Design method and system of micro-energy acquisition electronic tag

Abstract

The invention relates to a design method and system for a micro-energy acquisition electronic tag, and the method comprises the steps: obtaining an energy receiving parameter and environment condition data of the micro-energy acquisition electronic tag, and carrying out the matching analysis, and obtaining a corresponding tag energy receiving scheme; acquiring system architecture information of the micro-energy acquisition electronic tag, and performing energy management analysis on the system architecture information and the tag energy receiving scheme to obtain a corresponding energy management strategy group; acquiring tag power consumption data of the micro-energy acquisition electronic tag, and performing power consumption prediction on the energy management strategy group to obtain a corresponding energy utilization trend; performing module task identification on the tag power consumption data according to the system architecture information, and performing task planning according to the energy utilization trend to obtain a corresponding task execution strategy; and performing global optimization on the initial power consumption control scheme and the task execution strategy to obtain a corresponding global energy optimization strategy. According to the invention, fine management of system energy distribution can be realized.

Description

Technical Field

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

[0002] As a new type of energy-self-sufficient electronic device, micro-energy harvesting electronic tags play an important role in the field of 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 micro-energy harvesting electronic tags and achieve continuous and stable operation of the system has become one of the key research directions. Existing micro-energy harvesting electronic tag design methods 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 tag performance. This fragmented design approach often leads to inefficient energy management, fails to fully tap the potential of the micro-energy harvesting system, and restricts its promotion and development in practical applications. Summary of the invention

[0003] The main purpose of the present invention is to provide a design method and system for a micro-energy collection electronic tag, which can manage the energy distribution of the system in a refined manner and help the tag to achieve reasonable energy allocation between different functional modules.

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

[0005] Furthermore, the energy receiving parameters and environmental condition data of the micro-energy collection electronic tag are obtained, and matching analysis is performed to obtain a corresponding tag energy receiving scheme, including: 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.

[0006] Furthermore, the system architecture information of the micro-energy collection electronic tag is obtained, and energy management analysis is performed with the tag energy receiving scheme to obtain a corresponding energy management strategy group, including: 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.

[0007] Furthermore, the acquiring of the tag power consumption data of the micro-energy collection electronic tag and the power consumption prediction of the energy management strategy group to obtain the corresponding energy utilization trend include: 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.

[0008] Furthermore, the power consumption planning is performed 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: 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.

[0009] Furthermore, the energy utilization trend is subjected to Levy flight jump processing according to the hard enclosure 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, It is a parameter used to control the shape of the distribution, usually with a value of 1.5. is the variance factor.

[0010] Furthermore, 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.

[0011] Furthermore, the initial power consumption control scheme and the task execution strategy are globally optimized to obtain a corresponding global energy optimization strategy, including: 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.

[0012] The present invention further provides a design system for a micro-energy collection electronic tag, which is applied to any one of the above-mentioned design methods for a micro-energy collection electronic tag, and 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.

[0013] The present invention provides a method and system for designing a micro-energy collection electronic tag, which has the following beneficial effects: By matching and analyzing the energy receiving parameters and environmental condition data of micro-energy collection electronic tags, the energy acquisition capability of the system can be evaluated more accurately, thereby providing a reliable energy basis for the efficient operation of the tags. By combining the energy management strategy with the system architecture information for analysis, the refined management of the system energy allocation is realized, which helps the tags to achieve reasonable energy allocation between different functional modules and avoid energy waste. The power consumption of the energy management strategy group is predicted based on the tag power consumption data to ensure that the system can continue to operate stably in different environments and working conditions, reducing the blindness of energy utilization. The power consumption of the energy management strategy and energy utilization trend is planned through the HHO algorithm, and a more reasonable energy control plan is formulated to improve the overall operation efficiency of the system. By identifying the module tasks of the system architecture and planning the tasks in combination with the energy utilization trend, 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, the comprehensive improvement of the system energy utilization efficiency is achieved through global optimization, which effectively solves the problem of low energy management efficiency in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a design method of a micro-energy collection electronic tag provided by the present invention; Figure 2 This is a structural diagram of a design system for a micro-energy collection electronic tag provided by the present invention.

