A 5G energy storage circuit board and its preparation process

Abstract

The present invention relates to the technical field of circuit board preparation process, and in particular to a 5G energy storage type circuit board and its preparation process. The preparation process includes the following steps: according to the 5G energy storage type circuit board preparation demand data, the electromagnetic field theoretical fluctuation range of the components is analyzed to obtain the electromagnetic field theoretical fluctuation range; the electromagnetic field intensity change analysis of the electromagnetic field theoretical fluctuation range under the multi-band switching state is performed to obtain the electromagnetic field intensity change data of the frequency band switching; according to the electromagnetic field intensity change data of the frequency band switching, the preparation process is optimized to obtain the integrated circuit preparation process optimization data; the integrated circuit preparation process optimization data is subjected to the underlying mechanical operation firmware design to obtain the preparation process optimization underlying operation firmware, and the preparation process optimization underlying operation firmware is embedded into the external preparation mechanical arm. The present invention makes the circuit board preparation process more accurate by improving the circuit board preparation process technology.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit board preparation technology, and in particular to a 5G energy storage type circuit board and a preparation technology thereof. Background Art

[0002] The rapid development of 5G technology has promoted the upgrading of communication equipment, and the emergence of energy storage circuit boards has injected new vitality into this trend. An innovative 5G energy storage circuit board and its preparation process have attracted much attention. This circuit board combines efficient 5G communication technology and advanced energy storage technology, and can achieve efficient integration of data transmission and energy storage, bringing longer-lasting endurance and more reliable communication performance to mobile communication equipment. Its preparation process has been carefully designed in terms of material selection, process flow and equipment configuration, aiming to improve production efficiency and reduce costs, thereby promoting the widespread application of this technology in the market. Innovation in this context not only provides new possibilities for the application of 5G technology, but also brings more sustainable and intelligent solutions to the development of communication equipment. However, the preparation process of the traditional 5G energy storage circuit board has inaccurate analysis of the performance loss caused by electromagnetic influence in the 5G multi-band switching state and the problem of unstable output energy consumption. Summary of the invention

[0003] Based on this, it is necessary to provide a 5G energy storage circuit board and its preparation process to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above object, a preparation process of a 5G energy storage circuit board is provided, the preparation process comprising the following steps:

[0005] Step S1: Acquire the preparation demand data of 5G energy storage type circuit boards; perform theoretical operating power analysis of components according to the preparation demand data of 5G energy storage type circuit boards to obtain theoretical operating power data of components; perform theoretical electromagnetic field fluctuation range analysis of components according to the theoretical operating power data of components to obtain theoretical electromagnetic field fluctuation range;

[0006] Step S2: Perform 5G multi-band energy consumption evaluation on the 5G energy storage circuit board preparation demand data to obtain 5G multi-band energy consumption evaluation data; perform component output power consumption analysis based on the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; perform output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range based on the component output power consumption data to obtain output power consumption electromagnetic field limitation range; perform electromagnetic field intensity change analysis on the output power consumption electromagnetic field limitation range under multi-band switching state to obtain frequency band switching electromagnetic field intensity change data;

[0007] Step S3: performing multi-band performance loss evaluation according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic related performance loss data; optimizing the integrated circuit structure design of the energy storage circuit board according to the multi-band electromagnetic related performance loss data to obtain integrated circuit structure optimization data; optimizing the preparation process based on the integrated circuit structure optimization data to obtain integrated circuit preparation process optimization data;

[0008] Step S4: Automated script writing is performed on the integrated circuit preparation process optimization data to obtain an automated preparation process optimization script; underlying mechanical operation firmware is designed according to the automated preparation process optimization script to obtain underlying operation firmware for preparation process optimization, and the underlying operation firmware for preparation process optimization is embedded into an external preparation robot arm to execute the preparation process of the 5G energy storage circuit board.

[0009] By acquiring the preparation demand data of 5G energy storage circuit boards and analyzing the theoretical operating power of components, the present invention can better understand the power required by each component under theoretical operating conditions, which is helpful to control and optimize the power consumption of components in the design stage and improve the overall energy efficiency; analyzing the theoretical electromagnetic field fluctuation range of components can help determine the fluctuation range of the electromagnetic field, so as to consider electromagnetic compatibility and interference issues in the design stage and ensure that the product meets relevant standards. Evaluating the energy consumption of 5G multi-bands can help understand the overall energy consumption and guide subsequent energy consumption optimization work, such as adopting different energy-saving strategies for different frequency bands. Analyzing the output power consumption of components can find out components with higher energy consumption, optimize them in a targeted manner, reduce the overall power consumption, and extend the equipment. Endurance time, limiting the theoretical fluctuation range of the electromagnetic field according to the output power consumption, helps to control the electromagnetic radiation range in practical applications, ensure the safety and stability of the equipment, and understand the electromagnetic field strength change data under the frequency band switching state, which can optimize the electromagnetic radiation control strategy of the equipment during frequency band switching and reduce interference with the surrounding environment and other equipment; by evaluating the changes in electromagnetic field strength caused by frequency band switching, it is possible to accurately grasp the performance loss under multiple frequency bands, and then guide the optimization design of the integrated circuit structure. The optimized circuit structure can reduce the electromagnetic related performance loss, improve the overall performance and stability, and optimize the preparation process based on the optimized integrated circuit structure, which can improve the preparation efficiency, reduce costs, and ensure the quality and stability of the circuit board. This step helps to optimize the process parameters, select appropriate preparation equipment and materials, thereby improving the preparation efficiency and product quality. Writing automated scripts can realize the automated control of the preparation process and improve production efficiency and consistency. At the same time, the design of the underlying operating firmware can better control the movement of the robotic arm and ensure the accuracy and stability of the preparation process. Therefore, the present invention improves a preparation process of a 5G energy storage type circuit board, which is an improvement on the preparation process of a traditional 5G energy storage type circuit board. It solves the problems of inaccurate performance loss analysis and unstable output energy consumption caused by electromagnetic influence in the 5G multi-band switching state in the preparation process of the traditional 5G energy storage type circuit board, improves the accuracy of the performance loss analysis, and improves the stability of the output energy consumption.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining 5G energy storage circuit board preparation demand data;

[0012] Step S12: performing component material property matching processing on the 5G energy storage circuit board preparation demand data to obtain component material property matching data;

[0013] Step S13: performing a theoretical operating power analysis of the component according to the component material characteristic matching data to obtain the component theoretical operating power data;

[0014] Step S14: Analyze the theoretical electromagnetic field fluctuation range of the component on the component material characteristic matching data according to the component theoretical operating power data to obtain the electromagnetic field theoretical fluctuation range.

[0015] The present invention obtains the preparation requirement data of 5G energy storage type circuit boards: collects and organizes the preparation requirement data of 5G energy storage type circuit boards, including performance indicators, functional requirements, and size requirements, so as to clarify the basic requirements and restrictions of product design, and performs component material property matching processing on the preparation requirement data of 5G energy storage type circuit boards to obtain component material property matching data: according to the preparation requirement data, matches appropriate component material properties, including electrical properties, thermal properties, and mechanical properties, so as to ensure that the selection of component materials meets the requirements of product performance and preparation process, and performs component theoretical operation power analysis according to the component material property matching data to obtain component theoretical operation power data: by analyzing the material properties and design parameters of the components, the power consumption of the components under theoretical operation is calculated, so as to control the power consumption of the components and optimize the circuit design in the design stage, and performs component electromagnetic field theoretical fluctuation range analysis on the component material property matching data according to the component theoretical operation power data to obtain the electromagnetic field theoretical fluctuation range: in combination with the theoretical operation power and material properties of the components, the electromagnetic field fluctuation range of the components is analyzed to evaluate the electromagnetic compatibility and interference, and provide a reference for electromagnetic design.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing a 5G multi-band demand analysis on the 5G energy storage type circuit board preparation demand data to obtain 5G multi-band demand data; performing a 5G multi-band energy consumption assessment based on the 5G multi-band demand data to obtain 5G multi-band energy consumption assessment data;

[0018] Step S22: performing component output power consumption analysis on the component theoretical operating power data according to the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; performing output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range according to the component output power consumption data to obtain the output power consumption electromagnetic field limitation range;

[0019] Step S23: performing component power consumption fluctuation analysis on component output power consumption data in a multi-band switching state according to the 5G multi-band energy consumption evaluation data to obtain component power consumption data for frequency band switching;

[0020] Step S24: analyzing the change of electromagnetic field strength in the output power consumption electromagnetic field limit interval under the multi-band switching state according to the power consumption data of the frequency band switching components, and obtaining the electromagnetic field strength change data of the frequency band switching.

[0021] The present invention performs 5G multi-band demand analysis on the preparation demand data of 5G energy storage circuit boards, and performs 5G multi-band energy consumption evaluation. Through this step, the energy consumption of the product in different frequency bands can be accurately understood, thereby providing data support for subsequent power consumption optimization. This helps the design team to better control energy consumption and improve the energy efficiency of the product during product development; the output power consumption analysis of the theoretical operating power of the components is performed according to the 5G multi-band energy consumption evaluation data, and the theoretical fluctuation range of the electromagnetic field is limited according to the output power consumption data. The main effect of this step is to associate the energy consumption evaluation results with the electromagnetic field fluctuation range, so that the impact of energy consumption on the electromagnetic field can be considered in the design stage, thereby optimizing the design scheme and improving the stability and performance of the circuit; according to the 5G multi-band energy consumption evaluation data, the power consumption fluctuation analysis of the output power consumption data of the components under the multi-band switching state is performed. This step helps to understand the power consumption changes of components under different frequency band switching states, provide more accurate energy consumption prediction and optimization solutions for the actual application scenarios of the product, and analyze the changes in electromagnetic field intensity under the multi-band switching state according to the power consumption data of the frequency band switching components. Through this step, we can better understand the impact of frequency band switching on the electromagnetic field strength, so that electromagnetic compatibility can be considered in the design stage, and corresponding measures can be taken to reduce the interference of frequency band switching on the electromagnetic field and improve the reliability and stability of the product.

