture information and conducting energy management analysis begins with a detailed review of the tag's hardware structure and functional modules. The system architecture information includes core hardware information such as processor type, memory capacity, communication module specifications, sensor types, etc., and also includes the connection relationships and data flows between various modules. Based on this information, a complete energy consumption model is established to analyze the energy requirements of each module in different working states. Combining with the energy reception scheme, the balance relationship between energy supply and demand is calculated to determine the energy allocation strategy under different scenarios. This process generates multiple possible energy management strategies, and each strategy contains specific parameters such as the switching timing of module working modes, the triggering conditions of the sleep-wake mechanism, and the charge-discharge management of the energy storage unit. These strategies are preliminarily evaluated to screen out the strategy combinations most likely to meet the requirements of the system's stable operation.

[0099] Step S3: The process of obtaining the tag power consumption data and conducting power consumption prediction first requires establishing a detailed power consumption test plan. Measure the actual power consumption of each functional module in different working modes, including static power consumption and dynamic power consumption. These test data are used to build an accurate power consumption model. Based on the energy management strategy group, this power consumption model is used for simulation analysis to predict the energy consumption trend of the system under different strategies. The prediction process takes into account influencing factors such as load changes and environmental changes, and uses statistical methods and machine learning algorithms to improve the prediction accuracy. By analyzing these prediction results, the system can identify the strategy combinations with high energy utilization efficiency and give early warnings of possible energy shortage situations. The output of this step is a detailed energy utilization trend report, which contains the energy consumption prediction curves on different time scales and provides an important basis for subsequent power consumption optimization.

[0100] Step S4: The process of power consumption planning based on the preset HHO (Harris Hawks Optimization) algorithm first requires converting the energy management strategy group and energy utilization trend data into the input parameters of the optimization problem. The HHO algorithm simulates the predation behavior of Harris hawks, where each hawk represents a possible power consumption control scheme. The algorithm sets the optimization objective function, including multiple evaluation indicators such as energy utilization efficiency, system performance, and reliability. In the iterative optimization process, the algorithm searches the possible solution space through the exploration phase and conducts in-depth searches in the target area through the exploitation phase. Each iteration updates the positions of the hawk group, representing the adjustment of different power consumption control schemes. The algorithm dynamically adjusts the search strategy according to the characteristics of the energy management strategy group obtained in the previous steps, including adjusting parameters such as the step size and update speed. Through multiple iterations of optimization, an initial power consumption control scheme that balances energy efficiency and system performance is obtained.

[0101] Step S5: The process of module task identification and task planning begins with analyzing the working characteristics of each functional module in the system architecture. Conduct a fine-grained analysis of the tag power consumption data to identify the working modes and task types of different modules, such as data acquisition, data processing, communication transmission, etc. For each task type, establish a task feature model, including attributes such as the execution time, energy requirement, and priority of the task. Combining the results of the energy utilization trend analysis, formulate a detailed task scheduling strategy, including the execution order, time allocation, and resource allocation of tasks. During this process, consider the dependencies between tasks to ensure that critical tasks can be executed preferentially when there is sufficient energy. At the same time, the system will also establish a task dynamic adjustment mechanism to adjust the task execution plan according to the real-time energy status, ensuring that the system can maintain the normal operation of the core functions under energy constraints.

[0102] Step S6: The process of global energy optimization will comprehensively consider the output results of the previous several steps. Integrate the initial power consumption control scheme with the task execution strategy to establish a unified optimization model. This optimization process will consider factors at multiple levels, including power consumption control at the hardware level, task scheduling at the system level, and function optimization at the application level. Use a multi-objective optimization algorithm, considering multiple optimization objectives such as energy efficiency, system performance, and task completion quality. During the optimization process, dynamically adjust the weights of each objective to find the best balance according to the requirements of the actual application scenario. The optimization result will form a complete global energy optimization strategy, including detailed power consumption control parameters, task scheduling rules, energy allocation schemes, etc., ensuring that the system can achieve the optimal performance under energy constraints.

[0103] A design method of a micro-energy harvesting electronic tag provided by the present invention can more accurately evaluate the energy acquisition ability of the system by performing matching analysis on the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, thereby providing a reliable energy basis for the efficient operation of the tag. By combining the energy management strategy with the system architecture information for analysis, refined management of the system energy distribution is achieved, which helps the tag to achieve reasonable energy allocation among different functional modules and avoid energy waste. Based on the power consumption data of the tag, power consumption prediction is performed on the energy management strategy group to ensure that the system can operate continuously and stably under different environments and working conditions, reducing the blindness of energy utilization. Through the HHO algorithm, power consumption planning is carried out for the energy management strategy and the energy utilization trend, and a more reasonable energy control scheme is formulated, improving the overall operation efficiency of the system. By identifying the module tasks of the system architecture and combining the energy utilization trend for task planning, the task execution strategy can be flexibly adjusted according to the characteristics and energy requirements of different tasks, making the system more adaptable to complex and changeable application scenarios. Finally, through global optimization, the comprehensive improvement of the system energy utilization efficiency is achieved, effectively solving the problem of low energy management efficiency in the prior art.

[0104] In one embodiment, the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag are obtained and subjected to matching analysis to obtain the corresponding tag energy reception scheme, including:

[0105] For the classification processing of the energy reception parameters, the radio frequency energy reception parameters include characteristic quantities such as antenna gain, impedance matching coefficient, and rectification efficiency; the light energy reception parameters include indicators such as photoelectric conversion efficiency, photosensitive area, and light intensity response characteristics; the vibration energy reception parameters include parameters such as resonance frequency, mechanical quality factor, and piezoelectric coefficient; the thermoelectric energy reception parameters include numerical values such as Seebeck coefficient, thermocouple type, and thermoelectric response sensitivity.