[0015] 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

[0016] 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.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides a design method for a micro-energy collection electronic tag, which is characterized by comprising: Step S1: Obtain energy receiving parameters and environmental condition data of the micro-energy collection electronic tag, and perform matching analysis to obtain a corresponding tag energy receiving solution; Step S2: Obtain 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; Step S3: 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 the corresponding energy utilization trend; Step S4: Perform 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 plan; Step S5: Perform module task identification on the tag power consumption data according to the system architecture information, and perform task planning based on the energy utilization trend to obtain the corresponding task execution strategy; Step S6: globally optimize the initial power consumption control scheme and the task execution strategy to obtain a corresponding global energy optimization strategy.

[0019] Based on the above steps, the detailed steps are as follows: Step S1: The process of obtaining the energy receiving parameters and environmental condition data of the micro-energy collection 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 should be measured; for light energy, the light intensity at different time periods should be measured; for vibration energy, the vibration frequency and amplitude should be measured. At the same time, it is necessary to record environmental parameters such as ambient temperature and humidity that may affect the energy collection efficiency. By establishing an energy receiving parameter database, these data are classified and stored. According to the pre-established energy characteristic model, the availability and reliability of various energy sources are analyzed, and their energy supply capabilities under different environmental conditions are evaluated. Through comparative analysis, the most suitable energy receiving scheme is selected, which may be a single energy source or a combination of multiple energy sources.

[0020] Step S2: The process of obtaining the system architecture information of the micro-energy collection electronic tag and performing energy management analysis begins with a detailed review of the hardware structure and functional modules of the tag. The system architecture information includes core hardware information such as processor type, memory capacity, communication module specifications, sensor type, and the connection relationship and data flow between modules. Based on this information, a complete energy consumption model is established to analyze the energy requirements of each module under different working conditions. Combined with the energy receiving scheme, the balance between energy supply and demand is calculated to determine the energy allocation strategy in different scenarios. This process will generate multiple possible energy management strategies, each of which includes specific parameters such as the switching timing of the module working mode, the triggering conditions of the sleep wake-up mechanism, and the charge and discharge management of the energy storage unit. These strategies are preliminarily evaluated to screen out the strategy combination that is most likely to meet the requirements for stable system operation.

[0021] Step S3: The process of obtaining tag power consumption data and performing power consumption prediction requires the establishment of a detailed power consumption test plan first. The actual power consumption of each functional module is measured under different working modes, including static power consumption and dynamic power consumption. These test data will be 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 will take into account influencing factors such as load changes and environmental changes, and use statistical methods and machine learning algorithms to improve the accuracy of the prediction. By analyzing these prediction results, the system can identify strategy combinations with high energy utilization efficiency and issue early warnings for possible energy shortages. The output of this step is a detailed energy utilization trend report, which includes energy consumption prediction curves on different time scales, providing an important basis for subsequent power consumption optimization.

[0022] Step S4: The process of power consumption planning according to the preset HHO (Harris Hawks Optimization) algorithm first needs to convert the energy management strategy group and energy utilization trend data into 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. During the iterative optimization process, the algorithm searches for possible solution spaces through the exploration phase and conducts in-depth searches in the target area through the exploitation phase. Each iteration updates the position 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 step, including adjusting parameters such as step size and update speed. Through multiple iterative optimizations, an initial power consumption control scheme that balances energy efficiency and system performance is obtained.

[0023] 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. Perform fine-grained analysis on 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 the execution time, energy requirements, priority and other attributes of the task. Combined with 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. In this process, consider the dependencies between tasks to ensure that key tasks can be executed first when there is sufficient energy. At the same time, the system will also establish a dynamic task adjustment mechanism to adjust the task execution plan according to the real-time energy status to ensure that the system can maintain the normal operation of core functions under energy constraints.

[0024] Step S6: The process of global energy optimization will comprehensively consider the output results of the previous 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 functional optimization at the application level. Use a multi-objective optimization algorithm to consider multiple optimization goals such as energy efficiency, system performance, and task completion quality. During the optimization process, dynamically adjust the weights of each goal to find the best balance point according to the needs of the actual application scenario. The optimization results will form a complete global energy optimization strategy, including detailed power consumption control parameters, task scheduling rules, energy allocation plans, etc., to ensure that the system can achieve optimal performance under energy constraints.