[0022] Preferably, step S24 comprises the following steps:

[0023] Step S241: extracting the power consumption data of low-frequency band, medium-frequency band and high-frequency band components from the power consumption data of the frequency band switching components, and obtaining the power consumption data of low-frequency band components, the power consumption data of medium-frequency band components and the power consumption data of high-frequency band components respectively;

[0024] Step S242: performing frequency domain conversion on the power consumption data of low-frequency components, the power consumption data of mid-frequency components, and the power consumption data of high-frequency components, respectively, to obtain the power consumption frequency domain data of low-frequency components, the power consumption frequency domain data of mid-frequency components, and the power consumption frequency domain data of high-frequency components, respectively;

[0025] Step S243: performing time series alignment on the frequency domain data of power consumption of low-frequency components, the frequency domain data of power consumption of mid-frequency components, and the frequency domain data of power consumption of high-frequency components, respectively, to obtain multi-frequency-band time series alignment data;

[0026] Step S244: performing aliasing region removal processing on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band timing alignment data to obtain multi-band aliasing removal region data;

[0027] Step S245: performing frequency pairwise difference calculations on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band aliasing removal area data and the multi-band timing alignment data, to obtain mid-low frequency band frequency difference data, mid-high frequency band frequency difference data, and high-low frequency band frequency difference data, respectively;

[0028] Step S246: Calculating the power consumption difference in the multi-band switching state according to the mid-low frequency band frequency difference data, the mid-high frequency band frequency difference data, and the high-low frequency band frequency difference data, to obtain the multi-band switching power consumption difference data;

[0029] Step S247: analyzing the electromagnetic field intensity variation of the output power consumption electromagnetic field limited interval under the multi-band switching state according to the multi-band switching power consumption difference data, and obtaining the electromagnetic field intensity variation data of the frequency band switching.

[0030] The present invention can effectively separate the power consumption data under different frequency bands by extracting the power consumption data of the frequency band switching parts in low frequency band, medium frequency band and high frequency band, so that the subsequent analysis is more targeted and accurate. This helps to deeply understand the power consumption characteristics of the parts under different frequency bands, and provide accurate basic data for the power consumption difference analysis under the frequency band switching state; the power consumption data of the parts in the low frequency band, medium frequency band and high frequency band are converted into the frequency domain, and the power consumption data can be converted into the frequency domain to better show the characteristics of the power consumption signal in the frequency domain. This helps to identify and analyze the power consumption spectrum characteristics under different frequency bands, and provide more information support for the subsequent power consumption difference calculation; by aligning the power consumption frequency domain data of the parts in the low frequency band, medium frequency band and high frequency band in time series, it can ensure that the power consumption data under different frequency bands are consistent in time, eliminate the offset in the time series, so that the power consumption data under different frequency bands can be effectively compared and analyzed; the aliasing area removal processing of the multi-band time series alignment data helps to eliminate the aliasing areas between different frequency bands, further improve the accuracy and reliability of the data, and provide a clearer data basis for the subsequent power consumption difference calculation. By performing frequency difference calculations on the data after aliasing removal, the power consumption differences between different frequency bands can be effectively identified, so as to more accurately analyze the power consumption changes in the frequency band switching state. Based on the frequency difference data, the power consumption difference calculation in the multi-band switching state can quantify the power consumption differences in different frequency band switching states, and provide specific guidance for product design and optimization. By analyzing the power consumption difference data, we can have a deeper understanding of the impact of frequency band switching on the electromagnetic field strength, thus providing an important reference for the electromagnetic compatibility design of the product, and ensuring that the electromagnetic performance of the product in different frequency band switching states meets the requirements.

[0031] Preferably, step S3 comprises the following steps:

[0032] Step S31: Perform multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic correlation performance loss data;

[0033] Step S32: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data to obtain electromagnetic performance loss spatial distribution data;

[0034] Step S33: optimizing the integrated circuit structure of the energy storage circuit board according to the multi-band electromagnetic correlation performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data, to obtain integrated circuit structure optimization data;

[0035] Step S34: Optimizing the manufacturing process based on the integrated circuit structure optimization data to obtain integrated circuit manufacturing process optimization data.

[0036] The present invention performs multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data, so as to fully understand the performance loss of the system when switching frequency bands, help to find performance bottlenecks and optimization space, and improve the overall performance of the system. The spatial distribution correlation analysis of multi-band electromagnetic-related performance loss data can reveal the spatial distribution of electromagnetic performance loss, help determine the main sources and influencing factors of performance loss, and provide an important basis for subsequent optimization. The integrated circuit structure of the energy storage circuit board is optimized and designed according to the multi-band electromagnetic-related performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data. Under the premise of ensuring the functional integrity and performance of the system, the electromagnetic-related performance loss can be reduced, and the stability and reliability of the system can be improved. The preparation process is optimized based on the integrated circuit structure optimization data, which can effectively improve the preparation process of the integrated circuit, improve production efficiency and quality stability, and reduce production costs, thereby realizing the optimization and improvement of the integrated circuit preparation process.

[0037] Preferably, step S31 includes the following steps:

[0038] Step S311: performing electromagnetic field directivity analysis on the electromagnetic field intensity change data of frequency band switching to obtain electromagnetic field directivity data;

[0039] Step S312: Calculating the electromagnetic wave frequency density in different magnetic field directions for the frequency band switching electromagnetic field intensity change data according to the electromagnetic field directivity data to obtain electromagnetic wave frequency density data;

[0040] Step S313: performing density discrete analysis on the electromagnetic wave frequency density data to obtain frequency density discrete data; performing density discrete distribution difference calculation on the magnetic wave frequency density data according to the frequency density discrete data to obtain density distribution difference data;

[0041] Step S314: performing density error distribution constraint according to the density distribution difference data to obtain density error distribution constraint data; performing error distribution constraint linear fitting on the electromagnetic wave frequency density data according to the density error distribution constraint data to obtain electromagnetic wave frequency density fitting data;

[0042] Step S315: Perform output power loss analysis on the output power consumption data of components according to the electromagnetic wave frequency density fitting data to obtain output power consumption loss data; perform performance loss evaluation on the 5G multi-band energy consumption evaluation data according to the output power consumption loss data to obtain multi-band electromagnetic related performance loss data.

[0043] The electromagnetic field directionality analysis of the present invention helps to understand the directional distribution of the electromagnetic field when the frequency band is switched, so as to determine the main propagation direction and intensity distribution law of the electromagnetic field, and provide basic data for subsequent analysis. By calculating the electromagnetic wave frequency density under different magnetic field directions, a more comprehensive electromagnetic wave distribution can be obtained, which helps to understand the electromagnetic wave energy distribution in different directions, and provide detailed data for subsequent analysis. Density discrete analysis and density distribution difference calculation can reveal the discrete degree and difference of the electromagnetic wave frequency density, which helps to determine the stability and consistency of the electromagnetic field, and provide a basis for subsequent error analysis. Density error distribution constraints and error distribution constraint linear fitting can perform error analysis and fitting on the electromagnetic wave frequency density, so as to accurately evaluate the propagation and energy loss of electromagnetic waves, and provide accurate data for subsequent power consumption analysis. By performing output power consumption reduction analysis on the electromagnetic wave frequency density fitting data, the power consumption changes of the system under different electromagnetic field conditions can be evaluated, and guidance can be provided for system optimization. At the same time, the performance loss evaluation of the 5G multi-band energy consumption evaluation data is performed according to the output power consumption reduction data, and the performance loss of the system under multi-band conditions can be fully understood, providing an important reference for system design and optimization.

[0044] Preferably, step S32 includes the following steps:

[0045] Step S321: performing time series change analysis on the multi-band electromagnetic correlation performance loss data to obtain multi-band performance loss time series change data; constructing a two-dimensional space loss coordinate system for the multi-band electromagnetic correlation performance loss data according to the multi-band performance loss time series change data to obtain a multi-band space loss coordinate system;

[0046] Step S322: marking the positions of the multi-band loss data points on the multi-band spatial loss coordinate system to obtain the position data of the multi-band loss data points;

[0047] Step S323: performing spatial interpolation processing on the multi-band spatial loss coordinate system according to the multi-band loss data point position data to obtain multi-band performance loss spatial interpolation data;

[0048] Step S324: performing loss gradient analysis on the multi-band performance loss spatial interpolation data to obtain loss gradient data;

[0049] Step S325: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data according to the loss gradient data and the multi-band performance loss spatial interpolation data to obtain electromagnetic performance loss spatial distribution data.

[0050] The present invention can understand the changing trend of multi-band performance loss over time by performing time series change analysis on multi-band electromagnetic correlation performance loss data, thereby providing a basis for the dynamic optimization of system performance. Constructing a two-dimensional spatial loss coordinate system can intuitively display the spatial distribution of multi-band performance loss, which helps to identify the main performance loss areas and trends in the system; marking the positions of multi-band loss data points can correspond the performance loss to the spatial position, providing an accurate data basis for subsequent spatial interpolation processing, making the interpolation results more credible. By performing spatial interpolation processing on the positions of multi-band loss data points, the data gaps in space can be filled, and a more continuous and smooth performance loss distribution map can be obtained, which helps to accurately evaluate the spatial characteristics of system performance loss. Loss gradient analysis can reveal the rate and direction of change of performance loss in space, help understand the spatial distribution characteristics of performance loss, and guide subsequent optimization measures. Through spatial distribution correlation analysis, the correlation between performance losses in different regions can be deeply explored, and the existing correlation patterns and influencing factors can be identified, providing deeper insights for system performance optimization.

[0051] Preferably, S33 includes the following steps:

[0052] Step S331: performing electromagnetic impact assessment according to the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data to obtain performance-electromagnetic impact degree data;

[0053] Step S332: marking electromagnetic impact key nodes on the performance-electromagnetic impact degree data to obtain electromagnetic impact key node data;

[0054] Step S333: performing optimization constraint analysis based on electromagnetic impact key node data and component output power consumption data to obtain node constraint condition data;

[0055] Step S334: constructing an integrated line structure optimization model using a K-nearest neighbor algorithm and node constraint condition data to obtain an integrated line structure optimization model;

[0056] Step S335: Based on the integrated circuit structure optimization model and the node constraint condition data, the integrated circuit structure optimization design of the energy storage type circuit board is performed on the electromagnetic influence key node data to obtain the integrated circuit structure optimization data.