[0106] In the process of feature extraction of the environmental condition data, the environmental temperature data collects the spatial temperature distribution information through the temperature sensor array, and the sampling frequency is 1 Hz; the environmental light data obtains the light intensity values of different regions by the light sensor, and the sampling interval is 10 seconds; the environmental vibration data records the vibration acceleration time-domain waveform by the acceleration sensor, and the sampling rate is 1 kHz; the environmental radio frequency data scans the electromagnetic wave intensity in the frequency band of 300 MHz - 3 GHz by the spectrum analyzer.

[0107] In the calculation of the radio frequency energy collection coefficient, the environmental radio frequency data is subjected to fast Fourier transform to obtain the spectrum distribution, and the energy conversion efficiency of each frequency point is calculated in combination with the antenna gain curve, and its weighted average value is taken as the radio frequency energy collection coefficient. The weight coefficient is determined according to the signal intensity of each frequency point, and the greater the intensity, the higher the weight.

[0108] The determination of the light energy conversion coefficient is based on the light intensity distribution characteristics of environmental light data. The non-linear relationship curve between the photovoltaic conversion efficiency and the incident light intensity is fitted into a piecewise function, and the corresponding conversion function is used to calculate the light energy conversion coefficient in different light intensity intervals. When the light intensity distribution shows an obvious day-night change pattern, the parameters of the conversion function are dynamically adjusted over time.

[0109] The acquisition of the vibration energy collection coefficient is achieved by performing time-frequency analysis on environmental vibration data and extracting the main frequency components and amplitude characteristics of the vibration signal. When the vibration frequency is close to the natural frequency of the piezoelectric cantilever beam, the collection coefficient takes the maximum value; as the frequency difference increases, the collection coefficient decays exponentially. The relationship between the amplitude characteristic and the square of the mechanical strain energy is used to correct the collection coefficient.

[0110] The thermoelectric energy conversion coefficient is determined by the spatial gradient of environmental temperature data. The greater the temperature difference, the higher the conversion coefficient. The temperature difference gradient is calculated using the finite difference method. The temperature difference between adjacent measurement points is divided by the spatial distance to obtain the local gradient value, and the global gradient field is reconstructed through an interpolation algorithm. The conversion coefficient has an approximately linear relationship with the temperature difference gradient.

[0111] The calculation of the comprehensive energy conversion evaluation value uses a weighted fusion method. The weight coefficients of the four energy collection coefficients are jointly determined by the stability and availability of each type of energy. The weights of radio frequency energy and light energy are relatively high, followed by vibration energy, and the lowest is thermoelectric energy. The normalization processing of the evaluation value ensures that its value range is between 0 and 1.

[0112] The configuration of the tag energy reception scheme is optimized based on the comprehensive energy conversion evaluation value. When the evaluation value is greater than 0.8, the single energy source scheme is preferentially adopted; when the evaluation value is between 0.5 and 0.8, the dual energy source complementary scheme is adopted; when the evaluation value is less than 0.5, the multi-energy source combined power supply scheme is adopted. The scheme parameters are optimized to achieve the best match through an iterative optimization algorithm.

[0113] In this embodiment, through the refined classification processing of the energy reception parameters of the micro-energy collection electronic tag and the feature extraction and analysis of the environmental condition data, the efficient reception and conversion of multiple energy forms are realized. Based on the matching analysis of the reception parameters of radio frequency energy, light energy, vibration energy, and thermoelectric energy and the environmental data, a complete energy conversion evaluation system is established, enabling the tag to adaptively adjust the energy reception scheme according to the actual environmental conditions. The weighted fusion method is used to comprehensively evaluate different energy types, overcoming the limitation of unstable power supply of a single energy source. By establishing the quantitative relationship between the energy conversion coefficient and the environmental parameters, the energy collection efficiency is improved. The coordinated complementarity of multiple energy sources is realized, enhancing the adaptability of the tag in complex environments and ensuring the reliability and sustainability of the energy supply. At the same time, the dynamically configured tag energy reception scheme improves the system response speed, reduces energy loss, and significantly improves the overall performance of the micro-energy collection electronic tag.

[0114] In one embodiment, the system architecture information of the micro-energy harvesting electronic tag is obtained, and energy management analysis is performed in combination with the tag energy reception scheme to obtain the corresponding energy management strategy group, including:

[0115] When obtaining the system architecture information of the micro-energy harvesting electronic tag, the system architecture is designed with a hierarchical structure, including three main levels: the hardware layer, the driver layer, and the application layer. The system architecture is decomposed by the analytic hierarchy process to construct a system function module tree. The function module tree includes, from top to bottom in sequence: the system main control module, the radio frequency communication module, the energy harvesting module, the data storage module, etc. Each module is interconnected through a standard interface.

[0116] In the link of quantifying and calculating the module energy requirements, the energy consumption characteristics of each module in the function module tree are analyzed. The specific calculation method is as follows: measure the energy consumption data of each module in different working states, establish an energy consumption mathematical model, and obtain the energy requirement data of each module. These data are sorted into a module energy requirement matrix, which contains key information such as module identification, working state, and energy consumption value.

[0117] When performing the energy curve analysis of the tag energy reception scheme, a method combining experimental measurement and theoretical calculation is adopted. By collecting energy input data in different working environments, an energy input characteristic curve is plotted. This curve reflects the energy acquisition characteristics in the actual application scenario, including information such as energy amplitude change and time distribution.