[0025] The present invention provides a design method for a micro-energy collection electronic tag. By matching and analyzing the energy receiving parameters and environmental condition data of the micro-energy collection electronic tag, the energy acquisition capability 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, the refined management of the system energy distribution is realized, which helps the tag to achieve reasonable energy allocation between different functional modules and avoid energy waste. The energy management strategy group is predicted based on the tag power consumption data to ensure that the system can continue to operate stably in different environments and working conditions, reducing the blindness of energy utilization. The energy management strategy and energy utilization trend are planned for power consumption through the HHO algorithm, and a more reasonable energy control scheme is formulated to improve the overall operation efficiency of the system. By identifying the module tasks of the system architecture and planning the tasks in combination with the energy utilization trend, the task execution strategy can be flexibly adjusted according to the characteristics and energy requirements of different tasks, so that the system is more adaptable to complex and changeable application scenarios. Finally, the comprehensive improvement of the system energy utilization efficiency is achieved through global optimization, which effectively solves the problem of low energy management efficiency in the prior art.

[0026] In one embodiment, energy receiving parameters and environmental condition data of a micro-energy collection electronic tag are obtained, and matching analysis is performed to obtain a corresponding tag energy receiving solution, including: For the classification processing of energy receiving parameters, the RF energy receiving parameters include characteristic quantities such as antenna gain, impedance matching coefficient, and rectification efficiency; the light energy receiving parameters include indicators such as photoelectric conversion efficiency, photosensitivity area, and light intensity response characteristics; the vibration energy receiving parameters include parameters such as resonant frequency, mechanical quality factor, and piezoelectric coefficient; the temperature difference energy receiving parameters include values ​​such as Seebeck coefficient, thermocouple type, and temperature difference response sensitivity.

[0027] During the feature extraction process of environmental condition data, the ambient temperature data collects spatial temperature distribution information through a temperature sensor array with a sampling frequency of 1 Hz; the ambient light data obtains the light intensity values ​​of different areas through a light sensor with a sampling interval of 10 seconds; the ambient vibration data uses an acceleration sensor to record the vibration acceleration time domain waveform with a sampling rate of 1 kHz; the ambient radio frequency data uses a spectrum analyzer to scan the electromagnetic wave intensity in the 300 MHz-3 GHz frequency band.

[0028] In the calculation of the RF energy collection coefficient, the ambient RF data is subjected to a 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 the weighted average is taken as the RF energy collection coefficient. The weight coefficient is determined according to the signal strength of each frequency point, and the greater the strength, the higher the weight.

[0029] The determination of the light energy conversion coefficient is based on the light intensity distribution characteristics of the ambient light data. The nonlinear relationship curve between the photoelectric conversion efficiency and the incident light intensity is fitted into a piecewise function, and the light energy conversion coefficient is calculated using the corresponding conversion function in different light intensity ranges. When the light intensity distribution shows an obvious diurnal variation pattern, the conversion function parameters are dynamically adjusted over time.

[0030] The acquisition of vibration energy acquisition coefficient is achieved by performing time-frequency analysis on the ambient vibration data to extract 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 acquisition coefficient takes the maximum value; as the frequency difference increases, the acquisition coefficient decays exponentially. The square relationship between the amplitude characteristics and the mechanical strain energy is used to correct the acquisition coefficient.

[0031] The temperature difference energy conversion coefficient is determined by the spatial gradient of the ambient 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 measuring points is divided by the spatial distance to obtain the local gradient value, and the global gradient field is reconstructed through the interpolation algorithm. The conversion coefficient is approximately linearly related to the temperature difference gradient.

[0032] The calculation of the comprehensive evaluation value of energy conversion adopts a weighted fusion method. The weight coefficients of the four energy collection coefficients are determined by the stability and availability of each type of energy. The weights of RF energy and light energy are relatively high, followed by vibration energy, and temperature difference energy is the lowest. The normalization of the evaluation value ensures that its value range is between 0 and 1.

[0033] The configuration of the tag energy receiving scheme is optimized and calculated based on the comprehensive evaluation value of energy conversion. When the evaluation value is greater than 0.8, the single energy scheme is preferred; when the evaluation value is between 0.5 and 0.8, the dual energy complementary scheme is adopted; when the evaluation value is less than 0.5, the multi-energy joint energy supply scheme is adopted. The scheme parameters are optimally matched through an iterative optimization algorithm.