[0057] The present invention can comprehensively consider the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data through electromagnetic influence degree assessment, quantify the influence degree of different electromagnetic influences on system performance, provide quantitative indicators and important references for subsequent optimization, mark the electromagnetic influence key nodes to determine the key nodes with greater performance influence in the system, help to concentrate on subsequent optimization and improvement, and improve optimization efficiency, and by analyzing the electromagnetic influence key node data and the component output power consumption data, the optimization constraints can be determined, the optimization range can be limited, and the feasibility and effectiveness of the optimization scheme can be ensured, while avoiding potential conflicts and adverse effects, and the integrated circuit structure optimization model constructed by using the K nearest neighbor algorithm can quickly and accurately find the nodes most similar to the existing nodes based on the node constraint condition data, thereby establishing a reasonable and feasible optimization model, providing effective reference and guidance for optimization design, and optimizing the integrated circuit structure of the energy storage circuit board based on the integrated circuit structure optimization model and the node constraint condition data. Under the condition of considering the electromagnetic influence key nodes, the energy storage circuit board can be optimized and designed to improve the performance and reliability of the system, while satisfying the constraint conditions, providing a more scientific and reliable basis for system design.

[0058] Preferably, the node constraint condition data includes line size constraint data, component layout constraint data and component line direction constraint data, and step S333 includes:

[0059] Step S331: performing line size constraint analysis according to electromagnetic influence key node data to obtain line size constraint data;

[0060] Step S332: performing component layout analysis on electromagnetic impact key node data and component output power consumption data according to the line size constraint data to obtain component layout constraint data;

[0061] Step S333: Analyze the component line orientation according to the line size constraint data and the component layout constraint data to obtain the component line orientation constraint data.

[0062] The line size constraint analysis of the present invention determines the restriction conditions of the line size by considering the electromagnetic impact key node data. This helps to ensure that the line size meets the requirements of the system design and avoids the negative impact of excessive or small line sizes on system performance and electromagnetic compatibility. The component layout analysis is based on the line size constraint data, combined with the electromagnetic impact key node data and the component output power consumption data, to determine the layout position of the components in the system. This can effectively optimize the layout of the system so that the layout of each component meets the line size constraints, and help reduce the impact of electromagnetic interference and uneven power consumption distribution on system performance. The component line orientation analysis takes into account the line size constraint data and the component layout constraint data to determine the direction of the component line. This helps to optimize the line layout, ensure that the direction of the line meets the requirements of the system design, while minimizing electromagnetic interference and signal loss, and improving the reliability and stability of the system.

[0063] Preferably, a 5G energy storage type circuit board is prepared by the preparation process of the 5G energy storage type circuit board as described above.

[0064] The beneficial effects of the present invention are as follows: by obtaining the preparation demand data of 5G energy storage circuit boards and analyzing the theoretical operating power of components, the power required by each component under theoretical operating conditions can be better understood, which is helpful for controlling and optimizing the power consumption of components in the design phase and improving the overall energy efficiency; analyzing the theoretical electromagnetic field fluctuation range of components can help determine the fluctuation range of the electromagnetic field, so that electromagnetic compatibility and interference issues can be considered in the design phase to ensure that the product meets relevant standards; evaluating the energy consumption of 5G multi-bands can help understand the overall energy consumption and guide subsequent energy consumption optimization work, such as adopting different energy-saving strategies for different frequency bands; analyzing the output power consumption of components can identify components with higher energy consumption, and optimize them in a targeted manner to reduce overall power consumption and extend the life of the product. The battery life of the device is limited by the theoretical fluctuation range of the electromagnetic field according to the output power consumption, which helps to control the electromagnetic radiation range in practical applications and ensure the safety and stability of the equipment. Understanding the electromagnetic field strength change data under the frequency band switching state can optimize the electromagnetic radiation control strategy of the device during frequency band switching and reduce interference with the surrounding environment and other equipment; by evaluating the changes in electromagnetic field strength caused by frequency band switching, the performance loss under multiple frequency bands can be accurately grasped, and then the optimization design of the integrated circuit structure can be guided. The optimized circuit structure can reduce the electromagnetic related performance loss and improve the overall performance and stability. Based on the optimized integrated circuit structure, the preparation process is optimized, which can improve the preparation efficiency, reduce the cost, and ensure the quality and stability of the circuit board. This step helps to optimize the process parameters, select appropriate preparation equipment and materials, thereby improving the preparation efficiency and product quality. Writing automated scripts can realize the automated control of the preparation process and improve production efficiency and consistency. At the same time, the design of the underlying operating firmware can better control the movement of the robotic arm and ensure the accuracy and stability of the preparation process. Therefore, the present invention improves a preparation process of a 5G energy storage type circuit board, which is an improvement on the preparation process of a traditional 5G energy storage type circuit board. It solves the problems of inaccurate performance loss analysis and unstable output energy consumption caused by electromagnetic influence in the 5G multi-band switching state in the preparation process of the traditional 5G energy storage type circuit board, improves the accuracy of the performance loss analysis, and improves the stability of the output energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a 5G energy storage circuit board and its preparation process;

[0066] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0067] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0068] 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

[0069] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0072] To achieve this, please refer to Figures 1 to 3 , a preparation process of a 5G energy storage circuit board, the preparation process comprising the following steps:

[0073] Step S1: Acquire the preparation demand data of 5G energy storage type circuit boards; perform theoretical operating power analysis of components according to the preparation demand data of 5G energy storage type circuit boards to obtain theoretical operating power data of components; perform theoretical electromagnetic field fluctuation range analysis of components according to the theoretical operating power data of components to obtain theoretical electromagnetic field fluctuation range;

[0074] Step S2: Perform 5G multi-band energy consumption evaluation on the 5G energy storage circuit board preparation demand data to obtain 5G multi-band energy consumption evaluation data; perform component output power consumption analysis based on the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; perform output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range based on the component output power consumption data to obtain output power consumption electromagnetic field limitation range; perform electromagnetic field intensity change analysis on the output power consumption electromagnetic field limitation range under multi-band switching state to obtain frequency band switching electromagnetic field intensity change data;

[0075] Step S3: performing multi-band performance loss evaluation according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic related performance loss data; optimizing the integrated circuit structure design of the energy storage circuit board according to the multi-band electromagnetic related performance loss data to obtain integrated circuit structure optimization data; optimizing the preparation process based on the integrated circuit structure optimization data to obtain integrated circuit preparation process optimization data;

[0076] Step S4: Automated script writing is performed on the integrated circuit preparation process optimization data to obtain an automated preparation process optimization script; underlying mechanical operation firmware is designed according to the automated preparation process optimization script to obtain underlying operation firmware for preparation process optimization, and the underlying operation firmware for preparation process optimization is embedded into an external preparation robot arm to execute the preparation process of the 5G energy storage circuit board.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a preparation process of a 5G energy storage type circuit board of the present invention. In this example, the preparation process of the 5G energy storage type circuit board includes the following steps:

[0078] Step S1: Acquire the preparation demand data of 5G energy storage type circuit boards; perform theoretical operating power analysis of components according to the preparation demand data of 5G energy storage type circuit boards to obtain theoretical operating power data of components; perform theoretical electromagnetic field fluctuation range analysis of components according to the theoretical operating power data of components to obtain theoretical electromagnetic field fluctuation range;

[0079] In an embodiment of the present invention, various parameters, specifications, performance requirements and other data required for the preparation of 5G energy storage circuit boards are obtained by investigating, asking experts in related fields or referring to existing literature, and the obtained preparation requirement data is used to perform theoretical operating power analysis on components. This involves calculating the power consumption of components under specific working conditions, including considering factors such as operating frequency, operating voltage, and operating temperature, and obtaining the expected power data of each component during the preparation process based on the results of the theoretical operating power analysis. These data will serve as the basis for subsequent analysis, and the theoretical electromagnetic field fluctuation range will be analyzed based on the theoretical operating power data of the components. This includes considering the electromagnetic field fluctuations generated by the components during operation, as well as the amplitude and frequency range of the fluctuations, and obtaining the electromagnetic field fluctuation range that occurs in the components during operation based on the results of the theoretical electromagnetic field fluctuation range analysis. These intervals will help further design and optimization work.

[0080] Step S2: Perform 5G multi-band energy consumption evaluation on the 5G energy storage circuit board preparation demand data to obtain 5G multi-band energy consumption evaluation data; perform component output power consumption analysis based on the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; perform output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range based on the component output power consumption data to obtain output power consumption electromagnetic field limitation range; perform electromagnetic field intensity change analysis on the output power consumption electromagnetic field limitation range under multi-band switching state to obtain frequency band switching electromagnetic field intensity change data;

[0081] In an embodiment of the present invention, the obtained preparation requirement data is used to perform a 5G multi-band energy consumption evaluation. This includes analyzing the energy consumption in different frequency bands, taking into account the working characteristics and energy consumption of the 5G network in different frequency bands, and obtaining energy consumption evaluation data in 5G multi-bands according to the results of the energy consumption evaluation analysis. These data will be used for subsequent component output power consumption analysis, and the output power consumption of components will be analyzed based on the 5G multi-band energy consumption evaluation data. This involves taking into account the working state and energy consumption of components in different frequency bands, and estimating the power consumption of circuit elements, and obtaining the output power consumption data of each component in different frequency bands according to the results of the output power consumption analysis. These data will serve as the basis for the subsequent limitation of the theoretical fluctuation range of the electromagnetic field. Based on the output power consumption data of the components, the theoretical fluctuation range of the electromagnetic field is limited. This involves taking into account the influence of output power consumption on electromagnetic field fluctuations, as well as the limited range in different frequency bands. According to the limited theoretical fluctuation range of the electromagnetic field, the limited range of the output power consumption electromagnetic field is obtained. This will help to more accurately control the fluctuation of the electromagnetic field during the design process. For the limited range of the output power consumption electromagnetic field, the change of the electromagnetic field intensity under the multi-band switching state is analyzed. This involves considering the changes in electromagnetic field strength in different frequency bands and the impact of frequency band switching on the electromagnetic field. According to the results of the electromagnetic field strength change analysis, the electromagnetic field strength change data under the frequency band switching state is obtained. These data will help evaluate the impact of frequency band switching on the electromagnetic field in practical applications.

