Pfm pulse frequency modulation based air pressure sensor communication control method and system

Abstract

The application discloses a gas pressure sensor communication control method and system based on PFM pulse frequency modulation, in a multi-sensor networking scene, through signal conditioning, the environmental parameters are converted into analog electrical signals and then mapped into a unique pulse change sequence, and the matching degree of the pulse sequence and the parameter change is optimized, realizing high-fidelity and low-power data expression and transmission; through the communication link combined with the signal transmission protocol, interference filtering is implemented, the integrity of the pulse sequence is ensured, and the original parameter data is inversely mapped and restored; then, in the main control unit, multi-source sensor data is integrated to form a networking parameter data set, an abnormal deviation is judged through comparison logic, and a control instruction is generated and fed back to the terminal to perform accurate adjustment, forming a complete link from sensing, optimized transmission to closed-loop regulation, and finally realizing high-reliability real-time monitoring and adaptive intelligent regulation of environmental parameters.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of air pressure sensors, and particularly discloses an air pressure sensor communication control method and system based on PFM pulse frequency modulation. BACKGROUND

[0002] In the Internet of Things and industrial measurement and control systems, an air pressure sensor needs to reliably transmit real-time collected environmental air pressure data to a master control end to trigger precise device control actions, which is directly related to system safety and response efficiency. With the development of devices towards miniaturization, battery power supply and long-distance deployment, the power consumption requirement of sensor nodes is becoming more and more stringent, while the requirements for measurement accuracy and control timeliness are continuously increasing.

[0003] Existing air pressure transmission methods mostly use periodic sampling to send complete data packets through digital communication or use analog voltages to directly correspond to air pressure values. These methods often force the system to reduce the sampling frequency or shorten the communication distance in low-power consumption scenarios, otherwise the node power is quickly consumed; and once the sampling frequency or transmission distance is increased, data error accumulation or signal interference by external electromagnetic noise is caused, so that the air pressure value received by the master control end is deviated, and finally control instructions are delayed or misoperated.

[0004] The most core contradiction is concentrated in that when air pressure information must be carried and transmitted in a certain "frequency" form, how to make the frequency carrying information not only reflect the small changes of air pressure in high density, but also remain stable and unaltered for a long time under extremely weak signal energy. Because air pressure changes are usually slow and small in amplitude, but the master control end is required to distinguish differences of several pascals or even zero point several pascals, which means that the frequency needs to have extremely high resolution capability; at the same time, on the battery-powered node, sending high-frequency or complex waveform signals will quickly consume power, and sending too sparse or simple pulses cannot carry enough information, resulting in frequency drift or the master control end cannot accurately restore the real air pressure. For example, in high-altitude detection balloons or underground pipe network monitoring, the sensor may work for months or even years on a battery, and once the transmitted pulse frequency becomes too sparse due to power consumption limitations, or slightly shakes due to line noise, the master control end may misjudge the real air pressure fluctuation of tens of pascals as hundreds of pascals, and then trigger the wrong valve opening or alarm, causing serious consequences.

[0005] Therefore, how to establish a stable and distinguishable linear correspondence between the air pressure value and the pulse frequency under extremely low power consumption conditions, while ensuring that the frequency signal can still be accurately demodulated and restored after transmission in a long distance or noise environment, has become a key problem for realizing long-term reliable communication and control of high-precision air pressure sensors. SUMMARY

[0006] This invention provides a communication control method and system for a barometric pressure sensor based on PFM pulse frequency modulation, aiming to solve at least one of the defects existing in the prior art.

[0007] One aspect of the present invention relates to a communication control method for a barometric pressure sensor based on PFM pulse frequency modulation, comprising the following steps:

[0008] S100: Obtain parameter data from the environment through an environmental parameter sensor, process the parameter data through a signal conditioning unit including amplification and filtering functions to convert it into an analog electrical signal, and input the modulation unit to perform mapping processing on the analog electrical signal to obtain a pulse change sequence;

[0009] S200. Based on the pulse change sequence, the corresponding relationship is used for encoding optimization to determine the matching degree between the pulse change sequence and the parameter change amount. The matched pulse change sequence is then transmitted to the receiving end of the main control unit through the communication link.

[0010] S300: The signal transmission protocol is used to perform anti-interference processing on the transmitted pulse change sequence to filter out noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by performing inverse mapping processing on the filtered pulse change sequence through the demodulation unit.

[0011] S400: Based on the demodulated raw parameter data, integrate the corresponding data from the additional sensors under the network communication framework to obtain the network integrated parameter dataset. Compare the network integrated parameter dataset through the control logic to analyze and judge abnormal deviations.

[0012] The S500 uses a control mechanism to process the network integration parameter dataset that determines abnormal deviations, generates corresponding control commands, and feeds the control commands back to the terminal device through the communication link to perform parameter-related adjustment operations.

[0013] Further, step S100 includes:

[0014] S110. Collect at least one raw environmental parameter data such as temperature, humidity, air pressure and light intensity in real time through environmental parameter sensors to form a first parameter dataset. Perform offset compensation and linear correction on the first parameter dataset according to the pre-established sensor calibration table to obtain a second parameter dataset.

[0015] S120. Input the second parameter dataset into the signal conditioning unit, use an operational amplifier to amplify the weak signal with a fixed gain to obtain a first analog electrical signal, and use a low-pass filter to filter out high-frequency noise from the first analog electrical signal to obtain a second analog electrical signal.

[0016] S130. Input the second analog electrical signal into the modulation unit, and use a voltage-frequency converter to map the second analog electrical signal into a first pulse sequence with a corresponding frequency change. If the frequency of the first pulse sequence exceeds the preset threshold range, adjust the frequency division coefficient through the frequency divider to obtain a second pulse sequence with a stable frequency.

[0017] S140. The second pulse sequence is width encoded using a pulse width modulation encoder to form a pulse change sequence containing environmental parameter information.

[0018] Further, step S200 includes:

[0019] S210. Based on the pulse change sequence, a width-frequency dual mapping optimization is performed using a pre-established encoding correspondence table to obtain the first optimized pulse sequence.

[0020] S220. Perform point-by-point matching calculations between the first optimized pulse sequence and the pre-stored parameter change dataset using a comparator to determine the matching degree value;

[0021] S230. If the matching degree value is greater than the preset threshold, a convolutional neural network is used to perform feature extraction and recoding on the first optimized pulse sequence to obtain the second optimized pulse sequence.

[0022] S240. The second optimized pulse sequence is transmitted bit by bit through the serial communication link to the receiving end of the main control unit.

[0023] Further, step S300 includes:

[0024] S310. The transmitted pulse change sequence is subjected to anti-interference processing using a signal transmission protocol. Noise interference is filtered out by a median filter to obtain the first filtered pulse sequence.

[0025] S320. Based on the pre-established integrity check table, perform bit error rate calculation on the first filtering pulse sequence to determine the integrity index;

[0026] S330. If the integrity index is greater than the preset threshold, the first filter pulse sequence is optimized by using a Hamming code error corrector to obtain the second filter pulse sequence.

[0027] S340. The demodulation unit performs inverse width-frequency mapping processing on the second filtered pulse sequence to obtain the original parameter data.

[0028] Further, step S400 includes:

[0029] S410. Based on the demodulated raw parameter data, acquire corresponding data from the additional sensor through the networking communication protocol. Synchronize the corresponding data with the demodulated raw parameter data to obtain a preliminary fusion dataset.

[0030] S420. Based on the preliminary fusion dataset, a hash matching tool is used to perform data alignment processing. The data alignment processing merges the corresponding data to obtain the network integration parameter dataset.

[0031] S430. Based on the network integration parameter dataset, the control logic unit performs a domain-by-domain comparison to determine the deviation index sequence of the network integration parameter dataset.

[0032] S440. If the deviation index sequence exceeds the preset threshold, it is determined that there is an abnormal deviation.

[0033] Further, step S500 includes:

[0034] S510. Based on the network integration parameter dataset for judging abnormal deviations, a targeted deviation compensation value sequence is generated using a control mechanism to obtain a preliminary control command set.

[0035] S520: Obtain the current operating status parameters of the terminal device in real time through the communication link, perform priority sorting processing on the preliminary control command set, and determine the optimized control command sequence;

[0036] S530. If the single instruction deviation compensation value of the control instruction in the optimized control instruction sequence exceeds the safety adjustment limit of the terminal device, then the control instruction is split into multiple progressive sub-instructions to obtain the adjusted control instruction set.

[0037] S540: The adjusted set of control commands is sent one by one to the corresponding terminal device through the communication link. After receiving the adjusted set of control commands, the terminal device performs the parameter adjustment operation and returns an execution completion confirmation signal.