[0034] This embodiment realizes efficient reception and conversion of various energy forms by finely classifying and processing the energy receiving parameters of micro-energy collection electronic tags, combined with feature extraction and analysis of environmental condition data. A complete energy conversion evaluation system is established based on matching and analyzing the receiving parameters of radio frequency energy, light energy, vibration energy and temperature difference energy with environmental data, so that the tag can adaptively adjust the energy receiving scheme according to actual environmental conditions. The weighted fusion method is used to comprehensively evaluate different energy types, overcoming the limitation of unstable energy supply of a single energy source. By establishing a quantitative relationship between the energy conversion coefficient and environmental parameters, the energy collection efficiency is improved. The synergistic complementarity of multiple energy sources is achieved, the adaptability of the tag in complex environments is enhanced, and the reliability and sustainability of energy supply are guaranteed. At the same time, the dynamically configured tag energy receiving scheme improves the system response speed, reduces energy loss, and significantly improves the overall performance of the micro-energy collection electronic tag.

[0035] In one embodiment, the system architecture information of the micro-energy collection electronic tag is obtained, and energy management analysis is performed with the tag energy receiving scheme to obtain a corresponding energy management strategy group, including: When obtaining the system architecture information of the micro-energy collection electronic tag, the system architecture adopts a hierarchical structure design, which includes three main layers: hardware layer, driver layer, and application layer. The system architecture is decomposed through the hierarchical analysis method to construct a system function module tree. The function module tree includes: system main control module, radio frequency communication module, energy collection module, data storage module, etc. from the top to the bottom, and each module is interconnected through a standard interface.

[0036] In the module energy demand quantitative calculation phase, the energy consumption characteristics of each module in the functional module tree are analyzed. The specific calculation method is: measure the energy consumption data of each module under different working conditions, establish an energy consumption mathematical model, and obtain the energy demand data of each module. These data are organized into a module energy demand matrix, which contains key information such as module identification, working status, and energy consumption value.

[0037] When analyzing the energy curve of the tag energy receiving scheme, a method combining experimental measurement and theoretical calculation is used. By collecting energy input data under different working environments, an energy input characteristic curve is drawn. This curve reflects the energy acquisition characteristics in actual application scenarios, including energy amplitude changes, time distribution and other information.

[0038] The Fourier transform method is used to analyze the periodicity of energy fluctuation law, and the time domain energy fluctuation data is converted 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, reflecting the time characteristics of the system energy supply.

[0039] In the process of time-space mapping, the module energy demand matrix and the energy fluctuation cycle set are analyzed accordingly. An energy allocation mapping table is established, which contains information such as module working sequence, energy demand, energy supply time, etc. The establishment of the mapping table is based on the principle of minimum energy consumption to ensure the maximum energy utilization efficiency of the system as a whole.

[0040] In the energy allocation priority sorting, the priority of each functional module of the system is evaluated based on factors such as module importance, energy consumption characteristics, and timing requirements. The evaluation indicators include: module criticality, energy demand, response time requirements, etc. The energy allocation priority sequence is established through comprehensive evaluation.

[0041] In the multi-objective constraint optimization stage, the optimization objectives are to maximize system reliability and maximize energy utilization efficiency. Constraints include: total system energy consumption limit, module response time requirements, energy supply fluctuation range, etc. By solving the optimization model, the final energy management strategy group is obtained. This strategy group contains multiple energy management solutions for different scenarios.

[0042] This embodiment uses hierarchical structure design and hierarchical analysis method to construct the system function module tree, so that the system architecture is clear and the interfaces between the functional modules are standardized, which significantly improves the maintainability and scalability of the system. The module energy demand matrix established based on the analysis of energy consumption characteristics, combined with the measurement and analysis of the energy input characteristic curve, accurately grasps the energy demand and supply characteristics of the system, and provides reliable data support for energy management. Through the Fourier transform method for periodic analysis, the energy fluctuation law is accurately identified, and the space-time mapping relationship established on this basis realizes the precise allocation of system energy. The priority sequence established according to the multi-dimensional evaluation indicators, combined with the multi-objective constraint optimization model, not only ensures the efficient use of system energy, but also improves the overall reliability of the system. Through the systematic energy management strategy, the energy supply and demand contradiction faced by micro-energy collection electronic tags in practical applications is effectively solved, so that the system can maintain a stable and reliable operating state under limited energy conditions.

[0043] In one embodiment, the tag power consumption data of the micro-energy collection electronic tag is obtained, and the power consumption of the energy management strategy group is predicted to obtain the corresponding energy utilization trend, including: The tag power consumption data is divided into real-time power consumption data and historical power consumption data through time dimension splitting. The time dimension splitting is based on the preset time threshold T. 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 the value is generally between 1 / 4 and 1 / 2 of the tag working cycle.