[0082] Step S3: performing multi-band performance loss evaluation according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic related performance loss data; optimizing the integrated circuit structure design of the energy storage circuit board according to the multi-band electromagnetic related performance loss data to obtain integrated circuit structure optimization data; optimizing the preparation process based on the integrated circuit structure optimization data to obtain integrated circuit preparation process optimization data;

[0083] In an embodiment of the present invention, the influence of the change of electromagnetic field strength on the system performance during frequency band switching is analyzed, including signal transmission quality and system stability, and the performance loss of the system during frequency band switching is evaluated using multi-band electromagnetic correlation performance loss data; based on the multi-band electromagnetic correlation performance loss data, the circuit structure is optimized for electromagnetic interference and signal loss and other issues. For example, when designing the circuit layout, the electromagnetic interference between different components is reduced through reasonable wiring planning and isolation design. According to the integrated circuit structure optimization data, the preparation process of the energy storage circuit board is optimized. For example, in the circuit board manufacturing process, the process parameters are optimized, such as board selection, printing process, etc., to improve the electromagnetic compatibility of the circuit board.

[0084] Step S4: Automated script writing is performed on the integrated circuit preparation process optimization data to obtain an automated preparation process optimization script; underlying mechanical operation firmware is designed according to the automated preparation process optimization script to obtain underlying operation firmware for preparation process optimization, and the underlying operation firmware for preparation process optimization is embedded into an external preparation robot arm to execute the preparation process of the 5G energy storage circuit board.

[0085] In an embodiment of the present invention, according to the integrated circuit preparation process optimization data, an automation script is written to realize the automatic control of the preparation process. The script may include process parameter settings, equipment operation instructions, data processing and other functions to ensure the automatic execution of the entire preparation process. According to the requirements of the automated preparation process optimization script, the underlying mechanical operation firmware is designed to control the movement and operation of the external preparation robot arm. The underlying mechanical operation firmware needs to be compatible with the hardware structure and control system of the external robot arm to achieve accurate operation control. A Python script can be written to achieve automatic control. The script may include equipment initialization, process parameter settings, preparation process control and other functions. At the same time, according to the script requirements, the corresponding underlying mechanical operation firmware is designed, such as an Arduino control board. By writing a control program that communicates with the external robot arm and embedding it into Arduino, the underlying control of the robot arm is realized. In this way, when executing the preparation process, the external robot arm can be controlled to operate according to a predetermined process flow by running the written automation script, thereby realizing the automation of the preparation process of the 5G energy storage circuit board.

[0086] By acquiring the preparation demand data of 5G energy storage circuit boards and analyzing the theoretical operating power of components, the present invention can better understand the power required by each component under theoretical operating conditions, which is helpful to control and optimize the power consumption of components in the design stage and improve the overall energy efficiency; analyzing the theoretical electromagnetic field fluctuation range of components can help determine the fluctuation range of the electromagnetic field, so as to consider electromagnetic compatibility and interference issues in the design stage and ensure that the product meets relevant standards. Evaluating the energy consumption of 5G multi-bands can help understand the overall energy consumption and guide subsequent energy consumption optimization work, such as adopting different energy-saving strategies for different frequency bands. Analyzing the output power consumption of components can find out components with higher energy consumption, optimize them in a targeted manner, reduce the overall power consumption, and extend the equipment. Endurance time, limiting the theoretical fluctuation range of the electromagnetic field according to the output power consumption, helps to control the electromagnetic radiation range in practical applications, ensure the safety and stability of the equipment, and understand the electromagnetic field strength change data under the frequency band switching state, which can optimize the electromagnetic radiation control strategy of the equipment during frequency band switching and reduce interference with the surrounding environment and other equipment; by evaluating the changes in electromagnetic field strength caused by frequency band switching, it is possible to accurately grasp the performance loss under multiple frequency bands, and then guide the optimization design of the integrated circuit structure. The optimized circuit structure can reduce the electromagnetic related performance loss, improve the overall performance and stability, and optimize the preparation process based on the optimized integrated circuit structure, which can improve the preparation efficiency, reduce costs, and ensure the quality and stability of the circuit board. This step helps to optimize the process parameters, select appropriate preparation equipment and materials, thereby improving the preparation efficiency and product quality. Writing automated scripts can realize the automated control of the preparation process and improve production efficiency and consistency. At the same time, the design of the underlying operating firmware can better control the movement of the robotic arm and ensure the accuracy and stability of the preparation process. Therefore, the present invention improves a preparation process of a 5G energy storage type circuit board, which is an improvement on the preparation process of a traditional 5G energy storage type circuit board. It solves the problems of inaccurate performance loss analysis and unstable output energy consumption caused by electromagnetic influence in the 5G multi-band switching state in the preparation process of the traditional 5G energy storage type circuit board, improves the accuracy of the performance loss analysis, and improves the stability of the output energy consumption.

[0087] Preferably, step S1 comprises the following steps:

[0088] Step S11: Obtaining 5G energy storage circuit board preparation demand data;

[0089] Step S12: performing component material property matching processing on the 5G energy storage circuit board preparation demand data to obtain component material property matching data;

[0090] Step S13: performing a theoretical operating power analysis of the component according to the component material characteristic matching data to obtain the component theoretical operating power data;

[0091] Step S14: Analyze the theoretical electromagnetic field fluctuation range of the component on the component material characteristic matching data according to the component theoretical operating power data to obtain the electromagnetic field theoretical fluctuation range.

[0092] In an embodiment of the present invention, the preparation demand data of the 5G energy storage circuit board is obtained from the relevant departments or customers, including communication standards, frequency band requirements, and power consumption requirements, and the various components involved in the demand data, such as processors, radio frequency modules, and sensors, are analyzed, and appropriate material properties are matched to ensure that the components can meet the preparation requirements, such as high temperature resistance, anti-interference and other characteristics. The theoretical operating power of the components is analyzed and calculated by using the matched material property data of the components, and the power consumption of the components under different working conditions and the mutual influence with other components are considered. Based on the theoretical operating power data of the components, the influence range of the components on the electromagnetic field is analyzed, and the working state and influence degree of each component within the theoretical fluctuation range of the electromagnetic field are determined. Specifically, the communication standards and frequency band requirements of the 5G energy storage circuit board are obtained through step S11, for example, it is necessary to support the communication of 5G NR technology in the 3.5GHz frequency band. In step S12, the material property matching processing is performed on the radio frequency module, and a material with higher frequency response and anti-interference ability is selected. In step S13, according to the matched material property data, the power consumption of the radio frequency module is analyzed, and its theoretical operating power in the 3.5GHz frequency band is calculated. Finally, in step S14, based on the theoretical operating power data, the influence range of the RF module on the electromagnetic field is analyzed to determine its working state and performance within the electromagnetic field fluctuation range.

[0093] The present invention obtains the preparation requirement data of 5G energy storage type circuit boards: collects and organizes the preparation requirement data of 5G energy storage type circuit boards, including performance indicators, functional requirements, and size requirements, so as to clarify the basic requirements and restrictions of product design, and performs component material property matching processing on the preparation requirement data of 5G energy storage type circuit boards to obtain component material property matching data: according to the preparation requirement data, matches appropriate component material properties, including electrical properties, thermal properties, and mechanical properties, so as to ensure that the selection of component materials meets the requirements of product performance and preparation process, and performs component theoretical operation power analysis according to the component material property matching data to obtain component theoretical operation power data: by analyzing the material properties and design parameters of the components, the power consumption of the components under theoretical operation is calculated, so as to control the power consumption of the components and optimize the circuit design in the design stage, and performs component electromagnetic field theoretical fluctuation range analysis on the component material property matching data according to the component theoretical operation power data to obtain the electromagnetic field theoretical fluctuation range: in combination with the theoretical operation power and material properties of the components, the electromagnetic field fluctuation range of the components is analyzed to evaluate the electromagnetic compatibility and interference, and provide a reference for electromagnetic design.

[0094] Preferably, step S2 comprises the following steps:

[0095] Step S21: performing a 5G multi-band demand analysis on the 5G energy storage type circuit board preparation demand data to obtain 5G multi-band demand data; performing a 5G multi-band energy consumption assessment based on the 5G multi-band demand data to obtain 5G multi-band energy consumption assessment data;

[0096] Step S22: performing component output power consumption analysis on the component theoretical operating power data according to the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; performing output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range according to the component output power consumption data to obtain the output power consumption electromagnetic field limitation range;

[0097] Step S23: performing component power consumption fluctuation analysis on component output power consumption data in a multi-band switching state according to the 5G multi-band energy consumption evaluation data to obtain component power consumption data for frequency band switching;

[0098] Step S24: analyzing the change of electromagnetic field strength in the output power consumption electromagnetic field limit interval under the multi-band switching state according to the power consumption data of the frequency band switching components, and obtaining the electromagnetic field strength change data of the frequency band switching.

[0099] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0100] Step S21: performing a 5G multi-band demand analysis on the 5G energy storage type circuit board preparation demand data to obtain 5G multi-band demand data; performing a 5G multi-band energy consumption assessment based on the 5G multi-band demand data to obtain 5G multi-band energy consumption assessment data;

[0101] In an embodiment of the present invention, the 5G communication frequency band requirements in the demand data are analyzed, including the signal transmission requirements of different frequency bands, such as frequency range and bandwidth, to determine multiple 5G frequency bands that the circuit board needs to support, such as 3.5GHz and 28GHz. Combined with the determined multiple 5G frequency band demand data, the energy consumption of the circuit board in different frequency bands is evaluated, and the signal processing, transmission power consumption and energy consumption of the overall system in each frequency band are considered to obtain energy consumption evaluation data for multiple frequency bands.

[0102] Step S22: performing component output power consumption analysis on the component theoretical operating power data according to the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; performing output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range according to the component output power consumption data to obtain the output power consumption electromagnetic field limitation range;

[0103] In an embodiment of the present invention, the output power consumption of components is analyzed on the theoretical operating power data of components according to the 5G multi-band energy consumption evaluation data, and the output power consumption of each component under the multi-band working state is analyzed by combining the 5G multi-band energy consumption evaluation data and the theoretical operating power data of components, and the output power consumption data of each component under different frequency bands is calculated. According to the output power consumption data of each component under multi-bands, the influence range on the electromagnetic field is determined, and the theoretical fluctuation range of the output power consumption electromagnetic field is determined, that is, the limited range of electromagnetic field fluctuation caused by the output power consumption of each component under different frequency bands. Specifically, assuming that the 5G multi-band demand analysis determines that the circuit board needs to support two frequency bands of 3.5GHz and 28GHz, according to the energy consumption evaluation data, it is found that the overall energy consumption of the circuit board is high under the 28GHz frequency band. In step S22, it is found that the output power consumption of the RF module under the 28GHz frequency band is significantly increased, resulting in an expansion of the fluctuation range of the electromagnetic field. Therefore, according to the output power consumption data, the theoretical fluctuation range of the electromagnetic field under the 28GHz frequency band is limited to ensure the stability and performance of the system.