[0038] Another aspect of the present invention relates to a barometric pressure sensor communication control system based on PFM pulse frequency modulation, for executing the above-described barometric pressure sensor communication control method based on PFM pulse frequency modulation, comprising:

[0039] The pulse change sequence acquisition module is used to acquire parameter data from the environment through an environmental parameter sensor, process the parameter data through a signal conditioning unit including amplification and filtering functions to convert it into an analog electrical signal, and input the modulation unit to map the analog electrical signal to obtain a pulse change sequence;

[0040] The pulse change sequence transmission module is used to perform encoding optimization based on the pulse change sequence and the correspondence relationship, determine the matching degree between the pulse change sequence and the parameter change amount, and transmit the matched pulse change sequence to the receiving end of the main control unit through the communication link.

[0041] The original parameter data restoration module is used to perform anti-interference processing on the transmitted pulse change sequence using a signal transmission protocol to filter out noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by performing inverse mapping processing on the filtered pulse change sequence through the demodulation unit.

[0042] The abnormal deviation analysis module is used to integrate corresponding data from additional sensors under the network communication framework based on the demodulated raw parameter data to obtain the network integrated parameter dataset. The network integrated parameter dataset is compared by the control logic to analyze and judge abnormal deviations.

[0043] The parameter-related adjustment operation execution module is used to process the network integration parameter dataset that judges abnormal deviations using a control mechanism, generate corresponding control commands, and feed the control commands back to the terminal device through the communication link to execute parameter-related adjustment operations.

[0044] The beneficial effects achieved by this invention are as follows:

[0045] The present invention provides a communication control method and system for barometric pressure sensors based on PFM pulse frequency modulation. In a multi-sensor network scenario, environmental parameters are converted into analog electrical signals through signal conditioning and then mapped into a unique pulse change sequence. The matching degree between the pulse sequence and the parameter change is optimized through encoding to achieve high-fidelity, low-power data expression and transmission. Anti-interference filtering is implemented through the communication link combined with the signal transmission protocol to ensure the integrity of the pulse sequence before inverse mapping to restore the original parameter data. Subsequently, the main control unit integrates multi-source sensor data to form a network parameter dataset. Abnormal deviations are judged by comparison logic, and control commands are generated and fed back to the terminal for precise adjustment. This forms a complete link from sensing, optimized transmission to closed-loop control, effectively solving the problems of traditional analog or digital transmission being susceptible to noise interference, high power consumption, difficulty in fusion of multi-source network data, and slow response to abnormalities in integrated services. Ultimately, it achieves highly reliable real-time monitoring and adaptive intelligent control of environmental parameters. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an embodiment of the barometric pressure sensor communication control method based on PFM pulse frequency modulation according to the present invention.

[0047] Figure 2 This is a functional block diagram of an embodiment of the air pressure sensor communication control system based on PFM pulse frequency modulation of the present invention.

[0048] Explanation of icon numbers:

[0049] 10. Pulse change sequence acquisition module; 20. Pulse change sequence transmission module; 30. Original parameter data restoration module; 40. Abnormal deviation analysis module; 50. Parameter related adjustment operation execution module. Detailed Implementation

[0050] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0051] like Figure 1 As shown, the first embodiment of the present invention proposes a communication control method for a barometric pressure sensor based on PFM pulse frequency modulation, comprising the following steps:

[0052] S100: Obtain parameter data from the environment through an environmental parameter sensor, process the parameter data through a signal conditioning unit including amplification and filtering functions to convert it into an analog electrical signal, and input the modulation unit to perform mapping processing on the analog electrical signal to obtain a pulse change sequence.

[0053] With ambient air pressure as the core monitoring object, environmental parameter sensors (such as MEMS (Micro-Electro-Mechanical System) air pressure sensors and high-precision piezoelectric air pressure sensors) are deployed to collect air pressure parameter data in the environment in real time. Auxiliary environmental parameters such as temperature and humidity can be collected simultaneously. The raw parameter data is transmitted to a signal conditioning unit with integrated amplification and filtering functions. The weak sensor signal is amplified by an operational amplifier (the amplification factor is dynamically adjusted according to the sensor output sensitivity to ensure that the signal amplitude is suitable for subsequent processing), and high-frequency interference signals such as electromagnetic interference and sensor noise are filtered by a low-pass filter (such as RC (Resistor-Capacitor) filter and active filter). The processed parameter data is then converted into a stable analog electrical signal.

[0054] The analog electrical signal is input into the PFM (Pulse Frequency Modulation) modulation unit, and a mapping algorithm (such as linear mapping, piecewise nonlinear mapping, adapting the correspondence between air pressure parameters and pulse frequency) is used to modulate the analog electrical signal: based on the amplitude change of the analog electrical signal (corresponding to the magnitude change of air pressure parameters), a "pulse change sequence" with frequency changing with the parameter is generated - that is, the larger (or smaller) the air pressure parameter value, the higher (or lower) the frequency of the pulse sequence. The width and amplitude of each pulse in the sequence remain fixed, and only the frequency reflects the parameter change, ensuring accurate encoding of parameter information.

[0055] S200. Based on the pulse change sequence, the corresponding relationship is used for encoding optimization to determine the matching degree between the pulse change sequence and the parameter change amount. The matched pulse change sequence is then transmitted to the receiving end of the main control unit through the communication link.

[0056] For the pulse change sequence generated in step S100, the encoding is optimized based on the preset correspondence between "pulse frequency and parameter change" (e.g., the frequency range of 1kHz-10kHz corresponds to the air pressure range of 80kPa-120kPa). By establishing a precise mapping table, nonlinear errors are eliminated, ensuring a one-to-one correspondence between the pulse frequency and the air pressure parameter change. At the same time, the "matching degree" between the pulse change sequence and the parameter change is calculated (e.g., quantified by mean square error and correlation coefficient, a matching degree ≥95% is considered qualified), ensuring the accuracy of the encoding.

[0057] The matched pulse change sequence is transmitted to the receiving end of the main control unit through a designated communication link (such as wired links like UART (Universal Asynchronous Receiver and Transmitter), SPI (Serial Peripheral Interface), I2C (Inter-Integrated Circuit), or wireless links like LoRa (Long Range Radio) or Bluetooth, selected according to the application scenario). During transmission, the pulse sequence is encapsulated in a frame format (including start bit, data bit, parity bit, and stop bit). The parity bit is used to initially determine whether there are any errors in the data transmission, ensuring the integrity of the sequence transmission.

[0058] S300: The signal transmission protocol is used to perform anti-interference processing on the transmitted pulse change sequence to filter out noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by inverse mapping processing of the filtered pulse change sequence through the demodulation unit.

[0059] After the main control unit receives the transmitted pulse change sequence, it first uses a signal transmission protocol (such as CRC (Cyclic Redundancy Check) verification protocol and parity check protocol) to implement anti-interference processing. The verification algorithm filters out noise interference and data distortion caused by electromagnetic interference and link attenuation during transmission, eliminates erroneous pulse signals, and repairs slightly distorted sequence data.

[0060] Determine the integrity of the filtered pulse change sequence: If the sequence has a complete frame structure, matching check bits, and a pulse frequency within a preset valid range, then the integrity condition is met; otherwise, a retransmission command is sent to the transmitter. For pulse change sequences that meet the conditions, the input is to the PFM demodulation unit, where, through inverse mapping processing (i.e., the reverse operation of PFM modulation, based on the mapping lookup table in the coding optimization stage), the pulse frequency signal is restored to the corresponding analog electrical signal, and then converted into digital "raw parameter data" (i.e., quantized data consistent with the air pressure parameters collected by the sensor).

[0061] S400: Based on the demodulated raw parameter data, integrate the corresponding data from the additional sensors within the network communication framework to obtain the network integrated parameter dataset. Compare the network integrated parameter dataset through control logic to analyze and judge abnormal deviations.

[0062] Using the demodulated raw air pressure parameter data as the core, under the preset networking communication framework (such as star networking, Mesh networking, supporting the access of multiple sensor nodes), it integrates the corresponding timestamp data from additional sensors (such as temperature sensors, humidity sensors, wind speed sensors, etc., used to assist in judging air pressure-related environmental conditions), eliminates data redundancy and format heterogeneity issues, and forms a "networked integrated parameter dataset" according to the structure of "timestamp + core parameters + auxiliary parameters + sensor identifier", which comprehensively reflects the multi-dimensional state of the environment.

[0063] The network integration parameter dataset is compared and analyzed through preset control logic (such as threshold comparison logic, trend analysis logic, and multi-parameter association logic): the air pressure parameters and auxiliary parameters are compared with preset standard values ​​(such as normal working environment air pressure range and parameter linkage threshold), or the trend of parameter changes over time is analyzed to see if it exceeds the stable range, and it is determined whether there is an "abnormal deviation" (such as air pressure sudden rise / fall exceeding the safety threshold, abnormal linkage between air pressure and temperature parameters), and the abnormal deviation judgment result is output (including abnormal parameter type, deviation value, and occurrence timestamp).