[0118] For the periodic analysis of the energy fluctuation law, the Fourier transform method is used to convert the time-domain energy fluctuation data to the frequency domain for analysis. By extracting the main frequency components, the energy fluctuation period set of the system is determined. This period set contains multiple characteristic periods, which reflect the time characteristics of the system energy supply.

[0119] In the spatio-temporal mapping process, the module energy requirement matrix and the energy fluctuation period set are analyzed correspondingly. An energy allocation mapping table is established, which contains information such as module working timing, energy requirement, and energy supply time. The establishment of the mapping table is based on the principle of minimum energy consumption to ensure the maximization of the overall energy utilization efficiency of the system.

[0120] In the energy allocation priority ranking, the priority of each functional module of the system is evaluated according to factors such as module importance, energy consumption characteristics, and timing requirements. The evaluation indicators include: module criticality, energy requirement, response time requirement, etc. An energy allocation priority sequence is established through comprehensive evaluation.

[0121] In the multi-objective constrained optimization stage, the optimization objectives are to maximize the system reliability and the energy utilization efficiency. The constraint conditions include: the total system energy consumption limit, the module response time requirement, the energy supply fluctuation range, etc. By solving this optimization model, the final energy management strategy group is obtained. This strategy group contains multiple energy management solutions for different scenarios.

[0122] In this embodiment, by adopting a hierarchical structure design and the analytic hierarchy process to construct the system function module tree, the system architecture is clear and definite, the interfaces between various function modules are standardized, and the maintainability and expandability of the system are significantly improved. Based on the module energy demand matrix established through the analysis of energy consumption characteristics, combined with the measurement and analysis of the energy input characteristic curve, the energy demand and supply characteristics of the system are accurately grasped, providing reliable data support for energy management. Through the periodic analysis using the Fourier transform method, the energy fluctuation law is accurately identified, and based on this, the spatio-temporal mapping relationship is established to achieve the precise allocation of system energy. The priority sequence established according to the multi-dimensional evaluation index, combined with the multi-objective constrained optimization model, not only ensures the efficient utilization of system energy but also improves the overall reliability of the system. Through the systematic energy management strategy, the problem of the energy supply-demand contradiction faced by the micro-energy harvesting electronic tag in practical applications is effectively solved, enabling the system to maintain a stable and reliable operating state under limited energy conditions.

[0123] In one embodiment, the tag power consumption data of the micro-energy harvesting electronic tag is obtained, and the power consumption of the energy management strategy group is predicted to obtain the corresponding energy utilization trend, including:

[0124] The tag power consumption data is divided into real-time power consumption data and historical power consumption data by splitting in the time dimension. This time dimension splitting is based on a preset time threshold T for division. The power consumption data less than the time threshold T is classified as real-time power consumption data, and the power consumption data greater than the time threshold T is classified as historical power consumption data. The setting of the time threshold T is determined according to the working cycle of the tag, and generally takes a value between 1 / 4 and 1 / 2 of the tag working cycle.

[0125] The historical power consumption data is decomposed in multiple dimensions. The singular value decomposition method is used to transform the historical power consumption data into a historical energy feature matrix. During the multi-dimensional decomposition process, the historical power consumption data is segmented according to the time window W. The data within each time window forms a sub-matrix, and the singular value decomposition is performed on all sub-matrices to obtain the corresponding eigenvalues and eigenvectors. All eigenvectors are combined to form the historical energy feature matrix. The size of the time window is set to an integer multiple of the tag working cycle.

[0126] Perform Hilbert transform processing on the real-time power consumption data to obtain the instantaneous energy distribution sequence. The Hilbert transform extracts the envelope characteristics of the data through phase modulation of the real-time power consumption data, forming a time series describing the instantaneous energy distribution. The transformation process adopts a sliding window method, and the window size is set to 8 - 16 times the sampling period of the real-time power consumption data.

[0127] During the spectrum correlation mapping process, calculate the correlation degree between the historical energy feature matrix and each policy in the energy management strategy group to construct a historical correlation map; at the same time, calculate the correlation degree between the instantaneous energy distribution sequence and each policy in the energy management strategy group to construct a real-time correlation map. The correlation degree calculation uses the grey correlation analysis method, and the minimum correlation degree threshold is set to 0.6.

[0128] The spectrum feature correlation process fuses the features of the historical correlation map and the real-time correlation map, and uses the weighted summation method to obtain the policy energy correlation map, where the weight of the historical correlation map is 0.7 and the weight of the real-time correlation map is 0.3. The policy energy correlation map reflects the corresponding relationship between each energy management policy and the energy utilization state of the tag.

[0129] Calculate the energy entropy value based on the policy energy correlation map to construct an energy state transition matrix. The energy entropy value calculation uses the information entropy formula, and the correlation degree is used as the state probability for calculation. The energy state transition matrix describes the transition law of the tag energy state under different energy management policies.

[0130] Perform Markov chain analysis on the energy state transition matrix to predict the energy utilization trend of the tag under each energy management policy. The Markov chain analysis is based on the state transition probability matrix, calculates the steady-state distribution probability, and predicts the energy utilization efficiency. The prediction results are used to guide the optimization and adjustment of the energy management policy.