[0104] Step S23: performing component power consumption fluctuation analysis on component output power consumption data in a multi-band switching state according to the 5G multi-band energy consumption evaluation data to obtain component power consumption data for frequency band switching;

[0105] In the embodiment of the present invention, the output power consumption data of components in the multi-band switching state is collected, the power consumption fluctuation of each component in the different frequency band switching states is determined, the power consumption change trend of components in the different frequency band switching states is analyzed, including the increase or decrease of power consumption, and the power consumption data of each component in the frequency band switching state is determined. Specifically, by monitoring the power consumption of the RF module in the 5G multi-band switching state, it is found that the power consumption of the RF module will fluctuate significantly when the frequency band is switched. When switching from a low frequency band to a high frequency band, the power consumption of the RF module will increase, and when switching from a high frequency band to a low frequency band, the power consumption will decrease.

[0106] Step S24: analyzing the change of electromagnetic field strength in the output power consumption electromagnetic field limit interval under the multi-band switching state according to the power consumption data of the frequency band switching components, and obtaining the electromagnetic field strength change data of the frequency band switching.

[0107] In the embodiment of the present invention, the change of power consumption of each component during the frequency band switching is analyzed, the change of electromagnetic field strength under the frequency band switching state is determined based on the power consumption data, the change trend of electromagnetic field strength during the frequency band switching is analyzed, including the increase or decrease, and the change of electromagnetic field strength in the output power consumption electromagnetic field limited interval under the frequency band switching state is determined. Specifically, by monitoring the change of power consumption of each component during the frequency band switching, it can be inferred that the electromagnetic field strength will change during the frequency band switching process. For example, when the power consumption of the RF module increases, the electromagnetic field strength will increase accordingly, and when the power consumption decreases, it will decrease.

[0108] The present invention performs 5G multi-band demand analysis on the preparation demand data of 5G energy storage circuit boards, and performs 5G multi-band energy consumption evaluation. Through this step, the energy consumption of the product in different frequency bands can be accurately understood, thereby providing data support for subsequent power consumption optimization. This helps the design team to better control energy consumption and improve the energy efficiency of the product during product development; the output power consumption analysis of the theoretical operating power of the components is performed according to the 5G multi-band energy consumption evaluation data, and the theoretical fluctuation range of the electromagnetic field is limited according to the output power consumption data. The main effect of this step is to associate the energy consumption evaluation results with the electromagnetic field fluctuation range, so that the impact of energy consumption on the electromagnetic field can be considered in the design stage, thereby optimizing the design scheme and improving the stability and performance of the circuit; according to the 5G multi-band energy consumption evaluation data, the power consumption fluctuation analysis of the output power consumption data of the components under the multi-band switching state is performed. This step helps to understand the power consumption changes of components under different frequency band switching states, provide more accurate energy consumption prediction and optimization solutions for the actual application scenarios of the product, and analyze the changes in electromagnetic field intensity under the multi-band switching state according to the power consumption data of the frequency band switching components. Through this step, we can better understand the impact of frequency band switching on the electromagnetic field strength, so that electromagnetic compatibility can be considered in the design stage, and corresponding measures can be taken to reduce the interference of frequency band switching on the electromagnetic field and improve the reliability and stability of the product.

[0109] Preferably, step S24 comprises the following steps:

[0110] Step S241: extracting the power consumption data of low-frequency band, medium-frequency band and high-frequency band components from the power consumption data of the frequency band switching components, and obtaining the power consumption data of low-frequency band components, the power consumption data of medium-frequency band components and the power consumption data of high-frequency band components respectively;

[0111] Step S242: performing frequency domain conversion on the power consumption data of low-frequency components, the power consumption data of mid-frequency components, and the power consumption data of high-frequency components, respectively, to obtain the power consumption frequency domain data of low-frequency components, the power consumption frequency domain data of mid-frequency components, and the power consumption frequency domain data of high-frequency components, respectively;

[0112] Step S243: performing time series alignment on the frequency domain data of power consumption of low-frequency components, the frequency domain data of power consumption of mid-frequency components, and the frequency domain data of power consumption of high-frequency components, respectively, to obtain multi-frequency-band time series alignment data;

[0113] Step S244: performing aliasing region removal processing on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band timing alignment data to obtain multi-band aliasing removal region data;

[0114] Step S245: performing frequency pairwise difference calculations on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band aliasing removal area data and the multi-band timing alignment data, to obtain mid-low frequency band frequency difference data, mid-high frequency band frequency difference data, and high-low frequency band frequency difference data, respectively;

[0115] Step S246: Calculating the power consumption difference in the multi-band switching state according to the mid-low frequency band frequency difference data, the mid-high frequency band frequency difference data, and the high-low frequency band frequency difference data, to obtain the multi-band switching power consumption difference data;

[0116] Step S247: analyzing the electromagnetic field intensity variation of the output power consumption electromagnetic field limited interval under the multi-band switching state according to the multi-band switching power consumption difference data, and obtaining the electromagnetic field intensity variation data of the frequency band switching.

[0117] In the embodiment of the present invention, the power consumption data of the RF module in different frequency bands is extracted from the power consumption data in the 5G multi-band switching state, and the power consumption data of the low-band RF module, the power consumption data of the mid-band RF module and the power consumption data of the high-band RF module are obtained respectively. The power consumption data of the low-band RF module is Fourier transformed to be converted into the frequency domain, and the power consumption frequency domain data of the low-band RF module is obtained, and the power consumption frequency domain data of the components in the low-band, mid-band and high-band are obtained. The power consumption frequency domain data of the components in the low-band, mid-band and high-band are Fourier transformed. Align the time series to ensure that they are consistent in time and obtain multi-band timing alignment data for subsequent analysis; based on the multi-band timing alignment data, use wavelet transform to remove the aliasing area of ​​the component power consumption frequency domain data of the low-frequency band, medium-frequency band and high-frequency band to obtain multi-band aliasing removal area data, remove the aliasing area in the time domain or frequency domain to ensure that the obtained data is clear and not interfered with; use the aliasing removal area data and its timing alignment information to calculate the low-frequency band and medium-frequency band, medium-frequency band and high-frequency band, high-frequency band and low-frequency band Frequency difference, respectively obtain the frequency difference data of the mid-low frequency band, the frequency difference data of the mid-high frequency band, and the frequency difference data of the high and low frequency bands; according to the multi-band aliasing removal area data and the timing alignment information, calculate the frequency difference between the low frequency band and the mid frequency band, the mid frequency band and the high frequency band, and the high frequency band and the low frequency band, respectively obtain the frequency difference data of the mid-low frequency band, the frequency difference data of the mid-high frequency band, and the frequency difference data of the high and low frequency bands; use the frequency difference data of the mid-low frequency band, the mid-high frequency band, and the high and low frequency bands, combined with the power consumption data, to calculate the power consumption in the multi-band switching state difference, and obtain the multi-band switching power consumption difference data; according to the frequency difference data of the mid-low frequency band, the frequency difference data of the mid-high frequency band, and the frequency difference data of the high and low frequency band, combined with the power consumption data of the corresponding frequency bands, calculate the power consumption difference in the multi-band switching state to obtain the multi-band switching power consumption difference data; based on the multi-band switching power consumption difference data, analyze the output power consumption electromagnetic field limit range, understand the change of electromagnetic field intensity in the multi-band switching state, and obtain the band switching electromagnetic field intensity change data, so as to evaluate the impact of the electromagnetic field on the system performance.

[0118] The present invention can effectively separate the power consumption data under different frequency bands by extracting the power consumption data of the frequency band switching parts in low frequency band, medium frequency band and high frequency band, so that the subsequent analysis is more targeted and accurate. This helps to deeply understand the power consumption characteristics of the parts under different frequency bands, and provide accurate basic data for the power consumption difference analysis under the frequency band switching state; the power consumption data of the parts in the low frequency band, medium frequency band and high frequency band are converted into the frequency domain, and the power consumption data can be converted into the frequency domain to better show the characteristics of the power consumption signal in the frequency domain. This helps to identify and analyze the power consumption spectrum characteristics under different frequency bands, and provide more information support for the subsequent power consumption difference calculation; by aligning the power consumption frequency domain data of the parts in the low frequency band, medium frequency band and high frequency band in time series, it can ensure that the power consumption data under different frequency bands are consistent in time, eliminate the offset in the time series, so that the power consumption data under different frequency bands can be effectively compared and analyzed; the aliasing area removal processing of the multi-band time series alignment data helps to eliminate the aliasing areas between different frequency bands, further improve the accuracy and reliability of the data, and provide a clearer data basis for the subsequent power consumption difference calculation. By performing frequency difference calculations on the data after aliasing removal, the power consumption differences between different frequency bands can be effectively identified, so as to more accurately analyze the power consumption changes in the frequency band switching state. Based on the frequency difference data, the power consumption difference calculation in the multi-band switching state can quantify the power consumption differences in different frequency band switching states, and provide specific guidance for product design and optimization. By analyzing the power consumption difference data, we can have a deeper understanding of the impact of frequency band switching on the electromagnetic field strength, thus providing an important reference for the electromagnetic compatibility design of the product, and ensuring that the electromagnetic performance of the product in different frequency band switching states meets the requirements.

[0119] Preferably, step S3 comprises the following steps:

[0120] Step S31: Perform multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic correlation performance loss data;

[0121] Step S32: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data to obtain electromagnetic performance loss spatial distribution data;

[0122] Step S33: optimizing the integrated circuit structure of the energy storage circuit board according to the multi-band electromagnetic correlation performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data, to obtain integrated circuit structure optimization data;

[0123] Step S34: Optimizing the manufacturing process based on the integrated circuit structure optimization data to obtain integrated circuit manufacturing process optimization data.

