A method of miniaturizing a pressure resonant transmitter

Abstract

This invention relates to the field of sensor miniaturization technology and discloses a method for miniaturizing a pressure resonant transmitter. The method includes: analyzing design specifications to break down the transmitter into multiple functional units; obtaining the morphological and electrical parameters of each unit, and accordingly forming compression and fusion metrics to quantify the physical compression potential and functional fusion feasibility; deriving improvement coefficients for each unit based on these metrics, and then reconstructing these coefficients according to the dependencies between units to accurately reflect their optimization value in the overall system; prioritizing the processing of the reconstructed coefficients, and automatically generating an optimization scheme. This invention overcomes the shortcomings of existing technologies that rely on local experience and lack a global perspective through dual quantitative indicators and system-level dependency analysis, achieving precise miniaturization design and system optimization.

Description

Technical Field

[0001] This invention relates to the field of sensor miniaturization technology, specifically a method for miniaturizing a pressure resonant transmitter. Background Technology

[0002] Pressure resonant transmitters are widely used in industrial automation and process control, and their miniaturization is key to meeting the requirements for portable and integrated installation. Currently, common technical approaches to transmitter miniaturization mainly rely on the independent optimization of single functional units or components. One approach directly compresses the physical dimensions of key components such as the sensor core and circuitry, employing more precise manufacturing processes or highly integrated electronic components. Another approach, based on engineers' design experience, involves a compact layout of the transmitter's mechanical structure or attempts to achieve simple physical integration of some functional circuits.

[0003] These existing technological solutions have limitations. Direct reduction of physical dimensions often lacks systematic guidance and may affect the overall measurement accuracy, stability, or reliability of the transmitter by excessively miniaturizing a critical unit that is sensitive to electrical performance. Experience-based layout and integration are highly subjective and contingent, making it difficult to guarantee that every design will achieve the optimal solution, and resulting in long design cycles and low efficiency. Treating the transmitter as a collection of independent components rather than an interconnected organic whole fails to quantitatively assess the miniaturization potential of each unit and their interactions at the system level.

[0004] Current technology lacks an effective method to scientifically and systematically guide the miniaturization design of transmitters. The challenge lies in moving beyond traditional approaches that rely on local experience and physical compression, and establishing a quantitative evaluation model to accurately measure the inherent potential of each functional unit in terms of both physical space compression and functional integration. It is necessary to address how to incorporate the functional dependencies and electrical connections between units into decision-making, thereby avoiding the negative impact of local optimization on system performance and generating a globally optimal miniaturization implementation path. Summary of the Invention

[0005] The purpose of this invention is to provide a method for miniaturizing a pressure resonant transmitter to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for miniaturizing a pressure resonant transmitter, the method comprising:

[0007] The hardware composition is analyzed from the transmitter's design specifications, and the transmitter is broken down into multiple functional units.

[0008] Obtain the morphological and electrical parameters of each functional unit; generate compression and fusion metrics for the functional unit based on the morphological and electrical parameters;

[0009] Improvement coefficients of functional units are derived based on compressed and fused metrics; the improvement coefficients are reconstructed based on the dependencies between functional units;

[0010] Based on the improved coefficients after reconstruction, the processing priority of functional units is arranged to generate a miniaturized planning scheme.

[0011] Preferably, the step of analyzing the hardware composition from the transmitter's design specifications and dividing the transmitter into multiple functional units includes: reading the transmitter's substrate wiring diagram and packaging structure diagram; dividing the electrical connection area according to the substrate wiring diagram, with each electrical connection area corresponding to a chipset; calculating the occupied area of ​​each electrical connection area, and merging electrical connection areas with occupied area deviations within permissible values ​​into the same functional unit.

[0012] Preferably, the acquisition of the morphological and electrical parameters of each functional unit includes: using measuring tools to collect the external dimensions and weight data of the functional unit as morphological parameters; and using testing instruments to record the operating current and voltage characteristics of the functional unit as electrical parameters.

[0013] Preferably, the step of forming the compression metric and fusion metric of the functional unit based on morphological parameters and electrical parameters includes: extracting volume data and surface area data from morphological parameters to calculate space utilization; extracting power consumption data and signal integrity indicators from electrical parameters to calculate performance efficiency; combining space utilization and performance efficiency to generate a compression metric by weighted summation; and calculating a fusion metric based on the internal component density and number of interfaces of the functional unit.

[0014] Preferably, the improvement coefficients derived from the functional unit based on the compression metric and the fusion metric include: comparing the compression metric and the fusion metric with a preset benchmark range, respectively, and calculating the relative deviation of the compression metric and the relative deviation of the fusion metric; determining the level identifier of the compression metric and the level identifier of the fusion metric based on the magnitude of the relative deviation; and querying a predefined relational mapping table based on the level identifier to obtain the improvement coefficients.