[0064] The S500 uses a control mechanism to process the network integration parameter dataset that determines abnormal deviations, generates corresponding control commands, and feeds the control commands back to the terminal device through the communication link to perform parameter-related adjustment operations.

[0065] For the network integration parameter dataset that is identified as having abnormal deviations in step S400, a preset control mechanism (such as proportional control, PID (Proportion Integration Differentiation) control, fuzzy control, adapted according to the application scenario) is used for processing. Based on the type, magnitude, and scope of influence of the abnormal deviation, the optimal control amount is calculated, and corresponding "control commands" are generated (such as adjusting the air pressure compensation module of the terminal equipment, starting the ventilation / pressurization equipment, adjusting the sensor acquisition frequency, etc.). The control commands contain core information such as the control object, control parameters, target value, and execution time limit.

[0066] Control commands are fed back to the corresponding terminal devices (such as air pressure regulators, environmental control devices, and sensor terminals) via communication links. After receiving the commands, the terminal devices perform parameter adjustment operations. After the operation is completed, the terminal devices feed back the execution results to the main control unit, forming a closed loop of "monitoring-control-feedback" to ensure that abnormal deviations are corrected in a timely manner and that the ambient air pressure and related conditions are maintained within a reasonable range.

[0067] Furthermore, in the barometric pressure sensor communication control method based on PFM pulse frequency modulation proposed in this embodiment, step S100 includes:

[0068] S110. At least one raw environmental parameter data, such as temperature, humidity, air pressure, and light intensity, is collected in real time by environmental parameter sensors to form a first parameter dataset. Offset compensation and linear correction are performed on the first parameter dataset according to a pre-established sensor calibration table to obtain a second parameter dataset.

[0069] The following formula was used to perform offset compensation processing on the original environmental parameter data:

[0070] (1)

[0071] In formula (1), Indicates the second parameter in the dataset. Calibration data for each sensor, Indicates the first parameter in the dataset. The raw data collected by each sensor Indicates the first in the sensor calibration table The offset compensation value corresponding to each sensor.

[0072] The following formula is used to perform linear calibration correction on sensor data, eliminating non-ideal characteristics of the sensor:

[0073] (2)

[0074] In formula (2), This represents the sensor output value after linear correction. This represents the sensor's raw measurement value. Indicates the linear correction coefficient. This represents the bias term used for linear correction.

[0075] The following formula is used to weightedly fuse multiple environmental parameter data:

[0076] (3)

[0077] In formula (3), express The comprehensive environmental parameter values ​​at any given time. express Temperature data at any given time, in degrees Celsius (°C). express Humidity data at any given time, in units of relative humidity (%RH). express Light intensity data at any given time, in lux (lx). express The air pressure data at any given time is in kilopascals (kPa). , , , These represent the weighting coefficients of each environmental parameter, satisfying... The control logic of formula (3) is based on the weighted fusion of multi-dimensional environmental parameters according to the priority of the scenario. Through standardized processing, dynamic weight allocation and quantitative evaluation, it outputs an indicator that can comprehensively reflect the overall state of the environment, providing a scientific basis for subsequent anomaly judgment and control decision-making.

[0078] In intelligent agricultural monitoring systems, environmental parameter sensors collect raw data such as temperature, humidity, air pressure, and light intensity in real time to form a first parameter dataset. This data collection process typically relies on integrated sensor modules, such as the DHT22 sensor for temperature and humidity acquisition, the BMP180 for air pressure monitoring, and a photoresistor for capturing light intensity. These sensors are installed in the farmland environment, collecting data once per second to form a raw dataset containing multi-dimensional values ​​to ensure real-time performance. In one embodiment, assuming a greenhouse application scenario, the sensors detect a temperature of 25 degrees Celsius, humidity of 60%, air pressure of 1013 hPa, and light intensity of 500 lux. This data is directly used as the first parameter dataset for subsequent processing to support the optimization and adjustment of the crop growth environment.

[0079] The second parameter dataset is obtained by performing offset compensation and linear correction on the first parameter dataset according to a pre-established sensor calibration table. This calibration table is generated through laboratory testing, such as calibrating the DHT22 sensor multiple times and recording its offset values ​​and linear slope under standard conditions, such as 0 degrees Celsius. Specifically, offset compensation involves subtracting known deviations; for example, if the sensor reads 26 degrees Celsius at a standard 25 degrees Celsius, the compensation formula is simply adjusted to the actual value minus 1 degree Celsius. Linear correction applies a slope factor, such as multiplying by 0.98, to correct nonlinear errors. In the greenhouse scenario, the temperature of 25 degrees Celsius in the first parameter dataset becomes 24 degrees Celsius after compensation, and the humidity data is adjusted from 60% to 58% through linear correction, thus forming a more accurate second parameter dataset. This correction ensures data reliability and avoids the influence of inherent sensor errors on agricultural decisions, such as the automatic triggering of irrigation systems.

[0080] S120. Input the second parameter dataset into the signal conditioning unit, use an operational amplifier to amplify the weak signal with a fixed gain to obtain a first analog electrical signal, and use a low-pass filter to filter out high-frequency noise from the first analog electrical signal to obtain a second analog electrical signal.

[0081] The basic formula for fixed-gain operational amplifier amplification is shown below:

[0082] (4)

[0083] In formula (4), This represents the output voltage of the first analog electrical signal after amplification, and the unit is volts (V). This indicates the fixed gain factor of the operational amplifier; The second parameter represents the weak signal voltage of the input dataset, measured in volts (V). The bias voltage of the operational amplifier is expressed in volts (V). The control logic of formula (4) is broken down into two steps, which can be simply described as "amplification first, then bias superposition".

[0084] The attenuation characteristics of a low-pass filter for signals of different frequencies are described by the following formula:

[0085] (5)

[0086] In formula (5), This indicates that the low-pass filter operates at a certain frequency. The transfer function at that point, Represents pi; It represents the signal frequency, and the unit is Hertz (Hz). This indicates the resistance value of the low-pass filter, and the unit is ohms (Ω). This represents the capacitance value of the low-pass filter, measured in farads (F). The imaginary unit is represented. The control logic of formula (5) controls the attenuation of the signal through "frequency-dependent complex impedance".

[0087] The following formula describes how a low-pass filter processes the input signal in the time domain:

[0088] (6)

[0089] In formula (6), This represents the second analog electrical signal obtained after passing through a low-pass filter. This represents the impulse response function of the filter. This represents the first analog electrical signal input to the filter. Represents the integral variable. Indicates the current moment.

[0090] The second parameter dataset is input into the signal conditioning unit, where an operational amplifier amplifies the weak signal with a fixed gain to obtain the first analog electrical signal. This signal conditioning unit typically uses an operational amplifier such as the LM358, set to a gain of 10, to amplify the weak voltage signal from 0.1 volts to 1 volt. Specifically, in agricultural monitoring, the humidity signal may be very weak, and amplification facilitates subsequent processing. Then, a low-pass filter is used to filter out high-frequency noise from the first analog electrical signal to obtain the second analog electrical signal. The filter can be an RC circuit with a cutoff frequency set to 100 Hz to filter out high-frequency interference caused by fan vibration. In the greenhouse example, after the amplified signal noise is filtered out, a stable second analog electrical signal is obtained, representing accurate environmental parameters. This helps the system maintain data purity in noisy environments.

[0091] S130. Input the second analog electrical signal into the modulation unit, and use a voltage-frequency converter to map the second analog electrical signal into a first pulse sequence with a corresponding frequency change. If the frequency of the first pulse sequence exceeds the preset threshold range, adjust the frequency division coefficient through a frequency divider to obtain a second pulse sequence with a stable frequency.

[0092] The core conversion relationship of a voltage-frequency converter is described by the following formula:

[0093] (7)

[0094] In formula (7), This indicates the frequency of the first pulse sequence, measured in Hertz (Hz). This represents the voltage-to-frequency conversion factor, with units of Hertz per Volt (Hz / V). This represents the input second analog electrical signal voltage value, in volts (V). The reference frequency offset of the converter is expressed in Hertz (Hz). The control logic of formula (7) is "input voltage amplification and conversion + reference offset superposition", the core of which is to linearly convert the voltage signal into a frequency signal.

[0095] The frequency adjustment mechanism of a frequency divider is described by the following formula:

[0096] (8)

[0097] In formula (8), Indicates the frequency of the first pulse sequence. This indicates the frequency of the second pulse sequence obtained after frequency division, and the unit is Hertz (Hz). This represents the division factor of the frequency divider. The control logic of formula (8) is to reduce the input frequency by a fixed ratio.