[0131] In this embodiment, through the time dimension splitting and multi-dimensional decomposition methods, the accurate analysis of the power consumption data of the micro-energy harvesting electronic tag is realized, and the energy usage characteristics of the tag are accurately grasped. The Hilbert transform is used to process the real-time power consumption data, and the grey correlation analysis method is combined to construct the correlation map, effectively extracting the dynamic characteristics of the tag energy usage. Through the spectrum feature correlation and energy entropy value calculation, an energy state transition matrix is established, accurately reflecting the corresponding relationship between the energy management policy and the tag energy state. Based on the Markov chain analysis method, the energy utilization trend is predicted, providing a scientific basis for the optimization of the energy management policy. This method comprehensively analyzes the tag power consumption data, realizes the accurate evaluation of the energy management policy, improves the energy utilization efficiency of the micro-energy harvesting electronic tag, extends the working time of the tag, and enhances the reliability and stability of the tag in practical applications.

[0132] In one embodiment, a power consumption plan is made for the energy management strategy group and the energy utilization trend according to a preset HHO algorithm, and a corresponding initial power consumption control scheme is obtained, including:

[0133] When initializing the energy parameters in the energy management strategy group, set the initial population size to N, the maximum number of iterations to Max_iter, and the dimension to dim. Random numbers in the range of [0, 1] are generated by a random number generator, and the energy parameters of each dimension are assigned values to form an N×dim-dimensional initial strategy parameter matrix. This initialization process ensures the diversity of the initial population and lays a foundation for subsequent optimization.

[0134] In the energy distribution analysis stage, the system maps the initial strategy parameter matrix into the actual energy distribution space. By calculating the energy density function corresponding to each parameter, an energy distribution feature vector is constructed. This vector contains characteristic quantities such as energy density, energy gradient, and energy aggregation degree, which are used to characterize the energy distribution state of the system. The energy density function is calculated using a Gaussian kernel function to ensure the accuracy of feature extraction.

[0135] The exploration stage search process adopts a local search strategy. With the current optimal solution as the center in the feature space, a search radius R is set for local area exploration. The step size is dynamically adjusted during the search process, and the step size value is inversely proportional to the current iteration number, realizing refined search. The local search results form local energy optimization parameters, providing a reference for subsequent global optimization.

[0136] In the soft enclosure processing stage, the local energy optimization parameters are expanded to construct a soft enclosure area. The size of the soft enclosure area is related to the current iteration number and gradually shrinks as the iteration progresses. By calculating the fitness values of each point in the soft enclosure area, a soft enclosure energy control vector is formed. The soft enclosure mechanism enhances the local search ability of the algorithm.

[0137] In the hard enclosure stage, strict boundary constraint conditions are set on the basis of the soft enclosure. Through a boundary detection function, it is ensured that all optimization parameters are within the valid range, and the parameters exceeding the range will be mapped back to the boundary position. The parameters after boundary constraint processing form hard enclosure energy optimization parameters, ensuring the feasibility of the optimization results.

[0138] The Levy flight jump processing introduces random perturbations to break out of local optima. The Levy flight step size follows an α-stable distribution, and α is taken as 1.5. During the jump process, the information of the current optimal solution is combined to generate new candidate solutions, forming a jump optimization strategy set. This mechanism improves the ability of the algorithm to jump out of local optima.

[0139] The iterative convergence process adopts an adaptive weight mechanism to dynamically adjust the weight coefficients according to the importance of each optimization objective. When the improvement degree of the optimization objective is lower than the preset threshold, the corresponding weight coefficient is increased. The iterative optimization parameters are continuously updated during the iterative process until the convergence condition is met.

[0140] In the global search process stage, a global search operator is adopted to explore within the entire solution space. The search process comprehensively considers the historical optimal solution and the current population information, and selects high-quality solutions through a competition mechanism to form a global energy control vector. The global search ensures the wide-area search ability of the algorithm.

[0141] The weight fusion process performs a weighted combination of the global energy control vector and the energy management strategy group. The weight coefficients are determined based on the performance indicators of each strategy and are subjected to normalization processing. The fused result is the initial power consumption control scheme, which comprehensively considers the results of local optimization and global search.

[0142] In this embodiment, by adopting a power consumption planning method based on the HHO algorithm, precise control of the energy management of micro-energy harvesting electronic tags is achieved. This method uses a random initialization strategy to ensure the diversity of the initial population, extracts energy distribution characteristics through a Gaussian kernel function, and improves the representation accuracy of the system for the energy distribution state. Combining a double-layer constraint mechanism of soft enclosure and hard enclosure enhances the local search ability while ensuring the effectiveness of the parameters. Introducing Levy flight jump processing and an adaptive weight mechanism effectively avoids the algorithm falling into local optima and improves the global optimization effect. Through the weight fusion of the global energy control vector and the energy management strategy group, an organic combination of local optimization and global search is achieved, significantly improving the energy utilization efficiency. This method realizes the dynamic optimization of energy management while ensuring the normal operation of micro-energy harvesting electronic tags, providing effective technical support for improving the overall performance of the system.

[0143] In one embodiment, Levy flight jump processing is performed on the energy utilization trend according to the hard enclosure energy optimization parameters to obtain a jump optimization strategy set, including:

[0144] The design method of micro-energy harvesting electronic tags optimizes the energy utilization trend through Levy flight jumps. This design method conducts processing analysis based on the hard enclosure energy optimization parameters and quantitatively characterizes the energy distribution characteristics through mathematical modeling. During the calculation of the energy distribution probability vector, the probability density function is used to normalize the hard enclosure energy optimization parameters to obtain a probability vector representing the energy distribution law.

[0145] The Lévy step size parameter set is obtained through numerical operations on the energy distribution probability vector. Specifically, based on the Lévy distribution function, an integral transformation is performed on the energy distribution probability vector. The step size threshold range is set between 0.1 and 2.0, and through iterative calculations, a parameter set describing the jump amplitude is obtained. The Lévy random distribution matrix is generated by a random number generator based on the Lévy step size parameter set. The matrix dimension is n×m, where n represents the number of jumps and m represents the parameter dimension.