[0015] Preferably, the reconstruction of the improvement coefficients based on the dependencies between functional units includes: selecting a target functional unit; identifying other functional units that are directly or indirectly connected to the target functional unit; calculating the signal transmission delay and physical distance between the target functional unit and other functional units to generate a proximity index; matching the functional categories of the target functional unit and other functional units to generate a synergy index; calculating an adjustment factor using the proximity index and the synergy index; and applying the adjustment factor to modify the improvement coefficients of the target functional unit to obtain the reconstructed improvement coefficients.

[0016] Preferably, the calculation of signal transmission delay and physical distance between the target functional unit and other functional units to generate a proximity index includes: measuring the wire length and propagation time between the target functional unit and other functional units; normalizing the wire length to a distance fraction and the propagation time to a delay fraction; and linearly combining the distance fraction and the delay fraction to generate a proximity index.

[0017] Preferably, the process of matching the functional categories of the target functional unit with those of other functional units to generate a synergy index includes: querying the functional type of the target functional unit and the functional types of other functional units; if the functional types belong to a preset complementary combination, finding the synergy weight according to the complementarity strength table; if the functional types do not belong to a complementary combination, calculating the correlation based on historical co-occurrence data; and generating a synergy index based on the synergy weight or correlation.

[0018] Preferably, the calculation of correlation degree based on historical co-occurrence data includes: retrieving the transmitter's past design records and counting the frequency of the target functional unit appearing simultaneously with other functional units; obtaining a dependency table of functional types and determining the basic dependency value; and calculating the correlation degree by combining the simultaneous occurrence frequency and the basic dependency value through an arithmetic mean.

[0019] Preferably, the step of arranging the processing priorities of functional units according to the reconstructed improvement coefficients to generate a miniaturization planning scheme includes: sorting the reconstructed improvement coefficients in descending order to obtain a priority sequence; selecting functional units for size optimization according to the priority sequence; recording optimization steps and parameter changes; and outputting the miniaturization planning scheme.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] By establishing compression and fusion metrics for each functional unit, integrating both morphological and electrical parameters, miniaturization design shifts from relying on qualitative, empirical judgments to precise, data-driven quantitative analysis. The compression metric comprehensively considers morphological parameters such as physical dimensions, material properties, and heat dissipation requirements, as well as their correlation with electrical performance metrics like power consumption and signal integrity, generating an objective indicator reflecting the unit's potential for physical space compression. The fusion metric assesses characteristics such as interface compatibility, signal type, and functional coupling, quantifying its feasibility for functional integration with other units. This dual-quantification model accurately reveals the inherent miniaturization differences among units, freeing design decisions from excessive reliance on subjective experience. Designers can compare the optimization difficulty and value of different units based on explicit numerical values, improving the scientific rigor and reliability of design path selection and avoiding performance risks caused by blind compression.

[0022] By introducing an improved coefficient reconstruction mechanism based on inter-unit dependencies, the miniaturization process is elevated from local optimization of isolated units to global optimization considering system interconnections. The transmitter is an organic whole with interconnected functional units; the connections between units, such as signal flow, power dependence, and mechanical constraints, directly affect the overall effect of local optimization. The reconstruction algorithm analyzes these dependency networks and dynamically adjusts the independent improvement coefficients of each unit, ensuring that their values ​​accurately reflect the true optimization value of the unit within the system context. Optimizing a "bottleneck" unit on the critical path increases its reconstruction coefficient, thus prioritizing its processing. This dynamic prioritization allows miniaturization efforts to be invested in the links that generate the greatest system benefits, resulting in a more compact overall structure and a collaborative design scheme with minimal performance impact. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating the working principle of the method for miniaturized pressure resonant transmitter described in this invention.

[0024] Figure 2 A flowchart for generating compression and fusion metrics;

[0025] Figure 3 A flowchart for improving the coefficients of the reconstructed functional units;

[0026] Figure 4 A graph showing the correlation between proximity and synergy;

[0027] Figure 5 A comparison chart of the improved coefficient distribution after functional unit reconstruction. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1This invention provides a method for miniaturizing a pressure resonant transmitter. The method includes: analyzing the hardware composition from the transmitter's design specifications, which involves analyzing design documents or drawings to identify the transmitter's overall structure and breaking the transmitter down into multiple functional units, each representing an independent hardware module, such as a sensor module or a signal processing module. This breakdown is based on functional independence and physical layout, ensuring that each unit has clear boundaries and interfaces. Subsequently, the morphological and electrical parameters of each functional unit are acquired. The morphological parameters cover physical properties such as size and weight, while the electrical parameters include performance indicators such as current and voltage. These parameters are collected using standard measurement tools to provide basic data for subsequent quantitative analysis. Next, compression and fusion metrics are generated for the functional units based on the morphological and electrical parameters. The compression metric reflects the unit's spatial compactness and is derived by calculating indicators such as space utilization. The fusion metric assesses the unit's integrability based on internal component density and the number of interfaces. These two metrics are generated using a weighted or combined formula to quantify the unit's optimization potential. Improvement coefficients for functional units are derived based on compression and fusion metrics. Each improvement coefficient is a numerical indicator determined by comparing the metric with a preset benchmark and mapping it to a predefined table; it indicates the urgency of unit miniaturization. The improvement coefficients are reconstructed based on dependencies between functional units, including signal flow or physical connections. The reconstruction process uses proximity and synergy metrics to adjust the improvement coefficients, reflecting the mutual influence between units. Functional units are prioritized according to their reconstructed improvement coefficients, with units having higher improvement coefficients processed first. This priority sequence generates a detailed miniaturization plan, outlining optimization steps and parameter changes to guide actual design adjustments. The entire process ensures the systematic and efficient miniaturization of the transmitter.