[0098] The second analog electrical signal is input to the modulation unit, where a voltage-to-frequency converter maps it into a first pulse sequence with corresponding frequency variations. This converter, such as a 555 timer-based circuit, maps a voltage of 0 to 5 volts to frequency pulses of 1 to 10 kHz. If the frequency of the first pulse sequence exceeds a preset threshold range, such as 15 kHz, a frequency divider is used to adjust the division coefficient, resulting in a stable second pulse sequence. The frequency divider can use a counter chip, such as a division by 2, to reduce the high frequency to a safe range. In agricultural scenarios, if the frequency is too high after the temperature signal is converted into pulses due to high temperature, frequency division adjustment ensures sequence stability, facilitates wireless transmission, and avoids data loss.

[0099] S140. The second pulse sequence is width encoded using a pulse width modulation encoder to form a pulse change sequence containing environmental parameter information.

[0100] The following formula describes how a pulse width modulation encoder linearly maps environmental parameter information to pulse width:

[0101] (9)

[0102] In formula (9), Indicates the first The modulation width of each pulse. Indicates the reference pulse width. This represents the conversion coefficient from environmental parameters to pulse width. Indicates the first The environmental parameter values ​​at each sampling point. The control logic of formula (9) is "based on the reference pulse width + linear modulation of environmental parameters", the core of which is to convert the change of environmental parameters into a linear adjustment of the pulse width.

[0103] The mathematical representation of a pulse change sequence containing environmental parameter information is described by the following formula:

[0104] (10)

[0105] In formula (10), The time-domain expression representing the pulse variation sequence. Indicates the total number of pulses. Indicates the first The amplitude of each pulse, Indicates the pulse period. Indicates the first The duration of each pulse The function represents a rectangular window function. It represents the current moment.

[0106] The normalization coding process from environmental parameter values ​​to pulse width is described by the following formula:

[0107] (11)

[0108] In formula (11), Indicates the first The encoding width of each pulse, Indicates the first The measured values ​​of each environmental parameter, and These represent the minimum and maximum values ​​of the environmental parameter, respectively. and These represent the minimum and maximum widths of the pulse width modulation, respectively.

[0109] The second pulse sequence is width-coded using a pulse width modulation encoder to form a pulse variation sequence containing environmental parameter information. This encoder adjusts the pulse width, increasing as the temperature rises, ranging from 1 millisecond to 10 milliseconds, thus embedding multi-parameter information. In the greenhouse system, the final sequence can be transmitted to the central controller, decoded, and used to adjust ventilation or lighting for automated management. This encoding method improves data transmission efficiency and anti-interference capabilities.

[0110] Furthermore, in the barometric pressure sensor communication control method based on PFM pulse frequency modulation proposed in this embodiment, step S200 includes:

[0111] S210. Based on the pulse change sequence, a width-frequency dual mapping optimization is performed using a pre-established encoding correspondence table to obtain the first optimized pulse sequence.

[0112] The output value of the first optimized pulse sequence is obtained using the following formula:

[0113] (12)

[0114] In formula (12), This indicates that the first optimized pulse sequence is at time [time]. The output value, This represents the total number of mapping items in the code-to-database table. Indicates the first Width modulation coefficients of each mapping term, Indicates the first The frequency modulation coefficients of each mapping term, Indicates the input pulse change sequence at time... The value, Indicates the first The time delay parameter corresponding to each mapping item.

[0115] The output of the width-frequency dual mapping optimization function is obtained through the following formula:

[0116] (13)

[0117] In formula (13), This represents the output of the width-frequency dual-mapping optimization function. The parameter representing the width of the input pulse. This represents the frequency parameter of the input pulse. This represents the weighting coefficients of the width mapping. The weighting coefficients represent the frequency mapping. This represents a function that uses a pre-built width-encoded lookup table. This represents a function that pre-establishes a frequency encoding lookup table.

[0118] The pulse sequence value after processing by the encoding correspondence table is obtained by the following formula:

[0119] (14)

[0120] In formula (14), Indicates the first The pulse sequence values ​​at each time point after processing by the encoding correspondence table. Indicates the first Pulse change at each time point Represents the encoding mapping function, This represents a pre-established table of encoding correspondences.

[0121] For processing pulse change sequences, a pre-established encoding correspondence table is first used to perform width-frequency dual mapping optimization, resulting in a first optimized pulse sequence. This encoding correspondence table is a pre-defined mapping structure that associates the pulse width and frequency parameters with specific optimization rules. For example, in a smart agriculture monitoring system, this table might contain corresponding entries for different environmental parameters such as soil moisture or carbon dioxide concentration. Each entry defines a combination of a width ranging from 0.5 milliseconds to 5 milliseconds and a frequency ranging from 500 Hz to 2000 Hz. Specifically, this mapping optimization process involves looking up the corresponding entry in the table element by element of the input pulse change sequence. If a pulse has a width of 2 milliseconds and a frequency of 1000 Hz, it is adjusted to an optimized width of 1.8 milliseconds and a frequency of 950 Hz according to the rules in the table to meet transmission requirements. In a greenhouse crop monitoring scenario, assuming the collected pulse sequence represents real-time soil moisture changes, dual mapping can optimize the original sequence into a more compact form, facilitating subsequent analysis.

[0122] S220. Perform point-by-point matching calculations between the first optimized pulse sequence and the pre-stored parameter change dataset using a comparator to determine the matching degree value.

[0123] The matching degree value of the pulse sequence is obtained by the following formula:

[0124] (15)

[0125] In formula (15), Indicates the first The matching degree value of each pulse sequence. This represents the total number of sampling points in the sequence. Indicates the first optimized pulse sequence at the 1st... The amplitude of each sampling point This represents the first parameter in the dataset representing the change in pre-stored parameters. The data set in the first... The value of each sampling point This represents the standard deviation parameter used in the matching calculation.

[0126] A comparator performs point-by-point matching calculations between the first optimized pulse sequence and a pre-stored parameter change dataset to determine the matching degree. This comparator is an electronic component used to compare the differences between two signals. Here, it checks each pulse point of the optimized pulse sequence against a reference value in the pre-stored dataset. For example, the pre-stored parameter change dataset might be a collection of historical agricultural data containing humidity change curves under standard crop growing conditions. Specifically, the matching calculation process involves subtracting the corresponding reference point from each point in the sequence, calculating the absolute difference, then summing all differences and dividing by the number of points to obtain the average matching degree. If the average value is below a certain threshold, it is considered a high match. In one embodiment, in a system monitoring rice paddies, the point value sequence of the first optimized pulse sequence, such as [1.2, 1.5, 1.8], is compared point-by-point with the pre-stored dataset [1.1, 1.4, 1.7]. The calculated difference is [0.1, 0.1, 0.1], and the average matching degree is 0.1. If the preset threshold is 0.2, the matching degree is less than the threshold, indicating that the sequence highly matches the expected changes.

[0127] S230. If the matching degree value is greater than the preset threshold, a convolutional neural network is used to perform feature extraction and recoding on the first optimized pulse sequence to obtain the second optimized pulse sequence.

[0128] The second optimized pulse sequence is obtained using the following formula:

[0129] (16)

[0130] In formula (16), This represents the second optimized pulse sequence. This represents the feature vector extracted by the convolutional neural network. This indicates the number of basis functions of the encoder. Indicates the first The weight coefficients of each basis function Indicates the first Each basis function is used to generate an optimized pulse sequence through recoding.

[0131] The feature vector extracted by the convolutional neural network is obtained by the following formula:

[0132] (17)

[0133] In formula (17), This represents the first optimized pulse sequence. Represents the convolution kernel weight matrix. This represents the bias vector. This represents the activation function. This indicates the convolution operation.

[0134] If the matching degree value is greater than a preset threshold, a convolutional neural network is used to perform feature extraction and recoding on the first optimized pulse sequence to obtain the second optimized pulse sequence. A convolutional neural network is a deep learning model that captures local patterns in a sequence through convolutional layers. For example, in agricultural applications, this convolutional neural network may have multiple convolutional layers, each using a 3x3 kernel to scan the pulse sequence and extract features such as frequency abrupt changes or width patterns. These features are then recoded through fully connected layers, mapping the extracted features into a new pulse form. Specifically, the feature extraction process first converts the sequence into a one-dimensional tensor, applying convolutional operations to capture the relationship between adjacent pulses. For example, in orchard monitoring, the network extracts patterns representing light intensity fluctuations from the sequence, and after recoding, generates a more robust second optimized pulse sequence. This process ensures the stability of the sequence under noise interference.