[0146] The acquisition of the jump search direction set is achieved through vector operations on the Lévy random distribution matrix. During this operation, the inner product of the Lévy random distribution matrix and the unit direction vector is calculated to generate a vector set representing the search direction. The update mechanism of the initial jump position vector is to linearly combine the jump search direction set with the hard-bounding energy optimization parameter to determine the specific landing position of each jump.

[0147] In the boundary constraint processing section, physical constraint conditions are imposed on the initial jump position vector. The constraint conditions include energy collection range limitations, device power thresholds, etc. By modifying the position vector, it is ensured that the jump range is within the effective area. After the effective jump range set undergoes energy evaluation processing, a jump energy state matrix is formed, which contains the energy collection efficiency indicators corresponding to each jump position.

[0148] The final jump optimization strategy set is obtained by selecting the optimal jump path through the greedy algorithm based on the jump energy state matrix. The optimization objective function is set to maximize the energy collection efficiency while meeting the system stability requirements. The strategy set contains complete jump sequence information, providing a decision-making basis for the energy optimization of micro-energy collection electronic tags.

[0149] During the execution process, a closed-loop optimization process is formed, and each processing link is interconnected to jointly ensure the efficiency and reliability of the micro-energy collection process. The selection and update of the optimization parameters follow the adaptive principle.

[0150] Among them, the calculation formula for step size calculation processing is:

[0151] ;

[0152] L is the Lévy step size parameter set, and are random numbers drawn from the normal distribution, is a parameter used to control the distribution shape, usually taking a value of 1.5, is the variance factor.

[0153] In this embodiment, by adopting the Levy flight jump optimization method, the design method of the micro-energy harvesting electronic tag has achieved a significant improvement in energy utilization efficiency. Based on the probability density function, the characteristics of the hard enclosure energy optimization parameters are analyzed, realizing the accurate quantitative characterization of the energy distribution law and providing a reliable data basis for subsequent optimization. Combining the generation mechanism of the Levy step size parameter group and the random distribution matrix, the system has an adaptive search ability, effectively avoiding the dilemma of local optimal solutions. Through the cooperation of the boundary constraint processing and the energy evaluation mechanism, it is ensured that each jump is within the physically feasible range, while ensuring the continuous improvement of the energy harvesting efficiency. The finally formed optimization strategy set comprehensively considers the energy efficiency and system stability, improving the energy harvesting efficiency while ensuring the reliability of the system operation.

[0154] In one embodiment, based on the system architecture information, the tag power consumption data is used to identify module tasks, and task planning is carried out in combination with the energy utilization trend to obtain the corresponding task execution strategies, including:

[0155] In the system architecture analysis step, the system architecture information of the electronic tag is hierarchically decomposed to construct a system function module tree. This function module tree includes three main levels: the sensing layer, the processing layer, and the communication layer. The sensing layer includes data acquisition modules such as temperature sensing and humidity sensing; the processing layer includes function modules such as data processing and storage management; the communication layer includes communication modules such as data sending and protocol processing. The hierarchical decomposition process is based on the functional dependency relationship between modules, refining the system functions to the specific atomic operation level.

[0156] In the power consumption data processing step, based on the established function module tree, the power consumption data during the operation of the tag is segmented in the time domain. The time domain segmentation uses the sliding time window method, with a window size of 100 ms and a step size of 10 ms. By segmenting, the energy consumption data of each function module in different time periods is obtained, including parameters such as current, voltage, and power. The fluctuation characteristics of the segmented energy consumption data are extracted, including characteristic parameters such as energy consumption peak values, trough values, and periodicity, to generate the corresponding task activity sequence. The task activity sequence records the changes in the working states of each module.

[0157] In the task planning step, based on the task activity sequence, the correlation degree between modules is calculated. The correlation metric uses the Pearson correlation coefficient, and when the absolute value of the coefficient is greater than 0.8, it is determined that there is a strong correlation between modules. Through correlation analysis, a module task dependency graph is constructed, where the nodes in the graph represent function modules and the edges represent the dependency relationships between modules. The task dependency graph is analyzed for scheduling, and an initial scheduling plan is generated using the priority scheduling algorithm. In this algorithm, the communication task has the highest priority, followed by data processing, and the data acquisition has the lowest priority.

[0158] In the energy allocation stage, energy quotas are allocated to each module according to the initial scheduling plan. The energy allocation adopts a proportional allocation method based on task importance, and the importance is comprehensively determined by factors such as task priority, execution duration, and energy consumption requirements. The generated module energy configuration plan is optimized and adjusted based on the energy utilization trend curve. When it is predicted that the energy supply is sufficient, the energy quota of non-critical tasks is appropriately increased; when it is predicted that the energy is insufficient, the quota of non-critical tasks is reduced to ensure the normal execution of critical tasks.

[0159] The finally formed task execution strategy includes specific parameters such as task scheduling order, energy quota of each task, and execution time limit. This strategy ensures the continuous and stable operation of the micro-energy harvesting electronic tag under energy-constrained conditions. Through the comprehensive analysis of the system architecture, power consumption characteristics, and energy trend, the efficient utilization of the tag energy is realized.