[0030] Example 1: In specific implementation, reading the transmitter's substrate wiring diagram and package structure diagram is the initial step. The substrate wiring diagram is provided in Gerber format or a similar electronic design automation (EDA) file, while the package structure diagram exists as a STEP file or a 3D model file. These graphic data are loaded using dedicated drawing parsing software. The software identifies and vectorizes the graphic elements, extracting key geometric information such as circuits, pads, vias, and component outlines. In some embodiments, the parsing of the substrate wiring diagram focuses on identifying the connectivity of the electrical network, while the parsing of the package structure diagram focuses on obtaining the physical package's external dimensions and internal cavity structure. Electrical connection areas are divided according to the substrate wiring diagram. Each electrical connection area corresponds to a chipset or functional circuit module. The division process is based on the electrical isolation characteristics of the circuit network. In specific implementation, image processing algorithms are used to perform region growing segmentation on the wiring diagram. The algorithm uses the power network or ground network as seed points and gradually expands to all electrically connected lines and components, forming a closed electrical connection area. In practice, the boundaries of electrical connection areas are defined by insulation gaps or wiring layer switching points. The occupied area of ​​each electrical connection area is calculated using a pixel-based statistical method. First, the vector graphics are converted into high-resolution bitmaps. Then, the total number of pixels belonging to a specific electrical connection area is counted, and multiplied by the actual area scaling factor represented by each pixel to obtain the precise occupied area value. Electrical connection areas with occupied area deviations within the permissible value are merged into the same functional unit. The permissible value is a fixed percentage threshold preset by the designer based on miniaturization accuracy requirements. The merging operation is performed by comparing the difference in occupied area between adjacent electrical connection areas with this threshold. If the difference is less than the permissible value, the area is merged. The merged functional unit logically represents a hardware module with complete sub-functions.

[0031] Obtaining the morphological and electrical parameters of each functional unit is fundamental to subsequent quantitative analysis. Measuring tools are used to collect the external dimensions and weight data of the functional units as morphological parameters. In practice, for physical functional units, a 3D coordinate measuring machine or laser scanner is used to acquire their external dimensions. The measurement process involves multi-point sampling along the outer contour of the functional unit, recording the maximum values ​​of length, width, and height. Weight data is measured using a high-precision electronic balance in a static environment. It is understood that for functional units still in the design phase, their morphological parameters can be directly derived from the computer-aided design model without physical measurement. The operating current and voltage characteristics of the functional units are recorded as electrical parameters using testing instruments, including digital multimeters, oscilloscopes, or power analyzers. The functional units are placed in a standard test environment, and their rated operating voltage is applied. Their steady-state operating current and dynamic current fluctuations are measured. Voltage characteristics cover indicators such as input voltage range, output voltage accuracy, and ripple coefficient. In some embodiments, the acquisition of electrical parameters needs to be repeated under various load conditions to obtain a comprehensive performance profile.

[0032] It is understandable that the acquisition of morphological and electrical parameters must follow strict measurement procedures to ensure data consistency and comparability. All collected parameter data is structured and stored in a parameter database. Database fields explicitly record the parameter name, value, unit, measurement timestamp, and corresponding functional unit identifier, providing an accurate data source for subsequent calculations of compressed and integrated metrics. Optionally, the parameter database can integrate version control functionality to track parameter change history during design iterations. In practice, the parameter acquisition process can be scripted for batch execution, controlling measuring instruments and database read / write operations through an application programming interface (API), improving data acquisition efficiency. Optionally, for specific types of integrated functional units, some electrical parameters can also be directly imported from component datasheets as a supplement to measured data.

[0033] Example 2: See Figure 2In practice, the process of generating compression and fusion metrics begins with extracting volume and surface area data from morphological parameters. Morphological parameters are provided by measurement tools or design models, volume data is calculated from the external dimensions of the functional unit, and surface area data is derived from the external geometry of the functional unit. When calculating space utilization, the ratio of volume to surface area is used to quantify space compactness; a higher space utilization value indicates more efficient volume occupancy per unit surface area. Power consumption data and signal integrity indicators are extracted from electrical parameters. Electrical parameters are recorded by testing instruments; power consumption data is the product of operating current and voltage; signal integrity indicators are obtained through eye diagram measurements or bit error rate tests. When calculating performance efficiency, power consumption and signal quality are combined; performance efficiency can be the ratio of signal quality indicators to power consumption, reflecting the energy efficiency level of the functional unit. Combining space utilization and performance efficiency, a weighted summation is used to generate a compression metric. The weighted summation process assigns specific weights to space utilization and performance efficiency, with weights set according to design priorities. For example, when space compactness requirements are high, the weight of space utilization increases. The compression metric serves as a comprehensive indicator reflecting the optimization potential of the functional unit in terms of space and performance.