[0124] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0125] Step S31: Perform multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic correlation performance loss data;

[0126] In an embodiment of the present invention, the frequency band switching electromagnetic field intensity change data is compared and analyzed with the component output power consumption data and the 5G multi-band energy consumption evaluation data. According to the comparison results, the changes in the component output power consumption and the energy consumption evaluation data in the multi-band case are evaluated to obtain the multi-band electromagnetic related performance loss data. Specifically, according to the frequency band switching electromagnetic field intensity change data, the changes in the component output power consumption data and the 5G multi-band energy consumption evaluation data are analyzed to obtain the multi-band electromagnetic related performance loss data.

[0127] Step S32: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data to obtain electromagnetic performance loss spatial distribution data;

[0128] In an embodiment of the present invention, a spatial distribution analysis is performed on multi-band electromagnetic correlation performance loss data to understand the correlation between different regions. A statistical method or spatial analysis tool is used to determine the correlation between electromagnetic performance losses in different regions. A spatial distribution correlation analysis is performed on multi-band electromagnetic correlation performance loss data to determine the correlation between electromagnetic performance losses in different regions.

[0129] Step S33: optimizing the integrated circuit structure of the energy storage circuit board according to the multi-band electromagnetic correlation performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data, to obtain integrated circuit structure optimization data;

[0130] In an embodiment of the present invention, an energy storage type circuit board integrated circuit structure is optimized and designed based on multi-band electromagnetic correlation performance loss data, electromagnetic performance loss spatial distribution data, and component output power consumption data. The energy storage type circuit board integrated circuit structure is optimized and designed based on the electromagnetic correlation performance loss data and the spatial distribution data. Specifically, the energy storage type circuit board integrated circuit structure is optimized and designed based on the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data to reduce the electromagnetic correlation performance loss.

[0131] Step S34: Optimizing the manufacturing process based on the integrated circuit structure optimization data to obtain integrated circuit manufacturing process optimization data.

[0132] In the embodiment of the present invention, the manufacturing process is optimized based on the integrated circuit structure optimization data. According to the integrated circuit structure optimization data, the manufacturing process is optimized and the process parameters or process flow are adjusted to achieve the best performance of the integrated circuit structure.

[0133] The present invention performs multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data, so as to fully understand the performance loss of the system when switching frequency bands, help to find performance bottlenecks and optimization space, and improve the overall performance of the system. The spatial distribution correlation analysis of multi-band electromagnetic-related performance loss data can reveal the spatial distribution of electromagnetic performance loss, help determine the main sources and influencing factors of performance loss, and provide an important basis for subsequent optimization. The integrated circuit structure of the energy storage circuit board is optimized and designed according to the multi-band electromagnetic-related performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data. Under the premise of ensuring the functional integrity and performance of the system, the electromagnetic-related performance loss can be minimized, and the stability and reliability of the system can be improved. The preparation process is optimized based on the integrated circuit structure optimization data, which can effectively improve the preparation process of the integrated circuit, improve production efficiency and quality stability, and reduce production costs, thereby realizing the optimization and improvement of the integrated circuit preparation process.

[0134] Preferably, step S31 includes the following steps:

[0135] Step S311: performing electromagnetic field directivity analysis on the electromagnetic field intensity change data of frequency band switching to obtain electromagnetic field directivity data;

[0136] Step S312: Calculating the electromagnetic wave frequency density in different magnetic field directions for the frequency band switching electromagnetic field intensity change data according to the electromagnetic field directivity data to obtain electromagnetic wave frequency density data;

[0137] Step S313: performing density discrete analysis on the electromagnetic wave frequency density data to obtain frequency density discrete data; performing density discrete distribution difference calculation on the magnetic wave frequency density data according to the frequency density discrete data to obtain density distribution difference data;

[0138] Step S314: performing density error distribution constraint according to the density distribution difference data to obtain density error distribution constraint data; performing error distribution constraint linear fitting on the electromagnetic wave frequency density data according to the density error distribution constraint data to obtain electromagnetic wave frequency density fitting data;

[0139] Step S315: Perform output power loss analysis on the output power consumption data of components according to the electromagnetic wave frequency density fitting data to obtain output power consumption loss data; perform performance loss evaluation on the 5G multi-band energy consumption evaluation data according to the output power consumption loss data to obtain multi-band electromagnetic related performance loss data.

[0140] In an embodiment of the present invention, first, the data on the change in electromagnetic field intensity when switching frequency bands are collected and preliminarily processed to ensure the accuracy and consistency of the data, and a specific algorithm or tool (such as multivariate statistical analysis, Kalman filtering, and directional statistics model) is used to perform directional analysis on the data. The goal of the analysis is to determine the change of electromagnetic field intensity in different directions. In the analysis, multiple directions (for example, horizontal, vertical, and a certain angle direction) need to be considered, and the electromagnetic field intensity in each direction is calculated. Finally, the change data of the electromagnetic field intensity in each direction, that is, the electromagnetic field directionality data, are obtained; the electromagnetic field directionality data is used as a reference to analyze the change data of the electromagnetic field intensity of frequency band switching in different directions. In each direction, the electromagnetic wave frequency density data in that direction is calculated using the electromagnetic wave frequency density calculation method. The calculation process needs to consider the frequencies of different frequency bands and the influence of frequency band switching on frequency density. Finally, the electromagnetic wave frequency density data of each frequency band in different directions are obtained; density discrete analysis is performed on the previously calculated electromagnetic wave frequency density data, and statistical methods or other data analysis tools are used to analyze the distribution and discreteness of the frequency density data. The analysis results include the discreteness of the frequency density data and the type of distribution (for example, normal distribution, uniform distribution). The density discrete data is used to further analyze the magnetic wave frequency density data, compare the distribution differences of the electromagnetic wave frequency density in different directions, calculate the density distribution difference data, and determine the change of the electromagnetic wave frequency density in different directions by comparing the density distribution differences in different directions. A set of density distribution difference data represents the density difference of different points in a specific area. First, the density error distribution constraints are applied to these data, which can be achieved by a statistical method, such as normalizing the data, and then using certain constraints (such as mean, variance, etc.) to adjust the data to meet the preset density error distribution requirements. The obtained density error distribution constraint data can be used for the next step of processing. Apply the density error distribution constraint data to the electromagnetic wave frequency density data. There is a set of electromagnetic wave frequency density data, which represents the electromagnetic wave density of a device or system at different frequencies. The error distribution constraint linear fitting is performed on these frequency density data using the previously obtained density error distribution constraint data. This can be achieved by a fitting algorithm (such as linear regression) to find the linear relationship between the frequency density data and the density error distribution, so as to obtain the electromagnetic wave frequency density fitting data. A set of actually measured frequency density data and the corresponding density distribution difference data. The least squares method can be used for linear fitting, with the density distribution difference data as the independent variable and the frequency density data as the dependent variable to obtain the fitted linear relationship equation. In this way, the fitted electromagnetic wave frequency density data can be obtained for subsequent analysis. Output power loss analysis is performed using electromagnetic wave frequency density fitting data.This involves simulation or emulation experiments, which evaluate the output power loss of components at different frequencies by applying electromagnetic wave frequency density data to a specific output power model. Ultimately, based on the output power loss data, performance loss evaluation can be performed to obtain multi-band electromagnetic correlation performance loss data, thereby evaluating the performance of the device or system in multiple frequency bands.

[0141] The electromagnetic field directionality analysis of the present invention helps to understand the directional distribution of the electromagnetic field when the frequency band is switched, so as to determine the main propagation direction and intensity distribution law of the electromagnetic field, and provide basic data for subsequent analysis. By calculating the electromagnetic wave frequency density under different magnetic field directions, a more comprehensive electromagnetic wave distribution can be obtained, which helps to understand the electromagnetic wave energy distribution in different directions, and provide detailed data for subsequent analysis. Density discrete analysis and density distribution difference calculation can reveal the discrete degree and difference of the electromagnetic wave frequency density, which helps to determine the stability and consistency of the electromagnetic field, and provide a basis for subsequent error analysis. Density error distribution constraints and error distribution constraint linear fitting can perform error analysis and fitting on the electromagnetic wave frequency density, so as to accurately evaluate the propagation and energy loss of electromagnetic waves, and provide accurate data for subsequent power consumption analysis. By performing output power consumption reduction analysis on the electromagnetic wave frequency density fitting data, the power consumption changes of the system under different electromagnetic field conditions can be evaluated, and guidance can be provided for system optimization. At the same time, the performance loss evaluation of the 5G multi-band energy consumption evaluation data is performed according to the output power consumption reduction data, and the performance loss of the system under multi-band conditions can be fully understood, providing an important reference for system design and optimization.

[0142] Preferably, step S32 includes the following steps:

[0143] Step S321: performing time series change analysis on the multi-band electromagnetic correlation performance loss data to obtain multi-band performance loss time series change data; constructing a two-dimensional space loss coordinate system for the multi-band electromagnetic correlation performance loss data according to the multi-band performance loss time series change data to obtain a multi-band space loss coordinate system;

[0144] Step S322: marking the positions of the multi-band loss data points on the multi-band spatial loss coordinate system to obtain the position data of the multi-band loss data points;

[0145] Step S323: performing spatial interpolation processing on the multi-band spatial loss coordinate system according to the multi-band loss data point position data to obtain multi-band performance loss spatial interpolation data;

[0146] Step S324: performing loss gradient analysis on the multi-band performance loss spatial interpolation data to obtain loss gradient data;

[0147] Step S325: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data according to the loss gradient data and the multi-band performance loss spatial interpolation data to obtain electromagnetic performance loss spatial distribution data.