[0044] The historical power consumption data is decomposed in multiple dimensions, and the singular value decomposition method is used to convert the historical power consumption data into a historical energy feature matrix. In the multi-dimensional decomposition process, the historical power consumption data is segmented according to the time window W. The data in each time window constitutes a sub-matrix. The singular value decomposition is performed on all sub-matrices to obtain the corresponding eigenvalues ​​and eigenvectors, and all eigenvectors are combined to form a historical energy feature matrix. The size of the time window is set to an integer multiple of the label working cycle.

[0045] The real-time power consumption data is processed by Hilbert transform to obtain the instantaneous energy distribution sequence. The Hilbert transform extracts the envelope characteristics of the data by phase modulation of the real-time power consumption data to form 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.

[0046] In the process of graph correlation mapping, the historical energy feature matrix is ​​correlated with each strategy in the energy management strategy group to construct a historical correlation graph; at the same time, the instantaneous energy distribution sequence is correlated with each strategy in the energy management strategy group to construct a real-time correlation graph. The correlation calculation adopts the gray correlation analysis method, and the minimum correlation threshold is set to 0.6.

[0047] The graph feature association process fuses the historical association graph and the real-time association graph, and obtains the strategy energy association graph by weighted summation, where the weight of the historical association graph is 0.7 and the weight of the real-time association graph is 0.3. The strategy energy association graph reflects the correspondence between various energy management strategies and the energy utilization status of the tag.

[0048] The energy entropy value is calculated based on the strategy energy association graph, and the energy state transfer matrix is ​​constructed. The energy entropy value is calculated using the information entropy formula, and the correlation degree is calculated as the state probability. The energy state transfer matrix describes the transfer law of the tag energy state under different energy management strategies.

[0049] Markov chain analysis is performed on the energy state transfer matrix to predict the energy utilization trend of the tag under each energy management strategy. 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 strategy.

[0050] This embodiment realizes the accurate analysis of the power consumption data of micro-energy collection electronic tags through time dimension splitting and multi-dimensional decomposition methods, and accurately grasps the energy usage characteristics of the tags. The Hilbert transform is used to process the real-time power consumption data, and the gray correlation analysis method is combined to construct the correlation map, which effectively extracts the dynamic characteristics of the tag energy usage. Through the correlation of the map features and the calculation of the energy entropy value, an energy state transfer matrix is ​​established, which accurately reflects the correspondence between the energy management strategy and the energy state of the tag. The energy utilization trend is predicted based on the Markov chain analysis method, which provides a scientific basis for the optimization of the energy management strategy. This method comprehensively analyzes the tag power consumption data, realizes the accurate evaluation of the energy management strategy, improves the energy utilization efficiency of the micro-energy collection electronic tags, prolongs the working time of the tags, and enhances the reliability and stability of the tags in practical applications.

[0051] In one embodiment, power consumption planning is performed 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, including: When initializing the energy parameters in the energy management strategy group, set the number of initialization populations to N, the maximum number of iterations to Max_iter, and the dimension to dim. Generate random numbers in the range of [0, 1] through a random number generator, assign values ​​to the energy parameters of each dimension, and form an N×dim-dimensional initialization strategy parameter matrix. This initialization process ensures the diversity of the initial population and lays the foundation for subsequent optimization.

[0052] In the energy distribution analysis phase, the system maps the initialization strategy parameter matrix to the actual energy distribution space. By calculating the energy density function corresponding to each parameter, the energy distribution feature vector is constructed. This vector contains characteristic quantities such as energy density, energy gradient, and energy concentration, 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.

[0053] The search process in the exploration phase adopts a local search strategy. In the feature space, the current optimal solution is taken as the center, and the search radius R is set to perform local area exploration. The step size is dynamically adjusted during the search process, and the step size value is inversely proportional to the current number of iterations, realizing a refined search. The local search results form local energy optimization parameters, which provide a reference for subsequent global optimization.

[0054] In the soft encirclement processing stage, the local energy optimization parameters are expanded to construct a soft encirclement area. The size of the soft encirclement area is related to the current number of iterations and gradually shrinks as the iterations proceed. The soft encirclement energy control vector is formed by calculating the fitness value of each point in the soft encirclement area. The soft encirclement mechanism enhances the local search capability of the algorithm.

[0055] The hard encirclement stage sets strict boundary constraints based on the soft encirclement. The boundary detection function ensures that all optimization parameters are within the valid range, and the parameters that exceed the range will be mapped back to the boundary position. The parameters processed by the boundary constraint form the hard encirclement energy optimization parameters, which ensures the feasibility of the optimization results.