[0034] In some embodiments, space utilization calculations may consider the internal layout of functional units, such as analyzing the effective volume ratio through a 3D model. Performance efficiency calculations may include a comprehensive evaluation of dynamic and static power consumption to ensure completeness. A fusion metric is calculated based on the internal component density and the number of interfaces within the functional unit. The internal component density is obtained by counting the number of components within the functional unit and dividing by its volume. The number of interfaces is the sum of the functional unit's connection points to external systems. The fusion metric is generated by linearly combining or normalizing the component density and the number of interfaces. A higher fusion metric indicates that the functional unit is easier to integrate or miniaturize. It is understood that the formation of compression and fusion metrics depends on accurate data input and calculation algorithms. All calculation steps are automated through programming scripts or dedicated software to reduce human error.

[0035] Improvement coefficients for functional units are derived based on compression and fusion metrics. These metrics are compared to preset benchmark ranges derived from historical design data or industry standards. For example, the benchmark range for compression metrics might be 0.5 to 1.0, and for fusion metrics, it might be 0.3 to 0.8. The relative deviations of the compression and fusion metrics are calculated. The relative deviation is the difference between the measured value and the median of the benchmark range, divided by the width of the benchmark range, used to standardize the degree of deviation. Based on the magnitude of the relative deviation, level labels for compression and fusion metrics are determined. These labels are categorized into high, medium, and low levels. For example, a relative deviation greater than 20% is high, 10% to 20% is medium, and less than 10% is low. The level labels are determined based on a predefined threshold table. An improvement coefficient is obtained by querying a predefined relational mapping table based on the level label. The relational mapping table is stored in matrix form, with rows corresponding to compression metric levels and columns corresponding to fusion metric levels. Each cell contains an improvement coefficient value, typically between 0 and 1, indicating the urgency of miniaturization improvements.

[0036] In some embodiments, the comparison between compressed and fused metrics may use a sliding window benchmark to adapt to design changes, and the determination of grade identifiers may introduce fuzzy logic to handle continuous deviation values. It is understood that the derivation process of the improvement coefficients needs to ensure that the mapping table is updated in sync with design specifications to avoid outdated benchmarks affecting accuracy. Optionally, the relationship mapping table can be dynamically adjusted based on a machine learning model to optimize the allocation of improvement coefficients. The formula for calculating the compressed metric can be expressed as:

[0037] in: This represents the compression metric. Indicates space utilization rate, Indicates performance efficiency. and Represents the weighting coefficients, satisfying + =1, weighting coefficient and The design is set by the designers based on miniaturization goals, such as when space optimization is prioritized. Take 0.7, Take 0.3. In some embodiments, space utilization rate The calculations may be further refined, for example by introducing shape factor corrections, but the core remains based on volume and surface area data.

[0038] Example 3: See Figure 3In practical implementation, selecting the target functional unit is the starting point of the refactoring process. The target functional unit is the specific functional unit whose improvement coefficient is to be evaluated. Other functional units that are directly or indirectly connected to the target functional unit are identified. Direct connection refers to a physical wire or signal line connection between functional units, while indirect connection refers to communication between functional units through an intermediate unit or shared bus. The identification process is based on the transmitter's system topology diagram or network connection table, traversing all connection paths to list the relevant functional units. The signal transmission delay and physical distance between the target functional unit and other functional units are calculated. The signal transmission delay is obtained through simulation tools or actual measurement, and the physical distance is directly measured from the layout diagram as the straight-line length between the centroids of the functional units. When generating the proximity index, the proximity index comprehensively reflects the spatiotemporal proximity between functional units. The functional categories of the target functional unit and other functional units are matched. The functional categories are obtained from the design documents, generating a synergy index, which characterizes the tightness of cooperation between functional units. Adjustment factors are calculated using proximity and synergy indices. The adjustment factor is a numerical coefficient. The improvement coefficient of the target functional unit is modified by applying the adjustment factor. The modification operation is achieved through multiplication or addition operations to obtain the reconstructed improvement coefficient. The reconstructed improvement coefficient more accurately reflects the miniaturization requirements of the target functional unit at the system level.