[0135] S240. The second optimized pulse sequence is transmitted bit by bit through the serial communication link to the receiving end of the main control unit.

[0136] The signal strength received by the main control unit receiver is obtained using the following formula:

[0137] (18)

[0138] In formula (18), This indicates the signal strength received by the main control unit receiver. Indicates the number of bits received. Indicates the first The binary value of a bit. This represents the channel gain coefficient of a serial communication link.

[0139] The second optimized pulse sequence is transmitted bit-by-bit to the main control unit receiving end via a serial communication link. This serial communication link is a point-to-point data transmission method, such as using the RS-232 protocol, sending each bit of the pulse sequence one by one. In one embodiment, in a field crop management system, the second optimized pulse sequence is converted into a binary bit stream, such as encoding one pulse point as 8 bits of data, and transmitted to the main control unit at a baud rate of 9600 via the link. This unit may be an embedded controller. The receiving end parses the bit stream to recover the sequence for further agricultural decision support.

[0140] Preferably, in the barometric pressure sensor communication control method based on PFM pulse frequency modulation proposed in this embodiment, step S300 includes:

[0141] S310. The transmitted pulse change sequence is subjected to anti-interference processing using a signal transmission protocol. Noise interference is filtered out by a median filter to obtain the first filtered pulse sequence.

[0142] The following formula is used to filter noise from a pulse sequence:

[0143] (19)

[0144] In formula (19), This indicates the first filtered pulse sequence after median filtering at time [time value missing]. The output value, This indicates the original transmitted pulse sequence at time [time]. The sampled values, This represents the window radius of the median filter. This indicates the midpoint operation.

[0145] The process of using a signal transmission protocol to perform anti-interference processing on transmitted pulse-varying sequences first requires understanding that a signal transmission protocol is a standardized set of rules used to ensure the reliability and security of data during transmission. For example, in a smart agriculture monitoring system, this signal transmission protocol might be based on a custom wireless transmission standard, such as the LoRa protocol, which resists external interference by adding redundant bits and check mechanisms. Specifically, when processing pulse-varying sequences, the signal transmission protocol first groups the sequence, with each group containing a fixed number of pulse points. Then, it applies anti-interference algorithms, such as interleaving coding, to rearrange the data bits to disperse the impact of sudden interference.

[0146] Filtering noise interference using a median filter is a crucial step. A median filter is a non-linear filtering technique that works by sorting each point in the sequence with its neighboring points and replacing the original value with the median, thus effectively removing isolated noise points without obscuring edge information. In a greenhouse vegetable monitoring scenario, assuming the transmitted pulse change sequence represents real-time temperature fluctuations, the pulse change sequence may be subject to electromagnetic interference, introducing noise. For example, the original sequence point values ​​are [25.0, 26.5, 100.0, 27.0], where 100.0 is a noise point. Using a median filter with a window size of 3, for the third point, the median of [26.5, 100.0, 27.0], 26.5, is used to replace it, resulting in the first filtered pulse sequence [25.0, 26.5, 26.5, 27.0]. This process ensures the smoothness of the pulse change sequence, facilitating subsequent analysis.

[0147] S320. Based on the pre-established integrity check table, perform bit error rate calculation on the first filtering pulse sequence to determine the integrity index.

[0148] The following formula is used to calculate the bit error rate of the first filtering pulse sequence, and the accuracy of data transmission is quantified by statistically analyzing the ratio of erroneous bits to the total number of bits:

[0149] (20)

[0150] In formula (20), Indicates bit error rate. Indicates the number of bits detected as errors. Indicates the total number of bits transmitted.

[0151] The following formula is used to assess the system's reliability level by calculating the overall data integrity:

[0152] (twenty one)

[0153] In formula (21), Indicators representing completeness Indicates the first The number of errors in each data block Indicates the length of each data block. This indicates the total number of data blocks.

[0154] The following formula is used to perform data verification calculations based on a pre-established integrity check table:

[0155] (twenty two)

[0156] In formula (22), Indicates the integrity check value. This indicates the first check in the pre-established check table. A verification code, Indicates the corresponding data bits. This indicates the total number of check bits. This represents the XOR operation.

[0157] The process of calculating the bit error rate for the first filtered pulse sequence based on a pre-established integrity check table requires explanation. The integrity check table is a pre-defined database structure that stores the bit pattern and expected check value of a standard sequence, used to detect errors during transmission. Specifically, the bit error rate calculation involves converting the sequence into a binary bit stream, then comparing it bit-by-bit with a reference bit stream in the table, and calculating the proportion of mismatched bits as the error rate. For example, in orchard irrigation monitoring, the bit stream of the first filtered pulse sequence is 101010, while the reference in the table is 101011. The difference is in the last bit, and the bit error rate is approximately 1 / 6 ≈ 0.167. Then, the integrity index is judged; if the error rate is below 0.1, the index is considered good. This connection ensures continuity from filtering to verification, because if the filtered sequence has high integrity, it can directly proceed to the next step; otherwise, further processing is required.

[0158] S330. If the integrity index is greater than the preset threshold, the first filter pulse sequence is optimized by using a Hamming code error corrector to obtain the second filter pulse sequence.

[0159] The decision-making process for Hamming code error correction is described by the following formula:

[0160] (twenty three)

[0161] In formula (23), Indicates the second filtering pulse sequence One element, Indicates the first filtering pulse sequence One element, Indicates the detected error bit. This represents the Hamming code parity check matrix. This indicates the received codeword. Indicates transpose. This represents the XOR operation.

[0162] The second filtering pulse sequence is obtained using the following formula:

[0163] (twenty four)

[0164] In formula (24), This indicates the second filtering pulse sequence. Indicates the total number of pulses. Indicates the first The amplitude of each pulse, Indicates at time The unit impulse function at that point, Indicates the sampling period. Indicates the first The frequency corresponding to each pulse Indicates the first The phase of each pulse, Indicates the first The cosine oscillation waveform of a carrier signal.

[0165] If the integrity index exceeds a preset threshold, a Hamming code error corrector is used to optimize the first filtered pulse sequence. Hamming code is a forward error-correcting code that detects and corrects single-bit errors by adding parity bits. Its principle is to use multiple parity bits to cover different combinations of data bits. For example, in a (7,4) Hamming code, adding 3 parity bits to 4 data bits can correct 1 bit error. Specifically, in a paddy field environmental monitoring system, assuming there is an error in the bitstream of the first filtered sequence, the Hamming code error corrector first calculates the position of the parity bits. If an error bit is detected, it flips the bit to obtain the second filtered pulse sequence. This optimization improves the reliability of the data and provides accurate input for demodulation.

[0166] S340. The demodulation unit performs inverse width-frequency mapping processing on the second filtered pulse sequence to obtain the original parameter data.

[0167] The inverse width-frequency mapping function is defined by the following formula:

[0168] (25)

[0169] In formula (25), Represents the inverse width-frequency mapping function. Indicates the input frequency value. Indicates the maximum pulse width. Indicates the minimum pulse width. Indicates the maximum frequency value. This represents the minimum frequency value.

[0170] The demodulated raw parameter data is obtained using the following formula:

[0171] (26)

[0172] In formula (26), This indicates the first result obtained after demodulation. One set of original parameter data, This represents a filtered pulse sequence signal. Indicates the first demodulation unit The demodulation kernel function corresponding to each parameter. This indicates the integration time window.

[0173] The process of performing inverse width-frequency mapping on the second filtered pulse sequence through a demodulation unit, which is a hardware or software module used to recover the original signal, involves converting the optimized width and frequency parameters back into the original data. The principle is based on the inverse operation of a preset mapping table. For example, in crop growth monitoring, the pulse width of the second filtered pulse sequence is 2.0 milliseconds and the frequency is 1000 Hz. By looking up the table, the inverse mapping is converted to the original soil moisture value of 45%, thus obtaining the original parameter data. This step completes the closed loop of the entire processing, ensuring the accurate application of the monitoring data.

[0174] Furthermore, in the barometric pressure sensor communication control method based on PFM pulse frequency modulation proposed in this embodiment, step S400 includes:

[0175] S410. Based on the demodulated raw parameter data, acquire corresponding data from the additional sensor through the networking communication protocol. Synchronize the corresponding data with the demodulated raw parameter data to obtain a preliminary fused dataset.

[0176] The initial fusion of the datasets is obtained using the following formula:

[0177] (27)

[0178] In formula (27), Indicates the first A preliminary fusion of dataset elements, This represents the fusion weights of the original parameter data. Indicates the first One set of original parameter data, This indicates the overall weight of the additional sensor data. This indicates the number of additional sensors participating in the fusion. Indicates the first Specific weights of each additional sensor, Indicates the first The first additional sensor Each corresponding data point.