[0056] The Levy flight jump process introduces random disturbances and breaks the local optimum. The Levy flight step size follows the α stable distribution, and α is 1.5. During the jump process, the current optimal solution information is combined to generate new candidate solutions to form a jump optimization strategy set. This mechanism improves the algorithm's ability to jump out of the local optimum.

[0057] The iterative convergence process uses an adaptive weight mechanism to dynamically adjust the weight coefficient according to the importance of each optimization target. When the improvement of the optimization target is lower than the preset threshold, the corresponding weight coefficient is increased. The iterative optimization parameters are continuously updated during the iteration process until the convergence condition is met.

[0058] The global search processing stage uses a global search operator to explore the entire solution space. The search process comprehensively considers the historical optimal solution and the current population information, selects high-quality solutions through a competition mechanism, and forms a global energy control vector. Global search ensures the algorithm's wide-area search capability.

[0059] The weighted fusion process combines the global energy control vector with the energy management strategy group. The weight coefficient is determined based on the performance indicators of each strategy and normalized. The fusion result is the initial power consumption control scheme, which comprehensively considers the results of local optimization and global search.

[0060] This embodiment achieves precise control of energy management of micro-energy collection electronic tags by adopting a power consumption planning method based on the HHO algorithm. This method uses a random initialization strategy to ensure the diversity of the initial population, and extracts energy distribution features through a Gaussian kernel function, thereby improving the accuracy of the system's characterization of the energy distribution state. Combining the double-layer constraint mechanism of soft encirclement and hard encirclement, the local search capability is enhanced while ensuring the validity of the parameters. The introduction of Levy flight jump processing and adaptive weight mechanism effectively avoids the algorithm from falling into the local optimum and improves the global optimization effect. By fusion of the weights of the global energy control vector and the energy management strategy group, the organic combination of local optimization and global search is achieved, significantly improving the energy utilization efficiency. While ensuring the normal operation of the micro-energy collection electronic tags, this method realizes the dynamic optimization of energy management, providing effective technical support for improving the overall performance of the system.

[0061] In one embodiment, 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: The design method of micro-energy collection electronic tags optimizes the energy utilization trend through Levy flight jumps. The design method is based on the hard enclosure energy optimization parameter processing analysis, and the energy distribution characteristics are quantitatively characterized by mathematical modeling. In the process of calculating the energy distribution probability vector, the probability density function is used to normalize the hard enclosure energy optimization parameters to obtain the probability vector that characterizes the energy distribution law.

[0062] The Levy step parameter group is obtained by numerically calculating the energy distribution probability vector. Specifically, the energy distribution probability vector is transformed integrally based on the Levy distribution function, and the step threshold range is set between 0.1 and 2.0. After iterative calculation, the parameter group describing the jump amplitude is obtained. The Levy random distribution matrix is ​​generated by a random number generator based on the Levy step parameter group, and the matrix dimension is n×m, where n represents the number of jumps and m represents the parameter dimension.

[0063] The jump search direction set is obtained by performing vector operations on the Levy random distribution matrix. During this operation, the inner product operation of the Levy random distribution matrix and the unit direction vector is performed 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 encirclement energy optimization parameter to determine the specific landing position of each jump.

[0064] In the boundary constraint processing phase, physical constraints are imposed on the initial jump position vector. Constraints include energy collection range limits, device power thresholds, etc. By correcting the position vector, the jump range is ensured to be within the valid area. After energy evaluation, the valid jump range set forms a jump energy state matrix, which contains the energy collection efficiency index corresponding to each jump position.

[0065] The final jump optimization strategy set is obtained by selecting the optimal jump path through a greedy algorithm based on the jump energy state matrix. The optimization objective function is set to maximize the energy harvesting efficiency while meeting the system stability requirements. The strategy set contains complete jump sequence information, providing a decision-making basis for energy optimization of micro-energy harvesting electronic tags.

[0066] A closed-loop optimization process is formed during the execution process, and each processing link is interconnected to jointly ensure the efficiency and reliability of the micro-energy collection process. The selection and update of optimization parameters follow the principle of self-adaptation.

[0067] 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, It is a parameter used to control the shape of the distribution, usually with a value of 1.5. is the variance factor.