[0039] In some embodiments, target functional units can be selected according to their physical location or functional hierarchy within the system to ensure comprehensive coverage. When identifying connectivity, graph theory algorithms can be used to analyze the connectivity matrix and quickly identify all associated units. The signal transmission delay and physical distance between the target functional unit and other functional units are calculated to generate a proximity index. This includes measuring the wire length and propagation time between the target functional unit and other functional units. The wire length is calculated by extracting the metal trace paths from the layout diagram, and the propagation time is measured using a time-domain reflectometer or simulation software at typical operating frequencies to determine the delay from transmission to reception. The wire length is normalized to a distance fraction, scaled proportionally to the maximum wire length in the system, and the propagation time is normalized to a delay fraction, scaled proportionally to the maximum permissible signal delay. A linear combination of the distance and delay fractions generates the proximity index, with the weighting coefficients reflecting the degree of influence of distance and delay on proximity.

[0040] It is understandable that calculating proximity metrics requires a unified benchmark to ensure comparability, and all measurement data must be calibrated to the same conditions. In some embodiments, signal transmission delay measurements may consider worst-case scenarios under different operating modes, and physical distance measurements may use Manhattan distance or actual cabling length to improve accuracy. Generating Proximity Metrics The calculation formula can be expressed as:

[0041]

[0042] in: Indicates proximity index, Represents the normalized distance score. Represents the normalized delay score, and Denotes the weighting coefficients and satisfies + =1, weighting coefficient and The value is determined by signal integrity requirements, such as in high-frequency applications. The values ​​are relatively large. Optionally, the normalization of the distance and delay scores may employ logarithmic scaling to accommodate a large dynamic range.

[0043] The process involves matching the functional categories of the target functional unit with those of other functional units to generate a synergy index. This includes querying the functional types of the target functional unit and other functional units. Functional types are extracted from the functional description fields in the design specifications. If a functional type belongs to a predefined complementary combination, a synergy weight is retrieved based on a complementarity strength table. Complementary combinations are predefined functional pairing tables, and the complementarity strength table stores the weight value corresponding to each pair of functional types. If a functional type does not belong to a complementary combination, the correlation is calculated based on historical co-occurrence data, which comes from the transmitter's past design project library. A synergy index is generated based on the synergy weight or correlation, and this index is normalized to a standard range. It is understood that matching functional categories requires maintaining and updating a classification dictionary to cover all possible functional types. In some embodiments, the determination of complementary combinations may be based on data flow or control flow analysis between functional units, and the allocation of synergy weights may consider the strength of functional coupling.

[0044] Example 4: In specific implementation, matching the functional categories of the target functional unit with other functional units is the core step in generating the synergy index. The target functional unit is the functional unit to be evaluated, and other functional units are units connected to the target functional unit. Querying the functional types of the target functional unit and other functional units is achieved by accessing the design specification database. The functional types are extracted from the functional classification fields of the database. For example, functional types may include sensor type, amplifier type, or processor type, etc. The classification fields are coded based on a standardized functional dictionary to ensure consistency. If the functional types belong to a preset complementary combination, the synergy weight is found according to the complementarity strength table. The complementary combination is a predefined set of functional pairing relationships. The complementarity strength table stores the weight values ​​of different functional type pairings in tabular form. The weight values ​​reflect the synergy strength of the paired functions. If the functional types do not belong to a complementary combination, the correlation is calculated based on historical co-occurrence data. Historical co-occurrence data comes from the transmitter's past design project library. The correlation calculation is completed by statistically analyzing co-occurrence frequency and dependency relationships. A synergy index is generated based on the synergy weight or correlation. The synergy index is a numerical indicator used to quantify the degree of cooperation between functional units. The generation process may involve normalization processing to scale the index to a standard range.

[0045] In some embodiments, automated scripts may be used to batch process multiple functional unit pairs when querying function types, improving efficiency. The presupposition of complementary combinations is based on domain knowledge; for example, sensor type and amplifier type are often defined as complementary combinations because sensor output requires amplification. The complementarity strength table needs to be updated regularly to include new function type pairings. When calculating correlation based on historical co-occurrence data, the correlation calculation ensures that non-complementary functional units can also receive a reasonable synergy assessment. It is understood that the generation of synergy indicators depends on accurate function type classification and complete historical data; any classification errors or missing data may affect the accuracy of the indicators.

[0046] Calculating correlation based on historical co-occurrence data involves retrieving the transmitter's past design records, stored in a version control system. These records include historical design drawings, documents, and project lists. The retrieval process is achieved through keyword search or project identifier matching. The frequency of co-occurrence of the target functional unit with other functional units is calculated by dividing the number of times the target functional unit and other functional units appear together in the same transmitter design in past design records by the total number of design projects. A dependency table for functional types is obtained. This table is a static table describing the inherent dependencies between different functional types. The dependency table is derived based on functional logic; for example, power supply type functional units are usually dependent on but not on other units. Basic dependency values ​​are determined by looking up these values ​​in the dependency table, representing the basic correlation strength between functional types. Combining the co-occurrence frequency and basic dependency value, the correlation is calculated using an arithmetic mean. The arithmetic mean is the sum of the co-occurrence frequency and the basic dependency value divided by two. This correlation value is used to replace the co-occurrence weight when the functional types are not complementary combinations.