[0148] In an embodiment of the present invention, firstly, a time series change analysis of multi-band electromagnetic correlation performance loss data is performed, which involves statistical and trend analysis of performance loss data at multiple time points or time periods. Including, the average loss value, maximum loss value, and change trend of loss value in each time point or time period can be calculated; then, according to the time series change data, a multi-band spatial loss coordinate system can be constructed. This can be done by taking time as one dimension and loss value as another dimension, and plotting the loss data of multiple time points or time periods on a two-dimensional coordinate system. For example, a scatter plot or a line graph can be used to display the performance loss values ​​at different time points to facilitate observation of its time series change trend. In step S322, the multi-band spatial loss coordinate system is marked with the position of multi-band loss data points. This means that a specific loss value is associated with a corresponding position in the coordinate system, thereby obtaining multi-band loss data point position data. Including the loss values ​​of different time points or time periods can be marked in the coordinate system, or different symbols or colors can be used to represent different degrees of loss. Finally, in step S323, the multi-band spatial loss coordinate system is spatially interpolated according to the multi-band loss data point position data to obtain multi-band performance loss spatial interpolation data. This can be achieved through interpolation algorithms, such as Kriging interpolation and radial basis function interpolation. Interpolation processing can fill in the missing data points in the coordinate system to obtain a continuous performance loss spatial distribution map for spatial analysis and visualization. Loss gradient analysis of multi-band performance loss spatial interpolation data can be achieved by calculating the gradient of performance loss at each spatial point. Specifically, the gradient operator or numerical method in calculus can be used to calculate the gradient size and direction of each point. For example, for spatial interpolation data in a two-dimensional case, the central difference method or the finite difference method can be used to calculate the gradient at each point. Based on the loss gradient data and the multi-band performance loss spatial interpolation data, the multi-band electromagnetic correlation performance loss data is subjected to spatial distribution correlation analysis. This can be achieved by comparing the relationship between the loss gradient data and the performance loss spatial distribution data. For example, correlation analysis methods such as Pearson correlation coefficient or Spearman rank correlation coefficient can be used to evaluate the correlation between gradient data and performance loss data, so that the spatial distribution data of electromagnetic performance loss can be obtained, thereby understanding the spatial distribution of performance loss and its correlation with gradient.

[0149] The present invention can understand the changing trend of multi-band performance loss over time by performing time series change analysis on multi-band electromagnetic correlation performance loss data, thereby providing a basis for the dynamic optimization of system performance. Constructing a two-dimensional spatial loss coordinate system can intuitively display the spatial distribution of multi-band performance loss, which helps to identify the main performance loss areas and trends in the system; marking the positions of multi-band loss data points can correspond the performance loss to the spatial position, providing an accurate data basis for subsequent spatial interpolation processing, making the interpolation results more credible. By performing spatial interpolation processing on the positions of multi-band loss data points, the data gaps in space can be filled, and a more continuous and smooth performance loss distribution map can be obtained, which helps to accurately evaluate the spatial characteristics of system performance loss. Loss gradient analysis can reveal the rate and direction of change of performance loss in space, help understand the spatial distribution characteristics of performance loss, and guide subsequent optimization measures. Through spatial distribution correlation analysis, the correlation between performance losses in different regions can be deeply explored, and the existing correlation patterns and influencing factors can be identified, providing deeper insights for system performance optimization.

[0150] Preferably, S33 includes the following steps:

[0151] Step S331: performing electromagnetic impact assessment according to the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data to obtain performance-electromagnetic impact degree data;

[0152] Step S332: marking electromagnetic impact key nodes on the performance-electromagnetic impact degree data to obtain electromagnetic impact key node data;

[0153] Step S333: performing optimization constraint analysis based on electromagnetic impact key node data and component output power consumption data to obtain node constraint condition data;

[0154] Step S334: constructing an integrated line structure optimization model using a K-nearest neighbor algorithm and node constraint condition data to obtain an integrated line structure optimization model;

[0155] Step S335: Based on the integrated circuit structure optimization model and the node constraint condition data, the integrated circuit structure optimization design of the energy storage type circuit board is performed on the electromagnetic influence key node data to obtain the integrated circuit structure optimization data.

[0156] In an embodiment of the present invention, a quantitative analysis method is used to evaluate the electromagnetic impact degree by integrating multi-band electromagnetic correlation performance loss data and electromagnetic performance loss spatial distribution data. Including, a statistical analysis method, such as regression analysis or multivariate linear regression, can be used to model the correlation between electromagnetic performance loss data and associated performance loss data, so as to evaluate the electromagnetic impact degree and obtain performance-electromagnetic impact degree data. The electromagnetic impact key nodes of the performance-electromagnetic impact degree data are marked, and a threshold or a specific algorithm can be used to determine the key nodes. Among them, a threshold can be set, and nodes exceeding the threshold are marked as key nodes, or machine learning algorithms, such as cluster analysis or decision trees, are used to identify nodes with significant impact, so as to obtain electromagnetic impact key node data. According to the electromagnetic impact key node data and the output power consumption data of the components, optimization constraint analysis can be performed, and an optimization algorithm and constraint analysis method can be used. Among them, linear programming or genetic algorithm can be used to combine the electromagnetic impact key node data and power consumption data to determine the optimization constraints, so as to obtain node constraint data. The integrated circuit structure optimization model is constructed using the K nearest neighbor algorithm and the node constraint data, and the integrated circuit structure optimization model can be constructed by applying the K nearest neighbor algorithm to the node constraint data. The K nearest neighbor algorithm can find similar nodes according to the characteristics and constraints of the nodes, and optimize the circuit structure according to their characteristics; based on the integrated circuit structure optimization model and the node constraint condition data, the integrated circuit structure optimization design of the energy storage circuit board is carried out on the key node data of electromagnetic influence. The integrated circuit structure optimization model and the key node data can be combined, and the optimization algorithm and design method can be used to optimize the design of the energy storage circuit board to obtain the integrated circuit structure optimization data.

[0157] The present invention can comprehensively consider the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data through electromagnetic influence degree assessment, quantify the influence degree of different electromagnetic influences on system performance, provide quantitative indicators and important references for subsequent optimization, mark the electromagnetic influence key nodes to determine the key nodes with greater performance influence in the system, help to concentrate on subsequent optimization and improvement, and improve optimization efficiency, and by analyzing the electromagnetic influence key node data and the component output power consumption data, the optimization constraints can be determined, the optimization range can be limited, and the feasibility and effectiveness of the optimization scheme can be ensured, while avoiding potential conflicts and adverse effects, and the integrated circuit structure optimization model constructed by using the K nearest neighbor algorithm can quickly and accurately find the nodes most similar to the existing nodes based on the node constraint condition data, thereby establishing a reasonable and feasible optimization model, providing effective reference and guidance for optimization design, and optimizing the integrated circuit structure of the energy storage circuit board based on the integrated circuit structure optimization model and the node constraint condition data. Under the condition of considering the electromagnetic influence key nodes, the energy storage circuit board can be optimized and designed to improve the performance and reliability of the system, while satisfying the constraint conditions, providing a more scientific and reliable basis for system design.

[0158] Preferably, the node constraint condition data includes line size constraint data, component layout constraint data and component line direction constraint data, and step S333 includes:

[0159] Step S331: performing line size constraint analysis according to electromagnetic influence key node data to obtain line size constraint data;

[0160] Step S332: performing component layout analysis on electromagnetic impact key node data and component output power consumption data according to the line size constraint data to obtain component layout constraint data;

[0161] Step S333: Analyze the component line orientation according to the line size constraint data and the component layout constraint data to obtain the component line orientation constraint data.

[0162] In an embodiment of the present invention, for each key node, the location and characteristics of the surrounding environment are analyzed to determine the appropriate line size constraint. If a node is located in a densely laid out area, it is necessary to consider reducing the size of the line to save space and reduce electromagnetic interference. For other key nodes, due to their importance or the special requirements of the environment in which they are located, it is necessary to maintain a larger line size to ensure that performance is not affected. Combined with the line size constraint data, through optimization algorithms or layout rules, the components near each key node are reasonably laid out to meet the requirements of the line size constraint to the greatest extent and minimize electromagnetic interference. This involves adjusting the optimization of the position, direction and density of the components. According to the line size constraints and component layout constraints, the connection mode and line direction between the components are analyzed to ensure that the line layout is compact and meets the design requirements. This involves considerations such as selecting a suitable line direction, avoiding cross wiring, and optimizing line length to ensure the performance and electromagnetic compatibility of the circuit.

[0163] The line size constraint analysis of the present invention determines the restriction conditions of the line size by considering the electromagnetic impact key node data. This helps to ensure that the line size meets the requirements of the system design and avoids the negative impact of excessive or small line sizes on system performance and electromagnetic compatibility. The component layout analysis is based on the line size constraint data, combined with the electromagnetic impact key node data and the component output power consumption data, to determine the layout position of the components in the system. This can effectively optimize the layout of the system so that the layout of each component meets the line size constraints, and help reduce the impact of electromagnetic interference and uneven power consumption distribution on system performance. The component line orientation analysis takes into account the line size constraint data and the component layout constraint data to determine the direction of the component line. This helps to optimize the line layout, ensure that the direction of the line meets the requirements of the system design, while minimizing electromagnetic interference and signal loss, and improving the reliability and stability of the system.

[0164] Preferably, a 5G energy storage type circuit board is prepared by the preparation process of the 5G energy storage type circuit board as described above.

[0165] By acquiring the preparation demand data of 5G energy storage circuit boards and analyzing the theoretical operating power of components, the present invention can better understand the power required by each component under theoretical operating conditions, which is helpful to control and optimize the power consumption of components in the design stage and improve the overall energy efficiency; analyzing the theoretical electromagnetic field fluctuation range of components can help determine the fluctuation range of the electromagnetic field, so as to consider electromagnetic compatibility and interference issues in the design stage and ensure that the product meets relevant standards. Evaluating the energy consumption of 5G multi-bands can help understand the overall energy consumption and guide subsequent energy consumption optimization work, such as adopting different energy-saving strategies for different frequency bands. Analyzing the output power consumption of components can find out components with higher energy consumption, optimize them in a targeted manner, reduce the overall power consumption, and extend the equipment. Endurance time, limiting the theoretical fluctuation range of the electromagnetic field according to the output power consumption, helps to control the electromagnetic radiation range in practical applications, ensure the safety and stability of the equipment, and understand the electromagnetic field strength change data under the frequency band switching state, which can optimize the electromagnetic radiation control strategy of the equipment during frequency band switching and reduce interference with the surrounding environment and other equipment; by evaluating the changes in electromagnetic field strength caused by frequency band switching, it is possible to accurately grasp the performance loss under multiple frequency bands, and then guide the optimization design of the integrated circuit structure. The optimized circuit structure can reduce the electromagnetic related performance loss, improve the overall performance and stability, and optimize the preparation process based on the optimized integrated circuit structure, which can improve the preparation efficiency, reduce costs, and ensure the quality and stability of the circuit board. This step helps to optimize the process parameters, select appropriate preparation equipment and materials, thereby improving the preparation efficiency and product quality. Writing automated scripts can realize the automated control of the preparation process and improve production efficiency and consistency. At the same time, the design of the underlying operating firmware can better control the movement of the robotic arm and ensure the accuracy and stability of the preparation process. Therefore, the present invention improves a preparation process of a 5G energy storage type circuit board, which is an improvement on the preparation process of a traditional 5G energy storage type circuit board. It solves the problems of inaccurate performance loss analysis and unstable output energy consumption caused by electromagnetic influence in the 5G multi-band switching state in the preparation process of the traditional 5G energy storage type circuit board, improves the accuracy of the performance loss analysis, and improves the stability of the output energy consumption.