[0179] Based on the demodulated raw parameter data, the process of acquiring corresponding data from additional sensors via a network communication protocol first requires understanding that a network communication protocol is a standardized framework for data exchange between multiple devices. For example, in a smart agriculture monitoring system, this network communication protocol might be based on the ZigBee standard, which achieves low-power wireless networking by defining packet structures and routing mechanisms. Specifically, demodulated raw parameter data, such as a soil moisture value of 45%, will trigger the network communication protocol to send a request to additional sensors, such as an air temperature sensor. After the sensor responds, it returns corresponding data, such as a temperature of 28.0 degrees Celsius. Then, these data are synchronized, that is, the timestamps are aligned to ensure that the time point of the moisture data matches the temperature data, forming a preliminary fused dataset. This preliminary fused dataset contains a combination of multi-source parameters, providing a foundation for subsequent analysis.

[0180] S420. Based on the preliminary fusion dataset, a hash matching tool is used to perform data alignment processing. The data alignment processing merges the corresponding data to obtain the network integration parameter dataset.

[0181] The core matching logic for data alignment is implemented using the following formula:

[0182] (28)

[0183] In formula (28), Indicates source data With target data The alignment matching results between them This represents the hash value of the source data. The hash value representing the target data. This represents the threshold parameter for hash matching, when the source data... With target data A match is successful if the difference between the hash values ​​of two data items is less than or equal to the threshold, and otherwise it is 0.

[0184] The following formula describes the process of fusing and integrating all aligned data to form a complete network parameter dataset:

[0185] (29)

[0186] In formula (29), This represents the final generated network integration parameter dataset. This indicates the number of data sources participating in the fusion. Indicates the first One source data, This indicates the corresponding data after alignment. This represents the data fusion operator.

[0187] Based on the preliminary fusion dataset, a hash matching tool is used to perform data alignment. The hash matching tool is a software module based on hash functions used to quickly find and align data items. Its principle is to convert data keys into hash values ​​and store them in a table for easy comparison. Specifically, in the orchard environmental monitoring scenario, the preliminary fusion dataset includes a humidity sequence [45%, 46%] and a temperature sequence [28.0, 29.0]. The hash matching tool first calculates the hash value for each data point; for example, it calculates the hash value for humidity 45% to obtain the key value. Then, it matches the temperature data to achieve fusion, that is, merging corresponding items into pairs, such as (45%, 28.0), resulting in a network-integrated parameter dataset. This network-integrated parameter dataset integrates the parameters of all sensors, improving data consistency.

[0188] S430. Based on the network integration parameter dataset, the control logic unit performs a domain-by-domain comparison to determine the deviation index sequence of the network integration parameter dataset.

[0189] The following formula is used to calculate the dispersion of the entire deviation index series:

[0190] (30)

[0191] In formula (30), The standardized deviation value represents the sequence of deviation indicators. This indicates the total number of fields involved in the comparison. Indicates the first Deviation measurement values ​​for each domain, This represents the mean of all domain deviation values. The standard deviation represents the deviation value.

[0192] The process of determining a deviation index sequence through domain-by-domain comparison using a control logic unit (CLU). The CLU is an embedded processing module that executes the comparison algorithm. Its principle is to define domains such as humidity and temperature, and then calculate the differences one by one. Specifically, in a paddy field monitoring system, the humidity domain of the network-integrated parameter dataset is [45%, 46%], and the reference standard domain is [44%, 45%]. The CLU compares each point; for example, 45% differs from 44% by 1%, and 46% differs from 45% by 1%, forming a deviation index sequence [1%, 1%]. This deviation index sequence reflects the degree of data deviation, facilitating anomaly detection.

[0193] S440. If the deviation index sequence exceeds the preset threshold, it is determined that there is an abnormal deviation.

[0194] The following formula is used to define the criteria for judging abnormal deviations:

[0195] (31)

[0196] In formula (31), This indicates the result of the anomaly assessment. Indicates the first A series of deviation index values, This represents the preset anomaly detection threshold. When the deviation index sequence value exceeds the preset threshold, [the following occurs]. An equal value of 1 indicates the presence of an abnormal deviation; otherwise... A value of 0 indicates a normal state.

[0197] If the deviation index sequence exceeds a preset threshold, an abnormal deviation is identified. The preset threshold is, for example, 0.5%. When a value in the deviation index sequence, such as 1%, exceeds the preset threshold of 0.5%, the system determines an anomaly and triggers an alarm. Specifically, in greenhouse vegetable monitoring, if the deviation index sequence [1%, 1%] exceeds 0.5%, it is considered an abnormal deviation. This determination ensures timely monitoring and avoids potential problems.

[0198] Preferably, in the barometric pressure sensor communication control method based on PFM pulse frequency modulation proposed in this embodiment, step S500 includes:

[0199] S510. Based on the network integration parameter dataset for judging abnormal deviations, a targeted deviation compensation value sequence is generated using a control mechanism to obtain a preliminary control command set.

[0200] The following formula is used to generate targeted deviation compensation values ​​through a control mechanism:

[0201] (32)

[0202] In formula (32), Indicates the first Deviation compensation value for each control loop This represents the proportional adjustment coefficient. This represents the nonlinear control intensity parameter. This indicates the current deviation error signal. This represents the integral adjustment coefficient. Indicates the current moment. The integral variable is represented. The control logic of formula (32) is a composite control of "nonlinear proportional control + integral cumulative control", the core of which is to simultaneously deal with the "current nonlinear characteristics" and "historical cumulative effect" of the deviation.

[0203] The following formula is used to generate a discretized set of control commands:

[0204] (33)

[0205] In formula (33), Indicates the first The output value of each control command This indicates the total number of control strategy layers. Indicates the first Layer activation strength, Represents the step function. Indicates the first Layer status signals, Indicates the first The threshold parameter of the layer, Indicates the first The action amplitude of the layer. The control logic of formula (33) is "activation superposition of multi-layer strategy", the core of which is to generate the final control command by combining the state triggers of different layers.

[0206] Based on the network-integrated parameter dataset used to identify abnormal deviations, the process involves generating a targeted deviation compensation value sequence using a control mechanism. This control mechanism is a feedback control-based algorithm framework used to calculate deviation compensation. Its principle is to generate compensation values ​​by analyzing deviation indicators. Specifically, in the intelligent agricultural monitoring system, if the network-integrated parameter dataset shows a soil moisture deviation of 2%, the control mechanism will calculate a compensation value sequence according to preset compensation rules, such as linear interpolation. For example, it might generate a compensation sequence [0.5%, 1.0%, 0.5%] for moisture deviation. These values ​​aim to gradually correct the deviation. Then, the deviation compensation value sequence is transformed into a preliminary control instruction set, such as instructions like "increase irrigation by 0.5%." This preliminary control instruction set provides the basis for subsequent optimization. This generation process ensures the targeted nature of the compensation.

[0207] S520: Obtain the current operating status parameters of the terminal device in real time through the communication link, perform priority sorting processing on the preliminary control command set, and determine the optimized control command sequence;

[0208] The current operating status parameters of the terminal device are obtained using the following formula:

[0209] (34)

[0210] In formula (34), Indicates the first Each terminal device at time The comprehensive condition assessment value, Indicates equipment performance parameters, This represents the response time parameter. This represents a parameter indicating resource utilization rate. This represents the error rate parameter. , , , These are the corresponding weighting coefficients. The control logic of formula (34) is "weighted comprehensive evaluation of multi-dimensional equipment indicators", the core of which is to integrate the operating parameters of different dimensions of the equipment to obtain the overall state value.

[0211] The optimized control command sequence is determined using the following formula:

[0212] (35)

[0213] In formula (35), Indicates the first in the sequence Optimal control instructions for each bit. Represents the set of candidate instructions. Indicates the efficiency coefficient of instruction execution. Indicates the quality score of the instruction. This represents the execution cost coefficient. Indicates the instruction execution delay time. Indicates the first There are candidate control instructions. The control logic of formula (35) is "benefit-cost trade-off optimization", the core of which is to select the optimal instruction from the candidate instructions that has the "highest benefit and lowest cost delay".

[0214] The process of acquiring real-time operating status parameters of terminal devices through a communication link. A communication link is a wireless transmission channel, such as a network based on the LoRa protocol, used for data exchange. Its principle is to establish point-to-point or multi-point connections to achieve low-latency transmission. Specifically, in an orchard environment monitoring scenario, the system acquires current operating status parameters from irrigation terminal devices via the link, such as the current water pump speed being 80 rpm. Then, it performs priority sorting processing on the initial control command set. This processing is a weighted algorithm-based sorting method, which assigns priority scores. For example, emergency deviation commands have higher scores, so commands in the initial control command set, such as "increase irrigation" and "adjust temperature," are sorted first to determine an optimized control command sequence. For instance, the first command in the optimized control command sequence might be "immediately increase irrigation by 0.5%," thus improving the efficiency of command execution.