[0047] In practice, retrieving past design records may use database query languages ​​such as SQL to filter related projects. When calculating the frequency of simultaneous occurrences, the frequency calculation considers the weight of the design projects; for example, recent projects are given higher weight to improve relevance. The dependency table is loaded from a central knowledge base to ensure consistency. The determination of basic dependency values ​​may be based on the input-output relationship of functional types; for example, if the output of functional unit A is the input of functional unit B, the basic dependency value is higher. Arithmetic average calculation is simple and easy to implement, but optional, other weighted average methods may also be used in specific scenarios. The results of correlation calculations are stored in a temporary database for later use.

[0048] When matching the function category of a target functional unit with that of other functional units, the matching process is achieved by comparing function type strings or codes. Preset complementary combinations are stored in list form. See Table 1, which shows a complementary strength table containing function type pairings and corresponding collaborative weights.

[0049] Table 1: Complementary Strength Table

[0050]

[0051] In practice, the complementarity strength table is queried by finding matching function type pairs. If the target functional unit is a sensor and other functional units are amplifiers, the synergy weight is set to 0.8 from the table. If the function type pair is not in the table, a correlation calculation based on historical co-occurrence data is triggered. The synergy index is generated by directly assigning a value based on the found synergy weight or the calculated correlation, or by adjusting it to a range of 0-1 using a scaling function. It's understandable that the content of the complementarity strength table needs to match the specific application scenario of the transmitter; different applications may require customized tables.

[0052] Optionally, the preset complementary combinations may be dynamically updated, automatically adding new pairings by analyzing new design projects. Optionally, the statistics of historical co-occurrence data may include a time decay factor to reduce the impact of older projects. In implementation, after the synergy index is generated, its rationality needs to be verified, for example, by simulating functional unit interaction scenarios to check whether the index value reflects actual synergy requirements. The synergy index is ultimately integrated into the improvement coefficient reconstruction process to adjust the improvement coefficients of the target functional units.

[0053] In practice, the step of calculating correlation based on historical co-occurrence data must ensure data quality. Past design records may contain incomplete or erroneous entries; therefore, data cleaning is essential, such as filtering out test items or invalid designs. When calculating the frequency of co-occurrences, smoothing may be used to avoid the zero-frequency problem. The determination of the dependency table may be based on expert surveys or machine learning analysis to improve accuracy. The assignment of basic dependency values ​​may be hierarchical, for example, assigning high values ​​to direct dependencies and low values ​​to indirect dependencies. The arithmetic mean is a simple and effective method for calculating correlation, but alternatively, the geometric mean or harmonic mean may be used in specific situations to handle outliers.

[0054] See Figure 4 In the reconstruction of functional unit improvement coefficients for miniaturized pressure resonant transmitters, the correlation analysis between proximity and synergy indices relies on measured data from multiple functional units. The horizontal axis in the figure represents the proximity index, reflecting the quantification result of a linear combination of signal transmission delay and physical distance between functional units; the vertical axis represents the synergy index, generated by matching functional categories (complementary combination weights or historical co-occurrence correlation) between functional units. Different colored points correspond to functional units such as processor groups, sensor groups, power supply groups, and interface groups. The red fitted line presents the overall correlation trend between proximity and synergy, revealing the positive correlation between physical proximity and functional synergy between functional units, providing a quantitative basis for subsequent prioritization of functional unit processing based on reconstruction improvement coefficients and generation of miniaturization planning schemes.

[0055] Example 5: In specific implementation, prioritizing the functional units based on their reconstructed improvement coefficients is a crucial step in generating the miniaturization planning scheme. The reconstructed improvement coefficients are numerical indicators adjusted for dependencies, and the functional units are independent modules decomposed within the transmitter. The processing priority determines the order in which functional units undergo size optimization. The reconstructed improvement coefficients are sorted in descending order to obtain a priority sequence. This sorting process is implemented using a sorting algorithm, such as quicksort or bubble sort, to sort the array storing the reconstructed improvement coefficients in descending order. The sorted result is an ordered list of functional unit identifiers and their corresponding reconstructed improvement coefficients. Functional units are selected for size optimization based on the priority sequence. Size optimization involves reducing the physical size and layout of the functional units. The selection process starts from the top of the priority sequence, sequentially selecting the functional units with the highest reconstructed improvement coefficients for processing. Optimization steps and parameter changes are recorded. Optimization steps describe the specific modifications performed on the functional units, and parameter changes record the numerical changes in the morphological and electrical parameters of the functional units before and after optimization. The miniaturization planning scheme is output as a document or electronic file containing the optimization order, operation methods, and expected results. In some embodiments, the sorting process may consider type weights for functional units, such as assigning additional priority to core processing units. Size optimization may involve a combination of various techniques. When recording parameter changes, it is necessary to ensure the consistency of the data format for subsequent analysis. It is understood that the accuracy of the priority sequence directly depends on the calculation quality of the reconstructed improvement coefficients; any calculation deviation will affect the solution's effectiveness.