[0160] In this embodiment, through the functional module tree decomposition method based on system architecture information, the refined hierarchical management of the electronic tag system is realized, making the responsibility boundaries of each functional module clear and facilitating targeted energy consumption optimization. The sliding time window is used for time-domain segmentation processing of power consumption data, and combined with the fluctuation feature extraction technology, the energy consumption change rules of each module are accurately captured, providing reliable data support for subsequent task planning. The module correlation quantification method based on the Pearson correlation coefficient accurately identifies the dependency relationship between modules, avoiding blind task scheduling. Energy is allocated through the comprehensive evaluation of task importance to ensure the normal operation of critical tasks. The dynamic optimization mechanism combined with the energy utilization trend enables the system to adaptively adjust the task execution strategy according to the energy supply situation, improving the energy utilization efficiency of the system and extending the working time of the electronic tag.

[0161] In one embodiment, the initial power consumption control plan and the task execution strategy are globally optimized to obtain the corresponding global energy optimization strategy, including:

[0162] When extracting parameters from the initial power consumption control plan and the task execution strategy, the system obtains power consumption control-related parameters through the data acquisition module, including chip working voltage, working current, sleep time, etc., and at the same time collects key indicators such as timing parameters, processing cycles, and data transmission rates during task execution. These parameters are standardized to form a parameter correlation data matrix.

[0163] In the hierarchical clustering stage, the similarity between parameters is calculated based on the Euclidean distance, and the Ward minimum variance method is used to construct a clustering tree. The similarity threshold is set to 0.85 during the clustering process, and when the similarity between parameters is higher than this threshold, they are classified into the same category. The clustering result forms a multi-level parameter optimization structure, and each level contains parameter groups with relatively high mutual correlation.

[0164] In the hierarchical mapping process, an adaptive mapping algorithm is adopted to map the parameter optimization hierarchical data to three levels: the device level, the task level, and the system level. The device level contains hardware-related parameters; the task level contains task scheduling and resource allocation parameters; the system level contains global control parameters. Different optimization weights are set for each level to form a multi-level optimization parameter group.

[0165] Particle swarm optimization uses an improved PSO algorithm. The population size is set to 50, the maximum number of iterations is 100, and the inertia weight linearly decreases between 0.4 and 0.9. The optimization objective function combines two indicators of energy efficiency and task completion time, and the optimal parameter set is obtained through fitness evaluation.

[0166] During the dynamic merging process, the system integrates the power consumption control scheme and the task execution strategy according to the optimization parameter set. The integration rules are based on task priorities and energy states, and the optimal utilization of energy is achieved on the premise of ensuring the execution of critical tasks. The merged control scheme includes functional modules such as dynamic voltage regulation and task dynamic scheduling.

[0167] The energy balance calculation uses the sliding time window method. The window length is set to 1 hour and the overlap rate is 50%. During the calculation process, the energy collection rate, consumption rate, and storage capacity are monitored to generate an energy balance index. This index reflects the balance of the system's energy utilization, and its value range is from 0 to 1. The closer it is to 1, the more balanced the energy distribution is.

[0168] Iterative optimization uses the gradient descent method. The learning rate is set to 0.01 and the convergence threshold is 0.001. The system continuously adjusts the control parameters according to the energy balance index until the convergence condition is reached or the maximum number of iterations is completed. The finally formed global energy optimization strategy realizes the optimal configuration of the system's energy utilization.

[0169] In this embodiment, by performing an overall structural analysis and regional analysis on building data and combining building use information, the heat load requirements of different regions can be more accurately evaluated, thereby improving the accuracy of heat load estimation and providing a more reliable basis for system design and operation. By mapping system data into building structure data and performing correlation analysis based on building area data, refined management of the heat load requirements of different regions is achieved, which helps to achieve on-demand heating or cooling and avoid energy waste. Based on temperature data, an overall demand analysis of system data is carried out, which can ensure that the system can operate efficiently under different seasons and ambient temperatures, reduce unnecessary energy consumption, and improve the overall operating efficiency of the system. By comprehensively analyzing building area data and heat load data, a more reasonable regional scheduling strategy is formulated, and the optimal operation of the entire system is achieved through comprehensive adjustment strategies, thereby effectively utilizing energy, reducing energy consumption and environmental pollution. By considering the impact of building use on heat load, the heat load quantification strategy can be flexibly adjusted according to the spatial characteristics and demand changes of different uses, making the system more adaptable to diverse demand scenarios.

[0170] Referring to Figure 2 As shown, the present invention also provides a design system for a micro-energy harvesting electronic tag, which is applied to the design method of the micro-energy harvesting electronic tag according to any one of the above, and includes:

[0171] A collection module, which is used to obtain the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, and perform matching analysis to obtain the corresponding tag energy reception scheme;

[0172] An analysis module, which is used to obtain the system architecture information of the micro-energy harvesting electronic tag, and perform energy management analysis with the tag energy reception scheme to obtain the corresponding energy management strategy group;

[0173] An association module, which is used to obtain the tag power consumption data of the micro-energy harvesting electronic tag, and perform power consumption prediction on the energy management strategy group to obtain the corresponding energy utilization trend;

[0174] A processing module, which is used to perform power consumption planning on the energy management strategy group and the energy utilization trend according to the preset HHO algorithm to obtain the corresponding initial power consumption control scheme;

[0175] A control module, which is used to identify module tasks for the tag power consumption data according to the system architecture information, and perform task planning with the energy utilization trend to obtain the corresponding task execution strategy;

[0176] An execution module, which is used to globally optimize the initial power consumption control scheme and the task execution strategy to obtain the corresponding global energy optimization strategy.