[0166] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0167] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A preparation process for a 5G energy storage circuit board, characterized in that: The following steps are involved: Step S1: Obtaining 5G energy storage circuit board preparation demand data; According to the 5G energy storage circuit board preparation demand data, the theoretical operating power of the components is analyzed to obtain the theoretical operating power data of the components; according to the theoretical operating power data of the components, the theoretical electromagnetic field fluctuation range of the components is analyzed to obtain the theoretical electromagnetic field fluctuation range; Step S2: Perform 5G multi-band energy consumption evaluation on the 5G energy storage type circuit board preparation demand data to obtain 5G multi-band energy consumption evaluation data; perform component output power consumption analysis based on the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; perform output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range based on the component output power consumption data to obtain the output power consumption electromagnetic field limitation range; The electromagnetic field strength change analysis is performed on the output power consumption electromagnetic field limited interval under the multi-band switching state to obtain the electromagnetic field strength change data of the frequency band switching; wherein step S2 includes: Step S21: performing a 5G multi-band demand analysis on the 5G energy storage type circuit board preparation demand data to obtain 5G multi-band demand data; performing a 5G multi-band energy consumption assessment based on the 5G multi-band demand data to obtain 5G multi-band energy consumption assessment data; Step S22: performing component output power consumption analysis on the component theoretical operating power data according to the 5G multi-band energy consumption evaluation data to obtain component output power consumption data; performing output power consumption electromagnetic field theoretical fluctuation range limitation on the electromagnetic field theoretical fluctuation range according to the component output power consumption data to obtain the output power consumption electromagnetic field limitation range; Step S23: performing component power consumption fluctuation analysis on component output power consumption data in a multi-band switching state according to the 5G multi-band energy consumption evaluation data to obtain component power consumption data for frequency band switching; Step S24: analyzing the electromagnetic field intensity change of the output power consumption electromagnetic field limited interval under the multi-band switching state according to the power consumption data of the frequency band switching components, and obtaining the electromagnetic field intensity change data of the frequency band switching; wherein step S24 includes: Step S241: extracting the power consumption data of low-frequency band, medium-frequency band and high-frequency band components from the power consumption data of the frequency band switching components, and obtaining the power consumption data of low-frequency band components, the power consumption data of medium-frequency band components and the power consumption data of high-frequency band components respectively; Step S242: performing frequency domain conversion on the power consumption data of low-frequency components, the power consumption data of mid-frequency components, and the power consumption data of high-frequency components, respectively, to obtain the power consumption frequency domain data of low-frequency components, the power consumption frequency domain data of mid-frequency components, and the power consumption frequency domain data of high-frequency components, respectively; Step S243: performing time series alignment on the frequency domain data of power consumption of low-frequency components, the frequency domain data of power consumption of mid-frequency components, and the frequency domain data of power consumption of high-frequency components, respectively, to obtain multi-frequency-band time series alignment data; Step S244: performing aliasing region removal processing on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band timing alignment data to obtain multi-band aliasing removal region data; Step S245: performing frequency pairwise difference calculations on the low-band component power consumption frequency domain data, the mid-band component power consumption frequency domain data, and the high-band component power consumption frequency domain data according to the multi-band aliasing removal area data and the multi-band timing alignment data, to obtain mid-low frequency band frequency difference data, mid-high frequency band frequency difference data, and high-low frequency band frequency difference data, respectively; Step S246: Calculating the power consumption difference in the multi-band switching state according to the mid-low frequency band frequency difference data, the mid-high frequency band frequency difference data, and the high-low frequency band frequency difference data, to obtain the multi-band switching power consumption difference data; Step S247: analyzing the electromagnetic field intensity variation of the output power consumption electromagnetic field limited interval under the multi-band switching state according to the multi-band switching power consumption difference data, and obtaining the electromagnetic field intensity variation data of the frequency band switching; Step S3: performing multi-band performance loss evaluation according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic related performance loss data; optimizing the integrated circuit structure design of the energy storage circuit board according to the multi-band electromagnetic related performance loss data to obtain integrated circuit structure optimization data; optimizing the preparation process based on the integrated circuit structure optimization data to obtain integrated circuit preparation process optimization data; Step S4: Automated script writing is performed on the integrated circuit preparation process optimization data to obtain an automated preparation process optimization script; underlying mechanical operation firmware is designed according to the automated preparation process optimization script to obtain underlying operation firmware for preparation process optimization, and the underlying operation firmware for preparation process optimization is embedded into an external preparation robot arm to execute the preparation process of the 5G energy storage circuit board.

2. The preparation process of the 5G energy storage circuit board according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining 5G energy storage circuit board preparation demand data; Step S12: performing component material property matching processing on the 5G energy storage circuit board preparation demand data to obtain component material property matching data; Step S13: performing a theoretical operating power analysis of the component according to the component material characteristic matching data to obtain the component theoretical operating power data; Step S14: Analyze the theoretical electromagnetic field fluctuation range of the component on the component material characteristic matching data according to the component theoretical operating power data to obtain the electromagnetic field theoretical fluctuation range.

3. The preparation process of the 5G energy storage circuit board according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Perform multi-band performance loss evaluation on component output power consumption data and 5G multi-band energy consumption evaluation data according to the frequency band switching electromagnetic field intensity change data to obtain multi-band electromagnetic correlation performance loss data; Step S32: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data to obtain electromagnetic performance loss spatial distribution data; Step S33: optimizing the integrated circuit structure of the energy storage circuit board according to the multi-band electromagnetic correlation performance loss data, the electromagnetic performance loss spatial distribution data, and the component output power consumption data, to obtain integrated circuit structure optimization data; Step S34: Optimizing the manufacturing process based on the integrated circuit structure optimization data to obtain integrated circuit manufacturing process optimization data.

4. The preparation process of the 5G energy storage circuit board according to claim 3 is characterized in that: Step S31 includes the following steps: Step S311: performing electromagnetic field directivity analysis on the electromagnetic field intensity change data of frequency band switching to obtain electromagnetic field directivity data; Step S312: Calculating the electromagnetic wave frequency density in different magnetic field directions for the frequency band switching electromagnetic field intensity change data according to the electromagnetic field directivity data to obtain electromagnetic wave frequency density data; Step S313: performing density discrete analysis on the electromagnetic wave frequency density data to obtain frequency density discrete data; performing density discrete distribution difference calculation on the magnetic wave frequency density data according to the frequency density discrete data to obtain density distribution difference data; Step S314: performing density error distribution constraint according to the density distribution difference data to obtain density error distribution constraint data; performing error distribution constraint linear fitting on the electromagnetic wave frequency density data according to the density error distribution constraint data to obtain electromagnetic wave frequency density fitting data; Step S315: Perform output power loss analysis on the output power consumption data of components according to the electromagnetic wave frequency density fitting data to obtain output power consumption loss data; perform performance loss evaluation on the 5G multi-band energy consumption evaluation data according to the output power consumption loss data to obtain multi-band electromagnetic related performance loss data.

5. The preparation process of the 5G energy storage circuit board according to claim 3 is characterized in that: Step S32 includes the following steps: Step S321: performing time series change analysis on the multi-band electromagnetic correlation performance loss data to obtain multi-band performance loss time series change data; constructing a two-dimensional space loss coordinate system for the multi-band electromagnetic correlation performance loss data according to the multi-band performance loss time series change data to obtain a multi-band space loss coordinate system; Step S322: marking the positions of the multi-band loss data points on the multi-band spatial loss coordinate system to obtain the position data of the multi-band loss data points; Step S323: performing spatial interpolation processing on the multi-band spatial loss coordinate system according to the multi-band loss data point position data to obtain multi-band performance loss spatial interpolation data; Step S324: performing loss gradient analysis on the multi-band performance loss spatial interpolation data to obtain loss gradient data; Step S325: performing spatial distribution correlation analysis on the multi-band electromagnetic correlation performance loss data according to the loss gradient data and the multi-band performance loss spatial interpolation data to obtain electromagnetic performance loss spatial distribution data.

6. The preparation process of the 5G energy storage circuit board according to claim 3, characterized in that: S33 includes the following steps: Step S331: performing electromagnetic impact assessment according to the multi-band electromagnetic correlation performance loss data and the electromagnetic performance loss spatial distribution data to obtain performance-electromagnetic impact degree data; Step S332: marking electromagnetic impact key nodes on the performance-electromagnetic impact degree data to obtain electromagnetic impact key node data; Step S333: performing optimization constraint analysis based on electromagnetic impact key node data and component output power consumption data to obtain node constraint condition data; Step S334: constructing an integrated line structure optimization model using a K-nearest neighbor algorithm and node constraint condition data to obtain an integrated line structure optimization model; Step S335: Based on the integrated circuit structure optimization model and the node constraint condition data, the integrated circuit structure optimization design of the energy storage type circuit board is performed on the electromagnetic influence key node data to obtain the integrated circuit structure optimization data.

7. The preparation process of the 5G energy storage circuit board according to claim 6, characterized in that: The node constraint condition data includes line size constraint data, component layout constraint data, and component line direction constraint data. Step S333 includes: Step S331: performing line size constraint analysis according to electromagnetic influence key node data to obtain line size constraint data; Step S332: performing component layout analysis on electromagnetic impact key node data and component output power consumption data according to the line size constraint data to obtain component layout constraint data; Step S333: Analyze the component line orientation according to the line size constraint data and the component layout constraint data to obtain the component line orientation constraint data.

8. A 5G energy storage circuit board, characterized in that: It is produced by the preparation process of the 5G energy storage circuit board as described in claim 1.

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