[0056] In practical implementation, prioritizing functional units based on their reconstructed improvement coefficients can be illustrated with a specific example. For instance, suppose a pressure resonant transmitter contains three functional units: functional unit A has a reconstructed improvement coefficient of 0.85, functional unit B has a reconstructed improvement coefficient of 0.72, and functional unit C has a reconstructed improvement coefficient of 0.91. Sorting the reconstructed improvement coefficients in descending order yields a priority sequence of [functional unit C, functional unit A, functional unit B]. This sequence indicates that functional unit C has the highest processing priority. Based on this priority sequence, functional unit C is first selected for size optimization. Optimization measures may include replacing the integrated circuit with a smaller package or optimizing the wiring layout to reduce the physical size. The optimization steps are recorded in detail: "The analog-to-digital converter chip of functional unit C was changed from a QFP package to a CSP package." Parameter changes are recorded: the size of functional unit C changes from 10mm × 10mm to 8mm × 8mm, and the weight decreases from 5 grams to 3.5 grams. After optimizing functional unit C, functional units A and B are processed sequentially. The final miniaturization planning scheme is listed in tabular form, showing all optimization sequences and corresponding parameter changes.

[0057] Optionally, the priority sequence can be generated using a stable sorting algorithm to ensure that functional units with the same improvement coefficient after reconstruction maintain their original relative order. Optionally, an iterative optimization mechanism can be introduced during the size optimization process, i.e., after completing the first round of optimization, the improvement coefficient after reconstruction is recalculated and the priority sequence is updated. In some embodiments, the arrangement of processing priorities may be adjusted in conjunction with the criticality level of functional units; for example, even if the improvement coefficient after reconstruction is slightly lower, functional units that are crucial to system performance can have their priority appropriately increased. Recording optimization steps and parameter changes requires systematic document management. In specific implementations, optimization steps are recorded in a standard operating procedure format, including optimization methods, tool usage instructions, and acceptance criteria. Parameter changes are tracked through a parameter change log table, which includes fields such as parameter name, original value, new value, change time, and responsible person. When outputting a miniaturization planning scheme, the scheme content typically includes a list of functional unit processing priorities, detailed optimization step descriptions, a parameter change summary table, and verification method descriptions. The miniaturization planning scheme can be output as a PDF document, Excel spreadsheet, or XML file to facilitate data exchange between different design tools.

[0058] Optionally, the generation of miniaturization planning schemes can be integrated into a computer-aided design platform for automated output. Optionally, parameter change records can be linked to a product lifecycle management system for version control. In practical implementation, special cases may arise when prioritizing functional units based on their refactored improvement coefficients. For example, when multiple functional units have the same refactored improvement coefficient, their physical location or thermal distribution characteristics within the circuit can be used as a secondary ranking criterion. After generating a miniaturization planning scheme, its feasibility must be confirmed through a design review process. Only after approval can the scheme guide the actual miniaturization implementation.

[0059] It is understandable that the quality of the miniaturization planning scheme directly affects the final effect of transmitter miniaturization. Therefore, logical consistency and technical feasibility checks are necessary before the scheme is output. In specific implementation, the priority sequence is maintained using a dynamic data structure, which facilitates real-time adjustments during optimization. The specific implementation method for size optimization is selected based on the characteristics of the functional units. For example, a high-density integration scheme can be used for digital circuit units, while signal integrity must be prioritized for analog circuit units. Special attention should be paid to the precise expression of technical parameters when recording optimization steps to avoid ambiguity. Records of parameter changes should include measurement conditions and environmental parameters to ensure data reproducibility. Optionally, the output of the miniaturization planning scheme can include visual charts to aid understanding, such as a Gantt chart of functional unit optimization sequence or a size change trend chart. In the final stage of generating the miniaturization planning scheme, a scheme integrity verification is required to check whether all functional units are covered in the priority sequence and whether the optimization steps cover all key parameter change points. The verified miniaturization planning scheme is delivered to the design team as a guideline for transmitter miniaturization implementation. The actual data generated during the scheme execution can be fed back to optimize the coefficient calculation model, forming a closed-loop improvement process.