[0068] This embodiment adopts the Levy flight jump optimization method, and the design method of the micro-energy collection electronic tag achieves a significant improvement in energy utilization efficiency. Based on the probability density function, the hard enclosure energy optimization parameters are characterized and analyzed to achieve an accurate quantitative characterization of the energy distribution law, providing a reliable data basis for subsequent optimization. Combining the Levy step parameter group and the random distribution matrix generation mechanism, the system has an adaptive search capability and effectively avoids the dilemma of local optimal solutions. Through the coordination of boundary constraint processing and energy evaluation mechanism, it is ensured that each jump is within the physically feasible range, while ensuring the continuous improvement of energy collection efficiency. The final optimization strategy set comprehensively considers energy efficiency and system stability, while improving energy collection efficiency, ensuring the reliability of system operation.

[0069] In one embodiment, module task identification is performed on the tag power consumption data according to the system architecture information, and task planning is performed according to the energy utilization trend to obtain the corresponding task execution strategy, including: In the system architecture analysis phase, the system architecture information of the electronic tag is hierarchically decomposed to construct a system function module tree. The function module tree consists of three main layers: the perception layer, the processing layer, and the communication layer. The perception layer includes data acquisition modules such as temperature sensing and humidity sensing; the processing layer includes functional modules such as data processing and storage management; and the communication layer includes communication modules such as data transmission and protocol processing. The hierarchical decomposition process is based on the functional dependencies between modules, and the system functions are refined to the specific atomic operation level.

[0070] In the power consumption data processing link, the power consumption data of the tag during operation is segmented in the time domain based on the established functional module tree. The time domain segmentation adopts the sliding time window method with a window size of 100ms and a step size of 10ms. The energy consumption data of each functional module in different time periods are obtained through segmentation, 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 value, trough value, and periodicity, and the corresponding task activity sequence is generated. The task activity sequence records the changes in the working status of each module.

[0071] In the task planning phase, the degree of association between modules is calculated based on the task activity sequence. The Pearson correlation coefficient is used as the correlation measure. When the absolute value of the coefficient is greater than 0.8, it is determined that there is a strong association between modules. The module task dependency graph is constructed through correlation analysis. The nodes in the graph represent functional modules, and the edges represent the dependencies between modules. The task dependency graph is subjected to scheduling analysis, and the initial scheduling scheme is generated using the priority scheduling algorithm. The algorithm sets the communication task priority to the highest, followed by data processing, and the data collection priority to the lowest.

[0072] In the energy allocation phase, energy quotas are allocated to each module according to the initial scheduling plan. Energy allocation adopts a proportional allocation method based on task importance, and the importance is determined by factors such as task priority, execution time, and energy consumption requirements. The generated module energy configuration plan is optimized and adjusted based on the energy utilization trend curve. When the energy supply is predicted to be sufficient, the energy quota of non-critical tasks is appropriately increased; when the energy supply is predicted to be insufficient, the quota of non-critical tasks is reduced to ensure the normal execution of critical tasks.

[0073] The final 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 micro-energy collection electronic tags under energy-constrained conditions. Through a comprehensive analysis of system architecture, power consumption characteristics, and energy trends, efficient use of tag energy is achieved.

[0074] This embodiment realizes the refined hierarchical management of the electronic tag system through the functional module tree decomposition method based on the system architecture information, so that the responsibility boundaries of each functional module are clear, which is convenient for targeted energy consumption optimization. The sliding time window is used for time domain segmentation processing of power consumption data, combined with the fluctuation feature extraction technology, to accurately capture the energy consumption change law of each module, and provide reliable data support for subsequent task planning. The module association quantification method based on the Pearson correlation coefficient accurately identifies the dependency relationship between modules and avoids blind task scheduling. Energy allocation is carried out through a comprehensive evaluation of task importance to ensure the normal operation of key tasks. Combined with the dynamic optimization mechanism of energy utilization trends, the system can adaptively adjust the task execution strategy according to the energy supply status, improve the energy utilization efficiency of the system, and extend the working time of the electronic tag.

[0075] In one embodiment, the initial power consumption control scheme and the task execution strategy are globally optimized to obtain a corresponding global energy optimization strategy, including: When extracting parameters for the initial power consumption control scheme and task execution strategy, the system obtains power consumption control related parameters through the data acquisition module, including chip operating voltage, operating current, sleep time, etc., and also collects key indicators such as timing parameters, processing cycle, data transmission rate, etc. during task execution. After these parameters are standardized, a parameter correlation data matrix is ​​formed.