[0177] A design system for a micro-energy harvesting electronic tag provided by the present invention can more accurately evaluate the energy acquisition ability of the system by matching and analyzing the energy reception parameters and environmental condition data of the micro-energy harvesting electronic tag, thereby providing a reliable energy basis for the efficient operation of the tag. By combining the energy management strategy with the system architecture information for analysis, fine-grained management of the system energy distribution is achieved, which helps the tag to achieve reasonable energy allocation among different functional modules and avoid energy waste. Based on the power consumption data of the tag, power consumption prediction is performed on the energy management strategy group to ensure that the system can operate continuously and stably under different environments and working conditions, reducing the blindness of energy utilization. Through the HHO algorithm, power consumption planning is carried out for the energy management strategy and energy utilization trend, and a more reasonable energy control scheme is formulated, improving the overall operation efficiency of the system. By identifying the module tasks of the system architecture and combining with the energy utilization trend for task planning, the task execution strategy can be flexibly adjusted according to the characteristics and energy requirements of different tasks, making the system more adaptable to complex and changeable application scenarios. Finally, through global optimization, the comprehensive improvement of the system energy utilization efficiency is achieved, effectively solving the problem of low energy management efficiency in the prior art.

[0178] It should be noted that those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0179] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A design method for a micro-energy collection electronic tag, characterized in that: include: Obtain the energy receiving parameters and environmental condition data of the micro-energy collection electronic tag, and perform matching analysis to obtain the corresponding tag energy receiving solution; Obtaining system architecture information of the micro-energy collection electronic tag, and performing energy management analysis with the tag energy receiving scheme to obtain a corresponding energy management strategy group; Acquire the tag power consumption data of the micro-energy collection electronic tag, and perform power consumption prediction on the energy management strategy group to obtain the corresponding energy utilization trend; Performing power consumption planning on the energy management strategy group and the energy utilization trend according to a preset HHO algorithm to obtain a corresponding initial power consumption control scheme; Performing module task identification on the tag power consumption data according to the system architecture information, and performing task planning with the energy utilization trend to obtain a corresponding task execution strategy; The initial power consumption control scheme and the task execution strategy are globally optimized to obtain a corresponding global energy optimization strategy.

2. The design method of the micro-energy collection electronic tag according to claim 1 is characterized in that: The method of obtaining the energy receiving parameters and environmental condition data of the micro-energy collection electronic tag and performing matching analysis to obtain the corresponding tag energy receiving scheme includes: Classify and process the energy receiving parameters of the micro-energy collection electronic tag to obtain radio frequency energy receiving parameters, light energy receiving parameters, vibration energy receiving parameters and temperature difference energy receiving parameters; Extracting features from the environmental condition data to obtain environmental temperature data, environmental lighting data, environmental vibration data, and environmental radio frequency data; Performing spectrum analysis on the ambient radio frequency data according to the radio frequency energy receiving parameters to obtain a radio frequency energy collection coefficient; Calculate the light intensity distribution of the ambient light data according to the light energy receiving parameters to obtain a light energy conversion coefficient; Performing amplitude characteristic analysis on the environmental vibration data according to the vibration energy receiving parameter to obtain a vibration energy collection coefficient; Calculate the temperature difference gradient of the ambient temperature data according to the temperature difference energy receiving parameter to obtain a temperature difference energy conversion coefficient; Performing weighted fusion processing on the radio frequency energy collection coefficient, light energy conversion coefficient, vibration energy collection coefficient and temperature difference energy conversion coefficient to obtain a comprehensive energy conversion evaluation value; The energy receiving parameter configuration calculation is performed according to the comprehensive evaluation value of energy conversion to obtain the tag energy receiving scheme.

3. The design method of the micro-energy collection electronic tag according to claim 1 is characterized in that: The step of obtaining the system architecture information of the micro-energy collection electronic tag and performing energy management analysis with the tag energy receiving scheme to obtain a corresponding energy management strategy group includes: Performing hierarchical analysis on the system architecture information to obtain a corresponding system function module tree; Quantitatively calculate the energy demand according to the system function module tree to obtain the corresponding module energy demand matrix; Performing energy curve analysis on the tag energy receiving scheme to obtain a corresponding energy input characteristic curve; Performing periodic analysis on the energy fluctuation law according to the energy input characteristic curve to obtain a corresponding energy fluctuation period set; Performing space-time mapping on the module energy demand matrix and the energy fluctuation period set to obtain a corresponding energy allocation mapping table; Prioritizing system energy allocation according to the energy allocation mapping table to obtain a corresponding energy allocation priority sequence; The energy allocation priority sequence is subjected to multi-objective constraint optimization to obtain a corresponding energy management strategy group.

4. The design method of the micro-energy collection electronic tag according to claim 1 is characterized in that: The acquiring of the tag power consumption data of the micro-energy collection electronic tag and performing power consumption prediction on the energy management strategy group to obtain a corresponding energy utilization trend includes: Splitting the tag power consumption data by time dimension to obtain real-time power consumption data and historical power consumption data; Decomposing the historical power consumption data in multiple dimensions to obtain a historical energy feature matrix; Performing Hilbert transform processing on the real-time power consumption data to obtain an instantaneous energy distribution sequence; Performing graph correlation mapping on the historical energy feature matrix and the energy management strategy group to obtain a corresponding historical correlation graph; Performing graph correlation mapping on the instantaneous energy distribution sequence and the energy management strategy group to obtain a corresponding real-time correlation graph; Performing graph feature correlation on the historical correlation graph and the real-time correlation graph to obtain a corresponding strategy energy correlation graph; Calculate the energy entropy value according to the strategy energy association map to obtain an energy state transfer matrix; The energy state transfer matrix is ​​subjected to Markov chain analysis to obtain the energy utilization trend.