[0060] See Figure 5 In the functional unit processing priority planning of miniaturized pressure resonant transmitters, the post-reconstruction improvement coefficient is a key decision indicator. The figure shows the distribution of the post-reconstruction improvement coefficients for each functional unit (data storage unit, sensor unit, calibration unit, signal processing unit, power management unit, communication interface unit, protection circuit unit, and filtering unit), with a red dashed line marking a reference benchmark of 0.73 as the average value. Specifically, the data storage unit (unit 5) has the highest post-reconstruction improvement coefficient, significantly higher than the average, indicating its high priority in miniaturization operations such as size optimization. The improvement coefficients of the sensor unit (unit 1), calibration unit (unit 6), signal processing unit (unit 2), and power management unit (unit 3) are all close to or slightly higher than the average. The improvement coefficients of the communication interface unit (unit 4), protection circuit unit (unit 7), and filtering unit (unit 8) are lower than the average. This distribution provides a quantitative basis for prioritizing the processing of functional units in the miniaturization planning scheme: prioritize size optimization and other operations on functional units with high improvement coefficients (such as data storage units and sensor units), and then process units with lower improvement coefficients sequentially to achieve efficient miniaturization of the transmitter.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of miniaturizing a pressure resonant transmitter, comprising: The method performs the following operations: Resolving hardware components from the design specification of the transmitter, and splitting the transmitter into multiple functional units; Obtaining morphological parameters and electrical parameters of each functional unit; and forming compression metric values and fusion metric values of the functional units according to the morphological parameters and the electrical parameters; Deriving improvement coefficients of the functional units based on the compression metric values and the fusion metric values; and reconstructing the improvement coefficients through the dependency relationship between the functional units; Arranging processing priorities of the functional units according to the reconstructed improvement coefficients, and generating a miniaturization planning scheme; The forming of the compression metric values and the fusion metric values of the functional units according to the morphological parameters and the electrical parameters comprises: extracting volume data and surface area data from the morphological parameters, calculating space utilization; extracting power consumption data and signal integrity indicators from the electrical parameters, calculating performance efficiency; combining the space utilization and the performance efficiency, and generating the compression metric values through weighted summation; and calculating the fusion metric values based on the internal component density and the interface quantity of the functional units; The deriving of the improvement coefficients of the functional units based on the compression metric values and the fusion metric values comprises: comparing the compression metric values and the fusion metric values with preset reference ranges respectively, calculating relative deviations of the compression metric values and the fusion metric values; determining grade identifiers of the compression metric values and the fusion metric values according to the sizes of the relative deviations; and obtaining the improvement coefficients based on the grade identifiers and a predefined relationship mapping table; The reconstructing of the improvement coefficients through the dependency relationship between the functional units comprises: selecting a target functional unit, and identifying other functional units having direct or indirect connections with the target functional unit; calculating signal transmission delays and physical distances between the target functional unit and the other functional units, and generating proximity indicators; matching functional categories of the target functional unit and the other functional units, and generating synergy indicators; calculating adjustment factors using the proximity indicators and the synergy indicators; and applying the adjustment factors to modify the improvement coefficients of the target functional unit, to obtain the reconstructed improvement coefficients.

2. The method of miniaturizing a pressure resonant transducer of claim 1, wherein, The resolving of the hardware components from the design specification of the transmitter, and the splitting of the transmitter into multiple functional units comprises: reading a substrate wiring diagram and a packaging structure diagram of the transmitter; dividing electrical connection regions according to the substrate wiring diagram, each electrical connection region corresponding to a chip set; and calculating occupied areas of each electrical connection region, and merging electrical connection regions having occupied area deviations within a permitted value into the same functional unit.

3. The method of miniaturizing a pressure resonant transducer of claim 1, wherein, The obtaining of the morphological parameters and the electrical parameters of each functional unit comprises: using a measuring tool to collect shape dimensions and weight data of the functional unit as the morphological parameters; and recording working current and voltage characteristics of the functional unit by a test instrument as the electrical parameters.

4. The method of miniaturizing a pressure resonant transducer of claim 1, wherein, The calculating of the signal transmission delays and the physical distances between the target functional unit and the other functional units, and the generating of the proximity indicators comprise: measuring wire lengths and propagation times between the target functional unit and the other functional units; normalizing the wire lengths into distance scores and the propagation times into delay scores; and linearly combining the distance scores and the delay scores to generate the proximity indicators.

5. The method of miniaturizing a pressure resonant transducer of claim 1, wherein, The matching target function unit and the function category of other function units generate a synergy degree index, including: querying the function type of the target function unit and the function type of other function units; if the function type belongs to a preset complementary combination, looking up a synergy weight according to a complementary strength table; if the function type does not belong to the complementary combination, calculating a correlation degree based on historical co-occurrence data; and generating the synergy degree index according to the synergy weight or the correlation degree.

6. The method of miniaturizing a pressure resonant transducer of claim 5, wherein, The calculation of the correlation degree based on the historical co-occurrence data includes: retrieving past design records of the transmitter, and counting the frequency of simultaneous occurrence of the target function unit and other function units; obtaining a dependency relationship table of the function type, and determining a basic dependency value; combining the simultaneous occurrence frequency and the basic dependency value, and calculating the correlation degree through arithmetic averaging.

7. The method of miniaturizing a pressure resonant transducer of claim 1, wherein, The processing priority of the function unit is arranged according to the reconstructed improvement coefficient to generate a miniaturization planning scheme, including: sorting the reconstructed improvement coefficient in descending order to obtain a priority sequence; selecting the function unit for size optimization according to the priority sequence; recording the optimization steps and parameter changes, and outputting the miniaturization planning scheme.

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