[0076] In the hierarchical clustering stage, the similarity between parameters was calculated based on the Euclidean distance, and the cluster tree was constructed using the Ward minimum variance method. The clustering process set the similarity threshold to 0.85, and when the similarity between parameters was higher than this threshold, they were classified into the same category. The clustering results formed a multi-level parameter optimization structure, and each level contained parameter groups with high correlation.

[0077] The hierarchical mapping link uses an adaptive mapping algorithm to map the parameter optimization hierarchical data to three levels: device layer, task layer, and system layer. The device layer contains hardware-related parameters; the task layer contains task scheduling and resource allocation parameters; the system layer contains global control parameters. Different optimization weights are set for each level to form a multi-level optimization parameter group.

[0078] The particle swarm optimization adopts the improved PSO algorithm, the population size is set to 50, the maximum number of iterations is 100, and the inertia weight decreases linearly between 0.4 and 0.9. The optimization objective function combines the two indicators of energy efficiency and task completion time, and the optimal parameter set is obtained through fitness evaluation.

[0079] During the dynamic merging process, the system integrates the power consumption control scheme and task execution strategy based on the optimization parameter set. The integration rules are based on task priority and energy status, and optimize energy utilization while ensuring the execution of key tasks. The merged control scheme includes functional modules such as dynamic voltage regulation and dynamic task scheduling.

[0080] The energy balance calculation uses the sliding time window method, with the window length set to 1 hour and the overlap rate set to 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 system energy utilization, with a value range of 0 to 1. The closer to 1, the more balanced the energy distribution.

[0081] The iterative optimization adopts the gradient descent method, with the learning rate set to 0.01 and the convergence threshold set to 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 final global energy optimization strategy realizes the optimal configuration of system energy utilization.

[0082] This embodiment can more accurately evaluate the heat load demand of different areas by performing overall structural analysis and regional analysis on building data, combined with building use information, thereby improving the accuracy of heat load estimation and providing a more reliable basis for system design and operation. By mapping system data to building structure data and performing correlation analysis based on building area data, refined management of heat load demand in different areas is achieved, which helps to achieve heating or cooling on demand and avoid energy waste. Performing overall demand analysis on system data based on temperature data can ensure that the system can operate efficiently in 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 optimized operation of the entire system is achieved through a comprehensive adjustment strategy, thereby effectively utilizing energy and reducing energy consumption and environmental pollution. And 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, so that the system is more adaptable to diverse demand scenarios.

[0083] Reference Figure 2 As shown, the present invention also provides a design system for a micro-energy collection electronic tag, which is applied to any one of the above-mentioned design methods for a micro-energy collection electronic tag, including: 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 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; The association module is used to obtain the tag power consumption data of the micro-energy collection electronic tag, and to predict the power consumption of the energy management strategy group to obtain the corresponding energy utilization trend; A processing module, the processing module is used to plan power consumption for the energy management strategy group and the energy utilization trend according to a preset HHO algorithm, and obtain a corresponding initial power consumption control plan; The control module is used to identify module tasks based on the tag power consumption data according to the system architecture information, and to plan tasks based on the energy utilization trend to obtain the corresponding task execution strategy; The execution module is used to globally optimize the initial power consumption control scheme and the task execution strategy to obtain the corresponding global energy optimization strategy.

[0084] The present invention provides a design system for micro-energy collection electronic tags. By matching and analyzing the energy receiving parameters and environmental condition data of the micro-energy collection electronic tags, the energy acquisition capability of the system can be more accurately evaluated, thereby providing a reliable energy basis for the efficient operation of the tags. By combining the energy management strategy with the system architecture information for analysis, the refined management of the system energy distribution is realized, which helps the tags to realize reasonable energy allocation between different functional modules and avoid energy waste. The energy management strategy group is predicted based on the tag power consumption data to ensure that the system can continue to operate stably in different environments and working conditions, reducing the blindness of energy utilization. The energy management strategy and energy utilization trend are planned for power consumption through the HHO algorithm, and a more reasonable energy control scheme is formulated to improve the overall operation efficiency of the system. By identifying the module tasks of the system architecture and planning the tasks in combination with the energy utilization trend, the task execution strategy can be flexibly adjusted according to the characteristics and energy requirements of different tasks, so that the system is more adaptable to complex and changeable application scenarios. Finally, the comprehensive improvement of the system energy utilization efficiency is achieved through global optimization, which effectively solves the problem of low energy management efficiency in the prior art.

[0085] 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.

[0086] 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 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, It is a parameter used to control the shape of the distribution, usually 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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