5. The design method of micro-energy collection electronic tag according to claim 1, characterized in that: The performing power consumption planning on the energy management strategy group and the energy utilization trend according to the preset HHO algorithm to obtain a corresponding initial power consumption control scheme includes: Initializing the energy parameters in the energy management strategy group to obtain an initialized strategy parameter group; Performing energy distribution analysis on the energy utilization trend according to the initialization strategy parameter group to obtain an energy distribution feature vector; Performing an exploration phase search process on the energy distribution feature vector to obtain a local energy optimization parameter; Performing soft encirclement processing on the energy management strategy group according to the local energy optimization parameter to obtain a soft encirclement energy control vector; Performing hard encirclement stage processing on the soft encirclement energy control vector to obtain hard encirclement energy optimization parameters; Performing Levy flight jump processing on the energy utilization trend according to the hard enclosure energy optimization parameter to obtain a jump optimization strategy set; Performing iterative convergence processing on the jump optimization strategy set to obtain iterative energy optimization parameters; Performing a global search process on the energy utilization trend according to the iterative energy optimization parameter to obtain a global energy control vector; The global energy control vector and the energy management strategy group are weighted and fused to obtain the initial power consumption control solution.

6. The design method of the micro-energy collection electronic tag according to claim 5 is characterized in that: The energy utilization trend is subjected to Levy flight jump processing according to the hard encirclement energy optimization parameter to obtain a jump optimization strategy set, including: Performing distribution characteristic analysis on the hard enclosure energy optimization parameters to obtain an energy distribution probability vector; Performing step length calculation processing on the energy utilization trend according to the energy distribution probability vector to obtain a Levy step length parameter group; Performing random number generation processing on the Levy step size parameter group to obtain a Levy random distribution matrix; Calculating the direction vector of the energy utilization trend according to the Levy random distribution matrix to obtain a jump search direction set; Performing position updating processing on the jump search direction set and the hard encirclement energy optimization parameter to obtain an initial jump position vector; Perform boundary constraint processing on the energy utilization trend according to the initial jump position vector to obtain a valid jump range set; Performing energy evaluation processing on the effective jump range set to obtain a jump energy state matrix; Performing strategy optimization processing on the energy utilization trend according to the jump energy state matrix to obtain the jump optimization strategy set; The calculation formula for step length calculation is: ； L is the Levy step length parameter group, and is a random number drawn from a normal distribution, is a parameter used to control the shape of the distribution, with a value of 1.

5. is the variance factor.

7. The design method of micro-energy collection electronic tag according to claim 1, characterized in that: The module task identification is performed on the tag power consumption data according to the system architecture information, and task planning is performed with the energy utilization trend to obtain a corresponding task execution strategy, including: Performing hierarchical decomposition processing on the system architecture information to obtain a corresponding system function module tree; According to the system function module tree, the tag power consumption data is segmented in the module time domain to obtain energy consumption distribution data of each function module; Extracting task fluctuation characteristics from the energy consumption distribution data to obtain a corresponding task activity sequence; Quantifying module associations according to the task activity sequence to obtain a module task dependency graph; Performing an initial scheduling analysis on the module task dependency graph and the energy utilization trend to obtain an initial task scheduling solution; Performing module task energy allocation according to the initial task scheduling plan to obtain a module energy configuration plan; The module energy configuration scheme is task optimized and adjusted according to the energy utilization trend to obtain the task execution strategy.

8. The design method of micro-energy collection electronic tag according to claim 1, characterized in that: The globally optimizing the initial power consumption control scheme and the task execution strategy to obtain a corresponding global energy optimization strategy includes: Extracting parameters of the initial power consumption control scheme and the task execution strategy to obtain corresponding parameter correlation data; Performing hierarchical clustering on the parameter correlation data to obtain corresponding parameter optimization hierarchical data; Performing hierarchical mapping on the parameter optimization hierarchical data to obtain a corresponding multi-level optimization parameter group; Performing particle swarm optimization on the multi-level optimization parameter group to obtain a corresponding optimization parameter set; Dynamically merge the initial power consumption control scheme and the task execution strategy according to the optimization parameter set to obtain an initial dynamic merge control scheme; Performing energy balance calculation on the initial dynamic combined control scheme to obtain a corresponding energy balance index; The initial dynamic merging control scheme is iteratively optimized according to the energy balance index to obtain the global energy optimization strategy.

9. A design system for micro-energy collection electronic tags, characterized in that: The design method of the micro-energy collection electronic tag applied to any one of claims 1 to 8 above comprises: The acquisition module is used to obtain the energy receiving parameters and environmental condition data of the micro-energy collection electronic tag, and perform matching analysis to obtain the corresponding tag energy receiving solution; An analysis module, the analysis module is used to obtain the system architecture information of the micro-energy collection electronic tag, and perform energy management analysis with the tag energy receiving scheme to obtain a corresponding energy management strategy group; An association module, the association module is used to obtain the tag power consumption data of the micro-energy collection electronic tag, and perform power consumption prediction on the energy management strategy group to obtain a corresponding energy utilization trend; A processing module, the processing module is used to perform power consumption planning on the energy management strategy group and the energy utilization trend according to a preset HHO algorithm to obtain a corresponding initial power consumption control solution; A control module, the control module is used to perform module task identification on the tag power consumption data according to the system architecture information, and perform task planning with the energy utilization trend to obtain a corresponding task execution strategy; An execution module is used to globally optimize the initial power consumption control scheme and the task execution strategy to obtain a corresponding global energy optimization strategy.

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