[0215] S530. If the single instruction deviation compensation value of the control instruction in the optimized control instruction sequence exceeds the safety adjustment limit of the terminal device, then the control instruction is split into multiple progressive sub-instructions to obtain the adjusted control instruction set.

[0216] The following formula is used to define the conditions for splitting control instructions:

[0217] (36)

[0218] In formula (36), Indicates the first The single instruction deviation compensation value for each control command. Indicates the first The value of each control command. Indicates the reference control value. Indicates the upper limit of the terminal device's security adjustment. When the value is greater than 1, it indicates that the control command exceeds the safety adjustment limit and needs to be split. The control logic of formula (36) is "threshold judgment between command deviation and safety limit", the core of which is to determine whether the control command exceeds the safety adjustment range of the equipment.

[0219] The following formula is used to generate a progressive sub-instruction sequence in a linear increment, ensuring that the adjustment range of each sub-instruction is within a safe range:

[0220] (37)

[0221] In formula (37), Indicates the first The instruction value of a progressive subinstruction. Indicates the base instruction value. This indicates the total amount of instruction changes. This represents the total number of sub-instructions after splitting. A progressive sub-instruction sequence is generated using a linearly increasing method to ensure that the adjustment range of each sub-instruction is within a safe range. This indicates the sequence number of the currently generated sub-instruction. The control logic of formula (37) is "based on the basic instruction + linear step-by-step increment", the core of which is to break down instructions that exceed the safe range into multiple small-amplitude sub-instructions.

[0222] If the deviation compensation value of a single control command in the optimized control command sequence exceeds the safety adjustment limit of the terminal equipment, where the safety adjustment limit is a preset threshold of the equipment, such as a maximum adjustment range of 1%, to prevent overload. Specifically, in a paddy field monitoring system, if the compensation value of a control command in the optimized control command sequence is 1.5%, exceeding the upper limit of 1%, then the control command is split into multiple progressive sub-commands. For example, "increase irrigation by 1.5%" is split into three sub-commands: "increase by 0.5%", "increase by 0.5%", and "increase by 0.5%", resulting in an adjusted set of control commands. This adjusted set of control commands ensures the gradual nature of the adjustment and avoids equipment damage.

[0223] S540: The adjusted set of control commands is sent one by one to the corresponding terminal device through the communication link. After receiving the adjusted set of control commands, the terminal device performs the parameter adjustment operation and returns an execution completion confirmation signal.

[0224] The following formula is used to define the total time model for the complete execution of a set of control instructions:

[0225] (38)

[0226] In formula (38), This indicates the total time required for all control commands to complete. This indicates the total number of control commands after adjustment. Indicates the first The time it takes for a control command to be transmitted to the terminal device via the communication link. Indicates that the terminal device executes the first The time required for each control command to adjust parameters. This indicates that the terminal device has returned the first... The time of the confirmation signal for the completion of each instruction. The control logic of formula (38) is "single instruction full process time accumulation", the core of which is to calculate the total time of the entire process from the initiation to the completion of each instruction.

[0227] The following formula is used to define the calculation model for execution success rate:

[0228] (39)

[0229] In formula (39), This indicates the overall success rate of instruction execution within the system. Indicates the total number of control commands sent. Indicates the first The actual execution result status of the control instruction. Indicates the first The expected execution result status of the control instruction. The value is 1 when the actual execution result matches the expected result, and 0 otherwise. The control logic of formula (39) is "to count the number of instructions with consistent results + to calculate the proportion", the core of which is to calculate the proportion of instructions that actually execute and match the expected result.

[0230] The mathematical model for adjusting terminal device parameters is described by the following formula:

[0231] (40)

[0232] In formula (40), This indicates the adjusted equipment parameter values. This represents the original parameter values ​​of the device. This indicates the amount of parameter correction required based on the control command. This represents the retention coefficient of the original parameters. The coefficient representing the parameter correction is used to precisely control the adjustment range of equipment parameters. The control logic of formula (40) is "original parameter retention + weighted superposition of correction amount", the core of which is to precisely control the adjustment range of parameters.

[0233] The process of sending the adjusted set of control commands to the corresponding terminal devices one by one via a communication link also utilizes the aforementioned communication link for transmission. The principle is to serialize the commands and add checksums to ensure integrity. Specifically, in greenhouse vegetable monitoring, the system sends sub-commands from the set to the irrigation equipment one by one. Upon receiving the commands, the terminal devices execute parameter adjustment operations, such as gradually increasing humidity, and return a confirmation signal indicating completion. For example, the confirmation signal might include "Adjustment complete, current humidity 46%". This confirmation signal is fed back to the system, forming a closed-loop control, thereby achieving automation and reliability in monitoring.

[0234] Please see Figure 2This embodiment relates to a communication control system for a barometric pressure sensor based on PFM pulse frequency modulation, used to execute the aforementioned communication control method for a barometric pressure sensor based on PFM pulse frequency modulation. It includes a pulse change sequence acquisition module 10, a pulse change sequence transmission module 20, a raw parameter data restoration module 30, an abnormal deviation analysis module 40, and a parameter-related adjustment operation execution module 50. The pulse change sequence acquisition module 10 acquires parameter data from the environment through an environmental parameter sensor, processes the parameter data into an analog electrical signal via a signal conditioning unit including amplification and filtering functions, and inputs it to a modulation unit for mapping processing to obtain a pulse change sequence. The pulse change sequence transmission module 20 optimizes the encoding based on the pulse change sequence using a correspondence relationship, determines the matching degree between the pulse change sequence and the parameter change amount, and transmits the matched data through a communication link. The pulse change sequence is transmitted to the receiving end of the main control unit; the original parameter data restoration module 30 is used to perform anti-interference processing on the transmitted pulse change sequence using a signal transmission protocol to filter noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by inverse mapping processing of the filtered pulse change sequence through the demodulation unit; the abnormal deviation analysis module 40 is used to integrate the corresponding data from the additional sensors under the network communication framework based on the demodulated original parameter data to obtain the network integrated parameter dataset. The network integrated parameter dataset is compared by the control logic to analyze and judge abnormal deviations; the parameter related adjustment operation execution module 50 is used to process the network integrated parameter dataset judged by the control mechanism, generate corresponding control commands, and feed back the control commands to the terminal device through the communication link to execute the parameter related adjustment operation.

[0235] The PFM-based barometric pressure sensor communication control method and system provided in this embodiment, compared with the prior art, in a multi-sensor network scenario, converts environmental parameters into analog electrical signals after signal conditioning and maps them into a unique pulse change sequence. Encoding optimization is performed on the matching degree between the pulse sequence and the parameter changes to achieve high-fidelity, low-power data expression and transmission. Anti-interference filtering is implemented through the communication link combined with the signal transmission protocol to ensure the integrity of the pulse sequence before inverse mapping to restore the original parameter data. Subsequently, the main control unit integrates multi-source sensor data to form a network parameter dataset. Abnormal deviations are judged by comparison logic, and control commands are generated and fed back to the terminal for precise adjustment, forming a complete link from sensing, optimized transmission to closed-loop control. This effectively solves the problems of traditional analog or digital transmission being susceptible to noise interference, high power consumption, difficulty in fusion of multi-source network data, and delayed abnormal response in integrated services, ultimately achieving highly reliable real-time monitoring and adaptive intelligent control of environmental parameters.

[0236] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A communication control method for a barometric pressure sensor based on PFM pulse frequency modulation, characterized in that, Includes the following steps: S100: Obtain parameter data from the environment through an environmental parameter sensor, process the parameter data into an analog electrical signal through a signal conditioning unit including amplification and filtering functions, and input the modulation unit to perform mapping processing on the analog electrical signal to obtain a pulse change sequence; S200. Based on the pulse change sequence, the encoding optimization is performed using a preset pulse frequency-parameter change correspondence to determine the matching degree between the pulse change sequence and the parameter change, and the matched pulse change sequence is transmitted to the receiving end of the main control unit through the communication link. S300: The signal transmission protocol is used to perform anti-interference processing on the transmitted pulse change sequence to filter out noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by performing inverse mapping processing on the filtered pulse change sequence through the demodulation unit. S400. Based on the demodulated raw parameter data, integrate the corresponding data from the additional sensors under the network communication framework to obtain the network integrated parameter dataset. Compare the network integrated parameter dataset through control logic to analyze and judge abnormal deviations. S500: The control mechanism is used to process the network integration parameter dataset for judging abnormal deviations, generate corresponding control commands, and feed the control commands back to the terminal device through the communication link to perform parameter-related adjustment operations.

2. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 1, characterized in that, Step S100 includes: S110. At least one raw environmental parameter data, such as temperature, humidity, air pressure, and light intensity, is collected in real time by environmental parameter sensors to form a first parameter dataset. Offset compensation and linear correction are performed on the first parameter dataset according to a pre-established sensor calibration table to obtain a second parameter dataset. S120. Input the second parameter dataset into the signal conditioning unit, use an operational amplifier to amplify the weak signal with a fixed gain to obtain a first analog electrical signal, and use a low-pass filter to filter out high-frequency noise from the first analog electrical signal to obtain a second analog electrical signal. S130. The second analog electrical signal is input into the modulation unit, and the voltage-frequency converter is used to map the second analog electrical signal into a first pulse sequence with a corresponding frequency change. If the frequency of the first pulse sequence exceeds the preset threshold range, the frequency division coefficient is adjusted by the frequency divider to obtain a second pulse sequence with a stable frequency. S140. The second pulse sequence is width encoded using a pulse width modulation encoder to form a pulse change sequence containing environmental parameter information.

3. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 1, characterized in that, Step S200 includes: S210. Based on the pulse change sequence, a width-frequency dual mapping optimization is performed using a pre-established encoding correspondence table to obtain the first optimized pulse sequence. S220. Perform point-by-point matching calculation between the first optimized pulse sequence and the pre-stored parameter change dataset using a comparator to determine the matching degree value; S230. If the matching degree value is greater than a preset threshold, a convolutional neural network is used to perform feature extraction and recoding processing on the first optimized pulse sequence to obtain a second optimized pulse sequence. S240. The second optimized pulse sequence is transmitted bit by bit through a serial communication link to the receiving end of the main control unit.

4. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 1, characterized in that, Step S300 includes: S310. The transmitted pulse change sequence is subjected to anti-interference processing using a signal transmission protocol. Noise interference is filtered out by a median filter to obtain the first filtered pulse sequence. S320. Based on the pre-established integrity check table, perform bit error rate calculation on the first filtering pulse sequence to determine the integrity index; S330. If the integrity index is greater than a preset threshold, then a Hamming code error corrector is used to perform error correction optimization on the first filter pulse sequence to obtain a second filter pulse sequence. S340. The demodulation unit performs inverse width-frequency mapping processing on the second filtered pulse sequence to obtain the original parameter data.

5. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 1, characterized in that, Step S400 includes: S410. Based on the demodulated raw parameter data, corresponding data is obtained from the additional sensor through the networking communication protocol. The corresponding data is synchronized with the demodulated raw parameter data to obtain a preliminary fusion dataset. S420. Based on the preliminary fused dataset, a hash matching tool is used to perform data alignment processing, and the data alignment processing merges the corresponding data to obtain a network integration parameter dataset. S430. Based on the network integration parameter dataset, the control logic unit performs a domain-by-domain comparison to determine the deviation index sequence of the network integration parameter dataset. S440. If the deviation index sequence exceeds a preset threshold, it is determined that there is an abnormal deviation.

6. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 1, characterized in that, Step S500 includes: S510. Based on the network integration parameter dataset for judging abnormal deviations, a targeted deviation compensation value sequence is generated using a control mechanism to obtain a preliminary control command set. The following formula is used to generate targeted deviation compensation values ​​through a control mechanism: in, Indicates the first Deviation compensation value for each control loop This represents the proportional adjustment coefficient. This represents the nonlinear control intensity parameter. This indicates the current deviation error signal. This represents the integral adjustment coefficient. Indicates the current moment. Represents the integral variable; The following formula is used to generate a discretized set of control commands: in, Indicates the first The output value of each control command This indicates the total number of control strategy layers. Indicates the first Layer activation strength, Represents the step function. Indicates the first Layer status signals, Indicates the first The threshold parameter of the layer, Indicates the first The amplitude of the layer's motion; S520. Obtain the current operating status parameters of the terminal device in real time through the communication link, perform priority sorting processing on the preliminary control instruction set, and determine the optimized control instruction sequence; S530. If the single instruction deviation compensation value of the control instruction in the optimized control instruction sequence exceeds the safety adjustment limit of the terminal device, then the control instruction is split into multiple progressive sub-instructions to obtain the adjusted control instruction set. S540. The adjusted set of control instructions is sent one by one to the corresponding terminal device through the communication link. After receiving the adjusted set of control instructions, the terminal device performs the parameter adjustment operation and returns an execution completion confirmation signal.

7. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 6, characterized in that, In step S520, the current operating status parameters of the terminal device are obtained using the following formula: ； in, Indicates the first Each terminal device at time The comprehensive condition assessment value, Indicates equipment performance parameters, This represents the response time parameter. This represents a parameter indicating resource utilization rate. This represents the error rate parameter. , , , These are the corresponding weight coefficients; The optimized control command sequence is determined using the following formula: ； in, Indicates the first in the sequence Optimal control instructions for each bit. Represents the set of candidate instructions. Indicates the efficiency coefficient of instruction execution. Indicates the quality score of the instruction. This represents the execution cost coefficient. Indicates the instruction execution delay time. Indicates the first One candidate control instruction.

8. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 7, characterized in that, In step S530, the following formula is used to define the splitting judgment condition of the control instruction: ； in, Indicates the first The single instruction deviation compensation value for each control command. Indicates the first The value of each control command. Indicates the reference control value. Indicates the upper limit of the terminal device's security adjustment. When the value is greater than 1, it indicates that the control command has exceeded the safety adjustment limit and needs to be split. The following formula is used to generate a progressive sub-instruction sequence in a linear increment, ensuring that the adjustment range of each sub-instruction is within a safe range: ； in, Indicates the first The instruction value of a progressive subinstruction. Indicates the base instruction value. This indicates the total amount of instruction changes. This represents the total number of sub-instructions after splitting. A progressive sub-instruction sequence is generated using a linearly increasing method to ensure that the adjustment range of each sub-instruction is within a safe range. This indicates the sequence number of the currently generated sub-instruction.

9. The barometric pressure sensor communication control method based on PFM pulse frequency modulation according to claim 8, characterized in that, In step S540, the total time model for the complete execution of the control instruction set is defined using the following formula: ； in, This indicates the total time required for all control commands to complete. This indicates the total number of control commands after adjustment. Indicates the first The time it takes for a control command to be transmitted to the terminal device via the communication link. Indicates that the terminal device executes the first The time required for each control command to adjust parameters. This indicates that the terminal device has returned the first... The time of the instruction completion confirmation signal; The following formula is used to define the calculation model for execution success rate: ； in, This indicates the overall success rate of instruction execution within the system. Indicates the total number of control commands sent. Indicates the first The actual execution result status of the control instruction. Indicates the first The expected execution result status of the control instruction. This is an indicator function; its value is 1 when the actual execution result matches the expected result, and 0 otherwise. The mathematical model for adjusting terminal device parameters is described by the following formula: ； in, This indicates the adjusted equipment parameter values. This represents the original parameter values ​​of the device. This indicates the amount of parameter correction required based on the control command. This represents the retention coefficient of the original parameters. This represents the application coefficient for parameter correction, and the formula is used to precisely control the adjustment range of equipment parameters.

10. A barometric pressure sensor communication control system based on PFM pulse frequency modulation, used to execute the barometric pressure sensor communication control method based on PFM pulse frequency modulation as described in any one of claims 1 to 9, characterized in that, include: The pulse change sequence acquisition module (10) is used to acquire parameter data from the environment through an environmental parameter sensor, process the parameter data through a signal conditioning unit including amplification and filtering functions to convert it into an analog electrical signal, and input the modulation unit to perform mapping processing on the analog electrical signal to obtain a pulse change sequence. The pulse change sequence transmission module (20) is used to perform encoding optimization according to the pulse change sequence using the correspondence relationship, determine the matching degree between the pulse change sequence and the parameter change amount, and transmit the matched pulse change sequence to the receiving end of the main control unit through the communication link; The original parameter data restoration module (30) is used to perform anti-interference processing on the transmitted pulse change sequence using the signal transmission protocol to filter noise interference. If the integrity of the filtered pulse change sequence meets the conditions, the original parameter data is restored by performing inverse mapping processing on the filtered pulse change sequence through the demodulation unit. The abnormal deviation analysis module (40) is used to integrate the corresponding data from the additional sensors under the network communication framework based on the demodulated original parameter data to obtain the network integrated parameter dataset. The network integrated parameter dataset is compared by the control logic to analyze and judge abnormal deviations. The parameter-related adjustment operation execution module (50) is used to process the network integration parameter dataset for judging abnormal deviations using a control mechanism, generate corresponding control commands, and feed the control commands back to the terminal device through the communication link to execute parameter-related adjustment operations.

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